Chaotic-based particle swarm optimization algorithm for optimal PID tuning in automatic voltage regulator systems

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

An improved RUN optimizer based real PID plus second-order derivative controller design as a novel method to enhance transient response and robustness of an automatic voltage regulator

In this paper, a real-valued genetic algorithm (RGA) and a particle swarm optimization (PSO) algorithm with a new fitness function method are proposed to design a PID controller for the Automatic Voltage Regulator (AVR) system. The proposed fitness function can let the RGA and PSO algorithm search a high-quality solution effectively and improve the transient response of the controlled system. The proposed algorithms are applied in the PID controller design for the AVR system. Some simulation and comparison results are presented. We can see that the proposed RGA and PSO algorithm with this new fitness function can find a PID control parameter set effectively so that the controlled AVR system has a better control performance.

A self-competitive mutation strategy for Differential Evolution algorithms with applications to Proportional–Integral–Derivative controllers and Automatic Voltage Regulator systems

Optimal tuning of proportional-integral-derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a tuning fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.

Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic community and industry society throughout the world. To improve the performance of particle swarm optimization (PSO), we herein propose a chaotic proportional integral derivative (PID) controlling PSO algorithm by the hybridization of chaotic logistic dynamics and hierarchical inertia weight. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles’ search space. Moreover, the chaotic logistic map is not only used in the substitution of the two random parameters affecting the convergence behavior, but also used in the chaotic local search for the global best position so as to easily avoid the particles’ premature behaviors via the whole search space. Thereafter, the convergent analysis of chaotic PID controlling PSO is under deep investigation. Empirical simulation results demonstrate that compared with other several chaotic PSO algorithms like chaotic PSO with the logistic map, chaotic PSO with the tent map and chaotic catfish PSO with the logistic map, chaotic PID controlling PSO exhibits much better search efficiency and quality when solving the optimization problems. Additionally, the parameter estimation of a nonlinear dynamic system also further clarifies its superiority to chaotic catfish PSO, genetic algorithm (GA) and PSO.

The proportional-integral-derivative(PID) controllers in the process plants like thermal power plants, biomedical instrumentation is of popular use. The priority of such controllers is obviously a fine tuning as a high degree of industrial expertise is required for any other alternative. So in order to get the best results of PID controllers the optimal tuning of PID gains are required. This paper presents the design and performance analysis of proportional integral derivative (PID) controller for the Automatic Voltage Regulator (AVR) system using multi objective non dominated sorting genetic algorithm-II(NSGA-II). It is required to develop the electronic control system for the synchronous generator for the stable operation of power system. The Automatic Voltage Regulator (AVR) is widely used in electrical power field to obtain the stability and good regulation of the electric system. The characteristics of alternator output required are constant voltage and constant current. To get the constant output, alternator field excitation is controlled by Automatic Voltage Regulator (AVR). The Automatic Voltage Regulator maintains the constant voltage up to certain level of load current independently of generator speed and load. This paper further deals with the robustness analysis of the AVR system tuned by NSGA-II algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of-50% to +50% insteps of +25%.

Automatic voltage regulator (AVR) systems are critically required in many systems such as hydro, gas, steam turbines and engines. Effective control of an AVR system is complex and challenging due to its unstable open loop response characteristics. The AVR system is required to sustain appropriate voltage levels in spite of fluctuations in the main supply. Hence, the AVR system needs to be primarily designed for stability. This paper presents design implementation of the first and second generations of CRONE controllers (Commande Robuste d'Ordre Non Entier) for an AVR system. CRONE is a non-integer order robust controller which is far more effective than the standard PID controllers as it offers better tuning and flexibility in controlling the AVR process. In the present study, various time-domain performance criteria with and without controller were simulated to validate the accomplishment of AVR CRONE controllers. The proposed control procedure for enhancing AVR systems' efficiency has been shown to be effective and better in terms of improved phase and gain margins.

In this paper, various standard performance indices are minimized for Proportional Integral Derivative (PID) controller based Automatic Voltage Regulator(AVR) system using Harris Hawks Algorithm (HHO). This paper also proposes the best performance index for HHO-PID based AVR system. Efficient minimization of performance indices yields better AVR system controller parameters. Using obtained controller parameters, we found step responses and compared values of \(t_{r} ,t_{s} ,M_{p}\) and \(E_{ss}\) and then proposed the best efficient minimized performance index. HHO-PID based AVR system is also compared with GA-PID based AVR system for proving superiority of HHO algorithm. HHO-PID based AVR system with proposed best performance index showed great improvement in step response.

In this work, a hybrid PID controller, combining Zeigler–Nichols (ZN) theory and the dominant pole placement, has been developed for the automatic voltage regulator (AVR) systems. Furthermore, the reduced order models (ROMs) of AVR systems, namely, AVR-1, AVR-2, and AVR-3, have been established using Particle Swarm Optimization (PSO), Big Bang Big Crunch (BBBC), and Pade’s approximation, respectively. Then, the proposed PID controller has been applied to both the original and developed lower order models of AVR to check the efficacy and robustness of the developed hybrid controller. It is shown that the proposed hybrid PID controller provides a very small value of the percentage peak overshoot (%M p ) and settling time (t s ). Also, the gain margin (GM) and phase margin (PM) are found to be in the desired range. Finally, the sensitivity analysis has also been performed to check the robustness of the proposed controller. The sensitivity function, complementary sensitivity function, and various performance indices have been evaluated for the proposed controller based on Bode’s Integral (BI), Tyreus–Lyuben (T–L), and Internal Model Control (IMC) approaches. The comparative studies of the results show that the proposed controller has smaller values of performance indices and sensitivity function.

This paper combines the simulated annealing (SA) and white shark optimisation (WSO) algorithms to create a novel hybrid metaheuristic algorithm named SA–WSO. The proposed algorithm finds the best approach to tuning the parameters of three types of regulators. Three types of proportional–integral–derivative (PID) controllers are utilized for an automatic voltage regulator (AVR) system, including fractional–order (FOPID) –controllers. A novel filtered fractional–order PID controller is also presented to enhance the response of the AVR system. The simulation results demonstrate the highly effective optimisation of three controllers, including the rarely used filtrated–FOPID controller, outperforming other controller approaches in the state of the art. The controller performance analyses are also conducted to demonstrate the superiority of SA–WSO-optimized for tuning the controller parameters in different conditions for disturbance rejection and robustness. A comprehensive comparative analysis is performed to determine the most appropriate controller for the AVR system, and it is found that hybrid SA–WSO algorithm provides significant advantages in terms of improved convergence speed.

This paper presents a method for robust optimum tuning of Fractional-order Proportional Integral Derivative (FoPID) controller for Automatic Voltage Regulator (AVR) system. Optimal tuning of FoPID parameters for AVR system are accomplished by using Salp Swarm Algorithm (SSA) of optimization. The performance of the AVR system with FoPID controller tuned by SSA shows the robust performance against disturbances deliberated at the output of the system. To test the robustness of the system, in the presence of external disturbance $H_{\infty}$ norm is considered. AVR system with SSA tuned PID controller is also simulated with MATLAB platform. Results shows that the AVR system with SSA tuned FoPID controller outperform the SSA tuned PID controller in the presence of external disturbances at output of the system and shows its disturbance rejection capabilities.

This paper aims to develop a Proportional Integral Derivative (PID) controller for the Automatic Voltage Regulator (AVR) of the Synchronous Generator, Automatic Voltage Regulator is responsible for keeping the output voltage of the Generator constant and stable. The Automatic Voltage Regulator system can be represented by a fourth order Transfer Function. The parameters of the controller are evaluated by using the Genetic Algorithm tuning method, for this method the Genetic Algorithm Optimization toolbox in MATLAB is used. A PID controller and a PID Filter controller are developed in order to come up with the optimum controller design for the system. The performance of the Genetic Algorithm based controller has been compared with the classical Ziegler-Nichols based controller. The results show that the Genetic Algorithm based controller outperforms the Ziegler-Nichols based controller, the optimum controller design is achieved by the GA based PID filter controller.

Automatic voltage regulator is used to fix the output signal by correcting the voltage of the exciter and track the generator’s voltage. The improvement of automatic voltage regulator (AVR) response will reflected on the power system performance. In this paper two intelligent techniques fuzzy logic (FL) and Particle swarm optimization (PSO) are used to enhance the response of the Fourth order AVR system based on the merging between them and then the control action summed and coupled with PID controller action. Fuzzy controller has many benefits in design of AVR compared with conventional controllers, but the response of the AVR system still insufficient, therefore, the PSO and Fuzzy are used together for this purpose to produce Fuzzy-Swarm-PID controller (FSPID). Usually the cognitive (C1) and social acceleration (C2) are taken as a constant values in PSO algorithm, but here the Fuzzy logic system is used to get these accelerations to enhance the response.

Automatic voltage regulator is used to fix the output signal by correcting the voltage of the exciter and track the generator’s voltage. The improvement of automatic voltage regulator (AVR) response will reflected on the power system performance. In this paper two intelligent techniques fuzzy logic (FL) and Particle swarm optimization (PSO) are used to enhance the response of the Fourth order AVR system based on the merging between them and then the control action summed and coupled with PID controller action. Fuzzy controller has many benefits in design of AVR compared with conventional controllers, but the response of the AVR system still insufficient, therefore, the PSO and Fuzzy are used together for this purpose to produce Fuzzy-Swarm-PID controller (FSPID).Usually the cognitive (C1) and social acceleration (C2) are taken as a constant values in PSO algorithm, but here the Fuzzy logic system is used to get these accelerations to enhance the response.