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The purpose of this study is to suggest an optimal multi-objective fuzzy fractional-order P I λ D μ controller (MOFFOPID) for the speed control of EV systems with time-delay. It is presumed that while the EV is in movement, the armature winding resistance of the direct current (DC) motor varies with time due to temperature changes and this makes it impossible to develop an accurate dynamic model for an EV system. Consequently, in order to have a fast and accurate tracking of the set-point, besides a smooth control signal, a new multi-objective stochastic optimisation is used for the online adjustment of the parameters of MOFFOPID controller. Moreover, a comparison is made between the results of the current study and those of some of the most recent studies on the same topic, which have used online multi-objective PI and online multi-objective fuzzy PI, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are implemented on a DC motor to verify the effectiveness of the proposed MOFFOPID controller in controlling the speed of the DC motor which has non-linear features. The results of the simulation confirm the desirable performance of suggested controller.

This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function.

In this paper, a new modified fuzzy Two-Level Control Scheme (TLCS) is proposed to control a non-inverting buck-boost converter. Each level of fuzzy TLCS consists of a tuned fuzzy PI controller. In addition, a Takagi–Sugeno–Kang (TSK) fuzzy switch proposed to transfer the fuzzy PI controllers to each other in the control system. The major difficulty in designing fuzzy TLCS which degrades its performance is emerging unwanted drastic oscillations in the converter output voltage during replacing the controllers. Thereby, the fuzzy PI controllers in each level of TLCS structure are modified to eliminate these oscillations and improve the system performance. Some simulations and digital signal processor based experiments are conducted on a non-inverting buck-boost converter to support the effectiveness of the proposed TLCS in controlling the converter output voltage.

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

The fractional order calculus can represent systems with high-order dynamics and complex non-linear phenomena using fewer coefficients. In this study the extended fractional Kalman filter is developed for the class of non-linear discrete-time fractional order systems using observations with multiple delays contaminated by additive white noise. For a wide class of practical applications, the time delay cannot be neglected. In the time delay integer order systems, a common approach is partial differential equation. This method is very difficult to solve. The authors’ approach is applied the reorganised innovation technique. As a result of the reorganised innovation sequence, the Kalman filtering problem with multiple delayed measurements is converted to filter of a delay-free system. In the rest of paper, the covariance matrix of the prediction error of the states is presented in the form of the novel Riccati equations. Finally, the solution of the Kalman filtering problem is obtained by applying the re-organised innovation sequence and Riccati equations. A numerical example is given to illustrate the effectiveness of the proposed scheme.

Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the Traffic Information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study, a combination of the general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers.

The performance of a DC microgrid (MG) might degrade because of the dynamics of constant power loads (CPLs). In this paper, a novel adaptive controller is proposed to mitigate the destructive effect of time-varying uncertain CPLs. A nonlinear disturbance observer is developed to estimate the instantaneous power of the CPLs. The estimated CPLs powers are then employed in a Takagi–Sugeno fuzzy-based model predictive control strategy, aiming to adaptively modify the injecting current of the energy storage system. The proposed approach is applied to a dc MG testbed that feeds one CPL. Experimental results show that the proposed adaptive controller is able to increase the stability margin and improve the transient response of the dc MG.

This paper proposes a novel controller for the secondary load frequency control of a shipboard microgrid. The suggested controller is based on the linear matrix inequality technique and the Lyapunov stability theory. In the controller design procedure, the effects of the sensor-to-controller and controller-to-actuator delay communication links are considered, and a robust controller against the delay is proposed by utilizing a Lyapunov-Krasovskii functional. In addition, due to the practical space limitation of a ship, low-space, high-efficient renewable energy sources (RESs), and energy storage systems are considered. Furthermore, a diesel generator and a proton exchange membrane fuel cell are employed to effectively mitigate the frequency oscillations arise by the loads and RES power variations. Finally, to show the merits of the proposed method, several hardware-in-the-loop real-time simulations are performed. Comparison results illustrate the effectiveness of the proposed approach to the state-of-the-art methods.

This paper investigates a novel approach to design a nonlinear optimal model predictive controller for the speed control of constrained nonlinear electric vehicles (EVs). The proposed approach employs a linear parameter varying model including bias terms and a model predictive scheme. The controller design conditions are derived in terms of linear matrix inequalities (LMIs), which can be solved through convex optimization techniques. Due to considering bias terms in the system dynamic, the proposed approach can be regarded as the general case of the existing results. Furthermore, practical limitations on the amplitude of the input signal are considered and formulated in terms of LMIs. An EV dynamic with bias term is presented and hardware-in-the-loop real time and experiments are carried out to illustrate the effectiveness and merits of the proposed approach over the existing results.

This letter proposes a new modified model predictive control (MPC) to compensate for voltage and frequency deviations with higher bandwidth for an ac shipboard microgrid (MG). The shipboard power system (SPS) and islanded MGs have a reasonable analogy regarding supplying loads with local generations. However, a great number of vital imposing pulse loads and highly dynamic large propulsion loads in the SPS make the frequency and voltage regulation a complicated issue. Conventional linear control methods suffer from high sensitivity to parameter variations and slow transient response, which make big oscillations in the frequency and voltage of the SPS. This letter addresses the problem by proposing a novel finite control set MPC to compensate for primary frequency and voltage deviations with higher bandwidth and order of magnitude faster than the state of the art. Furthermore, a single input interval type-2 fuzzy logic controller is applied in secondary level to damp the steady-state deviations with higher bandwidth. Finally, hardware-in-the-loop experimental results prove the applicability of the proposed control structure.

The intermittent feature of renewable energy sources leads to the mismatch between supply and load demand on microgrids. In such circumstance, the system experiences large fluctuations, if the secondary load frequency control (LFC) mechanism is unable to compensate the mismatch. In this issue, this paper presents a well-structured combination of the fuzzy PD and cascade PI-PD controllers named FPD/PI-PD controller as a supplementary (secondary) controller for the secondary load frequency control in the islanded multi-microgrid (MMG). Additionally, two modifications to the JAYA algorithm are made to enhance the diversity of the initial population and ameliorate the global searching ability in the iterative process. Afterward, the improved JAYA algorithm, referred to as IJAYA, is employed for fine-tuning the proposed structured controller installed in areas of the studied MMG. The superiority of the proposed IJAYA is validated by comparative analysis with genetic algorithm and basic JAYA in a similar structure of the PID controller. Furthermore, it will be shown that the proposed FPD/PI-PD controller employing IJAYA provides a higher degree of stability in suppressing the responses deviations as compared with the conventional PID and FPID controller structures. Finally, the novel optimal proposed approach is validated and implemented in the hardware-in-the-loop (HIL) based on OPAL-RT to integrate the fidelity of physical simulation and the flexibility of numerical simulation.

This paper proposes a non-iterative state feedback design approach for polynomial systems using polynomial Lyapunov function based on the sum ofsquares (SOS) decomposition. The polynomial Lyapunov matrix consists of states of the system leading to the non-convex problem. A lower bound onthe time derivative of the Lyapunov matrix is considered to turn the non-convex problem into a convex one; and hence, the solutions are computedthrough semi-definite programming methods in a non-iterative fashion. Furthermore, we show that the proposed approach can be applied to a widerange of practical and industrial systems that their controller design is challenging, such as different chaotic systems, chemical continuous stirred tankreactor, and power permanent magnet synchronous machine. Finally, software-in-the-loop (SiL) real-time simulations are presented to prove the practical application of the proposed approach.

This letter investigates the fuzzy model predictive control synthesis of networked controlled power buffer for dynamic stabilization of a dc microgrid (MG). The proposed is based on Takagi-Sugeno fuzzy model and model predictive scheme to mitigate the network-induced delays from the sensor-to-controller and controller-to-actuator links. By employing the so-called time-stamp technique and network delay compensator (NDC), the delays are computed and compensated, which improves the effectiveness and robustness of the proposed controller. Due to the usage of two NDCs, the presented approach is robust against the network delays and results in small computational burden. Therefore, it can widely be employed on large distributed power systems. To show the merits of the proposed approach, it is applied to a dc MG that feeds one constant power load. Results show the simplicity of designing the controller and better robustness against the network's delays compared to the state-of-the-art methods. Additionally, hardware-in-the-loop simulations are presented to prove the practical applicability of the proposed controller.

This paper investigates the problem of designing a non-fragile polynomial fuzzy controller for a class of polynomial fuzzy models subjected to persistent bounded disturbances and system uncertainty. The proposed controller is considered to satisfy the input saturation constraint. Furthermore, the resilient controller is affected by linear fractional uncertainties. The overall structure of the controller is obtained by the polynomial parallel distributed compensation concept. The sufficient conditions guarantee an L1 performance of the perturbed system and exponential stability of the non-perturbed system is achieved in terms of sum-of-squares decomposition conditions. Subsequently, the sum-of-squares-based conditions lead to minimization of the L1 performance such that the above-mentioned hard constraints on the control input and robustness of the closed-loop system against the system and controller uncertainties will be guaranteed. The controller gain matrices will be achieved by numerically solving the sum-of-squares-based conditions with the third-party MATLAB toolbox ‘SOSTOOLS’. Finally, extensive studies and hardware-in-the-loop simulations on a ball-and-beam system are presented, which illustrate the proposed controller can accurately and smoothly track the set point frequency. Furthermore, the proposed control approach is more robust over prior-art controllers for all the simulation scenarios.

In this study, a single input interval type-2 fuzzy logic (SI-IT2-FLC) supervised adaptive neural-fuzzy interface system (ANFIS) controller is proposed for the velocity tracking task of a two-wheeled mobile robot (WMR). The suggested technique can handle the inherent nonlinearities, uncertainties and external disturbances in the system model by a new supervised controller. The robot control design is accomplished by two separate phases including kinematic controller, which is characterized using the kinematic model of the robot, and dynamic controller, which is designed using the physical features of the robot dynamics. In particular, an SI-IT2-fuzzy PD (SI-IT2-FPD) controller is initially applied for the trajectory tracking problem in the two-WMR. In this way, the impact of the footprint of uncertainty (FOU) on control surface (CS) generation is studied, i.e. several CSs were generated by changing a single coefficient which shapes the FOU. Then, a new SI-IT2-FPD supervised ANFIS controller is developed for the concerned robot system. To enhance the efficiency of the suggested controller, the baseline PD gains of the SI-IT2-FPD are adjusted in a heuristic manner. Finally, a prototype of the concerned robot is implemented to investigate the feasibility and applicability of the proposed framework in a real-time platform.

This paper proposes a new adaptive controller for air-feed on proton exchange membrane fuel cell (PEMFC) plants. The oxygen excess ratio is first regulated by a single input interval type-2 fuzzy PI (SIT2-FPI) controller during the current variation of the PEMFC system. Then, a deep deterministic policy gradient (DDPG) algorithm is adopted to adaptively adjust the baseline PI coefficients of the SIT2 controller by taking the benefits of the online learning and model-free features of reinforcement learning. In the DDPG structure, an actor-network determines the policy signals, while a critic-network evaluates the quality of the policy provided by the actor. The suggested DDPG algorithm naturally takes into account the baseline PI coefficients into the design objective and offers the SIT2-FPI structure with online coefficient adjusting ability through learning. Based on reward feedback of the oxygen excess ratio error, the weights of the actor and critic networks are updated by the gradient descent technique. Detailed real-time model-in-the loop (MIL) simulation outcomes and comparative analysis are presented to confirm the adaptation capability of the DDPG-based online SIT2-FPI coefficient tuning strategy.

This paper proposes a novel model predictive controller to minimise the effect of unknown pulsed loads on the DC microgrid (MG) side of shipboards. It is assumed that the level of the pulsed power loads (PPLs) is not determined in prior for which the authors propose a novel non-linear power observer. By employing the so-called freezing technique, a novel model predictive scheme is utilised to optimally stabilise the overall DC shipboard MG. Furthermore, different constraints on the current of the DC source and the energy storage system (ESS) are considered in the predictive controller to make it more realistic and practical. Compared to the exiting results, the proposed approach can optimally design the injecting current of the ESS so that the practical and physical constraints of the DC MG are also satisfied, which improves the effectiveness and robustness of the proposed controller. To show the merits of the proposed approach, it is tested on a DC MG that feeds one PPL. Real-time model-in-the-loop (MiL) results show the performance improvements in the transient and steady-state obtained by the proposed control method compared to the state-of-the-art methods.

This paper presents the design of a single-phase electric vehicle (EV) on-board bidirectional charger with the capability of power conditioning. This charger can control its charging/discharging active power based on the demand of EV battery/network or load. Also, it controls reactive power and harmonic current based on the characteristics of the nonlinear and linear loads. The topology of the proposed charger consists of the bidirectional ac/dc and buck-boost dc/dc converters, where it can operate in four quadrants in the active-reactive power plane with the capability of harmonic compensation. In the next step, this paper presents a suitable control strategy for the bidirectional charger according to the instantaneous active and reactive power (PQ) theory. Based on the PQ theory, the active and reactive power that includes average and oscillatory components obtained, based on the demand of nonlinear/linear loads and EV battery. Then, the reference current of ac/dc converter of the charger and battery is obtained, and in the next step, the situation of the charger switches is determined using output signals of the proportional-integral and proportional-resonant controllers and pulsewidth modulation. Finally, the proposed approach is validated and implemented in the OPAL-RT to integrate the fidelity of the physical simulation and the flexibility of the numerical simulations. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

For more than a century, conventional marine vessels spatter the atmosphere with CO2 emissions and detrimental particles when operated by diesel motors/generators. Fuel cells have recently emerged as one of the most promising emission-free technologies for the electrification of ship propulsion systems. In fuel cell-based ship electrification, the entire marine power system is viewed as a direct current (DC) microgrid (MG) with constant power loads (CPLs). A challenge of such settings is how to stabilize the voltages and currents of the ship’s grid. In this paper, we propose a new modified backstepping controller to stabilize the MG voltage and currents. Finally, to study the performance and efficiency of our proposal, we run an experiment simulation using dSPACE real-time emulator.

Summary In this paper, a fractional‐order Dadras‐Momeni chaotic system in a class of three‐dimensional autonomous differential equations has been considered. Later, a design technique of adaptive s...

As a consequence of increasing wind power penetration level, it will be a big challenge to control and operate the power system because of the inherent uncertainty of the wind energy. One of the ways to deal with the wind power variability is to predict it accurately and reliably. The traditional point forecasting-based technique cannot notably solve the uncertainty in power system operation. In order to compute the probabilistic forecasting, which yields information on the uncertainty of wind power, a novel hybrid intelligent method that incorporates the wavelet transform, neural network (NN), and improved krill herd optimization algorithm (IKHOA), is used in this paper. Also, the extreme learning machine is exerted to train NN and calculates point forecasts, and IKHOA is applied to forecast the noise variance. The robust method called bootstrap is regarded to create prediction intervals and calculate the model uncertainty. The efficiency of proposed forecasting engine is evaluated by usage of wind power data from the Alberta, Canada.

The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α-plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller.

This paper proposes novel linear matrix inequality (LMI) stability analysis and controller design conditions for nonlinear chaotic power systems. The proposed approach is based on the non-quadratic Lyapunov function (NQLF), non-parallel distributed compensation (non-PDC) schematic and Takagi–Sugeno (TS) fuzzy modeling. Utilizing NQLF causes membership functions (MFs) and their time derivative to appear in the design conditions. To solve this problem, an augmented state vector is proposed which results in removing the MFs and their time derivatives from the design conditions. Moreover, structural constraints on Lyapunov matrices are eliminated. The proposed approach provides relaxed stability analysis and controller design conditions due to the framework that is considered during the formulation derivation. Finally, two practical power systems that exhibit chaotic behaviors are considered to evaluate the proposed approach. Simulation results show advantages of the proposed method compared to the recently published works.

The intensive non-linear system and plants of modern industry highly motivating researcher to extend and evolve non-linear control systems. In this study, in order to control a class of non-linear uncertain power systems in the presence of large and fast disturbances, a new simple indirect adaptive proportional-integral is proposed. For handling dynamic uncertainties, the proposed controller utilises the advantages of least squares support vectors regression (LS-SVR) to approximate unknown non-linear actions and noisy data. The LS-SVR is used to approximate the non-linear uncertainties which must be bounded, whereas no requirement needs for bounds to be known. The globally asymptotic stability of the closed-loop system is mathematically proved by using Lyapunov synthesis. To show the merits of the proposed approach, a non-linear electric vehicle (EV) system is considered as a case study. The goal is to force the speed of EV to track a desired reference in the present of structured and unstructured uncertainties. The experimental data, new European driving cycle, is used in order to examine the performance of proposed controller. The simulation studies on a second-order EV with the presence of fast against slow and large against small disturbances demonstrate the effectiveness of the proposed control scheme.

To simplify decision making of market participants, a careful and reliable electricity market price forecasting method is indispensable. Nevertheless, due to the Instability in market clearing prices (MCPs), it is rather tough to forecast MCPs accurately. Using probabilistic forecasting is a new solution to overcome the low accuracy of forecast. Transformation from traditional point forecasts to probabilistic interval forecasts is too important to model the uncertainties of forecasts. Thus the decision making activities of market participants are supported against uncertainties and risks effectively. In this paper a hybrid approach to achieve prediction intervals (PIs) of MCPs is proposed that modified dolphin echolocation optimization algorithm (MDEOA) is applied to estimate point forecasts, model uncertainties, and noise variance. This proposed electricity price probabilistic forecasting method is evaluated by a generalized and comprehensive framework. To test the proposed hybrid method, real price data from Ontario, New England, and, Australian electricity markets are used and effectiveness of the method is validated.

This paper proposes a new method of fault detection and classification in asymmetrical distribution systems with dispersed generation to detect islanding and perform protective action based on applying a combination of wavelet singular entropy and fuzzy logic. In this method, positive components of currents at common coupling points are decomposed to adjust detailed coefficients of wavelet transforms and singular value matrices, and expected entropy values are calculated via stochastic process. Indexes are defined based on the wavelet singular entropy in positive components and three phase currents to detect and classify the fault. This protection scheme is put forward for fault detection and is investigated in different types of faults such as single-phase to ground, double-phase to ground, three-phase to ground and line to line in distribution lines in the presence of distributed generations, and different locations of faults are verified when the distributed generation is connected to the utility. The major priority of the proposed protection scheme is its reduction in time (10 ms from the event inception) in distinguishing islanding and protection transmission lines in the presence of distributed generations.

One of the main issues in engineering is the identification of nonlinear systems. Because of the complicated as well as unexpected behaviours of these chaotic systems, it is introduced as special nonlinear systems. A minute change in the primary conditions of such systems would lead to significant variations in their behaviours. On the other hand, due to simple structure of Permanent Magnet Synchronous Motors (PMSM) and its high applications in industry, the use of this machine is dramatically increasing these days. The reflection of a chaotic behaviour as the Permanent Magnet Synchronous Motor is positioned in a particular area. In the model of PMSM, the exact parameters of the system are required to properly control and spot the error. In this paper, Self-Adaptive Learning Bat-inspired Optimization algorithm is used for solving both offline and online parameter estimation problems for this chaotic system. In addition, noise is considered as one of influential factors in control of PMSM. According to simulation results, it can be claimed that the proposed algorithm is a very powerful algorithm for online parameter identification for PMSM.

Developing controller for uncertain nonlinear systems in the presence of disturbances is an important and still challenging problem. Adaptive control method asserts to adapt system parameters against uncertainties, if only uncertainties change sufficiently slowly. Alternatively, if uncertainties stay in known bounds, robust control approaches claim to ensure system stability. In this paper, a Proportional-Integral (PI) indirect adaptive Least Squares Support Vectors Regression (LS-SVR) control scheme for a class of uncertain nonlinear system in the presence of large and fast disturbances is proposed. The LS-SVR is used to approximate the nonlinear uncertainty which must be bounded, whereas in comparison to robust control methodologies no requirement needs for bounds to be known. The asymptotic stability of the control scheme is proved by using Lyapunov synthesis. The simulation study is performed on a second-order inverted pendulum system in the presence of fast against slow and large against small disturbances to demonstrate the effectiveness of the proposed control scheme.

The Isolated Hybrid Distributed Generation (IHDG) studied in this paper is consisted of a wind turbine generator and a diesel engine generator. The equivalent inertia of power grid reduces by increasing influence of variable speed wind turbines in power systems. Consequently, when a disturbance occurs in the power system the frequency fluctuations increases. To overcome this problem, a supplementary control loop is added to the converter of the variable speed wind turbine in order to share the inertia of the turbines in the power grid. But the appropriate rate of this contribution depends on the amount of load and must be suitably changed based on the load. In this paper, a Takagi–Sugeno (T–S) fuzzy system is designed to determine the contribution coefficient of variable speed wind turbine in such a way that variable wind turbine shares the maximum value of its inertia to compensate the reduced production in the power grid. However, the turbine does not pass its minimum speed limit while sharing the maximum value of inertia in the grid and prevents the cause of another disturbance in the power grid. In the proposed method, first, by using Particle Swarm Optimization (PSO) algorithm, the optimal values of contribution coefficient of wind turbines are attained proportional to the load in such a way that the minimum speed constraint is not violated. In the next stage, the initial T–S fuzzy system is extracted from the obtanied the optimal values of contribution by using subtractive clustering algorithm. In addition, Recursive Least Square (RLS) algorithm is used to adjust the consequent part of the T–S system. The efficiency of the proposed method is demonstrated through the simulation for different amount of load.

The control of electrical vehicles (EVs) should be designed robustly and adaptively to improve the system on both dynamic and steady-state performances, because EV systems are basically time variant (e.g. the operation parameters of EV and the road condition are always varying) that make the operation of controlling an EV difficult. In this study, general type-2 fuzzy logic sets and the modified harmony search algorithm techniques for adaptive tuning of the most popular existing proportional–integral (PI) controller is integrated in order to address these uncertainties. Although it is computationally expensive to carry out general type-2 fuzzy systems, it can be examined as a composition of several interval type-2 fuzzy logic systems with a corresponding level of α for each by using a new plan which is presented, general type-2 fuzzy set. The controller uses the linguistic rules directly. The achieved results are compared with optimal fuzzy-PI controller and optimal interval type-2 fuzzy-PI controller results which are the most recent researches into in the present issue to evaluate the proficiency of the proposed controller. The simulation results demonstrate the successfulness and effectiveness of the proposed controller.

The design and implementation of a new control system for reactive power compensation and mechanical torque, voltage regulation and transient stability enhancement for wind turbines equipped with fixed-speed induction generators (IGs) in power systems is presented in this study. The designed optimal linear quadratic regulator (LQR) controller provides an acceptable post fault performance for both small and large perturbations. Large disturbance simulations demonstrate that the designed controller enhances voltage stability as well as transient stability of the system during low-voltage ride-through transients and thus enhances the LVRT capability of fixed-speed wind generators. Further verifications based on detailed time-domain simulations are also provided. Calculations, simulations and measurements confirm how the increased STATCOM rating can provide an increased transient stability margin and consequently enhanced LVRT capability. A concept of critical clearing time has been introduced and its utility has been highlighted.

Probabilistic forecasting is an appropriate tool that helps electricity markets participants to improve their decision making. Due to changes in electricity prices, the point forecasting accuracy cannot be guaranteed. Hence, participants are more interested in results of probabilistic forecasting methods such as prediction intervals method. In this paper, a hybrid approach for probabilistic electricity price forecasting is presented. This model is based on using improved clonal selection algorithm and extreme learning machine for neural networks training process and wavelet preprocess. The wavelet is utilized to decompose data into well behaved subsets, which increases accuracy of the model. Also, due to the high required computational time for training the neural networks, autocorrelation function is used to reduce the number of neural networks inputs. Finally, in order to evaluate the proposed probabilistic forecasting method, the Ontario and Australian electricity markets data are used.

This paper proposes a multi-objective evolutionary algorithm method for Distribution feeder reconfiguration (DFR) with distributed generators (DG) in a practical system. Considering the low inertia constant of DG units in order to take the transient stability of DGs into account is one of the major issues in power systems. Especially when the penetration of DGs is low, the impacts of them on the distribution system transient stability may be neglected. However, when the penetration of DG increases, the transient stability of them must be taken into account (more DGs, more transient issues). To this end, the DFR problem has been solve by an enhanced Gravitational Search Algorithm (EGSA) to improve the transient stability index and decrease losses and operation cost in a distribution test system with multiple micro-turbines. The effectiveness of the proposed approach is studied based on a typical 33-bus test system. For getting close to the practical condition and considering the detailed dynamic models of the generators and other electric devices in power system, simulation and programming of this approach are done by the DIgSILENT® Power Factory software.

This paper introduces an observer strategy, namely a Sliding Mode Observer (SMO), to realise the fault detection and estimation of general uncertain non-linear systems. The use of a non-linear observer is considered for monitoring the states of a high incidence research model (HIRM) aircraft system. For a special class of Lipschitz non-linear system, a fault reconstruction scheme is presented where the reconstructed signal can approximate the fault signal to any accuracy. The proposed method is based only on the available plant input-output information and can be calculated online. Moreover, the globally asymptotic stability of the closed-loop system is mathematically proved. Finally, an HIRM aircraft system example is given to illustrate the efficiency of the proposed approach.

Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Controlling these systems is more difficult than ordinary form of power systems since, in most of them, their energy is provided by renewable energies which have uncertain and varying nature. These fluctuations in the generated power might cause some problems in the function of conventional controllers. As a result, modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. In this issue, the emotional controller which has a self-tuning nature is used to overcome these difficulties. This controller is based on emotional learning process of the human brain and can provide an appropriate control against changes in the system structure and occurrence of uncertainties. To evaluate the performance of the proposed controller, the results are compared with those obtained by conventional PI and fuzzy controllers, which is the latest research in the problem in hand. Simulation results show the effectiveness of the emotional controller.

The study system in this paper is an islanded DC-microgrid comprising DFIG wind turbines (WTs), microturbine and loads. The main focus of this paper is on local control strategies design for the DFIG-WTs and microturbine to obtain short time-scale coordination, seamless power management and proper load sharing between WTs in the study system. In this work, control strategies related to the DFIG-WTs are extracted and then modified by adding an auxiliary control block, known as wind power curtailment block (WPCB), that adjusts the WT reference power according to the local DC bus voltages. Hence, as long as there is sufficient load demand, the WTs inject available power to the microgrid. Once available wind power exceeds the load demand, WTs adaptively curtail the injected power and seamlessly track the varying load. Next, the control structure of the so-called WPCB is further modified to realize proper load sharing between different WTs. Hence, by using the improved control structure of each WT, not only the proper and seamless power management is realized, but also, the load power is appropriately shared between different WTs according to available wind power. At the end, theoretical results are verified by time-domain simulations in the Matlab-Simulink environment.

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

In this paper, a time-varying model-free nonlinear sliding mode control is applied for the secondary load frequency control (LFC) of a shipboard microgrid (MG) with time delay, system uncertainty, and multi-step load variation. Since the satisfactory performance of the suggested controller depends on its parameters’ values, a modified black hole optimization algorithm (MBHA) is utilized to set the optimal values of these coefficients. Finally, the new time-varying proposed technique is validated and implemented in hardware-in-the-loop (HiL) based on OPAL-RT to integrate the fidelity of physical simulation and the flexibility of numerical simulation.

The power system of the ship has less production capacity and inertia compared to large power systems and it behaves like a microgrid. In addition, in ship systems, pulse and dynamic loads absorb most of the capacity, which cause a severe fluctuation in the frequency and voltage of the power system. The topological structures of the network and control system are main factors affecting the stability of the power system of ships. Pulse power loads (PPLs) are very critical in the performance of the ship due to the safety of the system, which consists of electromagnetic weapons, radars, aircraft launch systems, and so on. Energy storage systems (ESSs) are used to decrease the fluctuations of the DC link voltage and adding more reliability to the system. Hence, in this paper, an interval type-2 fuzzy controller (IT2FC) is employed in order to improve the DC link voltage behavior as a robust and an intelligent control technique. Simulation results of the model are presented by Simulink/Matlab software to validate the proposed control technique.

This paper investigates the injecting power stabilization of nonlinear dc microgrids (MGs) with constant power loads (CPLs). By considering a centralized controller scheme, the limitations of the communication utilities are considered. Therefore, limited information is transferred through the nonideal noisy communication network. Consequently, a cubature Kalman filter (CKF) with a third degree is proposed to mitigate the effect of the noisy measurement and the noisy network on the system's information. Moreover, an estimation-based robust feedback controller is developed to design an optimal value for the injecting power. The considered CKF algorithm is robust against the system uncertainty and noisy environments and has a low computational time for high-order dc MGs with a high number of sources and CPLs. In addition, a systematic procedure to compute the feedback gain of the controller is presented, which can be numerically solved by linear matrix inequality techniques. Hardware-in-the-loop real-time simulation results verify the simplicity of the controller implementation, enhanced performance for the case of limited information, and better robustness against the noisy measurements compared to the state-of-the-art methods.

The electric spring (ES) is a novel voltage compensator which is series with a non-critical load to regulate the critical load voltage. The voltage fluctuation is caused by wind speed fluctuation, load fluctuation, and generator tripping. In busbar voltage drop situation, the electric spring decreases the voltage of non-critical load in order to support the critical load (busbar) voltage. All the non-critical loads couldn’t work under any voltage (for example 0.5 pu). In this paper, a control strategy founded on active and reactive power compensations has been proposed for voltage control of critical loads on a reference value while it controls the voltage of non-critical loads between an acceptable boundary. The proposed controller has two voltage control loops which adjusts active and reactive power of the electric spring. The experimental results from the case study show that the ES with the proposed control strategy can effectively mitigate double voltage control of both critical and non-critical loads while dynamically managing the demand response of the system at the same time.

Here, a multiple-input-multiple-output (MIMO) Phantom Omni robot made by SensAble Technologies Inc. is identified by using a least-square support vector regression (LS-SVR). To this end, a two-stage hybrid optimisation strategy combining coupled simulated annealing as a priori optimisation strategy and a derivative-free Simplex method as a complementary stage is proposed to solve the LS-SVR model selection problem. This extra step is a fine-tuning procedure to enhance the optimal tuning parameters and hence lead LS-SVR to a better performance. Generalised v -fold cross-validation is considered as the criterion of LS-SVR model selection problem. The Phantom robot model is implemented via OPAL-RT to assess the performance of the proposed algorithm compared with firefly algorithm and adaptive particle swarm optimisation in solving LS-SVR model selection in practical application of the Phantom robot modelling. Finally, the proposed approach is validated and implemented in the hardware-in-the-loop based on OPAL-RT to integrate the fidelity of physical simulation as well as the flexibility of numerical simulations.

Due to the fast development of renewable energy systems and the severe limitations enforced by the marine pollution protocol, the utilizing of wind turbines (WTs), solar generation, sea wave energy (SWE), and energy storage systems (ESS) in marine vessel power systems have been attracting a lot of attention. Hence, a marine vessel power system with photovoltaic, WT, SWE, and ESS can be considered as a specific mobile islanded microgrid. Consequently, the main target of this paper is to design a new optimal fractional order fuzzy PD+I load frequency controller (LFC) for islanded microgrids in a ship power system. Since the performance of the controller depends on its parameters, the optimization of these coefficients can play a significant role in improving the output performance of the LFC control. Accordingly, a modified black hole optimization algorithm is utilized for the adaptive tuning of the coefficients of noninteger fuzzy PD+I controller. The performance of the shipboard microgrid is evaluated by utilizing real-world wind power fluctuation and solar radiation data. Finally, the extensive studies and hardware-in-the-loop simulations are applied to prove that the proposed controller can track the reference frequency with lower deviation as well as it is more robust in comparison with the prior-art controllers utilized in the case studies.

This paper introduces a polynomial fuzzy model (PFM)-based maximum power point tracking (MPPT) control approach to increase the performance and efficiency of the solar photovoltaic (PV) electricity generation. The proposed method relies on a polynomial fuzzy modeling, a polynomial parallel distributed compensation, and a sum-of-squares (SOS) decomposition. The proposed method is a generalization of the standard Takagi–Sugeno fuzzy models and linear matrix inequality, which showed its effectiveness in decreasing the tracking time and increasing the efficiency of the PV systems. In this paper, a direct maximum power (DMP)-based control structure is considered for MPPT. Using the PFM representation, the DMP-based control structure is formulated in terms of SOS conditions. Unlike the conventional approaches, the proposed approach does not require exploring the maximum power operational point. Finally, the extensive studies and hardware-in-the-loop simulations are presented to show the effectiveness of the proposed method.

A marine energy system, which is fundamentally not paired with electric grids, should work for an extended period with high reliability. To put it in another way, by employing electrical utilities on a ship, the electrical power demand has been increasing in recent years. Besides, fuel cells in marine power generation may reduce the loss of energy and weight in long cables and provide a platform such that each piece of marine equipment is supplied with its own isolated wire connection. Hence, fuel cells can be promising power generation equipment in the marine industry. Besides, failure modes and effects analysis (FMEA) is widely accepted throughout the industry as a valuable tool for identifying, ranking, and mitigating risks. The FMEA process can help to design safe hydrogen fueling stations. In this paper, a robust FMEA has been developed to identify the potentially hazardous conditions of the marine propulsion system by considering a general type-2 fuzzy logic set. The general type-2 fuzzy system is decomposed of several interval type-2 fuzzy logic systems to reduce the inherent highly computational burden of the general type-2 fuzzy systems. Linguistic rules are directly incorporated into the fuzzy system. Finally, the results demonstrate the success and effectiveness of the proposed approach in computing the risk priority number as compared to state-of-the-art methods.

The main goal of this study is to introduce a new polynomial fuzzy model-based structure for a class of marine systems with non-linear and polynomial dynamics. The suggested technique relies on a polynomial Takagi–Sugeno (T–S) fuzzy modelling, a polynomial dynamic parallel distributed compensation and a sum-of-squares (SOS) decomposition. The new proposed approach is a generalisation of the standard T–S fuzzy models and linear matrix inequality which indicated its effectiveness in decreasing the tracking time and increasing the efficiency of the robust tracking control problem for an underactuated surface vessel (USV). Additionally, in order to overcome the USV control challenges, including the USV un-modelled dynamics, complex nonlinear dynamics, external disturbances and parameter uncertainties, the polynomial fuzzy model representation is adopted. Moreover, the USV-based control structure is formulated in terms of SOS conditions. Unlike the conventional approaches, the proposed approach can be easily implemented in the real-time and applied to a reasonably wide class of non-linear systems. Finally, the extensive studies and hardware-in-the-loop simulations are presented, which prove the effectiveness of the novel proposed method.