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Abstract In this study, a new multi input–multi output (MIMO) high step‐up DC–DC converter is proposed for battery charger applications. Continuous input current and capability of using several renewable energy sources with different voltage‐current characteristics are the main advantages of it. High voltage gain and low power losses on semiconductors are the other advantages of the proposed topology. In addition, the duty cycle of each input source is independent of other sources. The proposed converter has soft‐switching on some power diodes which leads to increasing the overall efficiency of the proposed topology. A laboratory prototype of the proposed topology in 12V–12 V/430 W is built and tested. The experimental results are verifying the proper performance of the proposed topology and confirm the theoretical analysis.

Abstract Here, a new bidirectional multi‐input‐multi‐output (B‐MIMO) high step‐up DC/DC converter with lower losses on switch/diode is proposed as suitable for electric vehicles. The proposed topology consists of five inputs and two outputs and the battery (energy storage) is considered one of the inputs. Due to the bidirectional nature of the proposed topology, different scenarios of it are described. In discharging mode, the stored energy of the battery is transferred to the output loads. In charging mode, the surplus energy by regenerative braking will be responsible for charging the battery in the related scenario. Also, each input can charge the battery separately in different scenarios. In general, the proposed topology is capable to work in different scenarios for bypassing/charging/discharging the battery. Other advantages of the proposed topology include continuous current of all the inputs, independent control of inputs from each other and lower normalized voltage stress on semiconductors. These features make the proposed bidirectional MIMO DC/DC converter (B‐MIMO‐C) suitable for electric vehicle applications. The proposed topology operation is provided in the laboratory testing for 870W prototype, and the obtained results confirm the theoretical calculations as well.

Abstract In this paper, a new ultra high step‐up soft‐switching DC/DC converter using coupled inductor and interleaved technique is presented. The primary side of the coupled inductors in up section and down section will be charged by the input power supply. The stored energy of the coupled inductors (at primary and secondary sides) is discharged to the output capacitors in up and down sections. Also, the capacitors on the coupled inductors path have clamped capacitors that cause the voltage stress across the power semiconductors to be low. Al diodes in up and down sections except output diodes are working at zero current switching turn‐off. Therefore, the reverse recovery problems of the proposed converter will be majorly solved. Therefore, power losses of the proposed converter will be decreased by using switches with lower on‐state resistance RDS(on) and lower rating power diodes. In addition, the leakage inductor causes to exclusive the current falling balance of the output diodes, significantly. In order to clarify the main advantages of the proposed converter, comparison between the proposed converter and other structures is provided. Finally, the proposed converter with turn ratio N(N2/N1) = 2 in about 300 W with operating 50 kHz is tested with real‐time TYPHOON HIL‐404 emulator in the laboratory.

The DC-Link capacitor is defined as the essential electronics element which sources or sinks the respective currents. The reliability of DC-link capacitor-banks (CBs) encounters many challenges due to their usage in electric vehicles. Heavy shocks may damage the internal capacitors without shutting down the CB. The fundamental development obstacles of CBs are: lack of considering capacitor degradation in reliability assessment, the impact of unforeseen sudden internal capacitor faults in forecasting CB lifetime, and the faults consequence on CB degradation. The sudden faults change the CB capacitance, which leads to reliability change. To more accurately estimate the reliability, the type of the fault needs to be detected for predicting the correct post-fault capacitance. To address these practical problems, a new CB model and reliability assessment formula covering all fault types are first presented, then, a new analog fault-detection method is presented, and a combination of online-learning long short-term memory (LSTM) and fault-detection method is subsequently performed, which adapt the sudden internal CB faults with the LSTM to correctly predict the CB degradation. To confirm the correct LSTM operation, four capacitors degradation is practically recorded for 2000-hours, and the off-line faultless degradation values predicted by the LSTM are compared with the actual data. The experimental findings validate the applicability of the proposed method. The codes and data are provided.

This multilevel inverter type summarizes an output voltage of medium voltage based on a series connection of power cells employing standard configurations of low-voltage components. The main problems of cascaded switched-capacitor multilevel inverters (CSCMLIs) are the harmful reverse flowing current of inductive loads, the large number of switches, and the surge current of the capacitors. As the number of switches increases, the reliability of the inverter decreases. To address these issues, a new CSCMLI is proposed using two modules containing asymmetric DC sources to generate 13 levels. The main novelty of the proposed configuration is the reduction of the number of switches while increasing the maximum output voltage. Despite the many similarities, the presented topology differs from similar topologies. Compared to similar structures, the direction of some switches is reversed, leading to a change in the direction of current flow. By incorporating the lowest number of semiconductors, it was demonstrated that the proposed inverter has the lowest cost function among similar inverters. The role of switched-capacitor inrush current in the selection of switch, diode, and DC source for inverter operation in medium and high voltage applications is presented. The inverter performance to supply the inductive loads is clarified. Comparison of the simulation and experimental results validates the effectiveness of the proposed inverter topology, showing promising potentials in photovoltaic, buildings, and domestic applications. A video demonstrating the experimental test, and all manufacturing data are attached.

Support Vector Machine (SVM) is a supervised machine learning algorithm, which is used for robust and accurate classification. Despite its advantages, its classification speed deteriorates due to its large number of support vectors when dealing with large scale problems and dependency of its performance on its kernel parameter. This paper presents a kernel parameter optimization algorithm for Support Vector Machine (SVM) based on Sliding Mode Control algorithm in a closed-loop manner. The proposed method defines an error equation and a sliding surface, iteratively updates the Radial Basis Function (RBF) kernel parameter or the 2-degree polynomial kernel parameters, forcing SVM training error to converge below a threshold value. Due to the closed-loop nature of the proposed algorithm, key features such as robustness to uncertainty and fast convergence can be obtained. To assess the performance of the proposed technique, ten standard benchmark databases covering a range of applications were used. The proposed method and the state-of-the-art techniques were then used to classify the data. Experimental results show the proposed method is significantly faster and more accurate than the anchor SVM technique and some of the most recent methods. These achievements are due to the closed-loop nature of the proposed algorithm, which significantly has reduced the data dependency of the proposed method.

This paper presents a new passivity-preserving order reduction method for linear time-invariant passive systems, which are also called positive real (PR) systems, with the aid of the balanced truncation (BT) method. The proposed method stems from the conic positive real balanced truncation (CPRBT) method, which is a modification of the BT method for PR systems. CPRBT presents an algorithm in which the reduced models are obtained from some Riccati equations in which the phase angle of the transfer function has been taken into consideration. Although CPRBT is a powerful algorithm for obtaining accurate PR reduced-order models, it cannot guarantee that the phase diagram of the reduced model remains inside the same interval as that of the original full-order system. We aim to address such a problem by modifying CPRBT in the way that the phase angle of the reduced transfer function always remains inside the conic and homolographic phase interval of the original system. This is proven through some matrix manipulations, which has added mathematical value to the paper. Finally, in order to assess the efficacy of the proposed method, two numerical examples are simulated.

In this paper, a new structure is proposed for a boost dc–dc converter based on the voltage-lift (VL) technique. The main advantages of the proposed converter are its lack of transformer, simple structure, free and low input current ripple, high voltage gain capability by using an input source, suitable voltage stress on semiconductors and lower output capacitance. Herein, the analysis of the proposed converter operating and its elements voltage and current relations in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are presented, and the voltage gain of each operating mode is individually calculated. Additionally, the critical inductance, current stress of switches, calculation of passive components’ values and efficiency are analyzed. In addition, the proposed converter is compared with other studied boost converters in terms of ideal voltage gain in the CCM and the number of active and passive components, maximum voltage stress on semiconductors, and situation of input current ripples. The correctness of the theoretical concepts is examined from the experimental results using the laboratory prototype.

While impressive progress has been already achieved in wide-bandgap (WBG) semiconductors such as 4H-SiC and GaN technologies, the lack of intelligent methodologies to control the gate drivers has prevented exploitation of the maximum potential of semiconductor chips from obtaining the desired device operations. Thus, a potent ongoing trend is to design a fast gate driver switching scheme to upgrade the performance of electronic equipment at the system level. To address this issue, this work proposed a novel intelligent scheme for the control of gate driver switching using the concept of quantum computation in machine learning. In particular, the quantum principle was incorporated into deep reinforcement learning (DRL) to address the hardware limitations of conventional computers and the growing amount of data sets. Taking potential benefit of the quantum theory, the DRL algorithm influenced by quantum specifications (referred to as QDRL) not only ameliorates the performance of the native algorithm on traditional computers but also enhances the progress of relevant research fields like quantum computing and machine learning. To test the practicability and usefulness of QDRL, a dc/dc parallel boost converter feeding constant power loads (CPLs) was chosen as the case study, and several power hardware-in-the-loop (PHiL) experiments and comparative analysis were performed.

With the qualitative development of DC microgrids, the usage of different loads with unique conditions and features is now possible in electric power grids. Due to the negative impedance features of some loads, which are called constant power loads (CPLs), the control of DC power converters faces huge challenges from a stability point of view. Despite the significant advances in semiconductors, there is no upgrade in the control of gate drivers to exploit all potential of power electronic systems. In this paper, quantum computations are incorporated into artificial intelligence (AI) to stabilize a full-bridge (FB) DC-DC boost converter feeding CPL. Aiming to improve the bus voltage stabilization of the FB DC-DC boost converter, a quantum deep reinforcement learning (QDRL) control methodology is developed. By defining a reward function according to the specification of the FB power converter, the desired performance and control objectives are fulfilled. The main task of QDRL is to adjust the control coefficients embedded in the feedback controller to suppress the negative impedance effect resulting from deploying the CPLs. By deploying the potential advantages of quantum fundamentals, the deep reinforcement learning improved by quantum specifications will not only enhance the performance of the DRL algorithm on conventional processes but also advance related research areas such as quantum computing and AI. Unlike the basic quantum theory, which requires real quantum hardware, QDRL can be executed on classic computers. To examine the feasibility of the QDRL scheme, hardware-in-the-loop (HiL) examinations are conducted using the OPAL-RT. The comparison of the proposed controller with the classic state-of-the-art methodologies reveals the superiority and feasibility of QDRL-based control schemes in both the transient and steady-state conditions to other schemes. Analysis using various performance criteria, including the integral absolute error (IAE), integral time absolute error (ITAE), mean absolute error (MAE), and root mean square error (RMSE), demonstrates the dynamic improvement of the proposed scheme over sliding mode control (approximately 50%) and proportional integral control (approximately 100%).

Abstract Futures contract is one of the useful financial derivatives for hedging the market players against the risks of undesirable price fluctuations in the power systems. Market players are allowed to trade electric energy in both futures and day‐ahead electricity markets. The behaviour of market players in each market affects the prices and power transactions of the other market. In this study, the mutual impacts of the futures and day‐ahead electricity markets are studied using a mixed supply function‐Cournot equilibrium model. The day‐ahead electricity market is modelled by Cournot method and futures market negotiations are modelled by the supply function method. The proposed model considers the strategic behaviour of consumers in the futures market, mark‐to‐marketing (MTM) settlement in the futures market, dynamics of market players’ behaviour during the trading period, and transmission system constraints. An upgraded risk management method is also introduced and evaluated. The proposed model is applied to a test system and the impacts of different parameters on the results are discussed.

Fuel cell-based hybrid electric vehicles are one of the most promising options to achieve zero-emission city buses. Efficient Energy Management (EM) plays a critical role to make such buses more efficient and practical. In this research, an available all-electric bus consisting of fuel cell (FC) and battery is considered and the efficiency of adding a Wireless Power Transfer (WPT) system to it is assessed. The proposed WPT system is only capable to receive energy in bus stations and use it to supply loads or charge the battery. To this end, the actual data of a city bus, its route and load profile were collected and utilized to ensure a realistic assessment. A full mathematical model of the energy system as well as the constraints governing the management issue is extracted and a Deep Deterministic Policy Gradient (DDPG) method is used to optimally manage the energy flows for the entire journey. All models are implemented in MATLAB software and the efficiency of the proposed system is investigated from economic and technical aspects. The results illustrate a high efficiency for the proposed WPT technique to be used in actual all-electric city buses.

The number of electric vehicles (EVs) is increasingly growing day by day and the charging infrastructure for covering this growing number of EVs should be developed. The construction of charging stations is one of the main solutions for supporting EVs while it costs huge investments for installation. Thus, this is not financially logical to invest in charging stations in remote areas with lower demands. An alternative way of constructing charging stations is to provide a peer-to-peer (P2P) energy exchange system in order to support out-of-charge EVs. In this paper, a private cloud-edge emergency energy trading framework is proposed to facilitate energy exchange among consumers and providers. Furthermore, a bidding system is suggested to encourage EVs with extra charges to exchange their energy. Moreover, a matching strategy for pairing consumers and providers is suggested in this paper that considers the benefit of both consumers and providers. In the proposed matching system, a measurement strategy is also suggested for considering the effect of the reliability and punctuality of the providers. To develop the accuracy and efficiency of the proposed framework, employing deep learning methods is also suggested in different layers of the framework. The performance of the proposed framework is evaluated on several case studies in the presence of EVs with realistic features to prove its efficiency, feasibility, and scalability.

Active power filters (APFs) are used to mitigate the harmonics generated by nonlinear loads in distribution networks. Therefore, due to the increase of nonlinear loads in power systems, it is necessary to reduce current harmonics. One typical method is utilizing Shunt Active Power Filters (SAPFs). This paper proposes an outstanding controller to improve the performance of the three-phase 25-kVA SAPF. This controller can reduce the current total harmonic distortion (THD), and is called fractional order PI-fractional order PD (FOPI-FOPD) cascade controller. In this study, another qualified controller was applied, called multistage fractional order PID controller, to show the superiority of the FOPI-FOPD cascade controller to the multistage FOPID controller. Both controllers were designed based on a non-dominated sorting genetic algorithm (NSGA-II). The obtained results demonstrate that the steady-state response and transient characteristics achieved by the FO (PI + PD) cascade controller are superior to the ones obtained by the multistage FOPID controller. The proposed controller was able to significantly reduce the source current THD to less than 2%, which is about a 52% reduction compared to the previous work in the introduction. Finally, the studied SAPF system with the proposed cascade controller was developed in the hardware-In-the Loop (HiL) simulation for real-time examinations.

The extensive use of electric vehicles (EVs) can reduce concerns about climate change and fossil fuel shortages. One of the main obstacles to accepting EVs is the limitation of charging stations, which consists of high-charge batteries and high-energy charging infrastructure. A new transformer-less topology for boost dc-dc converters with higher power density and lower switch stress is proposed in this paper, which may be a suitable candidate for high-power fast-charging battery chargers of EVs. Throughout this paper, two operating modes of the proposed converter, continuous current mode (CCM) and discontinuous current mode (DCM), are analyzed in detail. Additionally, critical inductances and design considerations for the proposed converter are calculated. Finally, real-time verifications based on hardware-in-loop (HiL) simulation are carried out to assess the correctness of the proposed theoretical concepts.

Extensive use of wind turbine (WT) systems brings remarkable challenges to the stability and safety of the power systems. Due to the difficulty and complexity of modeling such large plants, the model-independent strategies are preferred for the control of the WT plants which eliminates the need to model identification. This current work proposes a novel model-independent control methodology in the rotor side converter (RSC) part to ameliorate low voltage ride through (LVRT) ability especially for the doubly-fed induction generator (DFIG) WT. A novel model-independent nonsingular terminal sliding mode control (MINTSMC) was developed based on the principle of the ultra-local pattern. In the suggested controller, the MINTSMC scheme was designed to stabilize the RSC of the DFIG, and a sliding-mode supervisor was adopted to determine the unknown dynamics of the proposed system. An auxiliary dual input interval type 2 fuzzy logic control (DIT2-FLC) was established in a model-independent control structure to remove the estimation error of the sliding mode observer. Real-time examinations have been carried out using a Real-Time Model in Loop (RT-MiL) for validating the applicability of the proposed model-independent control in a real-time platform. To evaluate the usefulness and supremacy of the MINTSMC based DIT2-FLC, the real-time outcomes are compared with outcomes of RSC regulated conventional PI controller and MINTSMC controller.

All-electric ships, and especially the hybrid ones with diesel generators and batteries, have attracted the attention of maritime industry in the last years due to their less emission and higher efficiency. The variant emission policies in different sailing areas and the impact of physical and environmental phenomena on ships energy consumption are two interesting and serious concepts in the maritime issues. In this paper, an efficient energy management strategy is proposed for a hybrid vessel that can effectively consider the emission policies and apply the impacts of ship resistant, wind direction and sea state on the ships propulsion. In addition, the possibility and impact of charging and discharging the carried electrical vehicles’ batteries by the ship is investigated. All mentioned matters are mathematically formulated and a general model of the system is extracted. The resulted model and real data are utilized for the proposed energy management strategy. A genetic algorithm is used in MATLAB software to obtain the optimal solution for a specific trip of the ship. Simulation results confirm the effectiveness of the proposed energy management method in economical and reliable operation of the ship considering the different emission control policies and weather condition impacts.

This paper proposes a deep deterministic policy gradient (DDPG) based nonlinear integral backstepping (NIB) in combination with model free control (MFC) for pitch angle control of variable speed wind turbine. In particular, the controller has been presented as a digital twin (DT) concept, which is an increasingly growing method in a variety of applications. In DDPG-NIB-MFC, the pitch angle is considered as the control input that depends on the optimal rotor speed, which is usually derived from effective wind speed. The system stability according to the Lyapunov theory can be achieved by the recursive nature of the backstepping theory and the integral action has been used to compensate for the steady-state error. Moreover, due to the nonlinear characteristics of wind turbines, the MFC aims to handle the un-modeled system dynamics and disturbances. The DDPG algorithm with actor-critic structure has been added in the proposed control structure to efficiently and adaptively tune the controller parameters embedded in the NIB controller. Under this effort, a digital twin of a presented controller is defined as a real-time and probabilistic model which is implemented on the digital signal processor (DSP) computing device. To ensure the performance of the proposed approach and output behavior of the system, software-in-loop (SIL) and hardware-in-loop (HIL) testing procedures have been considered. From the simulation and implementation outcomes, it can be concluded that the proposed backstepping controller based DDPG is more effective, robust, and adaptive than the backstepping and proportional-integral (PI) controllers optimized by particle swarm optimization (PSO) in the presence of uncertainties and disturbances.

This paper proposes a novel adaptive controller based on digital twin (DT) by integrating software-in-loop (SIL) and hardware-in-loop (HIL). This work aims to reduce the difference between the SIL controller and its physical controller counterpart using the DT concept. To highlight the applicability of the suggested methodology, the regulation control of a horizontal variable speed wind turbine (WT) is considered for the design and assessment purposes. In the presented digital twin framework, the active disturbance rejection controller (ADRC) is implemented for the pitch angle control of the WT plant in both SIL and HIL environments. The design of the ADRC controllers in the DT framework is accomplished by adopting deep deterministic policy gradient (DDPG) in two stages: ( i ) by employing a fitness evaluation of wind speed error, the internal coefficients of HIL controller are adjusted based on DDPG for the regulation of WT plant, and ( ii ) the difference between the rotor speed waveforms in HIL and SIL are reduced by DDPG to obtain a similar output behavior of the system in these environments. Some examinations based on DT are conducted to validate the effectiveness, high dynamic performance, robustness and adaptability of the suggested method in comparison to the prevalent state-of-the-art techniques. The suggested controller is seen to be significantly more efficient especially in the compensation of high aerodynamic variations, unknown uncertainties and also mechanical stresses on the plant drive train.

In this paper, the concept of fractional calculus (FC) is introduced into the sliding mode control (SMC), named fractional order SMC (FOSMC), for the load frequency control (LFC) of an islanded microgrid (MG). The studied MG is constructed from different autonomous generation components such as diesel engines, renewable sources, and storage devices, which are optimally planned to benefit customers. The coefficients embedded in the FOSMC structure play a vital role in the quality of controller commands, so there is a need for a powerful heuristic methodology in the LFC study to adjust the design coefficients in such a way that better transient output may be achieved for resistance to renewable sources fluctuations. Accordingly, the Sine Cosine algorithm (SCA) is effectively combined with the harmony search (HS) for the optimal setting of the controller coefficients. The Lyapunov function based on the FOSMC is formulated to guarantee the stability of the LFC mechanism for the test MG. Finally, the hardware-in-the-loop (HIL) experiments are carried out to ensure that the suggested controller can suppress the frequency fluctuations effectively, and that it provides more robust MG responses in comparison with the prior art techniques.