Novel TIλDND2N2 Controller Application with Equilibrium Optimizer for Automatic Voltage Regulator

Abstract

Sustainability is important in voltage regulation control in grids and must be done successfully. In this paper, a novel tilt-fractional order integral-derivative with a second order derivative and low-pass filters controller, referred to as TIλDND2N2 controller, is proposed to enhance the control performance of an automatic voltage regulator (AVR). In this article, the equilibrium optimizer (EO) algorithm is used to optimally determine the eight parameters of the proposed controller. In this study, a function consisting of time domain specifications is used as the objective function. To evaluate the performance of the proposed controller, it is compared with the proportional-integral-derivative (PID), fractional order PID (FOPID), PID accelerator (PIDA), PID plus second order derivative (PIDD2), and hybrid controllers used in previous studies. Then, Bode analysis is performed to determine the achievement of the proposed controller in the frequency domain. Finally, the robustness test is realized to assess the response of the proposed controller against the deterioration of the system parameters. As a result, the proposed controller demonstrates outstanding control performance compared to studies in terms of settling time, rise time and overshoot. The proposed controller shows superior performance not only in frequency domain analysis but also in perturbed system parameters.

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.…”

“…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.…”

“…The African vultures optimization algorithm (AVOA) is adaptively modified for controller parameter design, resulting in superior AVR controller performance. In [ 23 ], the equilibrium optimizer (EO) is employed to optimize parameters for a novel AVR controller using a time-domain objective function. Comparative analyses with various controllers validate the proposed controller’s superior performance in settling time, rise time, and overshoot, supported by frequency domain analysis.…”

“…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 – 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.…”

A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

“…In the work by Mohd Tumari et al (2023), a novel optimization algorithm is used to obtain optimal control parameters for AVR, which substantially improves the related field. Similarly, in the work by Jegatheesh et al (2023), Zhang et al (2023), Tabak (2023) and Fergani (2022), different algorithms greatly contribute to optimizing voltage regulation.…”