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This paper proposes a method to reduce the output voltage distortions in voltage source inverters (VSI) working with impedance networks. The three main reasons for the voltage distortions include a discontinuous current in the coils of the impedance network, the double output frequency harmonics in the VSI’s voltage output caused by insufficient capacitance in the impedance network, and voltage drops on the bridge switches during the shoot-through time. The first of these distortions can be reduced by increasing the current of the impedance network when the output VSI current is low. This method requires storing energy in the battery connected to the DC link of the VSI during the “non-shoot through” time. Furthermore, this solution can also be used when the Z-source inverter works with a photovoltaic cell to help it attain a maximum power point. The Z-source inverter is essentially a voltage source inverter with the Z-source in the input. In this paper, the theory behind basic impedance networks of Z-source and quasi-Z-source (qZ-source) is investigated where simulations of the presented solutions and experimental verification of the results are also presented.

One of the key aspects of vehicle-to-grid technology (V2G) is the analysis of uncertainty in electric vehicle user behavior. Correct estimation of the amount of available energy from electric vehicles that are expected to provide ancillary services to the electricity system operator or to secure the end user’s demand is essential to design these services in an appropriate way. Therefore, it is necessary to analyze the probabilities of V2G service performance for different scenarios. This paper presents the author’s approach to determining the values of V2G service provision probabilities using survey data. It was found that estimating these values using simulation and forecasting tools makes sense when the coefficients resulting from survey responses are used as initial data. Thus, the paper also presents the results of the surveys that were conducted. As the results from the simulations show, the values of the probabilities of V2G services are not high, which should induce future operators of V2G services to offer a beneficial product for the customer.

New advances in small-scale generation and consumption technologies have shifted conventional buildings’ functionality towards energy-efficient active buildings (ABs). Such developments drew the attention of researchers all around the world, resulting in a variety of publications, including several review papers. This study conducts a systematic literature review so as to analyse the concepts/factors enabling active participation of buildings in the energy networks. To do so, a relatively large number of publications devoted to the subject are identified, introducing the taxonomy of control and optimisation methods for the ABs. Then, a study selection methodology is proposed to nominate potential literature that has investigated the role of ABs in the energy networks. The modelling approaches in enabling flexible ABs are identified, while the potential challenges have been highlighted. Furthermore, the citation network of included papers is illustrated by Gephi software and analysed using “ForceAtlas2” and “Yifan Hu Proportional” algorithms so as to analyse the insights and possibilities for future developments. The survey results provide a clear answer to the research question around the potential flexibility that can be offered by ABs to the energy grids, and highlights possible prospective research plans, serving as a guide to research and industry.

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. ‘Addition X of Y middle’ and ‘Addition Weighted average’ better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while ‘Multiplication X of Y high’ and ‘Multiplication X of Y middle’ better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%).

In this paper the idea of functioning of Building Management Systems and Object Management Systems in intelligent buildings is presented. New functionalities of intelligent buildings resulting from the introduction of microgeneration are described. Low-Power Wide-Area Networks (LPWAN) are characterized and compared. The selected Long-Range (LoRaWAN) technology is tested for its use for communication with energy meters and monitoring the power supply network in intelligent buildings. In the paper a new system for reading and monitoring the network is proposed, consisting of hardware, communication, and application layers. A key element of the system is a specially developed converter, which has been designed and tested in a real urban environment. Using our solution in practice could allow to change the architecture of a measurement data acquisition system to much more flexible and efficient.

This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method.

This paper focuses on the methods that ensure the rotor angle stability of electric power systems, which is most frequently analyzed with small-signal models. Over the past several decades, power system stabilizers (PSSs) for conventional excitation systems were the main tools for improving the small-signal stability of electromechanical oscillatory modes. In the last decade, power oscillation damping (POD) control implemented in photovoltaic (PV) inverters has been considered an alternative to PSSs. As PV generation undergoes massive rollout due to policy directions and renewable energy source integration activities, it could potentially be used as a source of damping, which is crucial for sustaining the rotor angle stability of the remaining in-service synchronous generators. Several studies have already been dedicated to the development of different damping strategies. This paper contributes to the existing research in power system stability by providing a comprehensive review of the effects of PV generation on small-signal stability, as well as the recent evolution of POD control through PV inverters. The features and impacts of the various ways to realize POD controllers are assessed and summarized in this paper. Currently, detailed information and discussions on the practical application of PV inverter PODs are not available. This paper is, thus, intended to initiate a relevant discussion and propose possible implementation approaches concerning the topic under study.

In time attack races, aerodynamics plays a vital role in achieving short track times. These races are characterized by frequent braking and acceleration supported by aerodynamic downforce. Usually, typical cars are modified for these races by amateurs. Adjusting the aerodynamic solutions to work with bodies developed for other flow conditions is difficult. This paper presents the results of a numerical analysis of the effects of installing a straight wing in front of or above the body on the modified vehicle system’s aerodynamic characteristics, particularly on the front wheels’ aerodynamic downforce values. The paper presents the methodology and results of calculations of the aerodynamic characteristics of a car with an additional wing placed in various positions in relation to the body. The numerical results are presented (Cd, Cl, Cm, Clf, Clr), as well as exemplary pressure distributions, pathlines, and visualizations of vortex structures. Strong interactions between the wing operation and body streamline structure are shown. An interesting and unexpected result of the analysis is that the possibility of obtaining aerodynamic downforce of the front wheels is identified, without an increase in aerodynamic drag, by means of a wing placed in a proper position in front of the body. A successful attempt to balance the additional downforce coming from the front wing on the front axle is made using a larger spoiler. However, for large angles of attack, periodically unsteady flow is captured with frequency oscillations of ca. 6–12 Hz at a car speed of 40 m/s, which may interfere with the sports car’s natural suspension frequency.

Considering loads when optimizing wind-farm layouts or designing farm-control strategies is important, but the computational cost of using high-fidelity wake models in the loop can be prohibitively high. Using simpler models that consider only the spatial variation of turbulence statistics is a tempting alternative, but the accuracy of these models with respect to the aeroelastic response is not well understood. This paper therefore highlights the effect of replacing wake meandering with spatially varying statistics (“profile functions”) in the inflow to a downstream turbine. Profile functions at different downstream and lateral locations are extracted from a large-eddy simulation with an upstream turbine and compared with two lower-fidelity models: one that prescribes both the mean and standard deviation of the turbulence and one that prescribes only the mean. The aeroelastic response of an NREL 5 MW wind turbine is simulated with the three different wake-model fidelities, and various quantities of interest are compared. The mean values for the power and rotor speed for the medium-and low-fidelity model match well, but the accuracy of the fatigue loads varies greatly depending on the load channel. Prescribing the profile function for the standard deviation is only beneficial for the tower-base fore-aft moment; all other DELs had similar accuracies for both the medium- and low-fidelity models. The paper concludes that blade DELs can be estimated using these simple models with some accuracy, but care should be taken with the load channels related to the shaft torsion and tower-base fore-aft bending moment.

Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.

In the transportation sector, electric battery bus (EBB) deployment is considered to be a potential solution to reduce global warming because no greenhouse gas (GHG) emissions are directly produced by EBBs. In addition to the required charging infrastructure, estimating the energy consumption of buses has become a crucial precondition for the deployment and planning of electric bus fleets. Policy and decision-makers may not have the specific tools needed to estimate the energy consumption of a particular bus network. Therefore, many state-of-the-art studies have proposed models to determine the energy demand of electric buses. However, these studies have not critically reviewed, classified and discussed the challenges of the approaches that are applied to estimate EBBs’ energy demands. Thus, this manuscript provides a detailed review of the forecasting models used to estimate the energy consumption of EBBs. Furthermore, this work fills the gap by classifying the models for estimating EBBs’ energy consumption into small-town depot and big-city depot networks. In brief, this review explains and discusses the models and formulations of networks associated with well-to-wheel (WTW) assessment, which can determine the total energy demand of a bus network. This work also reviews a survey of the most recent optimization methods that could be applied to achieve the optimal pattern parameters of EBB fleet systems, such as the bus battery capacity, charger rated power and the total number of installed chargers in the charging station. This paper highlights the issues and challenges, such as the impact of external factors, replicating real-world data, big data analytics, validity index, and bus routes’ topography, with recommendations on each issue. Also, the paper proposes a generic framework based on optimization algorithms, namely, artificial neural network (ANN) and particle swarm optimization (PSO), which will be significant for future development in implementing new energy consumption estimation approaches. Finally, the main findings of this manuscript further our understanding of the determinants that contribute to managing the energy demand of EBBs networks.

Organic Rankine cycle (ORC) is considered a promising heat-to-power technology to utilize waste heat and renewable energy, including solar, biomass and geothermal. However, since the thermodynamic, economic and environmental performance is usually conflict, the single objective design could no longer meet the requirements of the ORC system, putting forward urgent requirements for multi-objective optimization, which has attracted increasing attention with lots of papers published. However, due to these different decision variables, optimization objectives and approaches, existing research is significantly different from each other and is difficult to compare without a systematic summary. Therefore, this paper provides an overview of ORC multi-objective research from three perspectives: optimization objective, method and optimization parameters. Based on the classification of different objectives, this work summarizes the involved variables and provides a recommendation for selecting appropriate objectives in different scenarios. For the optimization method, this work compares different approaches and reveals their advantages and disadvantages. Finally, the decision variables are reviewed and classified into four levels. Then the integrated design approach considering “system-process-component-fluid” is proposed and recommended for further development.

This paper presents improved methods to detect cracks and thermal leakage in building envelopes using unmanned aerial vehicles (UAV) (i.e., drones) with video camcorders and/or infrared cameras. Three widely used contour detectors of Sobel, Laplacian, and Canny algorithms were compared to find a better solution with low computational overhead. Furthermore, a scheme using frame-based location identification was developed to effectively utilize the existing approach by finding the current location of the drone-assisted image frame. The results showed a simplified drone-assisted scheme along with automation, higher accuracy, and better speed while using lower battery energy. Furthermore, this paper found that the cost-effective drone with the attached equipment generated accurate results without using an expensive drone. The new scheme of this paper will contribute to automated anomaly detection, energy auditing, and commissioning for sustainably built environments.

This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the form of energy that is produced through ESS or that is pumped through water power generation without taking the amount of new renewable energy that is generated from sources such as thermal power, nuclear power, and hydropower into consideration. This occurs due to the difficulty of predicting power production using new renewable energy and the absence of a prediction system, which is a problem due to the inefficiency of changing energy types. Therefore, using game theory, the theoretical foundation of a power demand forecasting model based on big data-based renewable energy production forecasting was prepared.

The links of low power wireless sensor networks are error prone and the transmission on a wireless link is determined probabilistically by the packet reception rate (PRR) of the link. On the other hand, there is a very strict requirement in the end-to-end reliability and delay of sensor data in industrial wireless sensor networks (IWSNs). The existing approaches to provide the end-to-end reliability in IWSNs is retransmitting the packet when failure occurs. These approaches transmit a packet multiple times in successive time slots to provide the required reliability. These approaches, however, can increase the average delay of packets and the number of packets buffered in a queue. This paper proposes a new scheme to estimate the probabilistic amount of packets, called queue level (QL), in the buffer of each node based on the PRRs of the wireless links. This paper also proposes a QL-based centralized scheduling algorithm to assign time slots efficiently in TDMA-based IWSNs. The proposed scheduling algorithm gives higher priority to the nodes with higher QL. By assigning time slots first to the node with the highest QL, we can reduce the average end-to-end delay of packets and reduce the amount of buffered packets in the queue while satisfying the required end-to-end reliability. The performance of the proposed scheduling algorithm have been evaluated through a simulation using the Cooja simulator and compared with the existing approach. In the simulation on an sample network with the target end-to-end reliability of 99%, all of the flows were shown to guarantee the target reliability in both algorithms: on average, 99.76% in the proposed algorithm and 99.85% in the existing approach. On the other hand, the proposed algorithm showed much better performance than the existing approach in terms of the average end-to-end delay of packets (about 47% less) and the number of maximally buffered packets in the queue of each node (maximally, more than 90% less).

The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness.

The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.

The aim of the study is to explore the intellectual structure of the field and fronts in research on energy efficiency in the context of cloud computing and thus to contribute to science mapping of the research field. The research process was driven by the following study questions: (1) what are the most influential publications in the research field? and (2) what are the research fronts in the research field? The method of direct citation analysis was employed in the research process. Data for analysis were obtained from the Scopus database and analyzed with the use of VOSviewer science mapping software. In response to the first question, we identified the most influential publications in the research field and analyzed their types (i.e., whether they are original research papers or rather the “context” papers e.g., survey or review papers, framework papers, challenges papers, and study papers). Moreover, a comparison analysis between the types of papers among the most cited “classical” publications and “emerging stars” was conducted. In response to the second research question, we identified five research fronts concentrated around the issues of: virtual machine management (“VM”); task-focus, concerning data replication, task consolidation, and task scheduling (“task”); energy efficiency (“energy”); modelling and optimization (“model”); and energy efficiency in the networking context (“network”).

This paper presents a power control method for a hybrid electrical system, which enhances a conventional main AC grid by adding a DC sub-grid in parallel. In this system, each prosumer connects to the sub-grid via a couple of half-bridge inverters of a device called a power router. All of the power routers connected to the sub-grid are controlled such that they maintain the voltage of the sub-grid equally, which is an essential feature of peer-to-peer power accommodation without synchronization. This paper proposes an adaptive hysteresis current-control technique for controlling sub-grid-connected inverters that maintains the voltage of the sub-grid while accommodating a given power. The proposed control method also balances currents between positive and negative lines of the sub-grid simultaneously. The proposed control method is implemented on a high-speed FPGA-based digital controller and is evaluated on three prototypes of the power router. The experimental results show that the proposed method yields fast and stable responses and enables the power router to accommodate power with a high accuracy.

This paper addresses the problem of route planning for a fleet of electric vehicles departing from a depot and supplying customers with certain goods. This paper aims to present a permutation-based method of vehicle route coding adapted to the specificity of electric drive. The developed method integrated with an evolutionary algorithm allows for rapid generation of routes for multiple vehicles taking into account the necessity of supplying energy in available charging stations. The minimization of the route distance travelled by all vehicles was taken as a criterion. The performed testing indicated satisfactory computation speed. A real region with four charging stations and 33 customers was analysed. Different scenarios of demand were analysed, and factors affecting the results of the proposed calculation method were indicated. The limitations of the method were pointed out, mainly caused by assumptions that simplify the problem. In the future, it is planned for research and method development to include the lapse of time and for the set of factors influencing energy consumption by a moving vehicle to be extended.

The real-time application research on the Fuzzy Logic Systems (FLSs) and Artificial Neural Networks (ANN) is vast and, in this paper, a technique for a photovoltaic failure analysis using the type 2 FLS and ANN is proposed. The method is proposed to build T2 FLS with a guaranteed value equal to or lower than T2 and ANN. Several explanations are conducted to illustrate the effectiveness of the methodologies. It is found that both the type 2 Fuzzy and ANN can be configured for productive actions in applications for a PV fault analysis, and choice is typically applied. The methods discussed in this paper lay the groundwork for developing FLSs and ANNs with durable characteristics that will be extremely useful in many functional applications. The result demonstrates that specific fault categories can be detected using the fault identification method, such as damaged PV modules and partial PV unit shades. The average detection performance is similar in both ANN and fuzzy techniques. In comparison, both systems evaluated show approximately the same performance during experiments. The architecture of the type 2 fuzzy logic system and ANN with radial basic function, including the roles of the output port and the rules for identifying the type of defect in the PV structure is slightly different.

This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play a significant role in implementing smart energy. One of the approaches to implementing smart energy is to analyze consumption data and provide targeted contracts to consumers based on their individual consumption characteristics. To that end, the identification of individual consumption features is important for suppliers and utilities. Given the complexity of smart home load profiles, an appliance-based identification is nearly impossible. In this paper, we propose a different approach by grouping appliances based on their rooms, thus, we provide a room-based identification of energy consumption. To this end, this paper presents and tests an intelligent consumption identification methodology, that can be implemented in the form of an ensemble of artificial intelligence tools. The ensemble, which comprises four convolutional neural networks (CNNs) and four k-nearest neighbor (KNN) algorithms, is fed with smart submeter data and outputs the identified type of room in a given dwelling. Results obtained from real-world data exhibit the superiority of the ensemble, with respect to accuracy, as compared with individual CNN and KNN models.

In this paper, a distributed energy storage design within an electric vehicle for smarter mobility applications is introduced. Idea of body integrated super-capacitor technology, design concept and its implementation is proposed in the paper. Individual super-capacitor cells are connected in series or parallel to form a string connection of super-capacitors with the associated management unit to form a panel. These super-capacitor panels are shaped to fit the alternative concept of vehicle design, and it solves the design issues and prepares for configurable electric vehicles. Body integration of super-capacitors enhances the acceleration, and regenerative braking performances of the electric vehicle increases the operating life of the Li-ion battery and improves space utilization by giving more area for the main energy source, the Li-ion battery. Integrating super-capacitor into the car body involves special packaging technology to minimize space and promotes distributed energy storage within a vehicle. This pioneering design encourages future configurable electric vehicles. Model of both the Li-ion battery and the super-capacitor employed is studied with its series internal resistance determined at various C-rates. Loss and the efficiency analysis of the bi-directional converter, traits of body integrated super-capacitors system and control of the interleaved bi-directional converter to regulate the power-sharing in the hybrid energy storage system is presented.

Electromobility is one of the leading trends transforming public transport worldwide. Supported by international organizations, such as the European Union, and national cofounding, public transport operators and local authorities are taking strategic decisions on the direction and scope of the electrification of rolling stock. Most of the electric buses that are being put into operation replace the previously used conventional buses, and consequently, most of the electric buses are operating on existing bus lines. By applying a strategic approach to selecting bus routes for electrification, the advantages of electric vehicles can be maximized. Based on a case study of the Polish city of Gdynia, this paper explores the usefulness of the multi-criteria analysis for selecting the bus lines for electrification. Multi-criteria analysis methods help decision makers to consider and weigh diverse criteria that include, among others, economic, social, technological and environmental aspects. To fulfil the above purpose, the paper compares different methods for evaluating electromobility options at an early stage. The primary research methods include multi-criteria analysis, literature review and case study analysis. An example of using multi-criteria analysis in the decision-making process of in-motion charging trolleybuses to replace diesel buses on particular lines is discussed and concluded. It is found that the multi-criteria analysis method could be used at an early but important stage of the operational level when particular lines to be replaced are being discussed. Moreover, the case is made that the local context should always be considered, including features of the existing public transport systems, and that cost–benefit analysis should be conducted for the selected optimum scenario.

High voltage electric power equipment requires rigorous regulation testing to specific standards which ensure proper and safe operation in the grid. Manufacturers conduct these tests in order to prove standard compliance and product liability. Variable linear or nonlinear loads are necessary for testing medium voltage (MV) high power AC power converters. Generally, those AC power converters or power supplies require performance validation, burn-in and/or lifetime testing under different load conditions, defined by the end-user or standards for the given applications. For flexible and efficient MV verification testing, this paper presents a five-level multilevel converter-based MV regenerative AC electronic load with one-cycle control (OCC), which is based on five-level diode-clamped multilevel converters with back-to-back structure and can emulate any impedance load. In this paper, especially the dc-link capacitor voltage balance of the proposed multilevel MV regenerative AC load is deeply analyzed. Simulation and experimental results are presented to verify the dc-link voltage balance performance of the proposed multilevel MV regenerative AC electronic load.

Thermal analysis represents a key factor in electrical machine design due to the impact of temperature increase on insulation lifetime. In this context, there has been a wide investigation on thermal modeling, particularly for machines used in harsh working conditions. In this perspective, brake-by-wire (BBW) systems represent one of the most challenging applications for electrical machines used for automotive smart actuators. Indeed, electro-actuated braking systems are required to repeatedly operate the electric machine in high overload conditions in order to limit the actuator response time, as well as to enhance gravimetric and volumetric specific performance indexes. Moreover, BBW systems often impose unconventional supply conditions to the electric machine, consisting of dc currents in three-phase windings to keep the rotor fixed during the braking intervals. However, a dc supply leads to uneven temperature distributions in the machine, and simplified thermal models may not accurately represent the temperature variations for the different machine parts. Considering such unconventional supply conditions, this paper initially investigates the applicability of a conventional lumped-parameters thermal network (LPTN) based on symmetry assumptions for the heat paths and suitable for surface-mounted PM synchronous machines used in BBW systems. An extensive test campaign consisting of pulses and load cycle tests representative of the real machine operations was conducted on a prototype equipped with several temperature sensors. The comparison between measurements and predicted average temperatures, together with insights on the unbalanced heat distribution under the dc supply obtained by means of finite element analyses (FEA), paved the way for the proposal of a phase-split LPTN with optimized parameters. The paper also includes a critical analysis of the optimized parameters, proposing a simplified, phase-split lumped-parameters thermal model suitable to predict the temperature variations in the different machine parts for PM synchronous electric machines used in BBW systems.

The transition to a sustainable society and a carbon-neutral economy by 2050 requires extensive deployment of renewable energy sources that, due to the aleatority and non-programmability of most of them, may seriously affect the stability of existing power grids. In this context, buildings are increasingly being seen as a potential source of energy flexibility for the power grid. In literature, key performance indicators, allowing different aspects of the load management, are used to investigate buildings’ energy flexibility. The paper reviews existing indicators developed in the context of theoretical, experimental and numerical studies on flexible buildings, outlining the current status and the potential future perspective. Moreover, the paper briefly reviews the range of grid services that flexible buildings can provide to support the reliability of the electric power system which is potentially challenged by the increasing interconnection of distributed variable renewable generation.

The effect of the traditional energy saving mode on process engineering is decreasing, so the idea of system energy saving is developing vigorously. This paper summarizes the existing studies on energy saving from three aspects—energy saving technology, energy management, comprehensive evaluation or analysis—and clearly finds that related studies on energy saving have been gradually promoted to promote the development of system energy saving in the whole process engineering process. Among them, the calculation of energy saving brought by energy saving technology is a difficult point in the research. This paper proposes a novel calculation method of energy saving by taking the iron and steel production process (ISPP) as an example. According to the actual data provided by a steel plant in China, the calculation method is validated effectively.

Heat exchangers play an important role in power, the chemical industry, petroleum, food and many other industrial productions, while compact heat exchangers are more favored in industrial applications due to their high thermal efficiency and small size. This paper summarizes the research status of different types of compact heat exchangers, especially the research results of heat transfer and pressure drop of printed circuit heat exchangers, so that researchers can have an overall understanding of the development of compact heat exchangers and get the required information quickly. In addition, this paper summarizes and analyzes several main working fluids selected in compact heat exchangers, and puts forward some discussions and suggestions on the selection of working fluids. Finally, according to the existing published literature, the performance evaluation indexes of compact heat exchangers are summarized and compared, which is convenient for developers and researchers to better grasp the design direction.

Optimal planning of a remote area electricity supply (RAES) system is a vital challenge to achieve a reliable, clean, and cost-effective system. Various components like diesel generators, renewable energy sources, and energy storage systems are used for RAES systems. Due to the different characteristics and economic features of each component, optimal planning of RAES systems is a challengeable task. This paper presents an overview of the optimal planning procedure for RAES systems by considering the important components, parameters, methods, and data. A timely review on the state of the art is presented and the applied objective functions, design constraints, system components, and optimization algorithms are specified for the existing studies. The existing challenges for RAES systems’ planning are recognized and discussed. Recent trends and developments on the planning problem are explained in detail. Eventually, this review paper gives recommendations for future research to explore the optimal planning of components in RAES systems.

This paper presents the design and optimization of a wireless power transfer (WPT) charging system based on magnetically coupled resonant technology, applied to an Unmanned Aerial Vehicle (UAV). In this paper, a charging system, including dual active transmitter coils and a single receiver coil, is proposed. The dual transmitting coils adopt a coaxial structure with different radii. This structure simplifies the calculation of the complex mutual inductance between the coils to a function of mutual inductance only related to the value of the radial misalignment. Aiming toward a constant charging power, the optimal transmission efficiency of electric energy is achieved by controlling the input voltages of the active coils, which are solved via a set of equations defined as Lagrange multipliers. The simulation results of the 570 V and 85,000 Hz system verified the validity of the proposed wireless UAV charging scheme.

This paper presents a literature review of the dynamics of offshore floating wind turbine platforms. When moving further offshore, there is an increase in the capacity of wind power. Generating power from renewable resources is enhanced through the extraction of wind energy from an offshore deep-water wind resource. Mounting the turbine on a platform that is not stable brings another difficulty to wind turbine modeling. There is a need to introduce platforms that are more effective to capture this energy, because of the complex dynamics and control of these platforms. This paper highlights the historical developments and progresses in the design of different types of offshore floating wind turbine platforms needed for harvesting the energy from offshore winds. The relative advantages and disadvantages of the platform types with the design challenges are discussed. The major types of floating platforms included in this study are tension leg platform (TLP) type, spar type, and semisubmersible type. This study reviews the previous work on the dynamics of the floating platforms for a single turbine and multiple turbines under various operating environmental conditions. The numerical methods to analyze the aerodynamics of the wind turbine and hydrodynamics of floating platforms are discussed in this paper. This paper also investigates the performance of analytical wake loss models of Jensen, Larsen, and Frandsen that can provide guidelines for using these wake models in future applications. There are still a lot of challenges that need to be addressed to study the accurate behavior of floating platforms operating under combined wind–wave environmental conditions. With the current technological advancements, the offshore floating multi-turbine platform can be a potential solution to harness the abundant offshore wind resource. Based on this literature review, recommendations for future work are suggested.

Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is proposed. In the first stage, a sequence-to-sequence (seq2seq) architecture, which can handle a multi-sequence of input to extract more features of historical data than that of single sequence, is used to make multistep predictions. In the second stage, the MLP is used for residual modification by perceiving other information that the LSTM cannot. To construct the model, we collected the electrical load, calendar, and meteorological records of Kanto region in Japan for four years. Unlike other LSTM-based hybrid architectures, the proposed model uses two independent neural networks instead of making the neural network deeper by concatenating a series of LSTM cells and convolutional neural networks (CNNs). Therefore, the proposed model is easy to be trained and more interpretable. The seq2seq module performs well in the first few hours of the predictions. The MLP inherits the advantage of the seq2seq module and improves the results by feeding artificially selected features both from historical data and information of the target day. Compared to the LSTM-AM model and single MLP model, the mean absolute percentage error (MAPE) of the proposed model decreases from 2.82% and 2.65% to 2%, respectively. The results demonstrate that the MLP helps improve the prediction accuracy of seq2seq module and the proposed model achieves better performance than other popular models. In addition, this paper also reveals the reason why the MLP achieves the improvement.

This paper presents a straightforward step-by-step procedure to design salient-pole synchronous generators, starting with its main specifications and finishing with all necessary data to put it on production. As most of the electricity is generated by synchronous generators, the design of these machines remains an interesting subject, but, although it is important, it is difficult to find a complete step-by-step procedure in the literature. The proposed procedure can be followed by an electrical engineer or student and, distinctively from most papers and books, all steps are presented. Such a procedure is based on analytic calculations, eventually relying on finite element simulation to verify if everything is all right and to adjust some design parameters. All calculations have been chosen to keep the design as simple as possible, otherwise, it would not be possible to present all steps and procedures. Therefore, it can be used for beginners in the art of design-synchronous generators, applied to obtain an initial design, or be adopted by any electrical engineering course, not only aiming to be an introductory electrical machine design course but mainly to enhance the students’ comprehension of synchronous machines. The results have been compared with finite element simulation, presenting very small differences.

In this paper, an automated umbilical system based on a 6-dof (degree of freedom) hydraulic parallel mechanism is proposed to automate the rocket propellant loading process. The mechanical structure, vision acquisition algorithm, and control algorithm used in the system are described in detail in the paper. To address the fluid nonlinearity problem of the hydraulic drive system, nonlinear compensation and three-state feedback control are used in the paper to enhance the performance of the hydraulic system. For the problem of force tracking during the docking process between the umbilical system and the rocket, an adaptive impedance control algorithm based on the online environmental parameter estimation is proposed in the paper, which effectively reduces the contact force during the docking process. The dynamic tracking and docking experiments indicate that this automated umbilical system features rapid reaction speed, high measurement precision, and good flexibility, which can be used to realize the auto-mating and following task for the propellant loading robot in a hazardous environment.

The intensive electrification of the automotive sector means that the energy system must be able to adapt to the current market situation. The increase in energy demand is a major factor associated with electric vehicles. The study analyzed the operation of a grid-connected facility operating a vehicle fleet providing transport services in the region Halle/Saale, Germany. Measurement data were used in the analysis, including global positioning system data of the vehicles and technical data, including average fuel consumption on a given route section, daily load demand of the industrial facility, and energy generation from photovoltaics. This paper shows the impact of using a battery electric vehicles (BEVs) fleet in the load distribution for the industrial facility considered. The NEDC energy consumption profile for the Nissan e-NV200 were used in this study. Furthermore, the paper presented simulation results allowing one to determine the usage potential, energy demand, and consumption of EVs using real data, reliably representing the processes related to EV daily use. The measurement data were captured using available specialized equipment: Dako-Key (GPS data), PV power generation (Siemens 7KM PAC4200), and load (Janitza UMG 604-Pro) in September, 2018. On this basis, it is possible to identify the effects and variations in load on the power grid during the replacement of combustion vehicle fleets used currently by EVs for the provision of transport services. Three models were presented, making it possible to calculate changes in energy demand for each scenario. In the first model, EVs were charged exclusively from the distribution network. In the second, the energy generation from a renewable source was considered and the possibility of compensating the energy demand of the vehicles from this source was demonstrated. In the third model, the daily load profile and the period of maximum load in the electricity grid were considered. The results are presented in graphical and tabular form. Finally, the potential of using an EV fleet to increase the functionality of a modern industry object was determined and discussed. Based on data for the adopted scenarios, electrification of transport can increase demand for energy by 40.9% for individual enterprises. The electrification of the automotive sector will increase the instantaneous energy demand of businesses, forcing the integration of renewable energy sources during designing new invests.

In this paper, a methodology for optimal decision making for electrical systems is addressed. This methodology seeks to identify and to prioritize the replacement and maintenance of apower asset fleet optimizing the return of investment. It fulfills this objective by considering the riskindex, the replacement and maintenance costs, and the company revenue. The risk index is estimatedand predicted for each asset using both its condition records and by evaluating the consequence ofits failure. The condition is quantified as the probability of failure of the asset, and the consequence isdetermined by the impact of the asset failure on the whole system. Failure probability is estimatedusing the health index as scoring of asset condition. The consequence is evaluated considering afailure impact on the objectives of reliability (energy not supplied -ENS), environment, legality, andfinance using Monte Carlo simulations for an assumed period of planning. Finally, the methodologywas implemented in an open-source library called PywerAPM for assessing optimal decisions, wherethe proposed mathematical optimization problem is solved. As a benchmark, the power transformerfleet of the New England IEEE 39 Bus System was used. Condition records were provided by a localutility to compute the health index of each transformer. Subsequently, a Monte Carlo contingencysimulation was performed to estimate the energy not supplied for a period of analysis of 10 years.As a result, the fleet is ranked according to risk index, and the optimal replacement and maintenanceare estimated for the entire fleet. In this paper, a methodology for optimal decision making for electrical systems is addressed. This methodology seeks to identify and to prioritize the replacement and maintenance of a power asset fleet optimizing the return of investment. It fulfills this objective by considering the risk index, the replacement and maintenance costs, and the company revenue. The risk index is estimated and predicted for each asset using both its condition records and by evaluating the consequence of its failure. The condition is quantified as the probability of failure of the asset, and the consequence is determined by the impact of the asset failure on the whole system. Failure probability is estimated using the health index as scoring of asset condition. The consequence is evaluated considering a failure impact on the objectives of reliability (energy not supplied -ENS), environment, legality, and finance using Monte Carlo simulations for an assumed period of planning. Finally, the methodology was implemented in an open-source library called PywerAPM for assessing optimal decisions, where the proposed mathematical optimization problem is solved. As a benchmark, the power transformer fleet of the New England IEEE 39 Bus System was used. Condition records were provided by a local utility to compute the health index of each transformer. Subsequently, a Monte Carlo contingency simulation was performed to estimate the energy not supplied for a period of analysis of 10 years. As a result, the fleet is ranked according to risk index, and the optimal replacement and maintenance are estimated for the entire fleet. View Full-Text

With the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper’s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI.

The main emphasis in the operation of an electricity system is placed on its safe and reliable operation. The flow of reactive power in a network can affect voltage conditions in individual nodes of the transmission system. In recent years, there have been changes in the network that have resulted in increased capacitive reactive power flows from lower voltage levels to higher ones. These flows can cause the voltage to rise above the limit. This paper examines recent changes in the reactive power transmission in the network, especially at lower voltage levels. The possible impact of these changes on the flow of capacitive reactive power at higher voltage levels is analyzed. This paper also presents a description and the simulated impact of power lines at different voltage levels on reactive power flows. Real measurements of different types of consumers at the low-voltage (LV) level are analyzed. Finally, a simulation model was created to simulate the impact of a customer’s power contribution to the reactive power flows from the point of view of a 110 kV voltage node. This node is characterized as a supply point.

For sustainable electrification of surface transportation, a viable charging infrastructure is necessary. Firstly, this paper focuses on emphasizing the viability of a free fuel-based photovoltaics and/or wind turbines and lithium-ion battery-based power network to enable sustainable electric power. The importance of power electronics for a DC-based power network and extremely fast charger based on DC power is presented. Finally, the core design concepts of intelligent charging infrastructure using an intelligent energy management system are discussed. The paper aims to cover all aspects associated with a clean, reliable, efficient, and cost-effective solution to the novel charging infrastructure.

The most important element of the new active distribution system concept is the grid connected converter that needs to offer fault ride through capabilities. The new system topologies require new tools for fault state calculation that would consider different control methodologies. In that regard, this paper investigates the initial response of the grid connected inverter under fault that operates using new control methodology based on the integration of the delay signal cancellation. Using modern laboratory setup for testing of renewable energy sources and their integration in the power system the technique is weighed against the classical technique that does not provide the adequate control under unbalanced faults. Furthermore, through a set of specific experiments the paper demonstrates the behavior of the converter under fault, preparing the outline for the fault response modeling of distributed energy resources. Experimental results present the sub-transient period and the transient period of the response, giving the attention to the inrush current (initial peak current) of the converter. It has been shown that the new technique has similar behavior as the classical control for the balanced faults (symmetrical voltage states), while the values of the peak current for different type of unbalanced faults (asymmetrical voltages where classical technique can be proven to be ineffective) has also been demonstrated.

A control algorithm for Parallel Connected Offshore Wind Turbines with permanent magnet synchronous Generators (PCOWTG) is presented in this paper. The algorithm estimates the optimal collective speed of turbines based on the estimated mechanical power of wind turbines without direct measurement of wind speed. In the proposed topology of the wind farm, direct-drive Wind Turbine Generators (WTG) is connected to variable low-frequency AC Collection Grids (ACCG) without the use of individual power converters. The ACCG is connected to a variable low-frequency offshore AC transmission grid using a step-up transformer. In order to achieve optimum wind power extraction, the collective speed of the WTGs is controlled by a single onshore Back to Back converter (B2B). The voltage control system of the B2B converter adjusts voltage by keeping a constant Volt/Hz ratio, ensuring constant magnetic flux of electromagnetic devices regardless of changing system frequency. With the use of PI pitch compensators, wind power extraction for each wind turbine is limited within rated WTG power limits. Lack of load damping in offshore wind parks can result in oscillatory instability of PCOWTG. In this paper, damping torque is increased using P pitch controllers at each WTG that work in parallel with PI pitch compensators.

New grid-connected systems have imposed additional requirements regarding reliability, power quality, high levels of power processing capacity, and fault support, where power converters have a crucial role in fulfilling these requirements. Overcoming one of these challenges, this paper proposes a new alternative application to improve the low-voltage ride-through (LVRT) support based on the arm impedance employment of the modular multilevel converter (MMC) by attenuating the fault impacts, avoiding overcurrents and overvoltages. This proposal does not require additional hardware or control loops for LVRT support, only using PI controllers. This paper evaluates symmetrical and asymmetrical grid fault impacts on the converter DC side of four converter topologies: two-level voltage source converter topology (2L-VSC), neutral point clamped (NPC), MMC, and 2L-VSC equipped with a DC-chopper, employing the same control structure for the four topologies, highlighting that the MMC contributed better to LVRT improvement under severe grid conditions.

The building sector continues to register a significant rise in energy demand and environmental impact, notably in developing countries. A considerable proportion of this energy is required during the operational phase of buildings for interior heating and cooling, leading to a necessity of building performance improvement. A holistic approach in building design and construction represents a step to moderate construction costs in conjunction with reduced long-term operating costs and a low impact on the environment. The present paper presents an experimental evaluation of the energy efficiency of a building under real climate conditions, the building, which represents a holistically designed modular laboratory, is located in a moderate continental temperate climate, characteristic of the south-eastern part of the Pannonian Depression, with some sub-Mediterranean influences. Considerations for the holistic design of the building, including multi-object optimization and integrated design with a high regard for technology and operational life are described. The paper provides a genuine overview of the energy efficiency response of the building during six months of operational use through a monitored energy management system. The energetic analysis presented in the paper represents an intermediary stage as not all the energetic users were installed nor all the energetic suppliers. However, the results showed a reliable thermal response in the behaviour of recycled-PET thermal wadding used as insulation material in the building and for the intermediary stage in which the building has only secondary energy users, the energetic balance proves its efficiency, keeping the buffer stock of energy high values over 90%.

Power systems optimization is generally subject to the compromise between performance and cost. The 2021 Texas grid outage illustrates the worldwide dangers for the regional-centralized power grid, with comparable advantages to safety and flexibility for the distributed energy system. The storage of household batteries helps balance grid load and increase system stability and flexibility. However, household storage battery is still not widely used today because of its high costs. Currently, research on increasing household battery storage applicability is focused largely on optimizing economic strategies, such as configuration, dispatching and subsidy policies, which rely substantially more on technologies and financial perspectives. Consumers are not ‘rational’ individuals, and non-economic incentives can affect their decisions without raising prices. This paper consequently proposes to encourage users to acquire household battery storage to increase efficiency of power dispatching and economic advantages based on behavioral economics. In this paper, an empirical research builds upon the utility model of behavioral economics incentives and purchase willingness. Moreover, the multi-objective genetic algorithm is utilized to optimize the dispatching of household battery storage by using grid variance and user revenues as optimizing goals. The results of this paper show that the behavioral economics incentive improves intention to buy the household battery energy storage by 10.7% without raising subsidies. By improving the energy dispatching strategy, peak-load shifting performance and user revenues are improved by 4.2% and 10.6%, respectively.

The paper presents a new method for modelling the warming up process of a water system with elements regulating the flow in a stochastic manner. The paper presents the basic equations describing the work of typical elements which the water installation is composed of. In the proposed method, a new computational algorithm was used in the form of an iterative procedure enabling the use of boundary conditions that can be stochastically modified during the warming-up process. A typical situation, when such a modification is processed, is the regulation of the medium flow through two-way or three-way valves or applying additional heat source. Moreover, the presented method does not require the transformation of the differential equations, describing the operation of individual elements, into a linear form, which significantly facilitates analytical work and makes it more flexible. The example of analysis of the operation of water installation used for controlling temperature of the process gases in a chemical installation shows the functionality and flexibility of the method. The adopted calculation schematics enable changing the direction of the heat flow while the heat exchanger is in operation. Additionally, the sequence of calculation processed in modules describing operation of installation elements is elective (there is no situation that output parameters from one element are used as input parameters for other element in the same calculation step).

The introduction of an energy storage system plays a vital role in the integration of renewable energy by keeping a stable operation and enhancing the flexibility of the power flow system, especially for an islanding microgrid which is not tied to a grid and for a self-contained microgrid which tries to stay independent from a grid as much as possible. To accommodate the effects of power fluctuations of distributed energy resources and power loads on power systems, a power flow assignment under power balance constraint is essential. However, due to power limitations of power devices, the capacity of storage devices, and power flow connections, the power balance may not be achieved. In this paper, we proposed a system characterization which describes the relation among power generators, power loads, power storage devices, and connections that must be satisfied for a system to operate by keeping SOC limitations of power storage devices. When we consider one power generator, one power load, and one power storage device connected at a single node, the generated energy by the generator minus the consumed energy by the load from some start time will increase/decrease the state of charge (SOC) for the storage device; hence, keeping SOC max/min limitations relies on whether the difference between the generated energy and the consumed energy stays within a certain range or not, which can be computed from the capacity Ess and other parameters. Our contribution in this paper is an extension and generalization of this observation to a system that consists of multiple fluctuating power generators, multiple fluctuating power loads, multiple storage devices, and connections that may not be a full connection between all devices. By carefully enumerating the connection-dependent flow paths of generated energy along the flow direction from generators to storages and loads, and enumerating the connection-dependent flow paths of consumed energy along the counter-flow direction from loads to storages and generators, we have formulated the increase/decrease of SOCs of storage devices caused by the imbalance between generated energy and consumed energy. Finally, considering the max/min limitations of SOCs and fluctuations of power generators and power loads, the conditions that the power generators and the power loads must have for SOCs of storage devices to maintain individual max/min limitations have been derived. The system characterization provides guidelines for a power flow system that can continue safe operation in the presence of power fluctuations. That is, in order for a system to have a feasible power flow assignment, there are the issues of how large the capacity of a power storage device should be, how large/small the maximum/minimum power/demand levels of the power generators and the power loads should be, and how the connection should be configured. Several examples using our system characterization are demonstrated to show the possible applications of our results.

The Brushless Doubly-Fed Reluctance Generator (BDFRG) is a potential alternative to the Doubly Fed Induction Generator (DFIG) in wind power applications owing to its reasonable cost, competitive performance, and high reliability. In comparison with the Brushless Doubly-Fed Induction Generator (BDFIG), the BDFRG is more efficient and easier to control owing to the cage-less rotor. One of the most preferable advantages of BDFRG over DFIG is the inherently better performance under unbalanced grid conditions. The study conducted in this paper showed that conventional vector control of the BDFRG results in excessive oscillations of the primary active/reactive power, electromagnetic torque, and primary/secondary currents in this case. In order to address such limitations, this paper presented a new control strategy for the unbalanced operation of BDFRG-based wind generation systems. A modified vector control scheme was proposed with the capability to control the positive and the negative sequences of the secondary currents independently, thus greatly reducing the adverse implications of the unbalanced supply. The controller performance has been validated by simulations using a 1.5 MW BDFRG dynamical model built upon the positive and negative sequence equations. The main benefits of the new control strategy are quantified in comparison with conventional PI current control design.

A climate resilient city, perforce, has an efficient and robust energy infrastructure that can harvest local energy resources and match energy sources and sinks that vary over space and time. This paper explores the use of an urban building energy model (UBEM) to examine the potential for creating a near-zero carbon neighbourhood in Dublin (Ireland) that is characterised by diverse land-uses and old and new building stock. UBEMs are a relatively new tool that allows the simulation of building energy demand across an urbanised landscape and can account for building layout, including the effects of overshadowing and the potential for facade retrofits and energy generation. In this research, a novel geographic database of buildings is created using archetypes, and the associated information on dimensions, fabric and energy systems is integrated into the Urban Modelling Interface (UMI). The model is used to simulate current and future energy demand based on climate change projections and to test scenarios that apply retrofits to the existing stock and that link proximate land-uses and land-covers. The latter allows a significant decoupling of the neighbourhood from an offsite electricity generation station with a high carbon output. The findings of this paper demonstrate that treating neighbourhoods as single energy entities rather than collections of individual sectors allows the development of bespoke carbon reducing scenarios that are geographically situated. The work shows the value of a neighbourhood-based approach to energy management using UBEMs.

Vibration signals contain abundant information that reflects the health status of wind turbine high-speed shaft bearings ((HSSBs). Accurate health assessment and remaining useful life (RUL) prediction are the keys to the scientific maintenance of wind turbines. In this paper, a method based on the combination of a comprehensive evaluation function and a self-organizing feature map (SOM) network is proposed to construct a health indicator (HI) curve to characterizes the health state of HSSBs. Considering the difficulty in obtaining life cycle data of similar equipment in a short time, the exponential degradation model is selected as the degradation trajectory of HSSBs on the basis of the constructed HI curve, the Bayesian update model, and the expectation–maximization (EM) algorithm are used to predict the RUL of HSSBs. First, the time domain, frequency domain, and time–frequency domain degradation features of HSSBs are extracted. Second, a comprehensive evaluation function is constructed and used to select the degradation features with good performance. Third, the SOM network is used to fuse the selected degradation features to construct a one-dimensional HI curve. Finally, the exponential degradation model is selected as the degradation trajectory of HSSBs, and the Bayesian update and EM algorithm are used to predict the RUL of the HSSB. The monitoring data of a wind turbine HSSB in actual operation is used to validate the model. The HI curve constructed by the method in this paper can better reflect the degradation process of HSSBs. In terms of life prediction, the method in this paper has better prediction accuracy than the SVR model.