Search

Trap plays an important role in the charge transportation of three-layer oil-paper and the investigation of trap parameters at different degradation degree is of great significance for further research of the partial discharge (PD) mechanism. This article considers the turn-to-turn structure of on-board traction transformer. A defect model of three-layer oil-paper insulation was designed for PD test. The PD degradation samples are selected from the inception of PD to the breakdown of PD to study the characteristics of surface potential decay on degradation samples by using isothermal surface potential decay method. Given that, the trap distribution at different degradation stages was calculated and its influence on PD characteristics was investigated. The result shows that AC fluctuation voltages caused by inter-harmonic has a great influence on PD distribution, surface charge distribution and trap distribution. With the increase of degradation, the decay rate of surface potential increases and the decay rate of AC fluctuation voltages is larger than that pure AC. And also, the number of shallow trap with AC fluctuation voltages is larger than that with pure AC. The rate of free charge increases with the number of shallow trap increasing. More free electrons involves in the process of PD, accelerating the aging rate of three-layer oil paper samples. Therefore, it can be concluded that surface charge and trap distribution are helpful to clarify PD mechanism.

Insulated tubular busbar is a new type of current-carrying equipment with excellent performance. However, the research on this equipment is insufficient because of the short application and the lack of technical digestion, which has resulted in many accidents. In this paper, the full-scale model of epoxy-impregnated-paper insulated tubular busbar with bubble defects was designed and produced, its electrical breakdown process was investigated by withstand voltage test. In the experiment, the failure mode of breakdown caused by gas expansion was found for the first time. The research showed that the electrical-mechanical breakdown process could be divided into two stages: gas increase stage and breakdown threshold stage. The research also obtained the characteristics and the change criterions of the two stages. The gas in the bubble defects generated from epoxy decomposition which caused by partial discharge. When the gas pressure exceeds the critical fracture toughness of the bubble defects wall, the unstable crack extension occurs, therefore, improving the ability about resistance to partial discharge of epoxy resin or improving the fracture toughness of epoxy resin by modifying would enhance the breakdown performance of insulated tubular busbar. The result also has reference significance for other epoxy-impregnated-paper insulated electrical equipment.

Oil-immersed insulation paper (OIP) bushing is one of the most important equipment in power transmission and distribution systems. Moisture is one of the most serious factors that affect the insulation status of OIP bushing. In this paper, the OIP bushing was assumed to be in a stable and simplicity condition, and then the transient moisture distribution of capacitor core and its effects on the electric field were studied. First, by dissecting a 110 kV OIP bushing, the specific sizes for modelling were obtained. Afterwards, a moisture diffusion model for OIP bushing was established based on COMSOL Multiphysics software, and it was also verified by moisture diffusion experiment. Then, the moisture distribution for different conditions were obtained and analyzed. Finally, a model for the electric field of OIP bushing was established, and then the effects of moisture on the electric field were studied.

Oil-paper insulation will inevitably endure impulse voltage with a superimposed dc voltage (superimposed voltage) because of introduced lightning or switching, which can induce charge accumulation and trigger flashover during the operation of a converter transformer. Temperature rise is also a nonnegligible factor that can significantly influence the performances of oil-paper insulation. This paper aims to reveal the effects of ambient temperature and superimposed voltage on surface charge and flashover behaviors in an oil-paper insulation system. Surface potential decay (SPD) experiments were performed under superimposed voltage with various polarity and amplitude combinations and at the temperature ranging from 20 °C to 80 °C. The results show that the initial surface potential and decay rate increase as the amplitude of the impulse voltage increases at 20 °C. The initial surface potential declines, and the process of SPD is accelerated at higher temperatures because of the increased concentration of shallow traps. Subsequently, flashover tests were conducted under superimposed voltage. It is found that flashover occurs after applying superimposed voltages even with a rather low dc voltage. In addition, the dc voltage component is primarily responsible for flashover. Weibull results also indicate that it is prone to trigger flashover at higher temperatures, because thermal excitation helps more carriers to escape from deep traps and participate in the process of flashover. Accordingly, the insulation characteristics of oil-paper system can be significantly influenced by superimposed voltage and temperature, which should draw great concern among the power grid.

This paper presents a thorough review of transmitarray devices particularly aiming antenna beamsteering, gathering some of the most relevant solutions published by the scientific community in the field. First, the background for realizing 1-D and 2-D antenna beamsteering with a transmitarray is introduced. Subsequently, several examples of unit-cells for transmitarray implementation and complete transmitarray designs presented in the literature are outlined. Each solution is analyzed in detail, identifying the nature of its layout, e.g., based on microstrip patches, frequency selective surfaces (FSS), or metamaterials (MMs), and the method employed to enable electronic reconfigurability, e.g., p-i-n diodes, varactor diodes, or microelectromechanical systems (MEMS). In addition, some models with the capability of controlling the wavefront polarization modes are also included herein since these are the base of hybrid transmitarrays, i.e., transmitarray with both electronic beamsteering and polarization control. Finally, all the models are compared against each other in order to highlight their benefits and limitations, summarizing their main characteristics, such as the frequency of operation and bandwidth, insertion loss, physical dimensions, and maximum beamsteering range, when available.

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.

Lifetime estimation of power semiconductor devices have been widely investigated to improve the reliability and reduce the cost of maintenance of power converters. However in most reported work, the aging effect is not considered in the lifetime evaluation process due to the omission or limitation of thermal cycle counting method. Additionally, the electrical/thermal simulation and lifetime estimation are usually implemented in different simulators/platforms, for the same reason. Thus, to tackle these problems, a concise but comprehensive MOSFET model that enables electro-thermal modeling, aging and lifetime estimation on LTspice® circuit simulator is proposed in this paper. The idea comes from the fact that, MOSFET on-state resistance Rds,on is not only temperature dependent, but also widely accepted as the device failure precursor. In other words, as it carries critical information about instantaneous temperature and aging progress. Hence, co-simulation can be achieved by constructing electrical, thermal, and aging and lifetime sub-modules exclusively first, and using Rds,on, to build linkages among them. Averaged modeling technique is adopted due to the ease of establishing links among these three sub-modules, and fast simulation speed as compared to a switched converter model. Behavioral models are employed to realize the thermal cycles counting, stress accumulation and degradation evaluation. This paper demonstrates that it is possible to use a single simulation software to monitor performances of devices and circuits, and their lifetime estimation simultaneously. High-stress thermal cycling and long-term random mission profiles are applied to verify the correctness of the model and to mimic a 10-year load respectively. An accelerated aging trend can be observed in the long-term mission profile simulation, which is in agreement with the theory. Facilitated by the employment of averaged circuits, the proposed method is a good simulation/analytical tool to implement a long-term mission profile that requires reliability assessment.

In this paper, we propose a simple framework for closed-loop system identification: the stabilized output error method. Traditional closed-loop system identification methods rely on the linearity of the target system, and they require the identification of noise models or prior knowledge or identifiability of the feedback controllers to obtain unbiased estimates. But in many real-world applications, the noise dynamics and feedback controllers are complex and difficult to identify, and the nonlinearity may not be ignored. The proposed framework introduces a virtual controller that stabilizes the error between model prediction and the output of the target system. This enables us to apply the output error method, which gives unbiased estimates without depending on the noise model and is applicable to a wide range of models, including nonlinear systems, to closed-loop system identification problems. The paper describes the framework and gives the theoretical support and design guidelines for virtual controllers. Through numerical examples, we show the effectiveness of the proposed framework in various situations, which include identification of (a) linear gray box models, (b) systems in the presence of disturbances having realistic complexity, and (c) a nonlinear unstable system in a human-in-the-loop environment.

The aim of this paper is to design, analyze and optimize the “Multi-Stack Slotless Axial Flux Switching Permanent Magnet Machine”. In the design process, mathematical models are implemented, and the Finite Element Method (FEM) is performed to analyze the machine performances. The paper aims to minimize the occurrence of cogging torque and torque ripple in the multi-stack slotless stator AFPM machine. As a consequence, it reduces the vibrations in the machine and increases its life span. Multi-stack slotless stator AFPM machine with a right-angled trapezoid-shaped PM is proposed and comparison is done with conventional shape AFPM machine. In order to examine the performance of multi-stack slotless stator AFPM machine Finite Element Analysis (FEA) is used. To further enhance the characteristics of the designed machine with the proposed right-angled PM shape, optimization is done by considering inner and outer pole pitch as the design variables. In optimization process, krigging method assigned with Latin Hyper-cube Sampling and a genetic algorithm (GA) is performed due to suitability with non-linear data. Then, finite element analysis by JMAG-Designer is performed to verify the results. It is determined that optimized model has achieved 65% reduction in torque ripples as compared with the conventional design. Hence, this work attempts to optimize the performance of the AFPM machine.

This paper reveals the fundamental reason for the asymmetrical issue in magnetic-geared machines (MGMs) based on flux modulation theory. The analysis indicates that the magnetic circuits of three-phase windings in MGMs are inherently asymmetrical. This asymmetrical issue is even more severe in conventional consequent-pole MGMs (CP-MGMs) because of the more distorted magnetic field. Hence, to address the asymmetrical issue, a novel structure featuring enhanced harmonic elimination capability is proposed in this paper. Consequently, torque density can also be improved. In particular, the proposed CP-MGM employs modular structure and axially embedded permanent magnets (PMs) to achieve symmetrical back electromotive force (EMF) waveforms and improved torque capability, respectively. To further improve the electromagnetic performance of the proposed CP-MGM, the PM arc ratio and flux modulator width ratio are analytically designed, which provides a general design guideline for CP-MGMs. To illustrate the merits of the proposed CP-MGM, a few other MGMs are included for a fair comparison based on finite element analysis (FEA). Results show the proposed CP-MGM can achieve more symmetrical back EMF waveforms and lower torque ripple, as well as lower PM consumption and higher torque density, as compared with its MGM counterparts. Published version Schaeffler Group Research Grant M4062765.040.601001

The present article outlines the core technologies needed to realize the Hyperloop transportation system (HTS). Currently, the HTS vacuum tube train concept is viewed as the fastest way to cross the earth’s surface. However, the concept has not yet been demonstrated for subsonic or near-sonic speeds in large-scale implementations. Among the challenging technical areas are the tube’s depressurization, the capsule’s air resistance, and choked flows occurring around the capsule. There is also a need for effective levitation and propulsion solutions compatible with the velocities being proposed. Moreover, several conflicting objectives have been identified in the HTS design. It is highlighted that the tubes should be wide enough to lower the capsule’s drag forces but that a large-sized tube comes at the expense of higher operational energy costs and infrastructure investments. Another struggle is aiming at a low-cost passive track design and, and at the same time, a lightweight vehicle. One technical path is to turn the whole guideway into an electric propulsor, arriving at a ’lightweight capsule solution’ (LCS). Alternatively, the vehicle could be transformed into a ’modified airplane’ that stores massive amounts of onboard energy, yielding an energy-autonomous ’low infrastructure solution’ (LIS). In a case study, it is shown that even LIS technology is compatible with short-haul flights (1500km) but that it requires an energy reservoir of about 30% of the capsule’s overall mass. Results and discussions presented in this paper are supported by analytical predictions with parameters or input data either supported by referred simulations or experiments. In general, the paper aims to open up the discussion, provide a sufficient understanding of the Hyperloop field’s multidisciplinary aspects, and establish a foundation for further investigations. In the end, new research tasks have been defined, which have the potential to go beyond the state of current knowledge. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

The increasing number of megawatt-scale photovoltaic (PV) power plants and other large inverter-based power stations that are being added to the power system are leading to changes in the way the power grid is operated. In response to these changes, new grid code requirements establish that inverter-based power stations should not only remain connected to the grid during faulty conditions but, also provide dynamic support. This feature is referred in the literature to as momentary cessation operation. The few published studies about momentary cessation operation for PV power plants have not shed much light on the impact of these systems on the overall power system stability problem. As an attempt to address this issue, this paper proposes a control scheme for PV inverters that improves the transient stability of a synchronous generator connected to the grid. It is shown through the paper that the proposed control scheme makes the PV inverter’s dc link capacitors absorb some of the kinetic energy stored in the synchronous machine during momentary cessation. Besides that, the proposed solution is also able to improve voltage stability through the injection of reactive power. Experimental and simulation results are presented in order to demonstrate the effectiveness of the proposed control scheme.

This paper discusses a novel fault location approach using single ended measurement. The natural dissipation of the circuit parameters are considered for fault location. A relationship between the damped natural frequency of oscillation of the transmission line current and fault location is established in this paper. The hybrid dc circuit breaker (dcCB) interrupts the fault current and the line current attenuates under the absence of any driving voltage source. The line capacitance discharges into the fault at a specific frequency of oscillation and rate of attenuation. Utilizing this information, the fault location in a multi-terminal direct current (MTdc) network can be predicted. A three terminal radial model of a MTdc is used for performance evaluation of the proposed method using Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc)., 8 pages, 12 Figures, 6 Tables

Industry 4.0 is a revolution in manufacturing by introducing disruptive technologies such as Internet of Things (IoT) and cloud-computing into the heart of the factory. The resulting increased automation and the improved production synergy between stocks, supply chains and customer demands, come along with the threats and attacks from the Internet. Despite extensive literature on the cybersecurity topic, many actors in manufacturing factories are just realizing the impact of cybersecurity in the preservation of their business. This paper introduces step-by-step the concepts and practical aspects of an Industry 4.0 manufacturing factory that are related to cybersecurity. Based on a subdivision of a typical factory into several generic perimeters, we present the vulnerabilities and threats regarding the network and devices usually found in each perimeter. Therefore, it is more efficient to present the recent proposals of the literature regarding cybersecurity guidelines and solutions in Industry 4.0. Instead of spreading a lot of references regarding every aspect of cybersecurity, we focused on a limited number of papers among the recent references. However, for each paper, we provide the details about the purpose of the proposal, the methodology adopted, the technical solution developed and its evaluation by the authors. These solutions range from classical cybersecurity countermeasures to innovative ones, such as those based on honeypots and digital twins. In order to deliver a review also useful to non scientists, we present our guidelines along with those of some organizations involved in cybersecurity harmonization and standardization in the world.

This paper proposes a linear compensator algorithm to improve the performance of an absolute multipolar magnetic encoder (AMPME). An AMPME is an absolute magnetic rotary encoder that uses a multipolar magnet (MPM) to increase the resolution. The resolution can be dramatically increased in proportion to the number of poles in the MPM. However, various hardware problems that occur during the encoder manufacturing process degrade the AMPME performance. Also, harmonic components occur in the raw data due to various problems, such as the resistance error of the analog circuit, magnetic field overlap between the magnets, position error between the sensor and magnet, and pole-pitch difference. In particular, during the magnetization process of the MPM, the pole-pitch difference becomes a problem when the sizes of each pole are not uniform. This problem causes harmonic components that reduce the absolute position accuracy. To solve these problems, this paper proposes a linear compensation method. The proposed linear compensator consists of two parts. The first part is the enhanced ratiometric linearization for phase calculation and calibration. The second is the phase compensator for removing the phase difference via the pole-pitch difference of the MPM. The linear compensator improves various parameters by precomputing the offset, amplitude, and phase corrections. After compensating for sinusoidal signals, the linear compensator applies appropriate parameters at the appropriate times. This method is faster, easier to set up, and more accurate than the conventional method. Furthermore, this method is experimentally verified against the existing harmonic rejection method. Experimental results are provided to verify the effectiveness of the proposed method.

The Internet of Things is based on a communication network to automatically receive various information from the natural world. It uses intelligent objects with perception, communication and computers to automatically receive various information from nature. All independently managed physical objects are connected to each other to achieve integrated perception, reliable transmission and intelligent processing, and to establish intelligent information between people and objects, and objects and object service systems. This article mainly introduces the impact of industrial agglomeration on the regional economy in a simulated intelligent environment based on machine learning. This paper proposes a method to detect industrial agglomeration index to analyze industrial agglomeration. Through the establishment of the industrial agglomeration index system, the level of integration of the manufacturing industry in our city was objectively analyzed, and the impact of the integration of manufacturing industry on our city was tested empirically. Finally, the relationship between industrial integration and regional economic development was tested. The experimental results in this paper show that industrial integration in a smart environment based on simulation learning has a significant positive impact on regional economic development. Among them, the level of cooperation between the manufacturing service industry and the manufacturing industry increased by 1%, and the level of regional economic development increased by 0.025%.

This paper considers the economy and reliability of the microgrid cluster system, and proposes a bi-level optimized operation strategy for the microgrid cluster, which aims to improve the economic benefits of the microgrid cluster and reduce the operating risk of microgrid. The upper layer takes the microgrid cluster as the research object, with the goal of minimizing the total operating cost of the system, and establishes the optimal model of the microgrid cluster. The lower layer takes the sub-microgrid as the research object, and establishes the microgrid optimization model with the goal of optimal operating cost and operating risk index. The method of combining mixed integer linear programming and IABC algorithm is used to solve the multi-objective bi-level optimization model, and to provide decision makers with two microgrid dispatching schemes with different focuses. Finally, an example is used to verify the effectiveness of the bi-level optimization strategy and improved algorithm proposed in this paper, as well as the practicability of the two dispatching schemes.

Energy router is an emerging device based on power electronic technology, which can realize the flexible distribution of electric energy in the power system. Therefore, the use of energy routers to improve the operating conditions of the power system not only has broad development prospects, but also brings economic benefits. In this regard, this paper proposes a steady-state model of a single energy router and considers the loss of the DC/DC transformer, establishes a power flow equation with multiple energy routers, and proposes a distribution network power flow alternate iteration that takes into account multiple energy routers Calculation method. A case study of an improved IEEE-33 system with multiple energy routers under different working conditions is carried out. The calculation results show that the alternate iterative power flow calculation method used in this paper is effective and feasible, and it reasonably reflects that the network analysis model with multiple energy routers can achieve stable operation, and it can improve the node voltage and reduce the active power loss of the grid. It makes the operation of the distribution network more flexible.

The widespread use of Unmanned Aerial Vehicles (UAV) has made the security and computing resource application efficiency of UAV a hot topic in the security field of the Internet of Things. In this paper, an optimized lightweight identity security authentication protocol, Optimized Identity Authentication Protocol (ODIAP) is proposed for Internet of Drones (IoD) networks. The protocol is targeted to the security risks faced by IoD networks, and proposes the security authentication mechanism consisting of 3 phases and 7 authentication processes, which enables the protocol has both forward and backward security, and can resist mainstream network attacks. Meanwhile, this paper fully considers the computational load and proposes the identity information generation and verification method based on the Chinese residual theorem, which reduces the computational load of resource-constrained nodes and shifts the complex computational process to server nodes with abundant computational resources. Moreover, after security protocol analysis and tool verification based on the automated security verification tool Proverif, the protocol in this paper has complete security. At the same time, the performance analysis and comparison with other mainstream protocols shows that this protocol effectively optimizes the use of computing resources without compromising security.

This paper proposes a novel method that uses stator current signals to detect motor faults under operational speed and load torque conditions. Previous studies on motor current signature analysis (MCSA) have been devoted to developing methods to detect faults in non-stationary conditions; however, they have limitations. Conventional methods require much domain knowledge or parameter selection for signal decomposition, and are applicable under limited variable conditions. Thus, this paper proposes a new feature, drive-tolerant current residual variance (DTCRV), for fault detection. This new approach requires no domain knowledge and is applicable under varying speed and load torque conditions. In the proposed method, first, the envelope of the current signal is calculated to extract its modulation. Second, the drive-related signal, which greatly varies based on speed and load torque conditions, is extracted from the enveloped current signal. Third, the drive-tolerant current residual (DTCR) is calculated; the DTCR is defined as the subtraction of the drive-related signal from the enveloped current signal. Finally, the new health feature is calculated as the variance of the DTCR. To demonstrate the proposed method, experimental studies were conducted under several operating conditions (i.e., different speed profiles and load torque levels) with two fault modes: 1) a stator inter-turn short and 2) misalignment. Results confirm the ability of DTCRV to promptly and accurately detect faults in a variety of conditions; in contrast, conventional methods are greatly affected by the operating conditions.

It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.

Due to the lack of inertial support of the physical rotor of reliable power, the problem of weak inertia of the system is prominent when large-scale grid connection. Aiming at the weak inertia problem of new energy power systems, this paper takes the photovoltaic power generation system with generalized droop control as the research object. Firstly, the paper introduces the structure of the photovoltaic power generation system and the mathematical principle of generalized droop control At the same time, the electrical torque model of the system is established in order to reveal the inertia characteristics of the grid-tied inverter under the generalized droop control, moreover, the influencing factors of the system’s inertia characteristics and the influence rules of parameters are analyzed and studied. Finally, simulation analysis verifies the correctness of the analysis. The research results show that the photovoltaic inverter system under the generalized droop control has obvious inertia characteristics, the change of inertia size is affected by the dominant zero pole of the controller, and the equivalent inertia coefficient has frequency domain characteristics.

Compared with silicon (Si) IGBTs, silicon carbide (SiC) MOSFETs have much faster switching speeds and high switching frequency operation capability, enabling compact and high-efficiency motor drive systems. However, the increased switching speed and frequency also cause increased-level of common-mode (CM) voltage with higher slew rates (dv/dt). The CM voltage leads to machine leakage currents as well as bearing currents such as electric discharge machining (EDM) type current and capacitive-type (dv/dt) current. These bearing currents can cause degradation or failure of motor bearings. In order to investigate the impact of SiC motor drives on the motor bearing currents, in this paper, the mechanism and modelling of bearing currents are analyzed first. Then, a direct measurement method is used to assess the bearing currents in the induction motor driven by the SiC converter. In the experiment, the number of EDM current events is counted and the amplitude of the capacitive-type of current is also examined under various switching speeds and frequencies of SiC devices. Furthermore, the bearing currents are also extensively tested under various bearing temperatures, motor speeds and dc-link voltages. The correlations between the bearing currents and various factors have been revealed in this paper.

This paper presents a complete procedure on prototyping using the FPGA of the STEMlab board and is intended to serve as a guide for developers, students and researchers interested in speeding up their projects and experiments. Due to the reconfigurability of its internal circuitry, being as simple as a code modification (using hardware description language), FPGA technology allows testing of several controller topologies and/or parameters without the need of any physical change at hardware. This feature allows a much faster development cycle of either commercial products or academic experiments. Besides the reconfigurability of the FPGA, the STEMlab board also offers the advantage of several peripheral already available, which includes, among others, high speed analog-to-digital and digital-to-analog converters, Ethernet communication and a dual-core ARM processor capable of running a Linux operating system. In this paper, a didactic method of use of this board is presented, from getting started to a complete academic and industrial application: detection of early damage on an induction motor using frequency response analysis.

In this paper, an extensive reliability analysis of wind energy conversion systems (WECS) is presented. Elaborate the analysis is presented starting from the subassembly level to the subsystem level, then the system or the overall WECS. The fault tree method with a Weibull probability distribution function is introduced as a complete model for estimating the wind turbine subassemblies’ reliability. The model was tested using a massive dataset of failure rates of various wind turbine subassemblies derived from relevant literature, comprising various operating concepts and the different climate conditions. In addition, ranking for various subassemblies of wind energy conversion systems concerning their impact on the overall system reliability is also presented in this paper to identify the weak items and subsystems. This identification guides the designers and planners in setting the appropriate maintenance strategies to increase the overall reliability of the considered systems and achieve a desired level of reliability. The results indicate that the model has practical applications for managing wind turbines, and the implementation demonstrates the proposed approach’s effectiveness and efficiency, which may significantly enhance the WECS reliability.

Circuit breaker (CB) and fault current limiter (FCL) are two major fault protection devices used in mesh-type DC microgrids. This paper proposes the parameters selection method to obtain a reliable and economic configuration scheme of CBs and FCLs applied in mesh-type DC microgrid. The breaking time of CB, the nominal breaking current of CB and the inductance of FCL are selected as three optimization parameters in an evaluation model. Since the reliability of fault protection scheme depends on whether the nominal breaking current of the CB is greater than the line current at the breaking moment of CB, it is necessary to accurately calculate the post-fault line current. Based on this, an accurate transient DC microgrid model is presented in this paper. And then, an improved non-dominated sorting genetic algorithm II (NSGA-II) is used to configure the parameters of CBs and FCLs quickly. Besides, a case study on six-terminal DC microgrid test system shows that the proposed method can obtain accurate post-fault line current and can achieve the parameters selection of CBs and FCLs.

Emergency rescue vehicles are special equipment for fire, flood, earthquake and other disasters. They need to have high efficiency and high mobility under various terrain and road conditions. The fuel economy of emergency rescue vehicles is very poor in climbing and complex terrain due to the heavy weight of vehicle chassis and upper loading. The dual-motor torque increasing hybrid power system designed in this paper greatly improves the dynamic performance and mobility of emergency rescue vehicles, and the use of dual-motor drive in steep slopes and complex terrain can avoid the engine working in low efficiency area. In order to further improve the fuel economy of vehicles under various working conditions, a fuzzy logic control strategy based on improved particle swarm optimization (PSO) is proposed in this paper. The torques of engine and dual-motor are reasonably distributed under different working conditions. A novel fuzzy parameter optimization mode is designed, which reduces the workload of optimization calculation and ensures the optimization accuracy. The off-line simulation and hardware-in-the-loop experiments are carried out for the dual-motor torque increasing hybrid power system model and the proposed improved energy control strategy. The results show that the fuzzy logic control strategy optimized by simulated annealing particle swarm optimization algorithm (SimuAPSO-FLC) has the best effect and meets the requirements of high efficiency, high mobility and stability of the emergency rescue vehicle.

The paper proposes a novel interleaved AC-DC ZCS-PWM boost converter that requires the use of just a single active auxiliary switch to assist in the turning off the main power circuit switches with ZCS. The auxiliary switch can be turned on and off with ZCS and its auxiliary circuit allows the converter to operate without an increase in the main switch peak current or voltage stress. The auxiliary switch in the converter is active for a much shorter time than in most other ZCS-PWM converter, which allows the converter to operate at higher power levels than other previously proposed interleaved ZCS-PWM converter with a single auxiliary switch. The proposed converter’s operation is explained in the paper and its key features and design considerations are discussed. Experimental results that confirm the operation of the proposed converter are presented as well.

The main purpose of this paper is to achieve ultra-low power Physical Unclonable Function (PUF) to meet the requirements for Internet of Things (IoT) applications. PUFs are promising hardware security primitives that are based on the uniqueness and the unclonability of the device's physical characteristics to provide a unique identifier for devices. PUFs can be used in device authentication and for secure key storage and generation. This paper presents Oscillator Collapse Physical Unclonable Function (OC-PUF), which is suitable for low power applications. The power consumption is reduced based on designing the most power-hungry modules in the OC-PUF. An Oscillator Collapse (OC) is designed to work in the near-threshold voltage region, which reduces the power consumption. More power reduction is achieved by designing a new Collapse Time Comparator (CTC). Due to the process variations, the oscillations period of each OC is different. The CTC generates the output response bit by comparing the oscillations periods of the two selected OCs. The simulation results demonstrate that the proposed OC-PUF can effectively reduce the power consumption. The proposed PUF is designed using 20 nm triple gate FinFET technology. The Simulation results for 1000 different chips with the same input challenge, show that the average power is 140 nW, with the worst case being 740 nW. The best case is 63 nW per challenge-response pair at supply voltage 0.5 V. The averaged reliability of the OC-PUF is 99.8%, at temperatures from -40 to 125 °C and supply voltage 0.5 ± 10%. The proposed OC-PUF decreases the power consumption while retaining high-performance metrics.

In this paper, a finite control set model predictive direct power control (FCS-MPDPC) method based on satisfactory optimization, for single-phase three-level PWM rectifier, is proposed to achieve global optimization that takes into account all its objectives and constraints. Satisfaction optimization aims to obtain the satisfactory solution after coordination of multiple goals, instead of the optimal solution of a single goal. By replacing “optimal” with “satisfaction”, more control degrees of freedom are acquired, so that low-priority auxiliary control objectives can participate in the optimization process. Meanwhile, in order to enhance the description accuracy of FCS-MPDPC method for the future trend of PWM rectifier, the prediction time domain is extended from the traditional single-step to multi-step, and the average switching frequency model is established to realize the low switching frequency control. The satisfactory optimized FCS-MPDPC method proposed in this paper is compared, through simulation and experimental results, with the standard FCS-MPDPC method, which verifies the effectiveness and superiority of the proposed algorithm.

This paper addresses the problem of decoding non-binary low density parity check codes(LDPC) over finite field $GF(q)$ using symbol flipping approach. To achieve low complexity reliable communication, three new algorithms for improving the bit error rate performance of the non-binary LDPC decoder are presented. The first type is the symbol flipping decoding algorithm using a flipping function based on the channel reliability to identify the least reliable symbol position. In this algorithm, if the predicted symbol value satisfies the check sum, then the value is declared as correct otherwise the value is adjusted and sent back to the QAM detector. Algorithms 2 in this paper is an improvement to iterative joint detection-decoding algorithm by using the method of iterative hard decision based majority logic to select the new candidate symbol value. The feedback value to the QAM detector is adjusted by using Euclidean distance between the current symbol and the newly selected symbol value. Algorithm 3 is a low complexity version of Algorithm 2 which is derived by applying a majority voting scheme. In the majority voting scheme, symbols are short listed first by voting and all the computation are carried out only for the short listed least reliable symbols which significantly lowers the processing complexity. Numerical results and complexity analysis show that the proposed methods have good bit error rate versus complexity trade-off for various applications when compared with some existing algorithms.

This paper proposes a two-stage stochastic energy management (EM) in an isolated microgrid (MG) to decide for the day-ahead optimal dispatch. The dispatch aims to effectively manage the MG power sources, including intermittent renewable energy sources (RESs), battery energy storage systems (BESSs), and diesel generators such that the expected operation costs, reactive power costs, spinning reserve, and load shedding are minimized. The Generative Adversarial Networks (GANs) is utilized in this paper as a data driven scenario generation method to model the uncertainties in the output power of the RESs to be used in the stochastic programming formulation. Then, the fast forward scenario reduction algorithm is used to reduce the number of scenarios with the help of SCENRED/GAMS software. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations in the dispatches from the optimal solutions. Hence, this paper co-optimizes the costs related to both active and reactive powers of diesel generators. In addition, this study considers the reactive power capability of inverter-interfaced distributed energy resources (DERs). Moreover, the detailed models for the different resources are presented, especially for diesel generators where the actual capability curves are used instead of the widely used box constraints. The problem is formulated as a nonlinear programming problem in the General Algebraic Modeling System (GAMS) software and is solved by the CONOPT solver.

Single-phase fault is the most commonly occurred and severe AC fault that causes most of the Commutation Failures (CF) in LCC HVDC systems. It has long been regarded as having the same level of adverse impact on commutations of both 6-pulse bridges in the 12-pulse LCC HVDC scheme. This has resulted in the same level of investment on both 6-pulse bridges, for CF mitigation/elimination, e.g. firing angle advancement and equipment investment. However, due to different winding configurations of converter transformers, single-phase fault actually affects each 6-pulse bridge to different degrees. Based on the previously proposed AC Filterless Flexible LCC HVDC, this paper shows that, compared with the Y-Y bridge, the Y- $\Delta $ bridge has much smaller undesired phase advancement of the actual commutation voltage (commutation voltage at secondary side of converter transformer) under severe single-phase fault. As a result, the required voltage rating of controllable capacitors for Y- $\Delta $ bridge can be reduced, leading to considerable savings from reduced equipment costs and lower losses of controllable capacitors without compromising the commutation performance. The detailed theoretical analysis in this paper provides a valuable foundation for the development of more efficient CF mitigation/elimination strategies. Simulation results of 1) CIGRE HVDC benchmark system and 2) the same benchmark system but connected to a 39-bus inverter AC network are presented to validate the correctness of the analysis.

Due to the rapid development of wireless communication technologies, the number of wireless users are radically increasing. Currently, $\sim 23$ billion wireless devices are connected to the internet, and these numbers are expected to increase manifolds in the years to come. The technology growth of the fifth-generation (5G) wireless systems will be needed to meet this high demand of the network. 5G wireless systems offer data-rates of up to 10Gbps, 1-ms latency, and reduced power consumption. It is a known fact that 5G wireless systems will be exploiting beyond the presently used 3 GHz microwave and millimetre-wave (mm-wave) frequency bands. This is the primary driver in the development of the 5G wireless system. Multi-beam Phased array antenna (PAA) systems are typically used in the deployment of 5G systems for high-gain and directionality. In current 5G and future Beyond 5G (B5G) antenna array systems, beamforming networks (BFNs) such as the Butler Matrix (BM) will play a key role in achieving multi-beam characteristics. So, this paper presents an extensive review of the BM based BFNs, and discusses which type of BM will be suitable for the phased array antenna (PAA) systems in the upcoming 5G and next-generation of B5G wireless systems. Moreover, this paper also summarizes the different types of BM designs based on the number of layers. The BMs are classified into the bi-layer, tri-layer, and four-layer structures. It includes different techniques that have been used to solve the problem of crossing, narrow bandwidth, and size reduction of the BM. From the previous studies, it is found that most of the past research work was performed using the bi-layer BM system, whereas the difficult geometries like tri- and four-layer BM are avoided due to their complex fabrication process. It is also found in this paper that the metamaterial (MTM) based bi-layer BM achieves low insertion-loss and phase-error, excellent bandwidth and compact size, and good S-parameter performance, which makes them an ideal BFN candidate for the upcoming 5G and next-generation B5G systems.

In this paper, a quaternion-based adaptive dynamic surface control method is proposed for attitude tracking control for small-scale unmanned helicopters with external disturbance and uncertain dynamics. The quaternion formalism is introduced and a quaternion-based multi-input-multi-output nonlinear model is derived from the attitude dynamics of a small-scale helicopter. The low-complexity controllers are designed by the dynamic surface control method as it eliminates the problem of the explosion of items. The singularity problem is avoided by substituting Euler kinematic equations in the nonlinear model with quaternion expressions and integrating the quaternion expressions into the design process of the dynamic surface control. For improving the robustness of the control system, the radial basis function networks are applied to approximate the uncertain dynamics. The external disturbance is also compensated in the controllers’ design. This paper proves that the proposed method can guarantee the uniformly ultimate boundness of this attitude system. Simulation results are presented finally and show the effectiveness of this control approach.

This paper introduces a new method of estimating the variation interval and the expected value of optimal economic operation cost for the energy hub (EH) model considering load and renewable energy uncertainties in the residential district. Firstly, renewable energy and load uncertainties are defined as uncertain sets of historical data. Due to dispatch factors, the traditional EH model is non-linear and difficult to calculate and analyze. In this paper, a multi-step modeling method is developed for the breakdown of a complex EH model (CEH) into several simple EH (SEH) models and variable substitutions in each SEH, to eliminate the non-linearity of CEH. The upper and lower bound of the operating costs are then obtained based on the duality theorem and the expected value is calculated by expected value model. Finally, the case study in Beijing residential district is carried out to confirm the efficiency of the proposed method.

Conventional power delivery networks (PDNs) and power management techniques using off-chip power converters with bulky passive components cannot meet the ever-evolving power delivery requirements of high-performance modern system-on-chips (SoCs). In SoCs, heterogeneous components, including multi-core processors and mixed-signals peripheral circuits, require state-of-the-art PDNs to provide high-quality power-on-demand with minimum latency, simultaneously achieving the small-factor, high conversion efficiency, and minimum current consumption. To satisfy these power delivery requirements, various PDNs have been developed over the past decades, such as the conventional architectures using off-chip power converters, architectures using in-package power converters and fully-integrated power converters, and heterogeneous architectures (off-chip power converters and on-chip regulators). This paper reviews these architectural advancements of the PDNs and their advantages and limitations, which leads us to discuss a heterogeneous PDN structure consisting of a highly efficient off-chip switching-mode power converter and multiple highly precise small linear regulators integrated on chip at point-of-load locations. The heterogeneous PDN has been proved one of the most suitable architectures to achieve high-quality fine-grained on-chip power delivery and management in SoCs. This paper also discusses unified voltage and frequency regulators (UVFRs), which support dynamic-variation-aware dynamic voltage and frequency scaling (DVFS) for fine-grained power management in multi-core processors. Based on the UVFR, we propose a modified heterogeneous PDN using frequency-referenced digital low-dropout regulators (FR-DLDOs) for more efficient DVFS, eliminating the need for band-gap circuits to provide reference voltages. As an exemplary implementation of FR-DLDO for this PDN, we present an FR-DLDO with a transient-boost control, which accelerates the transient response. The transient-boost control is activated dynamically only when an abrupt change happens out of the steady state. The implemented FR-DLDO fabricated in a 40-nm CMOS process outperforms other FR-DLDOs in the figure-of-merit and peak power efficiency while driving 40 mA of load current.

By the virtue of its highly nonlinear magnetic characteristics, the Switched Reluctance Machine (SRM) poses a formidable challenge for digital current regulators operating at a fixed switching frequency. Very fast tracking performance demanded by highly dynamic reference current profiles often surpass the conventional limits on closed-loop bandwidth posed by finite sampling frequency. The non-linear nature of matched disturbance to be compensated by the controller appearing in the form of induced emf grows in significance as a function of operating speed while the varying nature of inductance profile stipulates a need for gain adaptation by the control law in order to maintain consistency in closed-loop dynamic response. In the view of these unique SRM characteristics, the paper presents a detailed theoretical analysis of the widely implemented current control techniques from literature and provides illustrations in the context of their implementation in a digital controller. The analysis presented in this paper can also serve as a foundation for more advanced versions of these control techniques as well as their combinations.

In the era of paper-based contract, a lot of time, human labor and expenses are required to handle the process of contract drafting, contract signing, contract execution, and payment settlement. The emerging of electronic contract enhances the tedious signing process of paper-based contract and improves the efficiency of contract management. However, due to the centralized system architecture and the database-based storage schema, the stored contract data is at high risk of information leaking, data tampering and hacker attacks. In this study, we introduce Blockchain technology to the contract management, and develop a process-oriented contract management system (BEcontractor) for a Hangzhou-located power grid enterprise X, aiming to solve a series of security issues existing in the traditional electronic contract system. By deploying BEcontractor, procurement activities could be resumed online among X and its nation-widely commodity suppliers during COVID-19 epidemic. Up to September 2020, 6336 electronic contracts have been signed, with an accumulated amount of $\yen ~6.5$ billion. It is showed that the cost for accomplishing the contract signing process was significantly reduced, and the payment period was shortened from three months to around one month.

Impedance measurement is a practical method to obtain the impedance characteristic of the power electronic equipment (PEE), which is important for the small-signal stability analysis. In the impedance measurement, the measurement results may be influenced by the external noise. Therefore, it is essential to study how to avoid the measurement errors caused by the noise. To address this issue, this paper analyzes the method of suppressing the noise interference by enhancing signal-to-noise ratio (SNR) and concludes that there is limitation on improving the measurement accuracy by merely enhancing SNR due to the practical limitation on the perturbation amplitudes. And then this paper proposes an improved impedance measurement method, by which the measurement errors caused by the noise can be decreased effectively. By investigating the influence mechanism of the noise on the impedance measurement results, the measurement error can be divided into average component and fluctuant component. The average component can be eliminated by averaging two groups of measurement results with the phase difference of $\pi $ rad while the influence of the fluctuant component can be minimized by employing complex-linear fitting algorithm. Finally, experiments are conducted to verify the effectiveness of proposed impedance measurement method.

Pitting corrosion of rotating machinery is one of the most common faults in industrial engineering. The convolutional neural network (CNN) is increasingly applied to the fault diagnosis. However, the conventional CNN method will reduce the feature dimension of the collected signal and cause the loss of information during the pooling process. In this paper, a new method based on dilated CNN combined with spatial dropout (DCSD) is proposed to diagnose the early faults of rotating machinery. By filling the convolution kernel, the DCSD method can increase the receptive field of the CNN without increasing the number of parameters while retaining more features of the raw vibration signal of the rotating machine. To avoid the dropout method eliminates the adjacent elements with a strong correlation, the Spatial Dropout method is adopted to reduce the overfitting problem of deep networks. The early pitting gears experiment was designed to verify the DCSD method in this paper. The raw vibration signal data of 6 different healthy states were collected to verify the effectiveness of the method. The experimental results show that the DCSD method proposed can effectively distinguish the different early gears pitting, and the diagnostic accuracy is better than other popular deep learning methods.

In order to achieve improved performance and efficiency of domestic and industrial fan/pump applications using low-cost solutions, the synchronous reluctance motors (SynRMs) remains a better choice than induction motors (IMs) which are still widely used for such applications despite its low efficiency during low speed operation. However, due to the lack of self-start capability the SynRMs need external drive and speed control algorithm to properly operate, which increases the production cost. For the purpose of providing low cost solutions for the SynRMs speed control, this paper presents a stable, reliable and robust scalar control for SynRMs. This control strategy implements the maximum torque per ampere loop for high efficiency operation and online parameter estimation for robust performance. The proposed solution presented in this paper can be implemented using a low cost MCU in PWM inverter with lower carrier frequency, thereby making the low cost SynRM drive for fan/pump application feasible and practical.

Aero-turbines usually work in various non-stationary and harsh operating environments. Its blades easily appear early crack faults in long-time operation, and it is difficult to dig out discriminant features of early crack. To solve these problems, in the paper, a novel intelligent approach for early crack diagnosis of turbine blades using three-dimensional blade tip clearance is presented. In order to improve feature learning ability to obtain better generalization ability, the paper firstly develops a novel deep learning method based on deep belief networks (DBNs). Considering the fact that the feature degradation easily occurs in deeper layers because of the change of the distribution in each layer's outputs with the increase of the layers, it is hard to decide which layer to learn features is useful for fault diagnosis. Accordingly, in the pre-training process, the global back-reconstruction (GBR) mechanism is introduced into DBNs to optimize the feature learning ability. The GBR mechanism can be realized between the input layer and hidden layers by “shortcut connection”, and the layer to learn more discriminant features can be determined automatically without prior knowledge. Moreover, due to three-dimensional blade tip clearance (3-DBTC) acquired from three different directions of turbine blades contains much more useful crack failure multiscale information, it is suitable to be used as an input from which to extract multiscale discriminant features. Eventually, in the supervised training, the softmax regression model is employed to classify the health conditions of turbine blades using these sensitive features learned from 3-DBTC. The experimental results show that the proposed method can effectively identify the crack of turbine blades with fairly high diagnostic accuracies and significantly outperform other methods considered in the paper.

The fast development of electric vehicles (EVs) provides significant opportunities to further utilize clean energies in the automotive. On-board chargers (OBCs) are widely used in EVs because of their simple installation and low cost. Limited space in the vehicle and short charging time require an OBC to be power-dense and highly efficient. Moreover, the possibility for EVs to deliver power back to the grid has increased the interest in bidirectional power flow solutions in the automotive market. This paper presents a comprehensive overview and investigation on the state-of-the-art solutions of bidirectional OBCs. It reviews the current status, including architectures and configurations, smart operation modes, industry standards, major components, and commercially available products. A detailed overview of the promising topologies for bidirectional OBCs, including two-stage and single-stage structures, is provided. Future trends and challenges for topologies, wide bandgap technologies, thermal management, system integration, and wireless charging systems are also discussed in this paper.

Solar energy technology has gained significant attention in recent years. It has strongly emerged as an alternative to the conventional mode of electricity generation for developing countries like India for their rising energy needs. Solar energy based electricity generation not only addresses environmental concerns but also increases the energy security and reduces the carbon footprints generated by the coal-based power plant. To boost the share of Renewable energy in the energy mix, the renewable energy policy introduced by the government has acted as a catalyst for large scale deployment of solar energy system to push it as the major energy alternative for India. The normal intensity of solar radiation in India is 220 MW per km square with around 300 sunny days in a year, whereas the coal-based plant generates a huge amount of carbon footprints. Therefore, solar based power generation has the potential of not only reducing the emissions and but also it can cater the rising energy demand. Thus solar photovoltaic plants has become a major alternative energy option in India with the ability to give an improved power supply, reduced global warming and enhance the security of energy supply. In this paper, efforts have been made to look into the overall perspective of solar energy and coal based generation by studying and analyzing the present status, technologies use, accessibility, policies interventions, major contributions, future potentials and trends of solar energy and fossil fuels in the Indian power sector. A feasibility analysis of solar technology implementation with the replacement of coal based plant pant is carried out for Singrauli, India. Results of the proposed study shows that the coal-based power plant generates a huge amount of carbon footprints and it can be reduced by replacing the coal based power plants with solar energy based power generation system. Consequently solar energy based power generation system is a key alternative for developing countries like India having metrological parameters similar to the case study done in this paper with the capacity to provide an improved supply, decreased global warming in addition to improving the energy security.

Integrating Distributed Energy Resources (DERs) and Micro-Grid (MG) into a system evolved the traditional power system. In spite of their significant advantages, MGs may result in volatility and uncertainty in the power systems. For reliable operation of the grid, energy trading among MGs should be optimized to maintain a fair trading price, maximize participants’ profit, and satisfy network constraints. In this paper, the optimal power trading among multiple reconfigurable MGs is formulated as a Mixed-Integer Nonlinear Programming (MINLP) considering all energy resources and their dynamic prices. In spite of the other methods in the literature, the proposed method minimizes the total cost (increase sales and decrease purchases) and transmission loss considering all energy resources in the MGs. In order to flatten the load profile, a time-based load profile is considered for the demand response program. The performance of the proposed model is evaluated on an IEEE 6-bus network as well as a modified IEEE 33-bus test system. The results verify that the proposed method, (i) determines the best configuration among MGs with a switching reduction of about 30%, (ii) optimizes the power generation of energy resources with 12% reduction in energy production, and (iii) optimizes the power trading costs with a 10% reduction in costs compared with the basic model without DR and trade that is introduced as $Scen.1$ in this paper.

The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable based energy sources. In India, wind energy is gradually becoming an important and significant energy resource. Keeping in opinion the aforementioned wind energy prediction is becoming an essential study for harnessing the wind energy prospective. This paper proposes an effective technique based on intelligent approach for predicting wind power in different areas. This technique is based on using an intelligent model concerning the predicted gap to its similar one and two year old data. There are many intelligent and conventional models existed in literature for the wind power prediction like support vector machines (SVM), back propagation (BP) prediction etc. In this paper an effective fuzzy logic and model predictive control based models have been developed and offered for the wind power prediction for microgrid application by using air density and wind speed as the input parameters for fuzzy system. The outcomes are compared with the computed data and existing models and it can be observed that the different errors are found within the permissible limits. The outcomes obtained from fuzzy based technique are very close to calculated values if compared with model predictive based technique. Hence, the proposed models can be employed for the prediction of wind speed and wind power generation in the selected stations. The existing models results are compared with Kolkata city outcomes. The Error RMSE with Support vector machine, Back propagation, Model of forecast error correction +SVM and Model of forecast errors correction +BP, Neural Network method, model predictive based system, and proposed fuzzy logic based system are 30.48%, 32.83%, 26.81%, 28.58%, 1.1431%, 1.38% and 1.12% respectively. Therefore, the proposed techniques provide the best results and even these are observed within the suitable limits. Additionally, the achieved outcomes can be used for Microgrid/SmartGrid applications.

The modular multilevel converter is considered the state-of-the-art power electronic solution for high-power high-voltage applications that require a DC stage and that operate with fixed frequency at their AC terminals. However, in high-power AC-AC variable-frequency applications, it has been proved that this converter topology presents an unnecessarily large number of components and an intolerably high submodule-capacitor voltage ripple at low frequencies. The modular multilevel matrix converter was proposed as a promising alternative for high-power medium-voltage electrical-machine drives since it presented improved performance at low frequencies and a reduced number of components in comparison to the modular multilevel converter resulting in reduced costs and improved efficiency. Even though, the modular multilevel matrix converter is one of the converter solutions that stands out the most for high-power machine drives, it presents some operational drawbacks such the unstable behavior under unbalanced grid conditions and the extremely complex internal control and modulation. Recently, a new converter topology named modular multilevel series converter was proposed presenting attractive operational characteristics such as the high performance at low frequencies, high performance under unbalanced grid conditions, and straightforward control and modulation, which are important advantages compared to the modular multilevel converter and the modular multilevel matrix converter. Nonetheless, as demonstrated in this paper, the modular multilevel series converter presents large component count and poor efficiency. Thus, in this paper, a new converter solution is proposed, which is an enhanced version of the modular multilevel series converter that preserves the previously mentioned advantageous operational characteristics and that presents an improved efficiency and a considerably reduced number of components in comparison to the other three converter topologies, resulting in reduced cost, size and weight.

Meter wave radar height measurement is one of the most difficult problems in meter wave radar system. At present, the most popular height measurement methods of meter wave radar include beam splitting method, MUSIC algorithm and maximum likelihood estimation method, which can achieve good performance under flat ground. However, the real ground is not ideal condition, and the situation of undulating ground is also very complicated. Under this condition, the performance of the above algorithm dropped sharply. Many researchers solve this problem by improving the robustness of the algorithm. However, the authors think that the key to solve the problem is to establish a refined height measurement signal model. In this paper, the accurate model of reflection coefficient, reflection height and reflection point under the condition of undulating ground are established to provide accurate signal model for meter wave radar height measurement. Then we deduce the application of generalized MUSIC algorithm and maximum likelihood estimation algorithm for MIMO radar in the accurate terrain signal model. In fact, the method proposed in this paper is a terrain matching MIMO radar target height measurement, and the traditional algorithm can be regarded as terrain mismatch. The simulation results show that the accuracy of terrain matching estimation of the precise signal model established in this paper is much better than that of the traditional simplified signal model.

A proper battery management system (BMS) plays a vital role in ensuring the safety and reliability of electric vehicles (EVs) and other electronic products. Accurate State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is a key factor in a BMS. It is difficult to determine SOH because of the complexity of the electrochemical reactions within the battery. To improve the accuracy of SOH estimation, a dynamic spatial-temporal attention-based gated recurrent unit (DSTA-GRU) model is proposed in this paper. First, we extract six features from the battery's charging and discharging processes that can reflect the aging degree of the battery to some extent. Second, this paper proposes a model to combine spatial attention and temporal attention that can not only consider the effects of states at different time step on the results, but also consider the effects of different features in the space domain. Third, the proposed model is trained and tested on NASA battery datasets and compared with other conventional models. Experiments carried on these data sets demonstrate that our model achieves higher accuracy than other conventional models.