Rao algorithm based optimal Multi‐term FOPID controller for automatic voltage regulator system

Abstract

This article presents a novel application of an optimal Multi‐term Fractional‐Order PID (MFOPID) controller for improving the performance of the automatic voltage regulator (AVR) system. A recently developed Rao algorithm has been used to optimize the proposed Multi‐term FOPID controller. The effectiveness of the Rao algorithm tuned Multi‐term FOPID controller for the AVR system has been proved by performing transient response, robustness, and performance analyses. Statistical analysis of the proposed Multi‐term FOPID controlled AVR system has been carried out to explore the most effective performance index among ITAE, ITSE, ISE, and IAE. The external disturbance rejection capability of the Rao algorithm‐tuned MFOPID controller has been examined by injecting external disturbances into the AVR system of different capacities at different times. The superiority of the proposed Multi‐term FOPID controller is validated by comparing the simulation results with recently employed various PID, PID‐Acceleration (PIDA), and FOPID controllers tuned by different metaheuristic algorithms. In addition, the behavior of the proposed Multi‐term FOPID controller has been examined in a real synchronous generator that is connected to a 10,000 MVA, 230 kV electric power network via a 210 MVA transformer using a MATLAB/Simulink environment. From the various simulation results, it has been concluded that the proposed Rao algorithm tuned optimal and robust Multi‐term FOPID controller has significantly improved the performance, robustness and stability of the AVR system.

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach

“…In addition to the comparative assessment against the algorithms used in the above analyses, further comparisons were made with recently reported optimization techniques in the literature (See Table 11 ). This techniques include hybrid atom search particle swarm optimization (h-ASPSO) based PID controller [ 46 ], improved marine predators algorithm (MP-SEDA)-tuned FOPID controller [ 47 ], modified artificial bee colony (IABC) based LOA-FOPID [ 48 ], equilibrium optimizer (EO) based TI λ DND 2 N 2 [ 23 ], whale optimization algorithm (WOA) based PIDA [ 49 ], symbiotic organism search (SOS) algorithm-based PID-F controller [ 50 ], mayfly optimization algorithm based PI λ1 I λ2 D μ1 D μ2 controller [ 25 ], Levy flight improved Runge-Kutta optimizer (L-RUN) based PIDD 2 controller with master/slave approach [ 51 ], particle swarm optimization based 2DOF-PI controller with amplifier feedback [ 52 ], modified artificial rabbits optimizer (m-ARO) based FOPIDD 2 controller [ 53 ], genetic algorithm (GA) based fuzzy PID controller [ 54 ], sine-cosine algorithm (SCA) based FOPID controller with fractional filter [ 55 ], imperialist competitive algorithm (ICA) based gray PID controller [ 56 ], Rao algorithm based multi‐term FOPID controller [ 57 ], whale optimization algorithm (WOA) based 2DOF-FOPI [ 58 ], chaotic yellow saddle goatfish algorithm (C-YSGA) based FOPID controller [ 59 ] and crow search algorithm (CSA) based FOPI controller [ 31 ]. The results indicate that the QWGBO algorithm outperforms several state-of-the-art optimization methods, demonstrating its effectiveness in AVR system control.…”

“…To validate the proposed approach's superiority, extensive comparative analyses (statistical, boxplot, convergence profile, Wilcoxon signed-rank test, transient and frequency responses, performance against varying input reference and external load disturbance, controller effort and robustness) were conducted against other competitive algorithms and recently reported optimization techniques. In addition to the comparative assessment against the algorithms used in the above analyses, further comparisons were made with recently reported 17 optimization techniques in the literature [23,25,31,[46][47][48][49][50][51][52][53][54][55][56][57][58][59]. The simulation results unequivocally highlight the QWGBO algorithm's superior performance in optimizing the AVR system, as evident from lower objective function values, excellent convergence, and statistical assessments as well as stability and robustness analyses.…”

“…The AVR system plays a pivotal role in regulating and controlling the generator's output voltage. Table 3 provides a comprehensive overview of the critical components of the AVR system, along with their corresponding transfer functions and adopted values [23,25,[46][47][48][49][50][51][52][53][54][55][56][57]. These components include the Amplifier, Exciter, Generator, and Sensor, each contributing to the overall control system.…”

“…https://doi.org/10.1371/journal.pone.0299009.t009PLOS ONEapproach[51], particle swarm optimization based 2DOF-PI controller with amplifier feedback[52], modified artificial rabbits optimizer (m-ARO) based FOPIDD 2 controller[53], genetic algorithm (GA) based fuzzy PID controller[54], sine-cosine algorithm (SCA) based FOPID controller with fractional filter[55], imperialist competitive algorithm (ICA) based gray PID controller[56], Rao algorithm based multi-term FOPID controller[57], whale optimization algorithm (WOA) based 2DOF-FOPI[58], chaotic yellow saddle goatfish algorithm (C-YSGA)…”

Novel TIλDND2N2 Controller Application with Equilibrium Optimizer for Automatic Voltage Regulator

“…In the literature, PID [9][10][11][12][13][14], TID [15,16], and fractional order PID (FOPID) [10,14,[17][18][19][20], controllers are widely used in the control of the AVR. Apart from these, PID-derived controllers such as PID plus second order derivative (PIDD 2 ) [14,[21][22][23], PID acceleration (PIDA) [9,12], multi-term FOPID [24] FOPID plus second order derivative (FOPIDD 2 ) [22], FOPID plus derivative (FOPIDD µ ) [23], and FOPID with filters (FOPIDND 2 N 2 ) [18] have also been proposed to effectively fit the output voltage of the AVR to the reference point. PID-derived controllers add extra tuning parameters to PID, increasing their flexibility and improving control capabilities.…”