Hybridized gravitational search algorithm tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine

In this paper, the load frequency regulation problem of 2‐area interconnected power system is resolved using the sliding mode control (SMC) methodology. Interconnected 2‐area power systems with and without doubly fed induction generator wind turbines are considered for implementing the proposed optimal control methodology. Here, a heuristic gravitational search algorithm (GSA) and its variants such as opposition learning–based GSA (OGSA), disruption‐based GSA (DGSA), and disruption based oppositional GSA (DOGSA) are employed to optimize the switching vector and feedback gains of SMC. In order to overcome the inherent chattering problem in SMC, the control signals are considered in the objective function. The robustness of optimized SMC is analyzed by the inclusion of nonlinearities such as generation rate constraint (GRC), governor deadband, and time delay during the signal processing between the control areas, which are present in the real‐time power system. The insensitiveness of the optimal controller is shown by variation in system parameters like loading condition, speed governor constant, turbine constant, and tie‐line power coefficient. Further, the optimal SMC has been studied with significant load variations and wind power penetration levels in the control areas. The potential of proposed SMC design with chattering reduction feature is shown and validated by comparing the results obtained with the other reported methods in the literature.     

Design of a chattering-free integral terminal sliding mode approach for DFIG-based wind energy systems

This article proposes a Fuzzy Second Order Integral Terminal Sliding Mode (FSOITSM) control approach for DFIG-based wind turbines subject to grid faults and parameter variations. Since traditional terminal sliding mode control (SMC) suffers from singularity, a novel integral terminal sliding manifold is proposed to eliminate chattering and improve the wind turbine's performance in the presence of faults and disturbances. A fuzzy system is proposed to auto-tune the controllers' gains and ensures the invariance of the sliding surfaces even under heavy uncertainties, thus further improving the reliability and performance of the proposed controller. The performance of the proposed approach was assessed under various operating conditions. A comparison analysis with a standard SMC approach as well as the state of the art in voltage sag mitigation was also carried over. Reliability, robustness, and power availability under faulty grid conditions are among the main features of the proposed approach. In addition, the proposed approach exhibited chattering free dynamics and enabled the finite time convergence of the sliding manifold and overcame the singularity problem associated with standard TSMC.     

Meta-heuristics algorithms for optimization of gains for dynamic voltage restorers to improve power quality and dynamics

This article discusses metaheuristic algorithms for optimizing controller gains for dynamic voltage restorers (DVRs) that use an impedance control strategy to compensate for unbalance in source voltages, voltage harmonics, and sag/swell in source voltages. The gains of the proportional-integral (PI) controllers become critical for proper DVR load voltage extraction. Various techniques for optimization, such as whale optimization technique, gray wolf optimization technique, particle swarm optimization technique, and ant lion optimization technique, are used to obtain DC and AC PI controller gains for DVR. The impedance control strategy employs simple calculations to determine the resistance and reactance of a polluted source voltage, without the use of frame conversions as in synchronous reference theory, instantaneous reference power theory, and so on. The quick calculations of the impedance control scheme improve the power quality and dynamics. The Metaheuristic algorithms are used to calculate the number of iterations required to achieve the best possible controller gains, which further helps to improve power quality and dynamics. Among these optimization techniques, the antlion optimization technique provides fast convergence and the best possible controller gain values to improve the dynamics of the dc-link voltage of voltage source converter and terminal voltage, thereby improving power quality. The proposed antlion optimization technique-based DVR model is simulated in MATLAB R2019, and the results are validated with RT-LAB.     

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.     

Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality‐based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre‐synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed‐form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two‐link robotic manipulator system. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

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.     

Optimal control strategy–based AGC of electrical power systems: A comparative performance analysis

This study extensively addresses the application of optimal control approach to the automatic generation control (AGC) of electrical power systems. Proportional‐integral structured optimal controllers are designed using full‐state feedback control strategy employing performance index minimization criterion. Some traditional single/multiarea and restructured multiarea power system models from the literature are explored deliberately in the present study. The dynamic performance of optimal controllers is observed superior in comparison to integral/proportional‐integral controllers tuned using some recently published modern heuristic optimization techniques. It is observed that optimal controllers show better system results in terms of minimum value of settling time, peak overshoot/undershoot, various performance indices, and oscillations corresponding to change in area frequencies and tie‐line powers along with maximum value of minimum damping ratio in comparison to other controllers. The results are displayed in the form of tables for ease of comparison. Sensitivity analysis affirms the robustness of the optimal feedback controller gains to wide variations in some system parameters from their nominal values.     

Load frequency predictive control for power systems concerning wind turbine and communication delay

This article addresses a model predictive control (MPC) technique for load frequency control (LFC) system in the presence of wind power, communication delay, and denial-of-service (DoS) attack. In this article, communication delay is incorporated into a single area control error transmission for simplicity, wind power and load disturbance are regarded as Lipschitz nonlinear terms, as for the randomly occurring DoS attack, it is modeled as Bernoulli processes with known conditional probability. Thinking all these adverse factors to stability and the limitation of input constraint synthetically, the stability of LFC system can be guaranteed by delay-dependent Lyapunov function lemma and a state feedback MPC controller is designed to solve the LFC problems by minimizing the infinite-horizon objective function. Although some scholars have studied the performance degradation and instability of LFC system caused by cyber attack and/or communication delay and some very nice results have been addressed, limited works have considered the MPC approach to deal with both the problems of cyber attack and communication delay which explicitly considers the physical constraints. In addition, the delay-dependent Lyapunov function is adopted to deal with the problem of communication delay, which results in less conservatism of the presented method. Finally, the optimization problem with input constraint is solved and proven to be recursive feasibility, and the closed-loop system turns out to be stable. The reasonability and validity of the provided strategy is verified through several groups of simulation experiments. It illustrates that the proposed control method can keep the system frequency steady in the standard range in spite of various attack conditions.     

An elite hybrid strategy for solar photovoltaic system based optimized cascade controller under uniform and partial shading conditions

A hybrid technique for maximum power point tracking (MPPT) for a photovoltaic (PV) system is proposed in this paper. The proposed hybrid system is combination of Wing suit Flying Search (WFS) and modified Transient search optimization (MTSO), therefore it is called WFS-MTSO method. The proposed controller has three processes: (i) to identify the operating level of photovoltaic (uniform or in PSC), (ii) to estimate the maximal power point using WFS technique, and (iii) to ensure the photovoltaic system runs on the estimated maximum power point (MPP) by MTSO optimized cascade controller. This method begins with a sense of irradiance and temperature. The proposed photovoltaic system has two components. The first one is WFS maximal power point tracking algorithm attain maximal power point. The second one is MTSO optimized cascade controller to force the photovoltaic system to activate at maximal power point. Here, the proposed hybrid technique is utilized at MPPT to diminish tracking error and oscillation across MPP for optimizing power output. The proposed optimized cascade control improves the system efficiency by averting interruptions previously they propagate to the system. Finally, the performance of proposed hybrid system is executed on MATLAB/Simulink working platform and the performances are compared with various existing approaches. The statistical matrices, like mean, median, and standard deviation is analyzed the tracking efficiency of the proposed WFS-MTSO approach.     

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.     

Robust decentralized design of power system stabilizers taking into consideration system uncertainties

In complex power systems, changes in network configurations, various loading conditions, etc., cause system uncertainties. Without considering such uncertainties in the design, the conventional power system stabilizers (PSSs) may deteriorate the system robust stability. To overcome this problem, the proposed design incorporates the uncertainty model in the system representation. Then, the PSSs in a multi‐machine power system are arranged as the decentralized controller in a multi‐input multi‐output (MIMO) system. The robust stability margin of the closed‐loop control system is guaranteed in terms of the MIMO gain margin and phase margin. Control parameters of PSSs are optimized by a tabu search. Non‐linear simulation studies confirm the robustness of the designed PSSs against various uncertainties. Copyright © 2005 John Wiley & Sons, Ltd.     

Building-to-grid optimal control of integrated MicroCSP and building HVAC system for optimal demand response services

The world is shifting toward cleaner and more sustainable power generation to face the challenges of climate change. Renewable energy sources such as solar, wind, hydraulic are now the go-to technologies for the new power generation system. However, these sources are highly intermittent and introduce uncertainty to the power grid which affects its frequency and voltage and could jeopardize its stable operations. The integration of micro-scale concentrated solar power (MicroCSP) and thermal energy storage with the heating, ventilation, and air conditioning (HVAC) system gives the building greater leeway to control its loads which can allow it to support the power grid by providing demand response (DR) services. Indeed, the optimal control of the power flowing between the MicroCSP, the HVAC system, and the thermal zones can bring additional degrees of freedom to the building which can be relegated to the power grid based on the objective function and the incentives provided by the latter. This article presents an in-depth investigation of the MicroCSP potential to provide ancillary services to the power grid. It focuses on evaluating the effect of incentives provided by the power grid on the building participation to the load following programs. It also demonstrates how the MicroCSP can help the building deal with constraints related to load peak shaving and ramp-rate reduction set by the power grid as part of long-term DR contracts. A sensitivity analysis is carried out to confront the results to prediction uncertainties of the energy prices and the weather conditions.     

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

Designing an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning (HAFSL), which combines the genetic algorithm (GA) and gradient descent technique (GD) is proposed for constructing an efficient and robust fuzzy neural network controller (FNNC). Two phases of design and learning process are presented in this work. A GA is used for finding near optimal structure/parameters of the FNNC that minimizes the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on GD method to fine tune the consequent parameters of the controller. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the HAFSL are compared to these found by the traditional PI with genetic optimization (GA‐PI). Simulations demonstrate that the proposed HAFSL and GA‐PI algorithms have good generalization capabilities and robustness on the water bath temperature control system. Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

Optimal reduced‐order LQG synthesis: Nonseparable control and estimation design

The optimal linear‐quadratic‐Gaussian synthesis design approach and the associated separation principle are investigated for the case where the observer design model is a reduced model of the underlying system model. Performance of the resulting reduced‐order controller in the full‐state system environment is formulated in terms of an augmented state vector consisting of the system state vector and the reduced model state vector. Considering explicitly separated linear control and estimation laws, a calculus of variations/Hamiltonian approach is used to determine the necessary conditions for the optimal controller and observer gains for the simplified algorithms. Results show that the optimal gains are not separable, ie, the optimal controller and observer gains are coupled and cannot be computed independently. Numerical examples of an infinite‐horizon and finite‐horizon control and estimation large‐scale multiagent system problem clearly show the advantages of using the nonseparable coupled solutions.     

Tracking control of Wiener models with hierarchical and switching model predictive control

A tracking problem is considered for a Wiener model. A two‐layer hierarchical control structure is designed: the upper level controller operates at a slow rate and computes the inputs to be ideally provided to the system; the effective control actions are provided by actuators placed at the lower layer and having faster dynamics. Model predictive control (MPC) laws are synthesized for both layers. In order to cope with the discrepancy between the ideal and the effective control action, a robust MPC controller is designed at the upper level. Such a controller can switch among different operating conditions ensuring different level of robustness. In doing so, the overall controller guarantees steady‐state zero error regulation for constant reference signals while trading off robustness versus system performance. A numerical example is reported. Copyright © 2011 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.     

Optimal hybrid control of interarea oscillation in power system using energy storage systems

Unmanaged renewables integration into the power system will raise the possibility of small-signal instability due to higher load deviations in the transmission lines. In power grids with a higher penetration level of the renewables, the load deviation can cause interarea oscillation in the grid. Meanwhile, large-scale battery energy storage systems are promising solutions to enhance power system stability by smoothening the load profile and their fast response. To damp the interarea oscillations without compromising the voltage stability, we need to consider the battery's dynamic model in axis. The battery's dynamic model in axis allows us to integrate the battery into the power system and simulate both the battery's active and reactive power injection/absorption. In this paper, we model the battery energy storage on axis. Then, the battery model is augmented into the two-area four-machine power system. An optimum hybrid controller using linear quadratic regulator techniques is designed to damp generators' frequency deviations. The results show that the interarea oscillations are damped without losing the voltage stability of the system.     

Model predictive and linear quadratic Gaussian control of a wind turbine

Model Predictive and linear quadratic Gaussian controllers are designed for a 5MW variable‐speed pitch‐regulated wind turbine for three operating points – below rated wind speed, just above rated wind speed, and above rated wind speed. The controllers are designed based on two different linear dynamic models (at each operating point) of the same wind turbine to study the effect of utilising different control design models (i.e. the model used for designing a model‐based controller) on the control performance. The performance of the LQG controller is enhanced by improving the robustness, achieved by replacing the Kalman filter with a modified Luenberger observer, whose gain is obtained to minimise the effect of uncertainty and disturbance. Copyright © 2016 John Wiley & Sons, Ltd.