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Due to the increase in the number of electric vehicles (EVs) in the world, there is a need to design superior performance and lower operating costs of charging infrastructures to serve these vehicles. Merging extreme fast charging technology with the portable feature can provide a user-friendly and cost-effective structure for charging EVs. This brief proposes a Smart Extreme Fast Portable Charger (SEFPC) for Electric Vehicles which have several input ports (e.g., the power grid or Renewable Energy Sources (RESs)/Energy Storage Systems (ESSs)) and an output port with an optimal charging operation mode based on considering the condition of the available power sources and battery of EVs for saving the energy and time charging. Moreover, a model-free sliding mode controller-based machine learning algorithm is applied to find optimal charging operation mode based on the battery state and power of sources condition to increase battery life and overall system efficiency. Finally, real-time results based on the OPAL-RT are provided to validate the efficacy and feasibility of the proposed SEFPC and model-free sliding mode controller.

—Traditional marine power grids, using diesel motors/generators, pollute the atmosphere with their CO2 emissions and adverse particles. Nowadays, green energy supplies like solar systems and fuel cells play significant roles in the electrification of marine propulsion plants. In general, marine power systems with green energy units are often considered as a kind of mobile DC microgrids under constant power loads (CPLs). However, since the CPLs impose nonlinearity and negative impedance instability on marine power systems, traditional linear controllers no longer stabilize the system under various disturbances. This motivated this study to develop a non-integer model predictive control for the accurate control of DC/DC converters with CPLs to stabilize the main bus voltage and current in the full-electric ferry ship. In practice, the stability of ship systems is highly threatened due to the negative-independence effect of CPLs. To address the adverse challenges of CPLs, the deep deterministic policy gradient (DDPG) with actor and critic neural networks (NNs) is applied to effectively design the coefficients embedded in the non-integer MPC. In this approach, the actor NN provides the regulatory signals to adjust the noninteger MPC controller while the critic NN is trained to evaluate the quality of actions of actor NN. The experimental results with comparative analysis are carried out to validate the accuracy of the non-integer MPC under the change in the CPL’s power. The results demonstrated that the proposed controller offers a higher level of robustness than the state-of-art-schemes.

This letter applies a multi-agent fuzzy Q-learning algorithm, incorporated with a model-free non-linear controller, entitled FQL-MFNC for stabilized controlling of a recently introduced grid-connected nine-level Packed E-Cell (PEC9) inverter under dynamical operation. Unlike previous tuning schemes which concentrate on extracting mathematical formulation of a controlled plant, this letter investigates a fuzzy Q-Learning agent for optimal design of PEC9. In first step, the fuzzy reinforcement learning is adopted to tune the MFNC controller in the simulation environment. In fact, the FQL algorithm finds the optimal policy based on a reward function for adjustment of the MFNC control coefficients to guarantee the grid-connectivity requirements under PEC9 dynamical operation are met. The experimental tests are conducted to assure efficiency and practicability of the designed multi-agent FQL-MFNC scheme on the single-phase grid-connected PEC9 inverter.

Due to the negative impedance potential of constant power loads (CPLs), the stability of power electronic converters-based electrical distribution networks is prone to instability. This brief proposes a robust control technique based on deep reinforcement learning to stabilize a DC microgrid (MG) with parallel boost converters when feeding CPLs. For this purpose, a model-free sliding mode controller (MFSMC) has been applied to the DC MG. The MFSMC controller does not need any identification of converters in the DC MG system while ensuring the efficiency and the stability of the control synthesis. The key control coefficients are designed by Proximal Policy Optimization (PPO) Reinforcement Learning (RL). The PPO is made from two deep neural networks (NNs) (actor NN and critic NN) which are trained to adjust the coefficients of the MFSMC controller. The MFSMC controller designed by the PPO with actor-critic architecture is applied to the DC MG system feeding CPL in an OPAL-RT setup to verify the efficiency of the proposed controller through Hardware-in-the-Loop (HiL) simulations.

This paper introduces a new high step-up multi-input multi-output (MIMO) DC–DC converter control-based pole placement suitable for photovoltaic system applications. The suggested topology has a high stability current control. Low element number, high voltage conversion ratio, and low ripple input current are the main advantages of the proposed converter. The capability to use several energy resources with different voltage/current characteristics, increase the number of inputs and outputs arbitrary without duty-cycle limitation, and control each input unit power switch independently are other advantages. The operation principle of the suggested topology, steady-state analysis in continuous condition mode (CCM), and parameter design are analyzed. A comparison between the suggested topology and other converters is applied. Two-input two-output version of the suggested topology in 200 W at an operating 50 kHz switching frequency is analyzed. Finally, the Typhoon HiL-404 emulating setup is applied to verify the performance and efficiency of the suggested high-step-up MIMO DC–DC power converter.

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.

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.

Commonly, a simple topology consisting of a single diode-rectifier and pulse-width-modulation (PWM) converter is used for the connection of the doubly-fed induction generator-based wind turbines (DFIG-WTs) to the DC grid, in which the rotor-side converter (RSC) and the stator diode-rectifier are linked to the common DC bus. But, this topology suffers from less flexibility due to the requirement of matching the stator nominal voltage with the DC grid. Hence, in this paper, a buck converter is added to the DFIG-WT topology and two methods are proposed to control the power converters, where in the first one, the RSC regulates the active power and stator flux linkage, and in the second one, the RSC regulates the DC bus voltage and stator flux linkage. Then, the small-signal stability and the performance of the DFIG-WT system with the mentioned control strategies are compared analytically and through the off-line simulations. At last, a real-time hardware-in-the-loop (HIL) emulation-based OPAL-RT is carried out to investigate the performance of the proposed control methods from a practical perspective.

For the 5G base transceiver stations (BTSs), the effective stabilization of full-bridge (FB) converters is necessary to supply the connected loads without any interruption. The stability challenges of such technologies are more intensified when the 5G BTS supplies constant power loads (CPL) with negative impedance instabilities. To meet this need, this brief presents an adaptive interval type-3 fuzzy logic system (IT3-FLS) employing deep reinforcement learning (DRL) for the efficient voltage stabilization of 5G-telecom power system (5G-TPS) supplying CPL. The Hardware-in-the-Loop (HiL) examinations are accomplished using an OPAL-RT platform to test the usefulness of the adaptive IT3-FLS from a systematic perspective.

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. All experimental codes-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 deals with the integration and control of the doubly fed induction generator-based wind turbine (DFIG-WT) in a DC grid. In this way, the stator of the DFIG-WT is integrated with the DC grid via an uncontrolled diode-bridge rectifier as well as a buck converter to provide a flexible connection path. In addition, the rotor of the DFIG-WT exchanges power with the DC grid through a rotor-side converter (RSC). By considering the DFIG-WT system topology, three control strategies based on the stator flux-oriented vector control are developed for the RSC and buck converter. The control schemes consist of the outer and the inner control loops with the proportional–integral (PI) controllers. Additionally, the stability of control strategies and their dependence on the generator parameters are evaluated and compared using modal analysis and numerical simulations in the MATLAB/SIMULINK environment. Finally, a real-time hardware-in-the-loop emulation-based OPAL-RT 5600 is accomplished to assess the performance of the superior control method from a practical perspective.

The Wireless Charging Electric Transit Bus (WCETB) is an innovative electric transportation technology that receives power wirelessly from underground power transmitters. Since the battery in the bus can be wirelessly charged during the moving, the need to stop for the vehicle at the charging station is eliminated, and recharging time is remarkably decreased. One of the best methodologies to commercialize the WCETB technology is to economically allocate the power tracks on considered routes while optimally determining the battery size of the vehicle at the same time. In this paper, the planning of the power transmitters and the capacity of the battery size for a WCETB with a multi-route model is optimally designed by Deep Deterministic Policy Gradient (DDPG). In particular, the DDPG algorithm is adopted in this model, an appropriate reward function is defined for the multiple route problem, and the optimal problem is solved by adopting the training ability of deep neural networks (DNNs), i.e., Actor and Critic neural networks. A complex Nguyen-Dupuis (N-D) traffic network with multiple routes is considered a case study to evaluate the validity and performance of the proposed deep learning scheme from a systematic point of view. Numerical analysis along with sensitivity examination confirms the efficiency of the optimal design and solution process.

The constant power loads (CPLs) threaten the stability of the DC microgrids (DCMGs) due to the negative impedance characteristic, which leads to voltage oscillations or a blackout in the DCMG. This paper proposes a new droop control for paralleled DC–DC converters feeding CPLs in the DCMG to improve the stability of the whole system. In this approach, an optimization droop gain is conducted for each converter to guarantee current sharing among paralleled converters. Furthermore, an intelligent single input interval type-II fuzzy logic controller (SIT2-FLC) is developed in a model-free framework to further ameliorate the current sharing and voltage regulation in the DCMG when supplying the CPLs. An extended-state observer (ESO) is adopted in the model-free controller to estimate the unmodelled dynamics in the DC-DC converters feeding CPLs. Since an estimation observer error often appears in the presence of uncertainties, a sliding mode control (SMC) technique is proposed to remove the estimation error and enhance the system stability. The studied DCMG is developed in hardware-in-the-loop (HIL) with various levels of CPL's power to verify the applicability of the suggested mode-free intelligent controller.

In recent years, fractional order Laser chaotic systems have gained popularity in both theory and applications, and various classes of these systems have been introduced. This paper presents a dynamic-free sliding mode control (SMC) methodology to synchronize a class of unknown fractional order (FO) Laser chaotic systems with input saturation. The proposed method uses a defined continuous function instead of the discrete sign function and is based on the FO version of the Lyapunov stability theorem. The result is a novel finite-time SMC (FTSMC) methodology that effectively suppresses chaotic behavior in FO Laser chaotic systems without undesirable chattering. This approach is designed to take advantage of the boundedness feature of the FO chaotic system. The efficacy of the FTSMC is demonstrated by applying the method to a chaotic FO Laser system at two different non-integer orders, and its practical applicability is demonstrated by using it to encrypt/decrypt color pictures. The suggested encryption/decryption methodology uses an adaptation of the pre-diffusion permutation-diffusion structure to increase security. Performance and security analyses, including histogram analysis, neighboring pixel correlation analysis, and information entropy analysis, provide further support for the suggested encryption system's superiority.

The nonlinearities and unmodeled dynamics inevitably degrade the quality and reliability of power conversion, and as a result, pose big challenges on higher-performance voltage stabilization of dc–dc buck converters. The stability of such power electronic equipment is further threatened when feeding the nonideal constant power loads (CPLs) because of the induced negative impedance specifications. In response to these challenges, the advanced regulatory and technological mechanisms associated with the converters require to be developed to efficiently implement these interface systems in the microgrid configuration. This article addresses an intelligent proportional-integral based on sliding mode (SM) observer to mitigate the destructive impedance instabilities of nonideal CPLs with time-varying nature in the ultralocal model sense. In particular, in the current article, an auxiliary deep deterministic policy gradient (DDPG) controller is adaptively developed to decrease the observer estimation error and further ameliorate the dynamic characteristics of dc–dc buck converters. The design of the DDPG is realized in two parts: (i) an actor-network which generates the policy commands, while (ii) a critic-network evaluates the quality of the policy command generated by the actor. The suggested strategy establishes the DDPG-based control to handle for what the iPI-based SM observer is unable to compensate. In this application, the weight coefficients of the actor and critic networks are trained based on the reward feedback of the voltage error, by using the gradient descent scheme. Finally, to investigate the merits and implementation feasibility of the suggested method, some experimental results on a laboratory prototype of the dc–dc buck converter, which feeds a time-varying CPL, are presented.

All-electric ships (AES) are regarded as a promising solution for decreasing greenhouse gas emissions since they are able to utilize clean technologies like fuel cells instead of fossil fuel. A zero-emission hybrid energy system comprising the Proton Exchange Membrane (PEM) fuel cell, battery, cold-ironing, and Recuperative organic Rankine cycle (RORC) is scrutinized in this paper. To use the waste heat produced by PEM fuel cells, a hybrid system which is composed of PEM fuel cells and RORC is suggested. In order to analyze this zero-emission all-electric ship, the real data of a ferry boat, containing the load profiles and routes is used to appraise the possibility of the suggested hybrid system. The structure of the boat and energy sources, along with mathematical models, are represented and examined. Eventually, the hourly energy management of the boat for one specific day is analyzed, and also, to optimize the power dispatch, the Deep Deterministic Policy Gradient (DDPG) is applied in actor-critic architecture. To corroborate the proposed models, the MATLAB software is employed. According to obtained results, the proposed models could ensure effective and efficient ways in marine vessels due to not only their high performances but also permissible energy cost to be a zero-emission ship.

Reducing the order of positive real and bounded real systems has received great interest over the years. Several methods have been published regarding these issues which are able to produce positive real or bounded real reduced models. However, none of them can guarantee that the reduced models are both positive real and bounded real at the same time. In this brief, a novel model reduction scheme is presented which focuses on preserving two possible properties of the system (positive realness and bounded realness) simultaneously. This is done by using one positive real Riccati equation and one bounded real Riccati equation in the reduction procedure. Thereafter, the gramians extracted from these Riccati equations are balanced based on the Balanced Truncation (BT) concept. Finally, a numerical example is provided to investigate the efficacy of the proposed method.

In this study, virtual inertia control-based vehicle-to-grid (V2G) concept for the secondary load frequency control of AC islanded microgrids (MGs) is presented. Since autonomous modern power grids typically have much smaller inertia in comparison to conventional grids, a very fast and high-performance control plant is necessitated for controlling reserved power plants (e.g., batteries) to overcome any voltage and frequency instability. This issue can be solved by the short-term battery energy storage system, inverter, and proper inertia control technique. Because of the high cost of the battery energy storage system, utilization of controllable loads like electric vehicle (EV) to low-inertia modern power grids is presented for the decreasing of the needed capacity of the energy storage system. Hence, an optimal non-integer model predictive control is applied for the secondary frequency regulation in low-inertia stand-alone MGs with the V2G technology. Since the performance and accuracy of the applied non-integer controller are depended on its parameters’ value, a modified heuristic optimization algorithm is proposed for tuning the controller's parameters. In the end, the validity and feasibility of the proposed method are investigated by the model-in-the-loop experimental results.

In this article, a noninteger proportional integral derivative (PID)-type controller based on the deep deterministic policy gradient algorithm is developed for the tracking problem of a mobile robot. This robot system is a typical case of nonholonomic plants and is exposed to the measurement noises and external disturbances. To accomplish the control methodology, two control mechanisms are established independently: a kinematic controller (which is designed based on the kinematic model of the vehicle), and a dynamic controller (which is realized according to the physical specifications of the vehicle dynamics). In particular, an optimal noninteger PID controller is initially designed as the primary dynamic controller for the tracking problem of a nonholonomic wheeled mobile robot. Then, a DDPG algorithm with the actor-critic framework is established for the supplementary dynamic controller, which is beneficial to the tracking stabilization by adapting to the uncertainties and disturbances. This strategy implements the supplementary based control to compensate for what the original controller is unable to handle. A prototype of the WMR was also adopted to investigate the applicability of the suggested controller from a real-time platform perspective. The outcomes in experimental environments are presented to affirm the effectiveness of the suggested control methodology.

This study proposes a new application for delay-dependent stability analysis of a shipboard microgrid system. Gain and phase margin values are taken into consideration in delay dependent stability analysis. Since such systems are prone to unwanted frequency oscillations against load disturbances and randomness of renewable resources, a virtual gain and phase margin tester has been incorporated into the system to achieve the desired stabilization specification. In this way, it is considered that the system provides the desired dynamic characteristics (e.g. less oscillation, early damping, etc.) in determining the time delay margin. Firstly, the time delay margin values are obtained and their accuracy in the terms of desired gain and phase margin values are investigated. Then, the accuracy of the time delay margin values obtained by using the real data of renewable energy sources and loads in the shipboard microgrid system is shown in the study. Finally, a real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT is accomplished to affirm the applicability of the suggested method, from a systemic perspective, for the load frequency control problem in the shipboard microgrid.

Parkinson’s disease (PD) is one of the most common diseases that its main complications are hand and head tremors and inflexibility of muscles. One of the prevalent treatments that employ for reducing the symptoms of that is deep brain stimulation (DBS). In practice, a sensor is located in the patient’s finger for detecting and evaluating the tremor values in PD. Using an open-loop control structure for stimulating one area of basal ganglia (BG) is the common approach, but in this work, two areas of BG, named subthalamic nucleus (STN) and globus pallidus internal (GPi) are stimulated in a closed-loop manner separately for i) reducing the intensity of electric field and consequently disappearing the side effects of DBS ii) decreasing hand tremor. In particular, an adaptive Active Disturbance Rejection Control (ADRC) based on a deep deterministic policy gradient (DDPG) and a conventional feedback controller are presented for simultaneous stimulating STN and GPi, respectively. In this way, the control coefficients of the ADRC are considered as the control objective parameters that are designed by the actor and critic neural networks (NNs) of DDPG. The suggested scheme is applied to a BG system model which is frequently studied in the literature. The comprehensive simulation studies are accomplished to confirm the supremacy of the ADRC based DDPG scheme over the state-of-the-art strategies. Moreover, hardware-in-the-loop (HiL) simulations are performed to verify the efficiency of the proposed scheme from real-time perspective.

This paper presents a control method based on active disturbance rejection control (ADRC) for both the primary and secondary control layers in a hybrid DC/AC microgrid (MG). A DC bus inside MG includes a wind turbine generator, photovoltaic panels, a fuel cell and battery energy storage. Maintaining the DC link voltage under various scenarios is made possible by applying an energy management system (EMS) based on the deviation in the state of charge of the battery and using a fuel cell as a backup. Through a voltage source inverter, the DC bus of the MG is connected to the AC side, which supplies AC loads. Using the proposed ADRC-based control strategy for the inverter, the voltage, frequency, and power flow within the MG would be effectively controlled. In order to demonstrate the effectiveness of the proposed method, a comparison between the ADRC and PI controller has also been implemented in different scenarios, proving the superiority of the suggested controller over classical approaches.

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.

Recently, a modularized smart grid (SG) architecture, entitled the Internet of Things (IoT) grid, is developed that accommodates the IoT technology into the dc-dc converters to build a programmable grid with a single voltage bus. This modern architecture can be established with low computing hardware that facilitates the control and management of the IoT-based grids. Due to the uncertainties originated from the integration of the IoT technology and power electronic converters, the deterministic methodologies are unable to precisely model the SG anymore. In response to these challenges, this article addresses a novel adaptive data-driven method based on the active disturbance rejection controller (ADRC) for the voltage regulation of an IoT-based dc-dc buck converter feeding constant power loads. In particular, a deep deterministic policy gradient (DDPG) with the actor-critic architecture is adopted for the online adjusting of the ADRC controller. The established DDPG takes into account the ADRC controller coefficients into the design objective and offers the ADRC controller with the online coefficient setting ability through the neural network learning. The IoT-based system is tested on a real-time testbed with the constrained application protocol protocol and IEEE 802.11 (Wi-Fi) network to assess the applicability of the suggested controller in the presence of network degradations. The impact of both packet loss and interfering traffic on the reduction performance of the DDPG adaptive ADRC controller is investigated, simultaneously. The supremacy of the suggested adaptive data-driven controllers is verified by a comprehensive comparative analysis with the state-of-the-art methodologies.

This article is dedicated to model order reduction of linear time-invariant passive systems, i.e., positive real (PR) systems, based on the balanced truncation (BT) concept. The main feature of this article is that we have used the phase angle of the transfer function, G(s), in model order reduction for the first time in the proposed method, which results in more accurate reduced-order models. It is worth noting that this hypothesis has been never made before in any other model order reduction techniques. Furthermore, new gramians are calculated corresponding to their associated new Riccati equations and Lur'e equations, which are in turn achieved by using Kalman-Yakubovic-Popov (KYP) lemma and linear matrix inequalities (LMI). Thereafter, a novel passivity preserving model order reduction algorithm, which takes the phase angle of the transfer function, G(s), into account, is presented. It is demonstrated that the proposed method is a generalization of the positive real balanced truncation (PRBT) method, and it is perfectly capable of providing more accurate approximation error compared to PRBT. Finally, numerical examples are included to figure out the effectiveness of the presented method.

This brief proposes an intelligent and fast controller for the secondary voltage control in the islanded DC microgrids (DCMGs) for shipboard power system applications (SPSs). The existence of constant power loads (CPLs) and propulsion loads in the SPS makes the voltage regulation as an important issue in the islanded DCMG. In order to achieve fast response and proper voltage regulation as well as optimal current sharing in the stand-Alone shipboard DCMG, this brief proposes a new nonlinear droop control in the primary control level. The proposed method eliminates the voltage oscillation in dynamic situations and improves the current sharing among paralleled sources. Furthermore, a novel high bandwidth fractional order general type-2 fuzzy logic proportional-integral-derivative (FOGT2FPID) controller is applied in the dynamic consensus algorithm (DCA)-based method in the secondary control level to ensure fast and accurate voltage regulation in the DCMG. The effectiveness and applicability of the new suggested approach are validated with hardware-in-The-loop (HiL) experimental results.

The recent advances in power plants and energy resources have extended the applications of buck-boost converters in the context of dc microgrids (MGs). However, the implementation of such interface systems in the MG applications is seriously threatened with instability issues imposed by the constant power loads (CPLs). The objective is that without the accurate modeling information of a dc MG system, to develop a new adaptive control methodology for voltage stabilization of the dc–dc converters feeding CPLs with low ripples. To achieve this goal, in this letter, the deep reinforcement learning (DRL) technique with the Actor–Critic architecture is incorporated into an ultralocal model (ULM) control scheme to address the destabilization effect of the CPLs under the reference voltage variations. In the suggested control approach, the feedback controller gains of the ULM controller are considered as the adjustable controller coefficients, which will be adaptively designed by the DRL technique through online learning of its neural networks (NNs). It is proved that the suggested scheme will ensure the rigorous stability of the power electronic system, for simultaneous effects of CPL and reference voltage changes, by adaptively adjusting the ULM controller gains. To appraise the merits and usefulness of the suggested adaptive methodology, some dSPACE MicroLabBox outcomes on a real-time testbed of the dc–dc converter feeding a CPL are presented.

This article investigates the fuzzy model predictive control synthesis of a power buffer for dynamic stabilization of a dc microgrid (MG), which is controlled through a low-latency communication network, such as the one envisioned in fifth-generation (5G). The proposed approach employs a Takagi–Sugeno fuzzy model, a fuzzy observer, and a model predictive scheme to alleviate the effects of the 5G-network-induced delays and data loss of the sensor-to-controller and controller-to-actuator links on the dc MG plant response. By employing the so-called time-stamp technique and network delay compensator (NDC), the delays are computed, and the data loss effects are compensated, thus improving the effectiveness and robustness of the proposed controller. In addition, a time-delay-independent observer is proposed to estimate the states of the constant power loads (CPLs) and the power buffer, based on the measured information. Due to the usage of two NDCs, the presented approach is robust against the network delays and results in a small computational burden. To show the merits of the proposed approach, it is applied to a dc MG that feeds two CPLs. Results show the simplicity of designing the observer-based controller and better robustness against the network delays, compared with the state-of-the-art methods. Additionally, software-in-the-loop simulations are presented to prove the practical applicability of the proposed controller.

The negative impedance characteristic of the constant power loads (CPLs) causes instability in DC/DC converters in the DC microgrids. To improve the stability of the DC/DC converter feeding CPLs, a robust and fast controller is required. This paper presents a robust pulse-width modulation-based type-II fuzzy controller for a DC/DC boost converter feeding the CPL in a DC microgrid. Theoretical analysis and real-time simulation are presented to demonstrate the effectiveness of the proposed non-integer intelligent controller. Finally, the experimental results demonstrate that the proposed intelligent controller for the DC/DC converter has a faster and more robust response in comparison to the previously suggested control techniques.

This brief proposes a new scheme for the online minimization of harmonic distortion of an islanded microgrid based on a population-based optimization method. In this scheme, a new central controller is presented, which can optimize network voltage harmonics according to particle swarm optimization (PSO) algorithm, while active power is shared between distributed generation units. In the suggested intelligent controller, the optimal amplitude, and angle of harmonic components are generated by the PSO algorithm and added to the voltage reference generated by the droop controller. The objective functions of the problem are minimizing the harmonic distortion of the critical bus and the total harmonic distortion of the microgrid. Simulation and experimental results show the effectiveness of the proposed method for minimizing harmonic distortion in microgrid networks.

This brief proposes a new optimal intelligent controller for the secondary load frequency control (SLFC) in islanded microgrids (MGs). Particularly, this brief is applied a precise model of the tidal power generation system for the SLFC in the stand-alone MG. Moreover, in order to reduce the cost of energy storage (ES) devices in the MG, vehicle-to-grid (V2G) technology has utilized as a new idea to overcome this difficulty. Hence, a fractional type-2 fuzzy P+ID controller is applied to have a robust balance between generation and demand. Furthermore, it is quite clear that optimal tuning of the controller's parameters can cause a significant effect on the performance of the whole of the system. Consequently, a new heuristic optimization algorithm, entitled sine-cosine, is used to find the best value of the proposed controller's parameters. Finally, by using real-data, the performance and robustness of the suggested controller over other controller techniques have examined by the dSPACE real-time simulator.

Nowadays, sea traveling is increasing due to its practicality and low-cost. Ferry boats play a significant role in the marine tourism industry to transfer passengers and tourists. Nevertheless, traditional ferry ships consume massive amounts of fossil fuels to generate the required energy for their motors and demanded loads. Also, by consuming fossil fuels, ferries spatter the atmosphere with CO2 emissions and detrimental particles. In order to address these issues, ferry-building industries try to utilize renewable energy sources (RESs) and energy storage systems (ESSs), instead of fossil fuels, to provide the required power in the ferry boats. In general, full-electric ferry (FEF) boats are a new concept to reduce the cost of fossil fuels and air emissions. Hence, FEF can be regarded as a kind of dc stand-alone microgrid with constant power loads (CPLs). This article proposes a new structure of a FEF ship based on RESs and ESSs. In order to solve the negative impedance induced instabilities in dc power electronic based RESs, a new intelligent single input interval type-2 fuzzy logic controller based on sliding mode control is proposed for the dc–dc converters feeding CLPs. The main feature of the suggested technique is that it is mode-free and regulates the plant without requiring the knowledge of converter dynamics. Finally, we conduct a dSPACE-based real-time experiment to examine the effectiveness of the proposed energy management system for FEF vessels.

This paper discusses the effect of nonlinear loads of some equipment on the islanded microgrids. The nonlinear load with active and reactive power is considered, which highly threatens the stability of the microgrids. Moreover, a wind turbine, which is a nondispatchable distributed generation (DG), is included as a power fluctuation source in the microgrid. The maximum power point tracking is integrated to ensure that the wind turbine generator operates at its maximum power level. In order to enable efficient power sharing among DG units, active power–frequency droop control is adopted for a microgrid with three DG units. In particular, a single input interval type-2 fuzzy integral (SI-IT2-FI) controller is implemented for the self-frequency recovery control of the microgrid to restore the frequency deviation within the desired range from its nominal value. In this way, by varying an adjustable design parameter which shapes the footprint of uncertainty, the SI-IT2-FI controller with different control surfaces generation is developed. In order to ascertain the efficiency of the SI-IT2-FI controller, the output responses in the absence and presence of wind turbine are compared with the P–f droop control. Total harmonic distortion is adopted as the harmonic metric to investigate the impact of the nonlinear loads at the test microgrid. Finally, in order to ascertain the feasibility and applicability of the proposed technique, a software-in-the-loop real-time simulation using dSPACE 1202 board is carried out. The simulation outcomes of the islanded microgrid confirm the efficiency of the proposed scheme.

This paper proposes a novel robust controller design for a class of nonlinear systems including hard nonlinearity functions. The proposed approach is based on Takagi–Sugeno (TS) fuzzy modeling, nonquadratic Lyapunov function, and nonparallel distributed compensation scheme. In this paper, a novel TS modeling of the nonlinear dynamics with signum functions is proposed. This model can exactly represent the original nonlinear system with hard nonlinearity while the discontinuous signum functions are not approximated. Based on the bounded-input-bounded-output stability scheme and ${L_{1}}$ performance criterion, new robust controller design conditions in terms of linear matrix inequalities are derived. Three practical case studies, electric power steering system, a helicopter model and servo-mechanical system, are presented to demonstrate the importance of such class of nonlinear systems comprising signum function. Furthermore, to show the superiorities of the proposed approach, it is applied to these systems; and, the experimental real-time hardware-in-the-loop results are compared with the published literature with the same topic.

Power electronic devices are prone to instability when supplying constant power loads (CPL) due to their negative impedance specifications. The stability of such systems is more threatened in the cases of CPLs are uncertain or have time-varying nature. This necessitates an efficient control methodology to overcome the de-stabilization issue and regulate the response of the output voltage. In particular, this work proposes a novel Interval Type-III Fuzzy Neural Network-based Model Predictive Control, called (IT3FNNMPC), to mitigate the destructive effects of CPL in dc/dc boost converters implemented in the Microgrid (MG) and power system distribution (PSD). In a typical MGs configuration, the IT3FNNMPC controller is formulated and designed in a constraint control problem to achieve a fast-transient response of system voltage. The feasibility and applicability of the designed IT3FNNMPC controller have been validated by Hardware-in-the-Loop (HiL) real-time simulations reached using an OPAL-RT setup and a prototype dc/dc boost converter.

The dc/dc full-bridge (FB) converters are often utilized as the interface system in telecom power applications. The constant power loads (CPLs) in the telecom power systems demonstrate negative impedance which threatens the stability of the dc/dc converters. To address this issue, in this letter, a nonlinear controller is designed for the stabilization of the dc/dc full-bridge converter feeding CPLs. The soft actor-critic (SAC) algorithm with deep neural networks is adopted based on deep reinforcement learning (DRL) for optimal tuning of the controller parameters in the control law of an established nonlinear controller. According to the control requirements of the FB interface system, a reward signal is defined to train the neural networks of SAC. An efficient solution based on Hardware-in-the-Loop (HIL) is adopted for verifying and validating the feasibility of the proposed scheme using the OPAL-RT 5600.

This paper deals with the problem of synthesizing a fuzzy-logic-based adaptive proportional-integral sliding mode control (FAPISMC) for active suspension systems based on Kalman filtering approach. To improve the performance of the controller and eliminate the effect of the chattering, the switching input is designed based on the fuzzy-logic-based approach with a minimum number of rules. In order to facilitate the stability analysis, the estimation of the state variables is used in designing the sliding surface platform. Furthermore, the gain of the controller is updated by an adaptive law to avoid any pre-knowledge of the disturbance amplitude. Subsequently, the proposed approach is more implementable in real-world processes. Finally, in order to illustrate the effectiveness and merits of the proposed approach, a suspension system is considered and simulated by the real-time hardware-in-the-loop (HiL). In this example, a quarter-car model of suspension systems is considered. Then, the obtained real-time results are compared with the linear quadratic regulator approach.

This study focuses on the development of the future wireless sensors as communication of a cyber-physical Microgrid (WCPMG) under uncertain switching topologies. The impact of the network-induced delay is assessed under the switching CTs. In this paper, a new general type-2 fractional order fuzzy PD/fuzzy PI (GT2FO-FPD/FPI) supervised online ANFIS controller is proposed for the secondary load frequency control (LFC) of a networked multiple interconnected MG. To handle the switching network with communication topology (CT), a Markov model is adopted in the control design stage. To ameliorate further the performance of the supervised control strategy, an improved version of the whale optimization algorithm called IWOA is introduced to adjust the setting of the controller parameters. A real-time WCPMG system testbed is developed using hardware-in-the-loop (HIL) simulator and the testbed applicability is then studied in the terms of MG components and communication degradations from a systemic perspective.

This study presents a new approach for multi-agent systems (MASs). The agent dynamics are approximated by the suggested type-3 (T3) fuzzy logic system (FLS). Some sufficient conditions based on the event-triggered scheme are presented to ensure the stability under less activation of the actuators. New tuning rules are obtained for T3-FLSs form the stability and robustness examination. The effect of perturbations, actuator failures and approximation errors are compensated by the designed adaptive compensators. Simulation results show that the output of all agents well converged to the leader agent under disturbances and faulty conditions. Additionally, it is shown that the suggested event-triggered scheme is effective and the actuators are updated about 20–40% of total sample times.

The traditional power system structure is constantly changing due to the application of renewable energy sources (RESs) and microgrids (MGs) into the power system network. Due to the variations in demand and renewable generation profiles, load frequency control (LFC) is one of the most important issues in MGs. As a consequence of the relatively low inertia of these systems, LFC is more challenging compared to absence of DERs and MGs. A comprehensive review of the LFC problem in MGs is the primary objective of this paper. The review process for this paper is undertaken from two perspectives. As a first step, different models for MG components and benchmarks for LFC studies have been described, which can ease the researchers' task of finding suitable models. The second step of this review is a discussion of various control strategies, considering the merits and limitations of each. It is helpful to researchers that this comprehensive literature review gives them an overview of previous well-researched papers in this respect and enables them to connect the dots between recent developments, challenges, and future trends in load frequency strategies for MGs.

Dynamic wireless charging technology (DWC) is considered an innovative contactless way to charge the battery of electric vehicles (EV) in the modern transportation system. By installing charging infrastructure beneath the road, this technology can overcome the obstacles of conventional charging topology, including high battery cost, restricted driving range, limited availability of charging stations, and long recharging downtime. This paper proposes a long-term mathematical model for locating-routing electric freight trucks (EFTs), which belongs to the Multi-Depot Multi-product delivery supply chain. In the proposed framework, the placement of DWC infrastructure and routing of EFTs has been accomplished by considering the battery capacity of trucks, charging facilities installation cost, electrical energy losses, and the voltage deviation in the power distribution system (PDS). Finally, two comparative case studies have been performed on the interconnected network IEEE 13 node test feeder and Nguyen–Dupius traffic network to validate the proposed approach. We also conduct a sensitivity analysis to understand the model’s system behavior and confirm it with insight.

This article proposes an intelligent and fast controller for the parallel dc–dc converters in the islanded dc microgrids (DCMGs) in the shipboard power system (SPS) applications. The existence of constant power loads and propulsion loads in the SPS makes the voltage regulation an important issue in the islanded DCMG. In this article, the goal is to provide a fast response and proper voltage regulation and optimal current sharing in the stand-alone shipboard DCMG. To achieve this, a nonlinear $I-V$ droop control is first designed in the primary control level to ensure the basic performance. Then, a new model-independent technique, entitled intelligent single-input interval type-2 fuzzy logic controller (iSIT2-FLC) combined with sliding mode control (SMC), is suggested to further improve the voltage regulation and current-sharing accuracy. In this control scheme, an extended state observer (ESO) is employed to estimate the unknown DCMG dynamics, whereas the SMC is adopted to eliminate the ESO estimation error. Moreover, the dynamic consensus algorithm (DCA) is implemented at the secondary control level to achieve coordinated control between DGs in the SPS. The effectiveness and applicability of the new suggested control approach are validated with hardware-in-the-loop (HiL) experimental results.

The small size of isolated microgrids (IMGs), high intermittency and fluctuations of renewable generations, and low inertia of distributed energy resources make the frequency an important factor in the operation of IMGs that should be managed considering technical and economic constraints. The fast response time and long lifecycle of vanadium redox flow batteries (VRFBs) make them a promising candidate for energy and frequency management of IMGs. Accordingly, this paper proposes a novel energy management system (EMS) for IMGs which aims to optimize energy and reserve scheduling of VRFBs in coordination with droop controlled distributed generators. The objective of the EMS is to minimize the total operation cost of the IMG while dynamic and static frequency securities are preserved. In the proposed EMS, the hierarchical control structure of IMGs is precisely formulated and the VRFB is incorporated in the EMS to provide both energy and reserve in primary and secondary control levels. Moreover, to get accurate results, the nonlinear characteristics of VRFBs are considered in the developed model and linearized by an efficient approach. The proposed EMS is formulated as a two-stage stochastic mixed-integer linear programming problem (MILP) that guarantees the global optimal solution. Case studies on a test IMG indicate that optimal coordination of VRFBs in energy and reserve scheduling can reduce the operation cost more than 11.67%.

Wireless power transfer (WPT), in a general scene, is the transmission of electrical energy for various technologies by means of magnetic couple resonance. Its potential applications change from low power electric equipment (like smartphones) to high-power devices over short distances of centimeters to tens of centimeters. In this letter, a pulsewidth-modulation-based adaptive data-driven strategy is newly developed for the output voltage regulation of the series–series (SS) compensated WPT. In an attempt to achieve a fast and adaptive voltage regulation, a deep deterministic policy gradient (DDPG) based on an ultralocal model (ULM) control scheme is applied to the dc–dc buck–boost converter feeding constant power loads installed in the receiver side of the WPT. In the suggested scheme, an intelligent feedback controller based on the ULM is used to stabilize the voltage response of the buck–boost converter while the unknown dynamics of the converter are estimated by a sliding mode observer (SMO). An auxiliary deep DDPG algorithm with Actor-Critic architecture is implemented to employ its adaptive capability to eliminate the SMO estimation error and improve the voltage regulation of the WPT plant. Some experimental outcomes on a laboratory testbed of the WPT plant are presented to assess the merits and real-time implementation of the adaptive data-driven scheme from a systematic perspective.

This paper investigates the problem of dynamic stabilization of dc microgrids (MGs) through a robust non-fragile fuzzy control synthesis of power buffer. The suggested robust fuzzy controller is designed to quickly stabilize the MGs by circulating the power between the dc link and an energy storage system (ESS). By employing the exponential stability analysis and Takagi-Sugeno fuzzy modeling, sufficient controller design conditions are derived in terms of linear matrix inequalities, which bring about a simple, systematic, and effective controller. The proposed approach is resilient against the uncertainties of the dc MG and ESS parameters. To show the merits of the proposed approach, it is applied to a dc MG that feeds one constant power load. It is shown that the proposed approach is more robust against system and controller uncertainties compared to the existing results. Finally, experimental results are then presented that show the transient performance improvement of the closed-loop system compared to the state-of-the-art methods.

More electric aircrafts (MEAs) comprise a vast amount of power electronic loads, which usually behave as constant power loads (CPLs). The incremental negative impedance of CPLs threatens system stability. To ensure an effective control of power flow in MEAs, eliminating the undesired behavior of CPLs is a necessity. This aim requires spontaneous power estimation of the time-varying uncertain loads. In this paper, an adaptive backstepping controller, which is interconnected to a third-degree cubature Kalman filter, is developed for a dc microgrid (MG) feeding nonideal CPLs. At first, the load power is considered as an artificial state and augmented into the system states, which enables estimation of not only the dc MG states but also the unknown value of the load power. The estimated load power is then forwarded to a backstepping controller. The systematic approach of this controller allows obtaining the control signal, which is the duty ratio of the switch, to not only system stabilizing but also tracking a desired voltage of the dc bus under the load power variations. The proposed adaptive controller is tested on a dc MG that has one CPL. The conducted experimental results verify the proposed nonlinear control in tracking the desired voltage of the dc bus under slow and fast variations of the load power.

This paper proposes the design of optimal and robust coordinated controller of hybrid energy storage system in a naval dc microgrid (MG) application. It is able to mitigate the negative effects of the pulsed power loads and meet the practical limitations of both converters input control and state variables signals based on IEEE standards. To do this, first, the dynamic model of the dc MG, which can represent in either all-electric aircraft or shipboard power systems, is developed. Second, a novel model predictive controller (MPC) for energy storage converters is proposed such that all of the mentioned hard constraints are guaranteed. Third, a linear matrix inequality approach is used to solve the MPC conditions. Finally, to evaluate the applicability and effectiveness of the proposed approach, some experimental tests are extracted. Obtained results verify better performance of the proposed approach over other state-of-the-art control techniques.

The stabilisation and transient performance improvement of a non-linear direct current (DC) microgrid (MG) dynamic which is controlled by a power buffer is studied. The injecting current of the power buffer is designed based on a novel reset gain-scheduling controller. This controller is constructed via a polytopic linear parameter varying-based dynamic controller that is equipped with the resetting action. The dynamic controller theoretically assures the closed-loop stability of the DC MG through the continuous-time Lyapunov theory. Also, the resetting action enhances the transient performance. The sufficient conditions of the controller design are derived in terms of linear matrix inequality and are solved by convex numerical methods. Comparing with the existing results, the proposed hybrid approach provides a non-continuous current reference of the power buffer, which efficiently improves the transient performance. To show the merits of the proposed approach, it is applied to a non-linear DC MG feeding one constant power load (CPL). Results show the simplicity of designing the proposed controller and the settling time and overshoot of the DC bus voltage compared to the state-of-the-art methods. Also, software-in-the-loop simulations are presented to prove the practical applicability of the proposed controller.