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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.

Due to the random nature of renewable energy in the micro-grid (MG) configuration, the energy generators in such systems are not able to offer an uninterrupted power and a mismatch between the demand and generation will be occurred. To alleviate this unbalance, technically, conventional storage devices are one of the practical solutions to this issue. However, the implementation of storage system is not effective from the economic preceptive and, as an alternative, the battery of electric vehicle can be also embedded in the structure of the MGs. In this work, an ultra-local model (ULM) controller based on a single-interval type-II fuzzy logic (SIT2-FL) controller is applied for frequency regulation of multi-MG(s) with electric vehicles. In this scheme, the ULM controller plays a critical role in the reducing the dependency of the controller to the MG model. An extended observer error is embedded in the structure ULM controller to remove the uncertainties included in the system. The SIT2-FL is established as supplementary controller to remove the error of observer and further ameliorate the multi-MG performance. Comparative simulation analysis and robustness examination are made to ascertain the usefulness of the suggested ULM controller-based SIT-FL.

This paper presents a new type-2 fuzzy logic control (T2FLC) for active/reactive power (AP/RP) regulation and energy storage management of an independent microgrid (MG). The case-study MG includes sporadic renewable sources that lead to significant frequency and voltage fluctuations in the network. The rules of suggested T2FLCs are tuned such that a fractional-order sliding condition can be satisfied. Also a new approach using square-root-cubature Kalman filter is designed to optimize the membership parameters of T2FLCs. Beside AP/RP power regulation, an electrical storage system (battery energy storage system (BESS)) is managed to produce the rated power, rather than a conventional generator. It makes the device frequency separate from the synchronous generator’s mechanical inertia. Nevertheless, a BESS has a power limit – it uses a synchronous generator to sustain the BESS charge state at a certain value. Sustainable energies achieve a voltage-damping impact by supplementing an AP/RP droop strategy procedure that considerably reduces voltage oscillation created by variation of its own output power. In various disturbed scenarios the good performance of the suggested controller and energy management system is shown.

Recently, the Digital Twin (DT) technology, which joints the physical environment and virtual space, has drawn more attention in industry and research academic plans. In general, the virtual model representations of the physical objects are created in the DT manner to simulates the characteristics and behaviors of the real-word system. Applying a supervisory system not only can reduce the failures of components, but also preserve the overall costs associated with the system at a minimum. This paper reviews the DT applications in the power system, while its advantages in wind turbines, solar panels, power electronic converter, and shipboard electrical system will be briefly discussed. The potential benefits of contemporary technologies to ameliorate the DT in the industry are studied. Besides, it provides a great technique to assess and analyze system performance. As a basis for DT, various new emerging developments as an example of artificial intelligence (AI), big data, the internet of things (IoT), and 5 G are reviewed.

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.

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.

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

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.

Deep brain stimulation (DBS) is a powerful tool to treat the movement disorders created by Parkinson's tremor. The stimulation of one of the two main parts of basal ganglia (BG) in DBS is often provided without the feedback signal of the tremor which leads to undesirable side effects like cognitive impairment, anxiety, and muscle twitches. In this article, two distinct intelligent controllers are designed to stimulate the parts of a non-linear BG including subthalamic nuclei (STN) and globus pallidus internal (GPi) to overcome the current challenges of the DBS. For this purpose, an interval type-2 fractional-order fuzzy proportional derivative plus integrator (IT2FO-FPD+I) is suggested to control the two parts of BG. The control signals of the IT2FO-FPD+I controller are designed based on the tremor values which are measured by a sensor mounted on the patient's finger. Since the quality of output command of IT2FO-FPD+I controller highly depends on its coefficients, these parameters are adjusted by a sine-cosine algorithm-based wavelet mutation, called SCAWM, in a heuristic scheme. The suggested controller delivers both suitable stimulatory control signals to the brain and decreases the amplitude of tremor impressively. Comparative simulation explorations are performed subsequently to ascertain the superior performance of the suggested structured intelligent controller over that of 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.

Today’s remarkable challenge of maritime transportation industry is the detrimental contamination generation from fossil fuels. To tackle such a challenge and reduce the contribution into air pollution, different power solutions have been considered; among others, hybrid energy-based solutions are powering many ferry boats. This paper introduces an energy management strategy (EMS) for a hybrid energy system (HES) of a ferry boat with the goal to optimize the performance and reduce the operation cost. HES considered for the ferry boat consists of different devices such as proton exchange membrane fuel cell (PEMFC), LI-ION battery bank, and cold ironing (CI). PEMFC systems are appropriate to employ as they are not polluting. The battery bank compensates for the abrupt variations of the load as the fuel cell has a slow dynamic against sudden changes of the load. Also, CI systems can improve the reduction of the expenses of energy management, during hours where the ferry boat is located at the harbor. To study the performance, cost and the pollution contribution CO2, NOX, SOX of the proposed hybrid energy management strategy (HEMS), we compare it against three various types of HEM from the state-of-the-art and also available rule-based methods in the literature. The analysis results show a high applicability of the proposed HES. All results in this paper have been obtained in the MATLAB software environment.

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.

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.

In this paper, we discuss the problem of model order reduction for positive real systems based on balancing methods. The mixed gramian balanced truncation (MGBT) method, which is a modification of the positive real balanced truncation (PRBT) method, focuses on solving one Lyapunov equation and one Riccati equation resulting in less computational effort compared to PRBT requiring solving two Riccati equations. One major disadvantage of MGBT is that it cannot provide an error bound in contrast to PRBT. To overcome this issue, we have developed some novel modifications to MGBT which not only work with one Lyapunov and one Riccati equations but also provide error bounds. Thus, we can say that the presented methods take the key features of both MGBT and PRBT. These algorithms are presented with the aid of the new gramians which are extracted from new Lyapunov equations. The second algorithm is the frequency weighted version of the first algorithm. Additionally, it is also observed that the proposed methods can provide better error bounds compared to PRBT. Finally, comprehensive numerical examples are included to figure out the effectiveness of the presented method.

With seven switching devices and two capacitors, the configuration of PEC9 has significantly decreased the number of ingredients in a creative topology. By proper regulation of the PEC9, this inverter provides various levels of voltage under a faulty switch state, which is the most prominent feature of such inverters. In this paper, an ultra-local mode control (ULM) based on single input interval type-2 (SIIT2) fuzzy logic control (FLC) is proposed in a model-free framework to stabilize the inverter output without the model identification. The control structure is divided into two sections: i) a ULM controller is adopted to meet the primary control requirements of the system operation while an extended observer error (ESO) is embedded to estimate the unknown uncertainty included in the inverter, ii) a supplementary SIT2-FLC controller is established to decrease the ESO error and improve the performance of PEC9 operation accordingly. The time-domain outcomes are provided and discussed to appraise the simplicity of control design and superior feasibility of the suggested scheme than the state-of-the-art approaches.

This paper proposes a robust and intelligent droop control method to stabilize a cluster of DC nanogrids (DCNGs) with constant power loads (CPLs). Conventional droop methods suffer from voltage regulation in parallel converters due to the CPLs destructive effects, which cause instability in the DCNGs. To address the shortcomings of the conventional methodologies, this paper proposes a new droop-based single input interval type-2 fuzzy logic controller (SI-IT2-FLC), which enhances the transient response and improve the steady-state performance. The suggested method aims to boost the stability of the paralleled DCNGs in a cluster under the effect of the CPLs, which is inevitable in the practical applications. The efficacy of the suggested scheme is validated in different operating conditions. Moreover, real-time model-in-the-loop (MiL) results show the performance improvements in the transient and steady-state obtained by the novel suggested control approach.

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.

This paper investigates the energy management of a roll-on/roll-off emission-free ship (RoRoEF-ship) that can carry passengers and vehicles. Fuel cells (FCs), energy storage systems (ESS), and cold-ironing (CI) are considered to be the energy resources of the ship. Moreover, the charging and discharging abilities of the electric vehicles (EVs) carried by the ship are used as an auxiliary source for managing the energy flow in the ship. In this paper we propose three cost-effective strategies to use EVs as a backup in the ship energy system based on the agreements between the ship operator and EV owners. A mixed-integer linear programming (MILP) approach is proposed to a highly efficient and cost-effective control for the energy management of the integrated solution (ship resources and EVs). The proposed model considers the non-linearity in the efficiency of FCs, power ramp-rate constraints of FCs and ESSs, and preferences of the EV owners. The proposed control model is applied to a test system and the simulation results are discussed. GAMS software is used to achieve the optimization process.

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.

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.

Switching sliding mode control (SSMC) can be utilized as a robust control technique, which is appropriate for the control of highly non-linear power systems like chaotic systems. The present study proposes a switching sliding mode control technique for control and chaos suppression of non-autonomous fractional-order (FO) nonlinear power systems with uncertainties and external disturbances. In the first step, a novel fractional switching sliding surface is introduced as well as its stability analysis to the origin is demonstrated. In the second step, based on the fractional version of the Lyapunov stability theory, a robust non-singular control law is designed to ensure the convergence of the system trajectories to the proposed sliding surface. Next, the proposed SSMC approach is utilized for designing a single input switching control technique for the stabilization of a class of 3D FO chaotic power systems. In order to evaluate the effectiveness and robustness of the suggested approach in practice, two examples including control and the stabilization of FO chaotic electric motors are illustrated.

In this work, a novel nonlinear approach is proposed for the stabilization of microgrids with constant power loads (CPLs). The proposed method is constructed based on the incorporation of a pseudo-extended Kalman filter into stochastic nonlinear model predictive control (MPC). In order to achieve high-performance and optimal control in DC microgrids, estimating the instantaneous power flow of the uncertain constant power loads and the available power units is essential. Thus, by utilizing the advantages of the stochastic nonlinear model predictive control and the pseudo-extended Kalman filter, an effective control solution for the stabilization of DC islanded microgrids with CPLs is established. This technique develops a constrained controller for practical application to handle the states and control input constraints explicitly; furthermore, as it estimates the current by using the pseudo-EKF, it is a current-senseless approach. As noisy measurements are taken into account for the state estimation, it leads to a less conservative control action rather than the classical robust MPC, whereas it guarantees the global asymptotic stability in the presence of noisy measurements and parameter uncertainty. To validate the performance of the proposed controller, the attained results are compared to state-of-the-art controllers. Furthermore, the implementability of the proposed method is validated using real-time simulations on dSPACE hardware.

DC shipboard power systems are usually under the threat of instability due to the incremental negative impedance of the constant power loads connected to the DC bus. The instantaneous power flow, which moves along the time-varying uncertain constant power loads, is required to be estimated to enhance the control efficiency of the shipboard power grid. In this paper, at first, the estimation of the load power variation of uncertain constant power loads in a shipboard DC microgrid is conducted in the finite time by adopting the finite-time disturbance observer method. The estimated load power is then received by the fixed-time terminal sliding mode controller to stabilize the entire marine power grid as well as tracking the reference voltage of the DC bus in the fixed time independent of initial conditions. A Lyapunov-based stability analysis rigorously proves the fixed-time convergence of the proposed disturbance observer-based controller. Finally, Model-in-the-Loop real-time simulations using dSPACE emulator are carried out under various operating cases to evaluate the practical implementation of the presented disturbance observer-based scheme.

Cancer illness still is one of the most common illnesses in the world, which is constantly rising. Chemotherapy plays a crucial role in treating cancer patients. In this paper, we have presented a novel intelligent sensor for controlling and adjusting chemotherapy parameters which consist of an ultra-local (ULM) controller based on a deep deterministic policy gradient (DDPG). First, the feedback signal is provided using a sensor to calculate the population of cells. Then, a controller sends the proper control commands to the actuator (chemotherapy). In the suggested scheme, the ULM is applied to the dynamic model of cancer. In order to shrink tumor cells and rising immune and normal cells at the same time. Moreover, for improving the performance of the established ULM scheme, a DDPG algorithm with the actor-critic structure is used for tuning the parameters of ULM in an adaptive manner. To demonstrate the supremacy of the DDPG based ULM controller, the conventional ULM and proportional integrator (PI) are also designed for the cancer treatment. Simulation outcomes prove the improved cancer treatment compared to the ULM and PI schemes.

The power system of an all-electric ship (AES) establishes an independent microgrid using the distributed energy resources, energy storage devices, and power electronic converters. As a hybrid energy system (HES), the power system of an AES works as a unified system where each part can affect the reliability of the other parts. The systemic reliability centered maintenance (SRCM), which efficiently enhances the reliability and safety of the AES by identifying optimal maintenance tasks of the AES, is considered in this article to apply to the entire system. In order to calculate the reliability and optimal maintenance schedule, the Markov process and Enhanced JAYA (EJAYA) are utilized. A layer of protection analysis (LOPA), which is a risk management technique, is adopted to assess the safety of the system. A hybrid molten carbonate fuel cell, photovoltaic (PV), and lithium-ion battery are considered as energy sources of the AES. Based on two common standards, DNVGL-ST-0033 and DNVGL-ST-0373, the suggested maintenance planning method can be used in industrial applications. Eventually, in order to validate the proposed method, a model-in-the-loop real-time simulation using dSPACE is carried out. The obtained results show the applicability and efficiency of the proposed method for improving reliability and safety.

Due to the increasing impacts of ships pollutants on the environment and the preventive laws that are tightening every day, the utilization of all-electric ships is a recent emerging technology. Being a promising technology, the usage of fuel cells as the main energy resource of marine vessels is an interesting choice. In this article, an all-electric hybrid energy system with zero emission based on fuel cell, battery, and cold-ironing is proposed and analyzed. To this end, actual data of a ferry boat, including load profiles and paths, are considered to assess the feasibility of the proposed energy system. The configuration of the boat and energy resources as well as the problem constraints are modeled and analyzed. Finally, the boat's energy management in hourly form for a one-day period is implemented. The improved sine cosine algorithm is used for the power dispatch optimization, and all models are implemented in MATLAB software. Based on the analysis results, the proposed hybrid system and the energy management method have high performance as an applicable method for the marine vessels. In addition, to be a zero-emission ship, the proposed system has an acceptable energy cost.

Utilization of renewable energies in association with energy storage is increased in different applications such as electrical vehicles (EVs), electric boats (EBs), and smart grids. A robust controller strategy plays a significant role to optimally utilize the energy resources available in a power system. In this paper, a suitable controller for the energy resources of an EB which consists of a 5 kW solar power plant, 5 kW fuel cell, and 2 kW battery package is designed based on the linear parameter varying (LPV) controller design approach. Initially, all component dynamics are augmented, and by exploiting the sector-nonlinearity approach, the LPV representation is derived. Then, the LPV control method determines the suitable gains of the states’ feedbacks to provide the required pulse commands of the boost converters of the energy resources to regulate the DC-link voltage and supply the power of EB loads. Comparing with the state-of-the-art nonlinear control methods, the developed control approach assures the stability of the overall system, as it considers all component dynamics in the design procedure. The real-time simulation results demonstrate the performance of the designed controller in the creation of a constant DC-link voltage.

Thinking-green consciousness is pushing international authorities to impose new regulations about the use of energy and CO2 emissions. Many hybrid energy solutions using renewable energies have been proposed and deployed. Designing real-time controllers that perform efficient and safe energy management is a challenging task due strong safety requirements, dynamic operation cost and real-time power demands. In this paper, we consider a hybrid combination of Fuel Cells (FC) and batteries sub-systems to aliment a marine vessel. Our contribution is to design a safety-driven efficient energy management control to dynamically schedule the energy sources to supply the real-time power requests so that 1) we maintain the system safety by not overloading or heating up an energy source; 2) reduce the operation cost by considering the cheapest energy source in a real-time manner. We use the symbolic model checker Uppaal to verify the safety properties. To demonstrate the efficiency of our energy scheduling, we analyze an actual case study.

This chapter presents novel structured single-input interval type-2 fuzzy logic controllers (SI-IT2-FLCs) for the frequency damping of multi-microgrids (MMGs), whereas the application of electric vehicles (EVs) is considered in this context. For this purpose, a new SI-IT2-fuzzy PD/fuzzy PI (SI-IT2-FPD/FPI) controller is designed on two levels. Initially, an improved whale optimization algorithm, called IWOA, is adopted to adjust the setting of the gains embedded in the FPD/FPI section effectively. Then, the impact of the footprint of uncertainty (FOU), to offer extra design freedom, on control surface generation of SI-IT2-FLC has been investigated. In this way, various control surfaces were generated by varying a single coefficient which forms the FOU. Lastly, by adopting hardware-in-the-loop (HIL) simulator, the feasibility and usefulness of the suggested framework are verified from a real-time perspective.

Nowadays the use of power converters, in particular, DC/DC converters in a large number of power electronics applications such as DC micro-grids (MGs) is an interesting subject in power systems. However, the use of such converters in MGs results in instability problems inflicted by the constant power loads (CPLs) because of their negative impedance feature. Thus, a controller with specific characters including, robustness and fast response to system dynamics to address unsteadiness issues and better performance of DC/DC converters feeding CPLs is vital. In this paper, a model prediction control (MPC) approach is introduced to tackle the de-stabilization problem and mitigate the destructive effect of CPLs, leading to a robust control approach as well as enlarging the system stability margin. To demonstrate and verify the usefulness of the proposed scheme in a real-time setup, the performance of the MPC controller applied to the DC/DC buck-boost converter feeding CPL is examined in a Model-In-the Loop (MiL) environment.