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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.

The study pioneers in proposing a hybrid cloud-based framework for designing a totally stand-alone, green residential house, stable over load variations and fluctuations of renewable energy sources (RES). The framework uses wind turbine (WT) and photovoltaic panels (PV) as the main power supplies, while employing a fuel cell (FC), fed with an electrolyzer, as the secondary source of energy. A battery is also used, which together with the FC and electrolyzer, constitute the compensation system in the proposed framework. Taking into account the various types of residential electrical loads, including an electrical vehicle (EV), and applying a deep learning method, the proposed framework makes the day-ahead scheduling of all components in the house based on the forecasted profile of load demand and the energy generated by the RES. The compensation system comes into use to balance the real-time scheduling error caused by the uncertainties of the main sources of power. To ascertain the practicality of the proposed framework for real-life implementation, it is examined on a residential house considering components with authentic technical features. The real-time operation of the suggested residential system is also tested on the SpeadGoat real-time simulator, whose results corroborate the practicability of both the real-time and day-ahead operation of the proposed framework.

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.

In this study, a new robust non-integer order controller is presented for the secondary load frequency control (LFC) of the shipboard microgrid (MG). During the design phase of the controller, the time delays between the controller and the sensor as well as the actuator are also taken into account, and a robust controller design against these time delays has been realized accordingly. The system frequency is regulated by a single input interval type-2 fuzzy fractional-order PI (SIT2-FFOPI) controller. The basic FOPI parameters of the SIT2 controller are determined offline with the help of the stability boundary locus (SBL) method depending on the time delay and integral order. For the determination of basic FOPI parameters, the characteristic equation of the shipboard MG LFC model is obtained, and the necessary arrangements are made. The geometric centroid is calculated to obtain the basic parameter values in the SBL curve obtained. The performance of the proposed controller design is assessed using real-world marine wave and solar irradiation data in the shipboard MG. Also, the process of verification of the applicability of the proposed method from a systemic perspective is done by real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT.

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.

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.

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.

The high-penetration of distributed generation technologies in the form of MicroGrids (MGs), in recent years, has increased the risk of frequency instability since their energy is supplied by renewable energy resources (RESs) with uncertain nature. Under such circumstances, providing an MG model with an efficient load frequency control (LFC) has a fundamental role in restoring the stability of the unstructured power system. In this study, a hybrid power system with the application of the Tidal Power Unit (TPU) and Vehicle-to-Grid (V2G) is effectively planed as an isolated MG. A new fractional gradient descent (FGD) based on a single-input interval type-2 fuzzy logic controller (SIT2-FLC) is suggested as the main LFC controller, where the footprint of uncertainty (FOU) coefficient of the SIT2-FLC is specifically adjusted to enhance the LFC performance. Additionally, a deep deterministic policy gradient (DDPG) with the actor-critic framework is considered to generate the supplementary control action, which is useful for the frequency stabilization by adapting to the randomness of load disturbances and RESs. Lastly, a model-in-the-loop (MiL) simulation is conducted to appraise the feasibility and applicability of the suggested design method from a systemic perspective.

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.

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 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.

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 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 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 dc microgrid (MG) system has several advantages over the ac one. Therefore, it recently became a preferred architecture in numerous industrial applications. Many loads in dc MGs are electronically regulated and they challenge the stability of the system due to their constant power load (CPL) behavior. This letter proposes a systematic and simple approach to design an improved state feedback controller for the power buffer that can stabilize the dc MGs with multiple CPLs. Based on the so-called sector nonlinearity approach, the nonlinear dc MG with several CPLs is exactly represented in a Takagi–Sugeno fuzzy model. Then, by employing the quadratic D-stability theory, the sufficient conditions to guarantee the stability and transient performance of the closed-loop system are obtained in terms of linear matrix inequalities (LMIs), such that the decay rate and oscillatory behavior of the closed-loop dc MG system are guaranteed to lie inside a predefined region. The LMI conditions can be numerically solved by utilizing the YALMIP toolbox in the MATLAB. Finally, to illustrate the merits and implementation validity of the proposed approach, some hardware-in-the-loop real-time simulation (RTS) results on a dc MG, which feeds two CPLs, are presented. In comparison with the state-of-the-art techniques, the RTS results indicate the simplicity, validity, and better performance of the proposed approach. According to the results, one can conclude that the proposed approach not only theoretically assures the stability but also guarantees the fast convergence and less oscillatory response of the dc MGs with multiple CPLs.

The performance of de shipboard power systems (SPSs) may degrade due to the negative impedance of constant power loads (CPLs) connected to dc microgrids (MGs). To control the dcSPS effectively, estimation of the instantaneous power flow to the time-varying uncertain CPLs is necessary. Furthermore, fast adaptive control is needed to deal with changes in the CPL power flow and quick stabilization of the dc MGs. Such a controller typically uses injection current from an energy storage system for actuation. Since measuring the CPLs' powers require installing current sensors that are both costly and not optimal, an estimation of the CPLs' powers should be employed. In this paper, an extended Kalman filter (EKF) is developed to estimate a time-varying power of uncertain CPLs in a dc MG based on measuring capacitor voltages. The estimated power is then used in a Takagi-Sugeno fuzzy-based model predictive controller (MPC) to manipulate the energy storage unit. The proposed approach is tested experimentally on a dc MG that feeds a single CPL. The experimental results show that the proposed MPC controller alongside the developed EKF improves the transient performance and the stability margin of the dc MGs used in the SPSs.

Nowadays, due to the fact that motor efficiency is strictly related to the diminution of emissions, researchers pay much attention to find a robust and efficient motor control technique for hybrid electric vehicles (HEVs). In this study, an optimal type-2 fuzzy fractional proportional plus integral derivative (IT2FOFP+ID) controller is applied to solve the throttle position and speed control problem of the HEVs. It is undeniable that the performance and effectiveness of the fuzzy-based proportional-integral-derivative (PID) controllers are dependent on its gains’ value. Hence, a novel improved heuristic technique, called IJAYA algorithm, is employed for the online tuning of the coefficients embedded in the specific controller structure. In contrast with the classical control methodologies that suffer from the lack of the self-regulating feature, the established controller has been adjusted online automatically. As another advantage of this control strategy, it is a model-free scheme and does not need the mathematical computation to identify the system model. To appraise the supremacy of the optimal IT2FOFP+ID controller than the other prevalent methodologies, a highly nonlinear EV model is utilized as a case study. In addition, the usefulness and robustness of the proposed method are tested by the experimental data, the EPA New York City Cycle. In the end, the new time-varying proposed technique is validated and implemented in hardware-in-the-loop real-time simulation based on OPAL-RT to study the feasibility of the designed IT2FP+ID controller with check outcomes on a physical platform.

This study utilizes a new optimal model-free controller based on a sliding mode technique to make a balance between load demands and power generations in the time-delay stand-alone microgrid system with electric vehicles. Forasmuch as the performance of the proposed controller is related to its parameter values, a new modified optimization algorithm, entitled the Black Hole optimization search algorithm, is applied to tune the coefficients of the proposed model-free technique. Finally, in order to validate the feasibility of the suggested approach under load disturbances, detailed simulation, and hardware-in-the-loop experimental results are shown.

In restructured markets where transactions process is competitive, forecasting of electricity price is inevitably an important available tool for market participants. Due to the sensitivity of forecasting issues in market's performance, and high prediction error resulted from the behavior of price series, nowadays probabilistic forecasting highly attracted participants’ attention. In this paper, a probabilistic approach for the hourly electricity price forecasting is presented. In the proposed method, the uncertainty of predictor model is considered as the uncertainty factor. The bootstrapping technique is used to implement the uncertainty and since the method is needed to be fast and of low computational cost in the daily forecasting, a generalized learning method is applied, which has high accuracy and speed. This newly presented learning method is based on generalized extreme learning machine approach to be used for improved wavelet neural networks. Also in order to reach more accommodation, the predictor model with the changes of price time series, the wavelet preprocessing is used. Effective performance of the proposed model is validated by testing on data of Ontario and Australian electricity markets.