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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.

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

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.

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.

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 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.

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.

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.

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.

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.

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 article, a novel fuzzy proportional integral derivative (PID) controller with filtered derivative action and fractional order integrator (fuzzy PIλDF controller) is proposed to solve automatic generation control (AGC) problem in power system. The optimization task for fine-tuning parameters of the proposed controller structure is accomplished by cuckoo optimization algorithm (COA). To appraise the usefulness and practicability of proposed COA optimized fuzzy PIλDF controller, four extensively used interconnected test systems, that is, two-area non-reheat thermal, two-area multi-source, three-area thermal and three-area hydro-thermal power plants, are considered. Different nonlinearity such as generation rate constraint (GRC) and governor dead band (GDB) as a source of physical constraints are taken into account in the model of the three-area power systems to examine the ability of the proposed technique to handle practical challenges. The acceptability and novelty of COA-based fuzzy PIλDF controller to solve aforesaid test systems are evaluated in comparison with some recently reported approaches. The consequences of time domain simulation reveal that designed secondary controllers provide a desirable level of performance and stability compared with other existing strategies. Additionally, to explore the robustness of the proposed technique, sensitivity analysis is conducted by varying the operating loading conditions and system parameters within a specific tolerable range.

This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.

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.

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.

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.

Utilizing drones in many applications such as video and photography, farms, search and rescue operations, construction industry for mapping and site monitoring has increased. However, their operating time is limited according to the capacity of their batteries. Therefore, many researchers attempt to enhance the operating time of drones. In this study, to the best of the authors' knowledge for the first time, the feasibility assessment of a laser-based wireless power transfer (WPT) mechanism is theoretically investigated on drone applications to increase their operating time. In this method, drones can be charged during operation wirelessly at a considerable distance with acceptable efficiency. The Analytical code is developed in Engineering Equation Solver (EES) software. The acquired results for the main components of the system have a good agreement with published papers of other researchers. In this study, three commercial photovoltaic (PV) materials were evaluated. The results show that an equipped drone with laser-based WPT system is able to approximately receive net power of 73.5, 62.6, and 33.2 W at a 500 m distance by GaAs, CdTe, and c-Si PV materials, respectively, while the laser module consumes around 600 W electrical power. Moreover, since the optical power of the laser is concentrated on PV panel, a two-phase cooling system is suggested for PV panel to reduce the temperature of the under radiate area. Results demonstrate that the temperature of GaAs PV material without a two-phase cooling system is approximately three times more than the temperature of PV panels with a two-phase cooling system at high power transfer rates.

This brief proposes a dynamic consensus algorithm-based nonlinear I-V droop control for the balancing state of charge of energy storage systems (ESSs) in DC Nanogrids (DCNGs). The dynamic consensus algorithm (DCA) provides a coordinated secondary control with sharing information between Distributed Generation (DG) units to regulate the output power of each DG based on the capacity and state of charge (SoC) of the ESSs. Furthermore, a novel high bandwidth fractional order general type-2 fuzzy logic proportional-integral-derivative (FOGT2FPID) controller is applied in the secondary control level to ensure fast and accurate voltage regulation, and SoC balancing in the DCNG. In the primary control level, a nonlinear I-V droop control approach provides fast dynamic and accurate power-sharing among DGs. Moreover, the proposed control method can provide reliability, modularity, and flexibility. Compared with conventional methods, over-current failures and abruptly disconnection of DGs are prevented with the proposed controller. Experimental results are shown to verify the effectiveness of the proposed control scheme under different scenarios using facilities at the Microgrid Laboratory, Aalborg University.

In this paper, optimal energy management of a zero-emission ferry ship is studied. Fuel cells are considered as the main energy resources of the ship. Solar energy, batteries, and cold-ironing are also used as auxiliary energy resources. Non-linearity in the efficiency of the fuel cells is estimated using piecewise linear models. A mixed-integer linear programming method is applied to model the optimal energy management problem of the ship. Moreover, model predictive control approach is applied to update control parameters in the model and provide the ability of adapting the energy management system with forecast updates. The effectiveness of the proposed method is investigated through an actual case study. The analysis results demonstrate that the proposed method is able to consider different operation aspects of the zero-emission ferry ship effectively. GAMS software is used to solve the optimization problem. In this paper, optimal energy management of a zero-emission ferry ship is studied. Fuel cells are considered as the main energy resources of the ship. Solar energy, batteries, and cold-ironing are also used as auxiliary energy resources. Non-linearity in the efficiency of the fuel cells is estimated using piecewise linear models. A mixed-integer linear programming method is applied to model the optimal energy management problem of the ship. Moreover, model predictive control approach is applied to update control parameters in the model and provide the ability of adapting the energy management system with forecast updates. The effectiveness of the proposed method is investigated through an actual case study. The analysis results demonstrate that the proposed method is able to consider different operation aspects of the zero-emission ferry ship effectively. GAMS software is used to solve the optimization problem.

Today, regarding the increase in using renewable energy resources, DC/DC boost converters have become more popular than in the past, in particular, the multilevel DC/DC boost converter due to its high voltage gain and simple components to implement. However, the constant power loads (CPLs) are contributed to instability in these converters because of the intense non-linear nature of CPLs. This paper intends to address this issue by applying a new method based on a combination of the Nonlinear Integral-Backstepping technique (NIB) and Model-Free Control (MFC) to a DC/DC multilevel boost converter (MBC) in presence of a non-ideal CPL. To validate the effectiveness of the proposed controller theoretical analysis and simulation results are represented.

In this paper, we propose a safety-driven cost effective scheduling controller to arbitrate and operate the energy resources of a maritime hybrid energy application. The proposed control algorithm enables efficient energy management to dynamically schedule the energy sources to supply the realtime 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. The efficiency and safety of our scheduling algorithm have been examined using Uppaal model checker. The experiment outputs show that our controller maintains the system safety and guarantees the lowest operation cost.

Appearing faults in a practical system is dispensable, and if it is not compensated, it results in poor system performance or even dysfunction of the system. The fault detection has become a promising challenging issue to guarantee the safety and reliability of systems. In this paper, a novel fuzzy-based sliding mode observer for the simultaneous actuator and sensor fault reconstruction of nonlinear systems subjected to external disturbance is proposed. The proposed approach employs the Takagi-Sugeno fuzzy model, sliding mode observer and non-quadratic Lyapunov function. First, by filtering the system output, a fictitious system whose actuator faults are the original actuator and sensor faults is constructed. Then, by considering the [Formula: see text] performance criteria, the effect of disturbance on the state estimations is minimized. It is proved that the estimations asymptotically converge to their actual values for non-perturbed systems. In the process of designing the observer gains, some transformation matrices are obtained by solving linear matrix inequalities. The proposed approach has some superiority over the existing methods. First, considering the non-quadratic Lyapunov function leads to relaxed results and good estimation performance. Second, using the sliding mode observer makes the proposed approach insensitive to the uncertainties and unknown inputs and determines the shape and size of the fault. Third, assuming the premise variables are immeasurable makes the presented approach more applicable. In conclusion, two practical systems are considered and simulation results illustrate the merits of the proposed approach in comparison with the recent methods from the fast and precise fault detection performance viewpoints.

In this paper, a novel energy management system (EMS) with two operating horizons is proposed for a residential microgrid application. The microgrid utilizes the energies of a photovoltaic, a fuel cell (FC), and a battery bank to supply the local loads through a combination of electric and magnetic buses. The proposed microgrid operates in a large number of grid-connected and off-grid operation modes. The EMS includes a long-Term data prediction unit based on a 2-D dynamic programing and a short-Term fuzzy controller. The long-Term prediction unit is designed to determine the appropriate variation range of the battery state of charge and FC state of hydrogen. The efficiency performance of the microgrid components, predicted energy generation and demand, energy cost, and the system constraints are taken into account. The resultant data then are sent to the short-Term fuzzy controller which determines the operation mode of the microgrid based on the real-Time condition of the microgrid elements. A prototype of the proposed microgrid including the EMS is developed, and experimental tests are conducted for three different energy management scenarios. The proposed management technique is validated through energy distribution and cost analysis.

Environmental pollutants, as a global concern, have led to a general increase in the utilization of renewable energy resources instead of fossil fuels. Accordingly, the penetration of these resources in all-electric ships, as well as power grids, has increased in recent years. In this article, in order to have a zero-emission and cost-effective energy management in an all-electric ferry boat, a new reliable and optimal power scheduling is presented that uses fuel cell and battery energy storage systems. Furthermore, the real information including load profile and paths is considered for the case study to assess the feasibility and superiority of the proposed approach. In addition to the cost of energy management, to have a reliable combination of the proposed resources, the loss of load expectation (LOLE) as a reliability index is considered in the energy management context and the problem is solved by the deep reinforcement learning in a multiobjective manner. The results of the consideration of two common standards, including DNVGL-ST-0033 and DNVGL-ST-0373, demonstrate that the proposed energy management method is applicable in industrial applications. Finally, the real-time simulation-based hardware-in-the-loop (HIL) is conducted to validate the performance and efficacy of the suggested power scheduling for the emission-free ships.

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

Robotic manipulator control is a challenging task due to its nonlinear, interacting multi-input–multi-output dynamics. In this paper, we proposed an optimal robust fractional-order fuzzy PID controller based on multi-objective particle swarm optimization (MOPSO) algorithm for a two-link robotic manipulator. In order to minimize position and trajectory-tracking error, MOPSO finds the optimal parameters of fuzzy membership functions and order of the fractional operators. To show the effectiveness of the proposed controller, the software-in-the-loop real-time simulation is executed, and the results are compared to conventional fuzzy PID, fractional-order PID, and linear PID controllers.

This paper presents the applicability of the future fifth-generation network technology for a marine vessel power system with sea wave energy, photovoltaic, and energy storage systems. In this paper, a new optimal structured interval type-2 fractional order fuzzy proportional derivative/fuzzy proportional integral controller is proposed for the secondary load frequency control (LFC) of a networked shipboard multimicrogrid. The effect of the various degradation factors associated with the communication infrastructure such as the time delay and packet loss is modeled and addressed to assess the system performance in the networked control system (NCS) operation. The parameters embedded in the established structure are decisive factors, which significantly affect the quality of control output actions. Accordingly, by employing the concepts of the black-hole optimization algorithm and Lévy flight, an enhanced JAYA algorithm is proposed to adjust the setting of the established structured controller. Finally, comprehensive studies and hardware-in-the-loop real-time simulations are conducted to appraise the acceptability of the suggested controller for a secondary LFC problem in the face of the uncertain NCS.

This paper proposes a novel structure for industrial demand response aggregators (IDRAs) to provide operational flexibility for the power system. A robust self-scheduling approach is formulated for the first time to optimize different sub-processes of the whole production line of heavy industries. The new approach satisfies the customer order with the lowest energy cost. Numerical studies are implemented on eight integrated cement factories, from Khorasan Regional Electric Company, in the east of Iran. The results show that the integrated model of heavy industries provides guaranteed flexibility to the system when a power shortage occurs or system reliability is jeopardized.

This study proposes a new decentralised non-linear I-V droop control to achieve current sharing and voltage restoration for paralleled DC nanogrids (DCNGs) in a cluster. The performance of the conventional droop methods in current sharing among parallel converters degrades due to line impedances. The proposed method improves current sharing among the DCNGs with parallel-connected converters by considering the effect of line impedance, which is an important issue in practical applications. By the proposed method, desired current sharing from the light-load to heavy-load condition is achieved. Since only local information (the output voltage and output current of the DCNGs) is used, the proposed method is fully decentralised, and no communication infrastructure is required, which improves the reliability and stability of the overall system. Furthermore, Lyapunov stability theory is applied to investigate the stability of the proposed method. Additionally, the plug-andplay feature is achieved as an important desired functionality in the DCNGs as well as the proposed method provides scalability and modularity for the DCNGs in a cluster. In the end, theoretical analysis and experimental results validate the effectiveness of the proposed control framework for different scenarios.

In this paper, a new interleaved AC-DC converter with a reduced number of semiconductor components for high power energy storage applications is proposed. The interleaved structure reduces the AC side current ripple as well as increases the overall current rating. Moreover, the proposed switching pattern reduces switching and the developed controller provides reactive power control during both power flow directions. The proposed structure is simulated and analyzed using MATLAB/Simulink software, and a 3.5kW prototype of the system has been implemented in the Lab. The results confirm the performance of the proposed topology. There is a seamless transition between operation with different power factors and the current ripple is significantly reduced.

This brief proposes a novel approach to apply reliability centered maintenance (RCM) on a distance protection system in a smart grid. In the proposed approach, instead of focusing on individual equipment, the RCM is implemented for the whole protection system. The approach helps to calculate the reliability of protection systems by overcoming the limitation of their hidden function and also considering the effect of all system equipment on reliability. Markov process and improved Jaya algorithm (IJA) are used to calculate the reliability and find the optimal maintenance plan. The proposed approach has been applied to real data of smart grid distance protection systems, which results in a higher level of reliability and less cost. Furthermore, in order to validate the suggested technique and its efficiency, the simulation is carried out using a real-time digital simulators and hardware-in-the-loop testing equipment known as opal-RT technology.

This paper proposes a novel hybrid algorithm based on a combination of the simple quadratic interpolation and the symbiosis organisms search algorithm (SQI-SOS) for finding the optimal location and size of capacitors in radial distribution networks. The objective of the problem is to minimize the system operating cost so that the net yearly savings of the system are increased. The effectiveness of the SQI-SOS has been tested on 33-, 69-, and 119-bus radial distribution networks with different load models. The obtained results from the test system by the proposed SQI-SOS are compared with those from the conventional SOS and other mature optimization methods in the literature. The result comparison has shown that the proposed SQI-SOS algorithm can provide a better solution than the other methods. Accordingly, the proposed SQI-SOS can be a very effective and efficient method for dealing with the optimal capacitor placement problem in distribution networks.

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

Due to environmental and economic issues as well as the high performance of marine vessels, efficient energy using has been becoming more demanding. Also, in order to have a zero-emission ship, the utilization of a fuel cell combined with energy storage such as batteries gets more and more attention. In this work, a zero-emission hybrid energy system, including fuel cells, batteries, and cold-ironing, is employed to have an environmentally friendly vessel, and to create condition in which ship operates with high performance, both energy management and components sizing of fuel cells and batteries using real data of ferry boat and intelligent optimization method are done simultaneously. In addition, all constraints related to energy management and component sizing with the topography of the boat and electric power sources are represented and analyzed thoroughly. Ultimately, hourly energy management and component sizing for one specific day are considered in this work, and to optimize this problem, the Improved Sine Cosine Algorithm (ISCA) is utilized. According to obtained results, the proposed energy management and component sizing result in the high-performance ship which could be utilized in the marine industry.

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.