A computational intelligence based maximum power point tracking for photovoltaic power generation system with small-signal analysis

There are multiple peak functions in its output power characteristic curve of a photovoltaic (PV) array under partial shading conditions (PSCs), the perturb and observe (P&O) may fail to track the global maximum power point (GMPP). Therefore, a reliable maximum power point tracking (MPPT) technique is essential to track the GMPP within an appropriate time. This article proposes a hybrid technique by combining an evolutionary optimization technique, namely the modified invasive weed optimization (MIWO) with the conventional P&O algorithm to enhance the search performance for the maximum power output of the PV system. MIWO executes in the initial stages of the tracking followed by the P&O at the final stages in the MPPT search process. The combined approach ensures faster convergence and better search to the GMPP under rapid climate change and PSCs. The search performance of the hybrid MIWO+P&O technique is examined on a standalone PV system through both MATLAB/Simulink environment and experimentally using dSPACE (DS1103)-based real-time microcontroller hardware setup. The performance of the proposed hybrid MPPT scheme is compared with the recent state-of-the-art MPPPT techniques. In addition, the small-signal analysis of the PV system is carried out to evaluate the loop robustness of the controller design. For a given set of system parameters, simulations for the small-signal model and robustness studies are analyzed to verify the results. The overall results justify the efficacy of the proposed hybrid MPPT algorithm.     

Renewable energy management of an hybrid water pumping system (photovoltaic/wind/battery) based on Takagi–Sugeno fuzzy model

This research work presents an optimal energy management for a hybrid water pumping system driven by a photovoltaic generator (PVG) and a wind turbine. These two renewable energies are used as power generation sources, whereas a battery is added as an energy-storing system, for the purpose of controlling the power flow and providing a constant load supply. The proposed management system, serve to guarantee the pumping system autonomy in a rural region where's no access to the electrical network. As a result, a maximum power point tracking (MPPT) controller is created based on the fuzzy Takagi–Sugeno (TS) model, ensuring maximum power transfer to the moto-pump in spite of wind speed and insolation changes. The synthesis of MPPT control law involves TS fuzzy reference models which generate the desired trajectories to track. A supervisor has been developed for energy management and its major purpose is to effectively use the battery to satisfy the power load requirements, and that is by maintaining the state of charge (SOC) to extend the battery's life. Finally, simulation results have been done based on Matlab/Simulink with the aim of validating the efficiency of the proposed energy management supervisor.     

Experimental investigation of Harris Hawk optimization-based maximum power point tracking algorithm for photovoltaic system under partial shading conditions

An integrated quasi Z-source DC–DC converter (qZSC) along with Harris Hawk Optimization (HHO)-based maximum power point tracking (MPPT) algorithm is proposed in this paper to increase the efficiency of photovoltaic (PV) system. The qZSC-based PV system experiences more voltage and current stress during partial shading conditions (PSCs), which causes overheat on qZSC components hence, degrade the efficiency and reliability of the system. Conventional swarm intelligence-based MPPT algorithms track the G_MPP during PSC, but these take longer convergence time and fail to settle at G_MPP. This uncertainty of finding the G_MPP leads to fluctuations at output of qZSC, hence more stress on the converter components. HHO in tracking the G_mpp eliminates premature local MPPs, enhances convergence speed by expanding the search space for finding the G_MPP. The proposed system is developed in MATLAB/Simulink environment and verified the results by developing prototype model in the laboratory by using C2000™ Piccolo™ Launch Pad™, LAUNCHXL-F28027 controller. The tracking performance of the proposed HHO-based MPPT algorithm is tested under fast changing and PSCs in comparison with perturb & observe (P&O), particle swarm optimization (PSO), and artificial bee colony (ABC)-based MPPT algorithms. The simulation and experimental results show that the proposed HHO-based MPPT algorithm is robust, tracks maximum power point in minimum convergence time in comparison with P&O, PSO and ABC-based MPPT algorithms. Hence, voltage and current fluctuations at the output of qZSC are reduced. Therefore, voltages and current stress on qZSC components are reduced and the efficiency of the system is improved.     

Optimal control of a wind energy conversion system and a wind turbine

Currently, a maximum power point tracking (MPPT) unit implemented in a wind energy conversion system (WECS) extracts the maximum mechanical power from the wind turbine used in the WECS. Therefore, the MPPT unit acts as a maximum mechanical power tracker (MMPT) that performs optimal control of the wind turbine to extract the maximum mechanical power from the wind turbine. In this paper, the basic concept of a maximum electrical power tracker (MEPT) is presented both theoretically and technically. It is demonstrated that an MEPT implemented in a WECS maximizes the output electrical power of the WECS. Thus, in contrast with an MMPT, the proposed MEPT optimally controls the whole of the WECS, rather than the wind turbine, to extract the maximum electrical power from the WECS. Since, in the WECS, the power efficiency of the whole of the WECS, not the wind turbine, should be maximized to extract the maximum output electrical power from the WECS, the conventional MPPT unit acting as an MMPT should be replaced with the proposed MEPT, and this is the superiority of the proposed MEPT to an MMPT or a conventional MPPT unit. To provide experimental verifications, 2 novel MEPT and MMPT with simple structures and better performance compared to the MPPT techniques commonly used in WECSs have been constructed, which are presented in detail.     

Optimal control of electrical vehicle incorporated hybrid power system with second order fractional-active disturbance rejection controller

This article proposes a novel control methodology employing a fractional-active-disturbance-rejection-controller for the combined operation of load frequency control and automatic voltage regulator of a hybrid power system. A two area hybrid power system with diverse energy sources like solar-thermal, conventional-thermal and wind sources equipped with appropriate system nonlinearities is investigated. In order to ascertain the role of modern-day electric-vehicle (EV), the hybrid power system is incorporated with EVs in both the areas. To establish an effective frequency, voltage and tie line power control of the hybrid power system, a second order fractional-active-disturbance-rejection-controller with fractional-extended state observer is modeled as secondary controller. Magnetotactic-bacteria-optimization (MBO) technique is applied to obtain optimal values of the controller gains and the hybrid system parameters. The robustness of the controller gains is tested under different system parameter changes from their nominal values. In addition, the effect of incorporating a power system stabilizer on the hybrid power system is evaluated. Further, the impact of integrating renewable sources and EVs in the hybrid power system is explored. Moreover, the stability of the hybrid power system is monitored with the inclusion of FACTS device. The developed controller operates encouragingly with reference to system stability, rapidity and accuracy in comparison to testified control strategies available in the literature. The robustness test under load-perturbation, solar-insolation, wind input variations also proves the efficiency of MBO optimized second order fractional-active-disturbance-rejection-controller gains.     

Cost function optimization for predictive control of a five-phase IM drive

Model predictive control has been used, for some time now, as a method to directly control power converters in electrical systems. The usual practice is tuning the cost function of the controller to obtain a certain compromise solution over the whole operating range of the system. This method is extended here to consider multiple, locally optimal, and tunings. The design objectives (tracking error, switching frequency, etc) are used to define a unique performance index that is locally optimized. In this way, the parameters of the cost function are linked to the current operating point. The tuning at each operating point is obtained numerically solving the optimization of the performance index. Although the idea can be applied to induction machines with any number of phases, in this paper, a five-phase induction motor is considered for presentation. This system is a demanding case due to the extra number of phases compared with the traditional three-phase motor. Simulation and experimental results are presented to assess the proposed predictive controller.     

Optimal decentralized control of a wind turbine and diesel generator system

This article presents a decentralized optimal controller design technique for the frequency and power control of a coupled wind turbine and diesel generator. The decentralized controller consists of two proportional-integral (PI)-lead controllers which are designed and optimized simultaneously using a quasi-Newton based optimization technique, namely, Davidon–Fletcher–Powell algorithm. The optimal PI-lead controllers are designed in such a way that there are no communication links between them. Simulation results show the superior performance of the proposed controller with a lower order structure compared to the benchmark decentralized linear-quadratic Gaussian integral controllers of orders 4 and 11. It is also shown that the proposed controller demonstrates an effective performance in damping the disturbances from load and wind power, as well as a robust performance against the parameter changes of the power system.     

An optimized fuzzy continuous sliding mode controller combined with an adaptive proportional-integral-derivative control for uncertain systems

This article discusses the design of a hybrid fuzzy variable structure control algorithm combined with genetic algorithm (GA) optimization technique to improve the adaptive proportional-integral-derivative (PID) continuous second-order sliding mode control approach (APID2SMC), recently published in our previous article in the literature. In this article, first, as an improved extension to APID2SMC published recently in the literature, an adaptive proportional-integral-derivative fuzzy sliding mode scheme (APIDFSMC) is presented in which a fuzzy logic controller is added. Second, a GA-based adaptive PID fuzzy sliding mode control approach (APIDFSMC-GA) is introduced to obtain the optimal control parameters of the fuzzy controller in APIDFSMC. The proposed control algorithms are derived based on Lyapunov stability criterion. Simulations results show that the proposed approaches provide robustness for trajectory tracking performance under the occurrence of uncertainties. These simulation results, compared with the results of conventional sliding mode controller, APID2SMC, and standalone classical PID controller, indicate that the proposed control methods yield superior and favorable tracking control performance over the other conventional controllers.     

H∞ output tracking control over networked control systems with Markovian jumping parameters

The problem of H_∞ output tracking control over networked control systems (NCSs) with communication limits and environmental disturbances is studied in this paper. A wide range of time‐varying stochastic problem arising in networked tracking control system is reduced to a standard convex optimization problem involving linear matrix inequalities (LMIs). The closed‐loop hybrid NCS is modeled as a Markov jump linear system in which random time delays and packet dropouts are described as two stochastic Markov chains. Gridding approach is introduced to guarantee the finite value of the sequences of transmission delays from sensor to actuator. Sufficient conditions for the stochastic stabilization of the hybrid NCS tracking system are derived by the LMI‐based approach through the computation of the optimal H_∞ performance. The mode‐dependent robust H_∞ output tracking controller is obtained by the optimal iteration method. Numerical examples are given to demonstrate the effectiveness of the proposed robust output tracking controller for NCS. Copyright © 2016 John Wiley & Sons, Ltd.     

Performance evaluation of GRNN and ANFIS controlled DVR using machine learning in distribution network

A machine learning based generalized neural network estimator (GRNNE) and Takagi-Sugeno (T-S) fuzzy control system is implemented to accelerate the functional performance indices of dynamic voltage restorer (DVR). The GRNNE predictive model is recommended for the fast estimation of the reference load voltage under the distorted power supply. The fruit fly optimization learning strategy is employed to optimize the weights and smoothing parameters for the extraction of the reference voltage signals as well as unit vectors, resulting in the required sinusoidal load component. The dynamics in the DC-link voltage are optimized by the metaheuristic-based gray wolf optimization algorithm. The coefficients of the adaptive controller are updated automatically to achieve the best fitted adaptive neuro-fuzzy inference system predictive model with the least tracking voltage deviation error even in the presence of voltage disturbances. In comparison to classical techniques, the recommended neural-based approach offers a faster convergence speed with fewer parameters to tune, shorter training time, and lower risks of local entrapment. The performance metrics such as mean square error, root mean square error, mean absolute error, mean absolute percent error, coefficients of correlational and determination (R and R²) are used to evaluate the efficacy of the proposed controller and hence enhance the DVR performance. Finally, the simulation results of the hybrid approach confirm that the NN-based DVR estimator has proven its ability to alleviate voltage sensible issues at critical loads and outperform others in reducing power quality issues.     

Analysis of moth flame optimization optimized cascade proportional-integral-proportional-derivative controller with filter for automatic generation control system incorporating solar thermal power plant

In the fast developing electric power system network, ancillary services like automatic generation control (AGC) plays a vital and significant role to ensure good quality of power supply in the system. To distribute good quality of power, a hybrid AGC system along with an efficient and intelligent controller is compelled. So, in this article, a cascaded proportional-integral (PI)-proportional-derivative (PD) controller with filter (PI-PDF) is proposed as secondary controller for AGC system. A nature inspired optimization algorithm named as moth flame optimization (MFO) algorithm is employed for simultaneous optimization of controller gains. Initially, a two-area interconnected nonreheat thermal power system is investigated. Analysis revealed that MFO-tuned PI controller performs better than the different optimization techniques tuned PI controller for the same system and PI-PDF controller performs better than PI controller does. Then, the study is extended to a three-area interconnected hybrid system with proper generation rate constraint. Area-1 consists of solar thermal-thermal unit; area-2 consists of thermal-hydro unit and area-3 thermal-gas unit as generating sources. Performance of PI-PDF controller is compared with classical controllers such as PI, PID, PIDF, and PI-PD controller without filter. Result analysis divulges that MFO-tuned PI-PDF controller performs better than all other controllers considered in this article. Robustness of the PI-PDF controller is evaluated using parameter variations and random load variation.     

A novel hybrid model predictive control design with application to a quadrotor helicopter

In this paper, a novel control design strategy based on a hybrid model predictive control in combination with fuzzy logic control is presented for a quadrotor helicopter system. In the proposed scheme, a 2‐part control structure is used. In the first part, a linear model predictive controller with receding horizon design strategy is combined with a nonlinear model predictive controller, which is applied as the main controller. In the second part, a 2‐level fuzzy logic controller is utilized to assist the first controller when the error exceeds a predefined value. The proposed nonlinear predictive control method utilizes a novel approach in which a prediction of the future outputs is used in the modeling stage. Using this simple technique, the problem can be solved using linear methods and, thereby, due to considerable reduction in the computational cost, it will be applicable for the systems with fast dynamics. Moreover, the fuzzy logic controller is used as a supervisor to adjust a proportional‐integral‐derivative controller to enhance the system performance by decreasing the tracking error. The proposed scheme is applied to a model of quadrotor system such that the difference between the predicted output of the system and the reference value is minimized while there are some constraints on inputs and outputs of the nonlinear quadrotor system. Simulation results demonstrate the efficiency of the proposed control scheme for the quadrotor system model.     

Adaptive fuzzy finite-time optimal control for switched nonlinear systems

This article addresses the adaptive fuzzy finite-time control problem for a class of switched nonlinear systems whose powers are positive odd rational numbers and vary with the switching signal. The fuzzy logic systems (FLSs) are used to approximate the unknown nonlinearities of the controlled systems, and then by combining backstepping control algorithm with adding a power integrator technique, an adaptive finite-time controller is designed. By modifying and optimizing the relevant design parameters of the proposed adaptive finite-time controller, a novel adaptive finite-time optimal control approach is developed. The proposed two adaptive finite-time optimal control schemes can guarantee semi-globally practically finite-time stability of the closed-loop system. Moreover, the adaptive finite-time optimal controller can also achieve optimized performance in relation to the cost functional. Finally, a simulation example is given to illustrate the effectiveness of the proposed two adaptive finite-time control strategies.     

Modified whale optimization algorithm for fractional‐order multi‐input SSSC‐based controller design

In this paper, the design of a fractional‐order (FO) multi‐input–single‐output (MISO)–type static synchronous series compensator (SSSC) is proposed with a goal to improve the power system stability using modified whale optimization algorithm (MWOA). The proposed MWOA achieves an appropriate balance between exploitation and exploration stages of the original whale optimization algorithm. The performance of MWOA is validated by employing the benchmark test functions and further contrasted with whale optimization algorithm and other heuristic algorithms like gravitational search algorithm, particle swarm optimization, differential evolution, and fast evolutionary programming algorithms to demonstrate its strength. The proposed FO MISO SSSC controller is optimized by the MWOA technique and tested under single‐machine infinite bus system and further extended to a multi‐machine framework. To demonstrate the superiority of MISO‐type SSSC controller, the results obtained from it are compared with particle swarm optimization and differential evolution–based conventional single‐input–single‐output structured SSSC controllers. The comparison of results of MWOA with that of other methods validates its superiority in the present context.     

PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

In this article, proportional-integral (PI) control to ensure stable operation of a steam turbine in a natural gas combined cycle power plant is investigated, since active power control is very important due to the constantly changing power flow differences between supply and demand in power systems. For this purpose, an approach combining stability and optimization in PI control of a steam turbine in a natural gas combined cycle power plant is proposed. First, the regions of the PI controller, which will stabilize this power plant system in closed loop, are obtained by parameter space approach method. In the next step of this article, it is aimed to find the best parameter values of the PI controller, which stabilizes the system in the parameter space, with artificial intelligence-based control and metaheuristic optimization. Through parameter space approach, the proposed optimization algorithms limit the search space to a stable region. The controller parameters are examined with Particle Swarm Optimization based PI, artificial bee colony based PI, genetic algorithm based PI, gray wolf optimization based PI, equilibrium optimization based PI, atom search optimization based PI, coronavirus herd immunity optimization based PI, and adaptive neuro-fuzzy inference system based PI (ANFIS-PI) algorithms. The optimized PI controller parameters are applied to the system model, and the transient responses performances of the system output signals are compared. Comparison results of all these methods based on parameter space approach that guarantee stability for this power plant system are presented. According to the results, ANFIS- PI controller is better than other methods.     

Development of MADB of P-I controller using LMI technique in a renewable energy based AGC system and study its application in a deregulated environment including energy storage device

In recent decades, inclusion of renewable resources is considered as one of the most promising options for the long run uninterrupted power supply without depending on conventional resources. Thus, the renewable energy generation will get more attention and massive growing, so that the goal of 40% share of electricity in the worldwide energy portfolio in 2050 would be realized. But during replacement of renewable energy by conventional energy, engineers are facing a lot of problem due to solar generation, wind generation change their characteristic rapidly with weather condition, which may cause large synchronizing imbalance between different units and generate large system delay or communication delay in large interconnected grid. In this article, the authors propose linear matrix inequalities techniques to developed margin of allowable delay for delay dependent stable hybrid system. Initially, to judge the efficacy of proposed chaotic atomic search optimization (CASO) algorithm over other evolutionary algorithms with PID controller, a thermal-hydro gas system is considered. The second part of this article is motivated by the fact that proposed CASO algorithm with P-I controller is superior in contrast to bacterial foraging algorithm technique. In addition to this, some energy storage devices such as fuel cell, aqua electrolyzer, and ultra-capacitor are used to achieve a better dynamic response within specified delay margin. Moreover, to study the impact of communication delays (delay margin) due to the loss of synchronism between solar-wind (for their unpredicted environmental features) and thermal unit (nonlinearities like generation rate constraints, boiler dynamics, and governor with the dead band) are considered in thermal unit and the simulation results verify with the effectiveness of the proposed approach on providing a balance between the delay margin and the damping performances is evaluated under deregulated environment. The simulation results help to make an inter-relation between the delay margin and the controller gain (P-I) which help the system operator in designing controllers gain for stable operation of the proposed hybrid system.     

Comparative performance analysis of 2DOF state feedback controller for automatic generation control using whale optimization algorithm

This paper presents the design of two‐degree‐of‐freedom state feedback controller (2DOFSFC) for automatic generation control problem. A recently developed new metaheuristic algorithm called whale optimization algorithm is employed to optimize the parameters of 2DOFSFC. The proposed 2DOFSFC is analyzed for a two‐area interconnected thermal power system including governor dead band nonlinearity and further extended to multiunit hydrothermal power system. The supremacy of the 2DOFSFC is established comparing with proportional‐integral, proportional‐integral‐derivative (PID), and 2DOFPID controllers optimized with different competitive algorithms for the concerned system. The sensitivity analysis of the optimal 2DOFSFC is performed with uncertainty condition made by varying bias coefficient B and regulation R parameters. Furthermore, the proposed controller is also verified against random load variations and step load perturbation at different locations of the system.     

Quantitative feedback design for a variable-displacement hydraulic vane pump

In this paper the model development, problem specification, constraint formulation, and optimal feedback controller design for a variable-displacement hydraulic pump system are shown using the Quantitative Feedback Theory (QFT) technique. The use of variable-displacement pumps in hydraulic system applications has become widespread due to their efficiency advantages; however, this efficiency gain is often accompanied by a degradation of system stability. Here we develop a QFT controller for a variable-displacement pump based upon a linear, parametrically uncertain model in which some of this uncertainty reflects variation in operating point-dependent parameters. After presentation of a realistic non-linear differential equation model, the linearized model is developed and the effect of parametric uncertainty is reviewed. From this point, closed-loop performance specifications are formulated and the QFT design technique is carried out. An initial feasible controller is designed, and this design is optimized via a non-linear programming technique. In conclusion, a non-linear closed-loop system response is simulated. This paper is intended to have tutorial value, both in terms of the detailed hydraulic system model development, as well as in terms of the detailed exposition of the QFT controller design and optimal loop shaping processes. © 1998 John Wiley & Sons, Ltd.     

Design and performance analysis of elephant herding optimization based controller for load frequency control in thermal interconnected power system

In this present contribution, an attempt has been taken to design and analyze the performance of elephant herding optimization (EHO) based controller for load frequency control (LFC) applications of interconnected power system. The studied system is a two‐area nonreheat thermal interconnected system which is widely used in literature. A proportional‐integral‐differential controller is utilized for LFC of the studied system. EHO technique is applied to obtain the tuned set of controller parameters. The objectives considered for design of the controller are the minimization of settling times and integral‐time‐multiplied‐absolute‐error of frequency deviations (FDs) and tie‐line power deviation (TPD). The design objectives are integrated together to form a function with single objective by assigning equal weights after normalization. Several test cases of diverse set of disturbances are taken into account to test the performance of the proposed controller and the obtained results are compared with other controllers designed with differential evolution, gray wolf optimization, particle swarm optimization, teacher‐learner‐based optimization, and whale optimization algorithm. Furthermore, the time‐domain simulations of FDs and TPD are illustrated to support the tabulated results. In addition, comparative statistical analysis is presented to validate the robust behavior of the proposed controller.