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In renewable energy sources transformer-less grid-tied inverters are widely used due to their higher efficiency, reduced cost and size. With the absence of transformer, there exists leakage current due to PV-to-ground parasitic capacitance leads to deteriorated power quality, cause personal safety issues and electromagnetic interference (EMI). In this paper, a five-level transformer-less inverter topology is proposed to eliminates the leakage current based on the principle of switched capacitor. Besides, the proposed topology has naturally balanced voltage capability, so no dedicated control strategy/algorithm is required for balancing/regulating the flying capacitor voltages. The performance of the proposed inverter is verified by simulation results, proving the feasibility and merits of proposed inverter topology are presented. Results depicting the output voltage, load current and voltage across the auxiliary capacitor are presented for various operating conditions. Further, simulation results depicting the leakage current in the proposed, H-5 and H-6 topologies are included. Moreover, a quantitative comparison study is presented, highlighting the benefits of the proposed inverter topology regarding boosted voltage, elimination of leakage current and reduced component count.

This study explores the influence and control strategies of rising photo-voltaic (PV) penetration in power system networks. The power system network is simulated for varying levels of PV penetration and a non-linear pattern of voltage increase is observed with increasing PV penetration. Popular local control strategies to tackle the overvoltage problem: reactive power control; active power curtailment; and power factor control are then analysed. Simulations of these control techniques are carried out under different weather conditions to analyse the suitablility of these techniques. The combination of active power curtailment and reactive power control strategies is explored to provide higher efficiency and cater for the deficiency of both techniques. The analysis and simulation of these control techniques would be an aid for power system operators to evaluate penetration levels of PV in the network as well as provide technical support for local control techniques to alleviate overvoltages.

In scientific research, the debate on open data is still evolving. The development of open-source benchmark data set is essential to the entire community as it enables empirical analysis of developed algorithms, speedy development of novel solutions, and strategies for solving real problems. In this paper, we presented a hybrid approach based on OpenStreetMap (OSM) and Python integration to generate an open-source dataset for power distribution networks. Using Overpass Query Language, multiple queries are constructed to collect the different features of distribution networks such as power line segments, transformers, poles, end consumers (households), and substation locations. The queried data is further accessed and downloaded with the Overpass API in Python. The validation of concept is provided with a case study of small region in Australia and results are illustrated with geographical visualizations. The results show that developed datasets are effective as it supports the geographical validation of realistic distribution network models.

An improved multi-input multi-output (MIMO) positive position feedback (PPF) controller is designed for LCL-filtered voltage source inverter of solar powered islanded microgrid. The proposed controller is designed based on multi-input multi-output framework and simulated annealing optimization algorithm. This controller provides high performance in terms of damping, closed-loop bandwidth, tracking and cross coupling effects reduction to the LCL-filtered solar powered inverter unit. Moreover, the designed controller is robust against the shift in resonance frequency of LCL-filter due to filter parameters and line inductance variations. Simulation results of open- and closed loop system are presented and the result show that the proposed controller provides high performance for a 102.59 kW solar powered islanded microgrid.

The evolution of the traditional power system towards the modern smart grid has posed many new cybersecurity challenges to this critical infrastructure. One of the most dangerous cybersecurity threats is the False Data Injection (FDI) attack, especially when it is capable of completely bypassing the widely deployed Bad Data Detector of State Estimation and interrupting the normal operation of the power system. Most of the simulated FDI attacks are designed using simplified linearized DC model while most industry standard State Estimation systems are based on the nonlinear AC model. In this paper, a comprehensive FDI attack scheme is presented based on the nonlinear AC model. A case study of the nine-bus Western System Coordinated Council (WSCC)'s power system is provided, using an industry standard package to assess the outcomes of the proposed design scheme. A public FDI dataset is generated as a test set for the community to develop and evaluate new detection algorithms, which are lacking in the field. The FDI's stealthy quality of the dataset is assessed and proven through a preliminary analysis based on both physical power law and statistical analysis., Comment: 13 pages, 3 figures

A novel controller and its experimental implication for fast auto-focusing capability of a piezo-electrically driven micro-lens actuator is presented in this paper. Negative imaginary (NI) theory is used to design the feedback controller for the micro-lens actuator. A resonant controller is also a part of overall feedback control. The design is based on the identified model of the cantilever and it is implemented on a micro-lens actuator. The result shows that the application of the designed controller damps the first resonance mode of the actuator by 17 dB. The closed-loop system also demonstrated a reduction of settling time from 119 ms to 8 ms which indicates the significant improvement in auto-focusing as well as optical zooming of the micro-lens.

Although energy management of a microgrid is generally performed using a day-ahead scheduling method, its effectiveness has been questioned by the research community due to the existence of high uncertainty in renewable power generation, power demand and electricity market. As a result, real-time energy management schemes are recently developed to minimise the operating cost of a microgrid while high uncertainty presents in the network. This paper develops modified particle swarm optimisation (MPSO) algorithms to solve optimisation problems of energy management schemes for a community microgrid and proposes a scheduling approach after taking into consideration high uncertainty to effectively minimise the operational cost of the microgrid. The optimisation problems are formulated for real-time and scheduling approaches, and solution methods are developed to solve the problems. It is observed that the scheduling program demonstrates superior performance in all the cases, including uncertainty in prediction, as compared to the other energy management approaches, although solutions have significant deviations due to prediction errors.

One of the key requirements for designing reliable and scalable electrical distribution networks is the identification of the correct location of its electrical components. The location of transformers in the distribution network is crucial as they are the backbone for transforming one form of electricity into another. In this study, a transfer learning approach based on artificial intelligence is presented to determine the location of transformers in distribution networks. To implement the concept, a dataset of transformer images is collected from public platforms such as Google images. The collected dataset is then used to train the transfer learning model developed in Python. A custom object detection model for transformers is developed using the YOLOv3 architecture. To evaluate the potential of proposed approach, the developed model is tested on real-world images of transformers. The experimental results show that the transfer learning method can achieve a high detection accuracy for the detection of transformer locations. The presented research would be useful in the educational and practical fields as it could be used by electricity companies to develop, extend, and prepare modern distribution networks.

A distribution network is one of the main parts of a power system that distributes power to customers. While there are various types of power distribution networks, a recently introduced novel structure of an aromatic network could begin a new era in the distribution levels of power systems and designs of microgrids or smart grids. In order to minimize blackout periods during natural disasters and provide sustainable energy, improve energy efficiency and maintain stability of a distribution network, it is essential to configure/reconfigure the network topology based on its geographical location and power demand, and also important to realize its self-healing function. In this paper, a strategy for reconfiguring aromatic networks based on structures of natural aromatic molecules is explained. Various network structures are designed, and simulations have been conducted to justify the performance of each configuration. It is found that an aromatic network does not need to be fixed in a specific configuration (i.e., a DDT structure), which provides flexibility in designing networks and demonstrates that the successful use of such structures will be a perfect solution for both distribution networks and microgrid systems in providing sustainable energy to the end users.

This paper proposes a centralized robust controller for a partial feedback linearized model of a generalized DCDC voltage source converter in a standalone PV-BES system. Generally, a linear PI controller is applied to stabilize this type linearized model of power electronics interfaces; however, poor performance of such types of control schemes is observed under sudden change of operating points. To overcome this issue, this paper designs the centralized robust $H_{\infty}$ mixed-sensitivity loop shaping controller for partial feedback linearized model of nonlinear DC-DC power electronics converter for the PV-BES system. The performance of the proposed controller is demonstrated under sudden generation change of solar PV system where storage capability also associated in using simulations of a Simulink.

The fluctuations in PV power due to weather or passing clouds can reduce the stability and reliability of the solar power generated by grid interfaced PV system. This paper presents a grid interfaced photovoltaic (PV) and Electric Vehicle (EV) integration to smooth PV power fluctuations. A modified Moving Window Average (MWA) is used to smooth the PV power fluctuations. In this proposed method, a smooth PV power is estimated for 15 minutes time step. The difference between the predicted output power and the actual PV generation is provided by the EVs thus making the aggregated output power smooth. Real time one second interval solar data collected from the solar installation at the University of California, Los Angeles (UCLA) is used for testing the proposed method. The EV data is taken from UCLA, EV garage charging points. The smoother PV power for 15 min is considered from energy market prospective with its benefits to consumers. An evaluation based on comparative performance is made between the proposed and existing methods. The EV scheduling based on its availability and energy cost is presented for the cases of fully cloudy days and night time. During operation EVs are charged/discharged from/to grid keeping EV user benefits as priority.

This paper proposes a multi-timescale volt/var optimization for the optimal dispatch of battery energy storage system in smart distribution grids. It aims to coordinate the substation on-load tap changer operation on slow-timescale (hourly basis) with the photovoltaic inverters and battery storage operations on fast-timescale (15 min basis). This coordination is achieved by using two-stage stochastic programming and implemented via model predictive control. The power loss and energy purchase cost are reduced while maintaining voltages within limits. The forecasting uncertainties are modeled by generating a large number of random scenarios and then subsequently reducing scenario numbers to establish a tradeoff between computational burden and accuracy of the solution. The mixed-integer second-order cone program (MISOCP) is formulated with reduced scenarios, which achieves global optimum. Simulation results demonstrate the effectiveness of proposed MISOCP model in keeping the voltages within limits under forecasting uncertainties.

This paper presents a need-based distributed coordination strategy (NDCS) for multiple electric vehicle (EV) storages in an islanded commercial hybrid alternating-current (AC)/direct-current (DC) microgrid. The control capacity of the interlinking converter is enhanced by incorporating combined power-droop and voltage-droop strategies to leverage the coupling of AC and DC voltages. Therefore, the AC bus voltage can be regulated simultaneously by regulating only the DC bus voltage without affecting the power-sharing capabilities of the converter. The NDCS is proposed to coordinate the EV storages to regulate the DC bus voltage. The main objective of the NDCS is to decide whether the coordination of the available EV storages is to be done in a decentralised or a distributed manner. The mathematical model and the algorithm to deploy NDCS are developed to realise its application to a real system. The effectiveness of the control system is verified in a commercial hybrid AC/DC microgrid comprising one photovoltaic unit and four EV storages directly connected to the DC bus via DC/DC converters and four distributed-generation (DG) units connected to the AC bus using the conventional droop-control scheme. The performance of both controllers is tested under variable irradiation, commercial loading, and various fault conditions. The results of the case studies demonstrate the efficacy of the overall system in terms of robustness for a variable generation-demand scenario, time delay, EV plug-and-play, and fault conditions.

This paper proposes a nonlinear control scheme for power-sharing between parallel distributed generations in a standalone microgrids. Generally, a linear control-based droop method is applied to balance the power in between multigenerating units and demands; however, the poor performance of such types of schemes is observed under sudden change of operating point. To overcome this issue, this paper designs nonlinear output feedback linearized droop control for the active and reactive power sharing in a network. The performance of the proposed controller is demonstrated under load variation in using simulations of a Simulink for the standalone AC microgrids system.

The energy management of a microgrid becomes complex due to a number of reasons, including intermittent power generation, unpredictable load demand and variable real-time electricity prices. In this paper, different energy management schemes, such as real-time and scheduling, are analysed under uncertain environments to demonstrate their effectiveness in managing a microgrid’s energy. Optimisation problems are first formulated, then separate solution methods using particle swarm optimisation algorithms are applied to efficiently manage the microgrid’s energy. It is observed that the scheduling program in all the case studies can reduce the operational costs significantly.

This paper proposes a day-ahead energy management program for a residential house while taking into account the degradation costs of a battery to reflect the true operational costs. In the method, charging-discharging cycling costs are integrated into the energy exchange program for evaluating their effect on the operating costs. The optimisation problem formulated is solved using a framework, in which particle swarm optimisation (PSO) algorithm and Rainflow counting algorithm are used. Simulation results demonstrate that including cycling costs to the objective function estimates the appropriate operating costs of the scheduled energy. It is also found that the proposed algorithm can decrease electricity costs by around 11.29% as compared to the baseline approach. Simulation results also prove the effectiveness of the proposed method in managing battery energy effectively.

Growing electricity demand and scarcity of energy sources are the driving force of exploration of renewable energy sources. However, uncertain energy production of these sources creates difficulties in their control and operation with or without a grid utility connection. This paper proposes a novel power control strategy which employs a voltage band in the DC-link voltage to maintain voltage stability and thereby improve the power quality during temporary disturbances in a network. It is applied to act as an inverter’s inertia and obtain maximum benefit from the storage system, based on the features of a DC busbar to avoid a voltage limit violation. It also proposes a demand dispatch strategy based on a client’s terminal voltage, which avoids the complexity of communication lines, to balance the network power during power shortages. The developed control algorithm is tested on an IEEE 16 bus test feeder with multiple DG units and from the results it is found that the proposed control methods provide effective control of the voltage and power quality during transient conditions and scarce power generation.

A new method for the control of a battery energy storage system and its implementation on a 25 kW solar system to compensate solar power intermittency and improve distribution grid power quality is presented in this paper. The novelty of the proposed method is to provide a systematic way to optimize the size of the battery capacity for the desired level of solar power smoothing. This goal is achieved by designing a two-stage filter solution. The first stage is a fast response digital finite impulse response (FIR) filter that makes a trade-off between smoothing of the solar output and battery capacity. This paper proposes an optimal design of a minimum-length, low-group-delay FIR filter by employing convex optimization, discrete signal processing, and polynomial stabilization techniques. The new strategy proposed in this paper formulates the design of a length- $N$ low-group-delay FIR filter as a convex second-order cone programming, which guarantees that all the filter zeros are inside the unit circle (minimum-phase). A quasi-convex optimization problem is formulated to minimize the length of the low-group-delay FIR filter. The second-stage filter is designed to level the battery charging load. The effectiveness and performance of the proposed approach is demonstrated by simulation results and also over a real-case implementation.

In most nanotechnology applications, speed and precision are important requirements for obtaining good topographical maps of material surfaces using atomic force microscopes (AFMs), many o...

Payload hoisting and wind disturbance during crane operations are among the challenging factors that affect a payload sway and thus, affect the crane’s performance. This paper proposes a new online adaptive output-based command shaping (AOCS) technique for an effective payload sway reduction of an overhead crane under the influence of those effects. This technique enhances the previously developed output-based command shaping (OCS) which was effective only for a fixed system and without external disturbances. Unlike the conventional input shaping design technique which requires the system’s natural frequency and damping ratio, the proposed technique is designed by using the output signal and thus, an online adaptive algorithm can be formulated. To test the effectiveness of the AOCS, experiments are carried out using a laboratory overhead crane with a payload hoisting in the presence of wind, and with different payloads. The superiority of the method is confirmed by 82% and 29% reductions in the overall sway and the maximum transient sway respectively, when compared to the OCS, and two robust input shapers namely Zero Vibration Derivative-Derivative and Extra-Insensitive shapers. Furthermore, the method demonstrates a uniform crane’s performance under all conditions. It is envisaged that the proposed method can be very useful in designing an effective controller for a crane system with an unknown payload and under the influence of external disturbances.

This paper presents an approach to design a nonlinear controller for grid-connected fuel cell (FC) systems in order to provide ancillary services to low or medium voltage power networks. In this paper, these ancillary services mainly refer to the delivery of both active and reactive power from the FC to the grid under different operating conditions. The controller is designed based on the dynamical model of a proton exchange membrane fuel cell (PEMFC) which is developed from the electrical equivalent circuit of a grid-connected PEMFC through a voltage source converter (VSC). Partial feedback linearization technique is then employed to obtain the control laws with an aim of regulating respective currents related to both active and reactive power through the switching action of VSCs. The stability of the internal dynamics of the PEMFC system is also analyzed in this paper as the proposed control scheme cannot be implemented for unstable internal dynamics. A rate limiter is used with the proposed partial feedback linearizing controller to ensure the suitability under the worst-case conditions as well as to prevent the over-modulation in the rate of change of power references. The applicability and performance of the proposed controller is justified on a simple system as well as on a 12-bus balanced test distribution system and the CIGRE low voltage test distribution network under different grid events. The simulation results show the superiority of the proposed controller as compared to the vector controller.

In the last decade, there have been significant developments in the field of intelligent energy management systems (IEMSs), with various methods and new solutions proposed for managing the energy resources intelligently. An important issue related to finding the desired outcomes remains unexplored, i.e., how to determine key insights from the sparse academic literature in the age of digital publishing. To mitigate the issue, this study proposes a novel strategy to systematically survey the relevant studies by converting the sparse literature into visual presentations. We first apply a systematic approach called a PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) statement to provide the insights from the published literature of the past decade (2010–2020). Then, VOSviewer experiments are conducted to transform these sparse scholarly data into visual representations. In total, eighty-one papers published in high-impact journals are identified based on their scientific soundness and relevance, and a VOSviewer analysis is applied. The analysis revealed the existence of three research clusters focused on the following main thematic areas: the energy management in smart homes and smart grids (35 journal papers); the emerging concept of context-awareness (26 journal papers); and the role of privacy preservation in IEMSs (20 journal papers). This analysis uncovers the current state of IEMSs and explores existing issues, methods, findings, and gaps. Thus, future research directions have been recommended to fill the existing gaps. This systematic literature review is to assist both researchers and industry practitioners to understand the research gaps of previous studies.

In this paper, a nonlinear excitation control scheme is proposed to enhance transient stability and voltage regulation of higher-order synchronous generators. The controller is designed based on an adaptive backstepping method and the fifth-order dynamical model of synchronous generators is used to design the controller. The proposed controller is designed recursively to adapt some unknown stability sensitive parameters of synchronous generators and these unknown parameters are estimated through adaptation laws based on the formulation of control Lyapunov functions (CLFs). The proposed control scheme is also capable to overcome the over-parameterization problems of unknown parameters. Finally, the effectiveness of the proposed scheme is evaluated on a test single machine infinite bus system by applying a symmetrical three-phase short-circuit fault at different locations of the test system and compared to that of an existing adaptive backstepping controller. Simulation results demonstrate the superiority of the proposed control scheme over the existing controller in terms of settling time and providing additional damping into the system.

In this paper, a data driven controller is designed for the positioning control of a piezoelectric tube scanner (PTS) used in an atomic force microscope (AFM). A single-input single-output (SISO) model-based data driven controller is synthesized by using a mixed negative imaginary (NI) and small gain (SG) approach. The controller is implemented on an AFM and gives significant damping and tracking performance, with 16.64 dB and 19.33 dB damping at the resonance frequency of the X and Y-axes of the PTS, respectively. Moreover, this type of controller design is an effective approach to give intuition about how the controller frequency response should, depending on the design constraints being applied, which ensures optimized performance in vibration control. Refereed/Peer-reviewed

In this paper, a high-efficiency third harmonic injected pulse width modulation (PWM) scheme is presented for an ultracapacitor-based energy storage system (UCESS). Also, a nonlinear partial feedback linearizing controller is used with the proposed third harmonic injected PWM scheme where the main control objectives are to regulate the active and reactive power injection into the grid from the UCESS. The dynamical model of the grid-connected UCESS is developed from the electrical equivalent circuit, where the grid connection is accomplished through a three-phase voltage source converter (VSC). The grid current components corresponding to both active and reactive power are regulated through the proposed control action. Since the proposed control scheme reduces the order of the original UCESS, the stability of remaining dynamics (i.e., internal dynamics) is investigated before implementing the proposed controller. The implementation of the proposed control scheme also requires the injection of the third harmonic externally as the incorporation of this harmonic during the modeling does not affect the transient and steady-state characteristics of the system. A test distribution system is considered to justify the performance of the proposed scheme through the simulation results. The simulation results show the superiority of the proposed controller as compared to a finely-tuned proportion integral (PI) controller.

In this paper, a data-driven controller is synthesised for systems with negative imaginary (NI) properties such as the piezoelectric tube scanner (PTS) of an atomic force microscopy (AFM). The scanning speed of an AFM is limited by the dynamic behaviours of its scanning unit. The controller aims to damp vibrations, increase the closed-loop bandwidth and track reference signals with high accuracy. The measured frequency response of the PTS is used to calculate the controller frequency response at each frequency by minimising a cost function. The data-driven control optimisation problem is constrained by NI properties to guarantee that the controller is stable and identifiable. The designed controller is implemented on the AFM using a dSPACE ds1103 real-time prototyping system. Experimental results show that the controller is able to significantly damp the resonances in the scanner's lateral axes, provide a high-bandwidth closed-loop system and allow reference signal tracking. Refereed/Peer-reviewed

This paper proposes a nonlinear controller to mitigate subsynchronous control interaction (SSCI) in series-compensated doubly fed induction generator (DFIG)-based wind farms. The controller is designed based on partial feedback linearization (PFL) and the proposed design approach involves scrutinizing the partial feedback linearizability of the system. The stability of the internal dynamics, which is not transformed into linear autonomous subsystems by PFL, is also analyzed in the process of deriving the control laws. The frequency scanning method is used to evaluate the performance of the proposed PFL controller, and the performance is compared to that of a finely tuned conventional proportional integral controller. A grid-connected series-compensated 100-MW DFIG-based offshore wind farm is used to demonstrate the performance of the proposed scheme through the identification and mitigation of subsynchronous resonance. An analysis of the power system reveals that the resistance is negative across the entire subsynchronous frequency range, while the reactance becomes negative around 42 Hz. The proposed controller effectively mitigates SSCI, and it can be observed that it results in positive resistance and reactance values across the entire subsynchronous frequency range. Results from the eigenvalue (modal) analysis and electromagnetic transient simulation also confirm the results obtained from frequency scanning.

Nanotechnology is the branch of science which deals with the manipulation of matters at an extremely high resolution down to the atomic level. In recent years, atomic force microscopy (AFM) has proven to be extremely versatile as an investigative tool in this field. The imaging performance of AFMs is hindered by: 1) the complex behavior of piezo materials, such as vibrations due to the lightly damped low-frequency resonant modes, inherent hysteresis, and creep nonlinearities; 2) the cross-coupling effect caused by the piezoelectric tube scanner (PTS); 3) the limited bandwidth of the probe; 4) the limitations of the conventional raster scanning method using a triangular reference signal; 5) the limited bandwidth of the proportional-integral controllers used in AFMs; 6) the offset, noise, and limited sensitivity of position sensors and photodetectors; and 7) the limited sampling rate of the AFM's measurement unit. Due to these limitations, an AFM has a high spatial but low temporal resolution, i.e., its imaging is slow, e.g., an image frame of a living cell takes up to 120 s, which means that rapid biological processes that occur in seconds cannot be studied using commercially available AFMs. There is a need to perform fast scans using an AFM with nanoscale accuracy. This paper presents a survey of the literature, presents an overview of a few emerging innovative solutions in AFM imaging, and proposes future research directions.

This paper presents a distributed multiagent scheme to detect and identify cyber threats on the protection systems of power grids. The integration of information and communication technologies into existing power grids builds critical cyber-physical energy systems, in which digital relays are networked cyber-physical components subject to various cyber threats. Cyber attacks on protection systems may mimic real faults, cause component failure, and disable the communication links. Agents utilize both cyber and physical properties to reinforce the detection technique and further distinguish cyber attacks from physical faults. This paper also introduces the problem of secure communication protocols and highlights the comparative studies for enhancing the security of the protection systems. The proposed scheme is validated using a benchmark power system under various fault and cyber attack scenarios.

This paper presents a new robust nonlinear excitation controller design for synchronous generators in multimachine power systems to enhance the transient stability. The mismatches between the original power system model and formulated mathematical model are considered as uncertainties, which are modeled through the satisfaction of matching conditions. The exogenous noises appearing from measurements are incorporated with the power system model including the two-axis model of synchronous generators. The partial feedback linearization technique is used to design the controller which transforms the original nonlinear multimachine power system model into several reduced-order linear and autonomous subsystems. The desired control law is obtained for each subsystem and implemented in a decentralized manner provided that the dynamics of the autonomous subsystems have no effects on the overall stability of the system. The analysis related to the dynamics of noisy autonomous subsystems is also included and the proposed controller has the excellent capability to decouple these noises. Finally, the performance of the proposed control scheme is evaluated on an IEEE 39-bus benchmark power system following different types of large disturbances. The performance of the proposed controller is compared to that of a partial feedback linearizing controller, which is designed without robustness properties, to verify the effectiveness of the proposed control scheme.

A distribution network is one of the main parts of a power system as it is connected directly to the load center and can act as a microgrid during an islanding operation. The concept of integrating both renewable and distributed energy sources at the distribution level is currently of great interest for power system engineers. Further research by power companies and engineers on applying better techniques to improve the power quality and stability of dynamic devices in a distribution network is ongoing. This paper presents a review of fundamental distribution network architectures, including radial, ring, and mesh ones, which considers their types of operation, control and management, growth model, and advantages and disadvantages. The existing networks suffer from various technical and quality issues while they are also vulnerable to the natural disaster and any type of fault in the system. To overcome the problems associated with present networks, a novel architecture of aromatic structure is proposed. Simulations carried out show that this new network can perform better than existing ones in terms of load flow, power quality, reliability, and islanding operations. It is also a perfect choice for both a distribution network and microgrid in a cyclone-prone area as it has both overhead and underground network facilities.

In modern power systems the increasing penetration of renewables and power electronics, particularly converter-based wind and solar power generation, is transforming system dynamics and bringing new stability concerns. One major issue that is currently attracting considerable attention is the wide range of power oscillations associated with renewable power generators (RPGs). The unique characteristics of RPGs, especially their low inertia, power-electronic interface and wide-band response, not only impact the classical electromechanical and torsional oscillations of the power system (that were previously dominated by synchronous machines), but they also cause emerging oscillatory stability problems, such as sub/super-synchronous interactions, medium/high-frequency oscillations and harmonic instability. This Special Issue covers the analysis, modelling, monitoring and control of power oscillations associated with renewable power generations. The theme of the Special Issue is broadly divided into: Section 1 subsynchronous interaction: analysis & mitigation, Section 2 monitoring of subsynchronous oscillation, Section 3 modelling and stability of converter-interfaced RPGs, Section 4 low-frequency oscillations associated with RPGs, Section 5 virtual inertia techniques to support low-inertia RPGs and Section 6 power quality and ferroresonance issues in distributed RPGs. A brief discussion of the authors' contributions is given in this Editorial.

This paper addresses a power sharing method of electronically interfaced distributed generation (DG) units in the context of a multiple-DG microgrid system where there is no synchronous generator. The emphasis is primarily on electronically interfaced DG (EI-DG) units like DFIG connected wind generator, PV system and battery storage. The power sharing is mainly based on locally measured signals without communications. The inverter connected to the battery energy storage system (BESS) changes the frequency of the network with the change in load demand. The real power of each DG unit is controlled based on a frequency-droop characteristic and a complimentary frequency restoration strategy. A systematic approach to develop a small-signal dynamic model of a multiple-DG microgrid, including a real power management method, is also presented. The model is also used to investigate sensitivity of the design to the changes in parameters and operating point and to optimize performance of the microgrid system.

The integration of renewable energy sources together with an energy storage system into a distribution network has become essential not only to maintain continuous electricity supply but also to minimise electricity costs. The operational costs of this paradigm depend highly upon the optimal use of battery energy. This paper proposes day-ahead scheduling of the battery energy while considering its degradation costs due to charging-discharging cycles. The degradation costs with respect to the depth of charge are modelled and added to the objective function to determine the actual operational costs of the system. A framework to solve the function is developed in which particle swarm optimisation, the Rainflow algorithm and scenario techniques are integrated. Uncertainties of parameters, modelled by scenario generation and reduced by scenario reduction techniques, are discussed. Simulation results demonstrate that the proposed method can reduce the operational costs by around 40% compared to the baseline method. They also reveal that uncertainty in power generation and power demand has no influence on the energy schedule of the battery, but variation in electricity prices has an impact on the outcome. Several pragmatic tests verify the effectiveness of the proposed method.

The typical layout of power systems is experiencing significant change, due to the high penetration of renewable energy sources (RESs). The ongoing evaluation of power systems is expecting more detailed and accurate mathematical modeling approaches for RESs which are dominated by power electronics. Although modeling techniques based on state&ndash, space averaging (SSA) have traditionally been used to mathematically represent the dynamics of power systems, the performance of such a model-based system degrades under high switching frequency. The multi-frequency averaging (MFA)-based higher-index dynamic phasor modeling tool is proposed in this paper, which is entirely new and can provide better estimations of dynamics. Dynamic stability analysis is presented in this paper for the MFA-based higher-index dynamical model of single-stage single-phase (SSSP) grid-connected photovoltaic (PV) systems under different switching frequencies.

The corrosion of steel reinforcement in concrete often leads to huge unbudgeted expenses for maintaining, monitoring and renovating an infrastructure. This is mainly due to the presence of salts or chemical chlorides that pose a danger to the concrete structures. The determination of the existence of these corrosive salts is vital for defining the service life of concrete. This research looked at developing an electromagnetic induction (EMI) sensor for the detection of corrosive salts. The first design adopted a single-loop coil (SLC) concept, and the second was based on a multiple-loop coil (MLC) one using copper wire. Tests were conducted on these two techniques, and with the results obtained, the latter seemed more promising, thus, a prototype sensor was developed using the MLC concept. As this new prototype sensor was able to detect the manifestation of chemical contents in a concrete structure, it could be used as a non-destructive evaluation (NDE) technique for the detection of corrosive chemicals in concrete and has the further possibility of detecting corrosion in concrete.

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

In this paper, a robust H ∞ mixed-sensitivity loop shaping control synthesis is proposed to stabilize a output feedback linearized grid-connected voltage source inverter (VSI) system. Traditionally, linear controllers are applied for the reliable operation of VSI to tackle small disturbances around a specific operating point. However, the performance of such controllers degrades under severe disturbances. Though there are several nonlinear control scheme to overcome issues related to operating points, but the robustness against different factors is not covered. The dynamical nonlinear model of VSI is linearized by output feedback linearization scheme to provide independent operating point. However, the feedback linearized system performance is depend on system parameters. The main contribution to investigate the robustness on existing controller for feedback linearized grid-connected VSI system under severe disturbance. The proposed scheme will be developed also to provide robustness during measurment noise rejection of output feedback signal.

A process of critical transmission lines identification in presented here. The criticality is based on network flow, which is essential for power grid connectivity monitoring as well as vulnerability assessment. The proposed method can be utilized as a supplement of traditional situational awareness tool in the energy management system of the power grid control center. At first, a flow network is obtained from topological as well as functional features of the power grid. Then from the duality property of a linear programming problem, the maximum flow problem is converted to a minimum cut problem. Critical transmission lines are identified as a solution of the dual problem. An overall set of transmission lines are identified from the solution of the network flow problem. Simulation of standard IEEE test cases validates the application of the method in finding critical transmission lines of the power grid.

Although several energy management schemes are developed in the literature, they are not thoroughly examined in order to reduce the impact of uncertain power generation, demand and electricity prices to minimise the operating cost of a small-scale power system. This paper investigates energy management schemes under uncertain environments and proposes a scheduling scheme to minimise the operating cost of a grid-connected microgrid. Optimisation problems are formulated as real-time scheduling approaches, and are solved by developing modified particle swarm optimisation (MPSO) algorithms. The modification in the MPSO algorithm is performed through structural change in incorporating a mechanism that regulates the selection procedure for decision variables. The effectiveness of the proposed algorithm is justified by comparing the results with other recent algorithms. All the algorithms are separately tuned using the Taguchi technique to demonstrate a fair comparison in solving the scheduling problem. The scheduling program demonstrates superior performance in all cases, including when there is uncertainty in prediction, as compared to other energy management approaches, although solutions have significant deviations due to prediction errors. It is also shown that the proposed MPSO algorithm for the scheduling program can save 16.80 per cent operational cost as compared to the PSO algorithm.

Summary This paper presents a systematic approach to the design of a nonlinear robust dynamic state feedback controller for nonlinear uncertain systems using copies of the plant nonlinearities. The technique is based on the use of integral quadratic constraints and minimax linear quadratic regulator control, and uses a structured uncertainty representation. The approach combines a linear state feedback guaranteed cost controller and copies of the plant nonlinearities to form a robust nonlinear controller with a novel control architecture. A nonlinear state feedback controller is designed for a synchronous machine using the proposed method. The design provides improved stability and transient response in the presence of uncertainty and nonlinearity in the system and also provides a guaranteed bound on the cost function. An automatic voltage regulator to track reference terminal voltage is also provided by a state feedback equivalent robust nonlinear proportional integral controller. Copyright © 2016 John Wiley & Sons, Ltd.

Evacuation planning contains more than a few decisions which have to be made in a very short period of time and in the most appropriate way. Evacuation path optimization has vital importance in reducing the human and social harm and saving the aid time. Significant research efforts have been made in the literature to deal with evacuation optimization on the basis of deterministic optimization model, nevertheless the stochastic aspects or uncertainty of real-world evacuation have not been taken into account comprehensively. Inspired by the promising performance of heuristic algorithms to solve combinatorial problems, this paper proposes an improved quantum ant colony algorithm (QACA) for exhaustive optimization of the evacuation path that people can evacuate from hazardous areas to safe areas. In comparison with ACO (ant colony optimization) based method, QACA has the capability of finding a good solution faster using fewer individuals and possesses strong robustness, as a result of the quantum representation and updating of pheromone. Experiment results show that the proposed approach executes more effectively during evacuation.

In this paper, a new agent-based distributed reactive power management scheme is proposed to improve the voltage stability of energy distribution systems with distributed generation units. Three types of agents - distribution system agent, estimator agent, and control agent are developed within the multi-agent framework. The agents simultaneously coordinated their activities through the online information and energy flow. The overall achievement of the proposed scheme depends on the coordination between two tasks - (i) estimation of reactive power using voltage variation formula and (ii) necessary control actions to provide the estimated reactive power to the distribution networks through the distributed static synchronous compensators. A linear quadratic regulator with a proportional integrator is designed for the control agent in order to control the reactive component of the current and the DC voltage of the compensators. The performance of the proposed scheme is tested on a 10-bus power distribution network under various scenarios. The effectiveness is validated by comparing the proposed approach to the conventional proportional integral control approach. It is found that, the agent-based scheme provides excellent robust performance under various operating conditions of the power distribution network.

This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with solar generation, stationary Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicle (EV) integration. Taking into consideration of a comprehensive pricing model, we first formulate a deterministic DSM as a mixed integer linear programming problem, assuming perfect knowledge of the uncertainties in the system. A two-stage stochastic DSM is further developed that addresses the stochastic nature in solar generation, loads, EV availabilities and EV energy demands. The proposed DSMs are validated with real solar generation, loads, BESS and EV data using sample average approximation. Detailed case studies show that the stochastic DSM outperforms its deterministic counterpart for cost saving for a wide range of prices, though at the expense of higher computational time. Computational results also demonstrate that moderate number of EVs helps to cut down the overall operation cost, which sheds light on the benefit of future large scale EV integration to smart buildings.

This paper presents the design and experimental implementation of a novel multi-input multi-output (MIMO) control structure using two negative-imaginary damping controllers to damp the first resonant mode and attenuate cross coupling effects between the axes of a piezoelectric tube scanner. The dynamics of the scanner in the lateral and longitudinal axes are identified from measured data and the design of the controller using a MIMO framework is done based on a reference model matching approach. The controller proposed in the paper is able to achieve a bandwidth near to the first resonance frequency of the scanner and the proposed controller is robust against changes in resonance frequencies that are due to load changes on the scanner. Experimental results are presented to show the effectiveness of the proposed controller.

This paper proposes a new control scheme for a 3-dimensional (3D) overhead crane having simultaneous payload hoisting and persistent wind disturbance. Control of the 3D crane system under those effects is highly challenging as it results in a high unwanted payload sway and inaccurate payload positioning. The proposed control structure which combines a new adaptive command shaping technique designed based on output signals and an integral sliding mode (ISM) control provides a precise crane positioning and low payload sway. The two independent controllers are designed such that they can practically be combined and implemented without affecting the overall stability of the closed-loop system. To test the effectiveness of the proposed method, simulations and experiments on a laboratory overhead crane are carried out under various conditions with different payload masses, various cable lengths, payload hoisting and external wind disturbance. Under all operating conditions, by comparing with an ISM control, the combined controller gives smoother and more satisfactory transient responses with two to four-fold reductions in the overall payload sway. In addition, the controller provides substantial reductions in the overall chattering of the control signal and sliding manifold which resulted in smoother control signals and system responses.

The proportional power sharing is essential to guarantee reliability on the operation of an islanded microgrid. However, inaccurate reactive power sharing in scenarios with line impedance mismatches, and slow transient response remains important limitations of most of the conventional power sharing controllers. In this work, a novel method for power sharing control based on a decentralized Direct Current Primary Controller (DCPC) and a distributed Consensus-based Quadrature Current Controller (CQCC) is proposed. The main properties of the control approach are shown including accurate proportional active and reactive power sharing under mismatched feeder impedances and frequency regulation characteristics. Faster transient response, power sharing accuracy and minimum communication requirements are the advantages of the proposed method. The controller properties, the impact of constant communication time delays and communication failures on the power sharing performance are evaluated on different experimental study cases. These experimental results show that the accurate power sharing state is achieved in less than 2 s with frequency deviations lower than 40 mHz in all the study cases. Additionally, it is shown that the control approach tolerates constant communication time-delay of more than 200 ms and multiple communication failures that maintains the connectivity properties on the communication graph.

Transformerless inverters (TIs) are widespread in photovoltaic (PV) applications due to their low cost and high efficiency. However, TIs suffer due to leakage current as a consequence of PV parasitic capacitance, and high-frequency common-mode voltage, which has resulted in the emergence of several topological and control scheme modifications. In this article, a switched-capacitor (SC) based inverter capable of synthesizing a five-level voltage is proposed. In the devised structure, the negative pole of the dc bus is directly connected to the grid neutral, thereby resulting in zero leakage current. In addition, industrial standard half-bridge modules can be employed directly for implementing the proposed inverter without modifications, making it more practically feasible. The proposed topology has the capability of self-voltage balancing of SC, reactive power processing, and full dc-bus utilization as opposed to half-bridge topologies. The principle of operation and a simple logic-gate-based pulse generation scheme is presented in detail. Simulation and experimental results confirming the practical viability of the proposed topology are presented. Finally, the superior features and the merits of the proposed topology are shown through a detailed comparison against state-of-the-art topologies.

This paper proposes a Bidirectional Fuzzy Brain Emotional Learning (BFBEL) control system to control Aerial Robots. The proposed controller is based on the emotional and logical processing of the brain. The proposed control system merges fuzzy inference and a bidirectional brain emotional learning algorithm. The Bidirectional Fuzzy Brain Emotional Learning (BFBEL) control can learn from scratch and adapt rapidly in real-time to control the system without much prior information. The proposed controller is tested against simulations of both a 1-Degree-Of-Freedom (DOF) flapping wing and a 6DOF flapping wing model and successfully implemented on a 1DOF flapping wing experiment which showcases the learning and adaptation capability in a real-time environment.