Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system

Zhang, Jinzhong; Zhang, Tan; Zhang, Gang; Kong, Min

doi:10.1007/s12351-023-00787-5

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…The basic whale optimization algorithm (WOA), proposed by Australian scholar Mirjalili S. and colleagues in 2016, emulates three mechanisms employed by humpback whales for prey encirclement: shrinking and encircling of prey, spiral bubble net hunting, and prey search [13]. The WOA assumes that the prey captured by the whale represents the optimal solution.…”

Section: The Basic Woamentioning

confidence: 99%

Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved Whale Optimization Algorithm

Guo,

Qiao,

Yang

et al. 2024

Energies

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Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Lévy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q, tangent of the main influence angle tanβ, horizontal movement coefficient b, and mining influence propagation angle θ were all below 0.32. The average value of the fitted root mean square error for the subsidence value’s fitted root mean square error and horizontal movement value’s fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications.

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Section: The Basic Woamentioning

confidence: 99%

Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved Whale Optimization Algorithm

Guo,

Qiao,

Yang

et al. 2024

Energies

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“…Comparative analyses with various controllers validate the proposed controller’s superior performance in settling time, rise time, and overshoot, supported by frequency domain analysis. The enhanced whale optimization algorithm (EWOA) in [ 24 ] stabilizes PID controller parameters in AVR systems. Comparative analysis establishes EWOA’s faster convergence, higher precision, shorter execution time, and greater stability, making it a practical method for PID controller optimization.…”

Section: Introductionmentioning

confidence: 99%

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

Ekinci,

Snášel,

Rizk-Allah

et al. 2024

PLoS ONE

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Maintaining stable voltage levels is essential for power systems’ efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO’s effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO’s superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm’s performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO’s superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

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“…The use of optimization algorithms to improve AVR controllers is a major advancement in voltage regulation. The need for more flexible and efficient control strategies has grown exponentially, as contemporary power systems deal with increased complexities and evolving demand patterns, Recent studies and comparative analyses have explored a wide range of Intelligence Base Algorithms, and heuristic and metaheuristic optimization techniques, which are employed in AVR systems [2,[4][5][6][7][8][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Some of the prevalent algorithms in the literature include the Bat Algorithm (BA), Fruit Fly (FFA), Whale Optimization Algorithm (WOA), Harmony Search (HAS), Grey Wolf Algorithm (GWA), Monarch Butterfly Algorithm (MBA), Ant Colony (ACO), Dragonfly (DFO), Cuckoo Search (CSO), Moth Flame Optimization (MFO), Artificial Bee Colony (ABC), Grasshopper Optimisation Algorithm (GOA) the Genetic Algorithm (GA), Tabu Search (TSA), Water Cycle Algorithm (WCA), Local Unimodal Sampling (LUS), as well as adaptations and modifications of the Particle Swarm Optimization (PSO), and Teaching and Learning Based Algorithms (TLBA) [2,[4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…The subsequent sections of this paper provide the theoretical foundations of AVR systems, the integration of a single Extended State Observer (ESO)based DOB into the AVR system, the 3-DOF-PIDA controller, and the utilization of SSO for parameter optimization. Simulated results and comparative analyses are presented and compared to five other controller designs that are popular in the relevant literature, namely, WOA-PIDA [21], TLBO-PIDA [27], LUS-PIDA [25], HAS-PIDA [22], and BAT-PIDA [19], to demonstrate the effectiveness of the proposed novel approach in improving voltage regulation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Salp Swarm Optimization Oriented 3-DOF-PIDA Controller Design for Automatic Voltage Regulator System

Chetty,

Sharma,

Gandhi

et al. 2024

IEEE Access

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Voltage stability is critical in electrical power systems, and automatic voltage regulators play a crucial role in maintaining voltage levels within permissible limits. Due to their simplicity and effectiveness, traditional Proportional-Integral-Derivative (PID) controllers have been widely used in automatic voltage regulation. However, they may not always perform optimally in complex power systems, under varying operating conditions and external disturbances. This research introduces an integrated approach of employing a 3-degrees of freedom-PID-Acceleration (3-DOF-PIDA) controller coupled with a disturbance observer-based control strategy. This combination is embedded with a simple but effective salp swarm optimisation algorithm. This novel control approach of the combined 3-DOF-PIDA, disturbance observer and salp swarm optimisation will enhance the voltage regulation of the system. The proposed novel control strategy incorporates an acceleration component to continuously adjust its parameters based on system dynamics. Simultaneously, the disturbance observer is responsible for estimating and compensating for external disturbances, further improving the system's performance. The salp swarm optimisation is applied to optimise both the PIDA control parameters and the disturbance observer's parameters in the automatic voltage regulation system to find optimal solutions that improve voltage regulation and disturbance rejection capabilities. The results are established by performing statistical and graphical analysis with timevarying step load fluctuations, and under various system parameter variations. The results are validated by comparisons to five popular optimization algorithms found in the reviewed literature. The investigations demonstrate that this new proposed approach provides outstanding performance, in the presence of substantial system parameter fluctuations and uncertainties.

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Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system

Cited by 8 publications

References 34 publications

Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved Whale Optimization Algorithm

Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved Whale Optimization Algorithm

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

A Novel Salp Swarm Optimization Oriented 3-DOF-PIDA Controller Design for Automatic Voltage Regulator System

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