Robust analysis and design of PID controlled AVR system using Pattern Search algorithm

Sahu, Binod Kumar; Panda, Sidhartha; Mohanty, Pradeep Kumar; Mishra, Namrata

doi:10.1109/pedes.2012.6484294

Cited by 32 publications

(21 citation statements)

References 9 publications

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“…The controller types that have been studied for improving the dynamic response of AVR system are proportional-integral-derivative (PID), fractional order PID (FOPID), gray PID (GPID), and fuzzy logic PID (FLPID). In literature, heuristic optimization-based tuning methods that have been applied to improve performance of the fore mentioned controller types are particle swarm optimization (PSO) (Gaing, 2004), artificial bee colony (ABC) (Gozde and Taplamacioglu, 2011), teaching learning-based optimization (TLBO) (Chatterjee and Mukherjee, 2016; Priyambada et al, 2014), gravitational search algorithm (GSA) (Duman et al, 2016; Kumar and Shankar, 2015), chaotic ant swarm (CAS) optimization (Zhu et al, 2009), chaotic optimization based on Lozi map (COLM) (Coelho, 2009), pattern search algorithm (PSA) (Sahu et al, 2012), anarchic society optimization (ASO) (Shayeghi and Dadashpour, 2012), many optimising liaisons (MOL) (Panda et al, 2012), Taguchi combined genetic algorithm (TCGA) (Hasanien, 2013), local unimodal sampling (LUS) optimization (Mohanty et al, 2014), firefly algorithm (FA) (Bendjeghaba, 2014), bio-geography-based optimization (BBO) (Guvenc et al, 2016), Nelder-Mead algorithm (NMA) (Verma et al, 2015), ant colony optimization (ACO) (Suri babu and Chiranjeevi, 2016), cuckoo search (CS) algorithm (Sikander et al, 2018), grasshopper optimization algorithm (GOA) (Hekimoglu and Ekinci, 2018) and genetic algorithm (GA) tuned neural networks (NN) (Al Gizi et al, 2015). It is worth mentioning that, in literature, the most studied heuristic optimization methods that have either been proposed or used for comparison with other existing methods for AVR system are PSO, GA, ABC and DE.…”

Section: Introductionmentioning

confidence: 99%

Sine-cosine algorithm-based optimization for automatic voltage regulator system

Hekimoğlu

2018

Transactions of the Institute of Measurement and Control

105

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A novel design method, sine-cosine algorithm (SCA) is presented in this paper to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system. The proposed approach is a simple yet effective algorithm that has balanced exploration and exploitation capabilities to search the solutions space effectively to find the best result. The simplicity of the algorithm provides fast and high-quality tuning of optimum PID controller parameters. The proposed SCA-PID controller is validated by using a time domain performance index. The proposed method was found efficient and robust in improving the transient response of AVR system compared with the PID controllers based on Ziegler-Nichols (ZN), differential evolution (DE), artificial bee colony (ABC) and bio-geography-based optimization (BBO) tuning methods.

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Section: Introductionmentioning

confidence: 99%

Sine-cosine algorithm-based optimization for automatic voltage regulator system

Hekimoğlu

2018

Transactions of the Institute of Measurement and Control

105

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“…The intelligence of the SSA was exploited to obtain the best combination of FOPID gains by minimizing a time integrating error fitness function. In order to assess the effectiveness of the proposed design, comparisons of the performance metrics were carried out with different optimization algorithms including DE [14], PSO [15], ABC [14], GOA [16], BBO [17], and PSA [18] on the basis of transient response and stability. The results showed that the FOPID controller tuned by SSA expresses improvement in transient response in comparison to alternate techniques in terms of Mp%, ts, tr, and tp values.…”

Section: Discussionmentioning

confidence: 99%

“…It was concluded that the SSA offers a better solution for the FOPID gains optimal selection problem than other well-known algorithms in terms of transient response indicators. For example, the proposed algorithm based on FOPID tuning provided 52.82%, 17.63%, 38.02%, 0.12%, 24.50%, 7.98%, and 31.1% less overshoot than that of DE [14], PSO [15], ABC [14], BBO [17], GOA [16], PSA [18], and the whale optimization algorithm (WOA), respectively. Hence, the proposed algorithm duly validated its performance superiority over the other optimization methods for the same system configuration and parameters.…”

Section: Transient Response Analysismentioning

confidence: 99%

“…The obtained optimized parameters ensure the optimal dynamic response of the AVR system with reduced settling time and overshoot as compared to traditional methods utilized in recent literature. Furthermore, the effectiveness of the proposed method is reported along with a comparative analysis with differential evolution (DE) [14], the particle swarm optimization algorithm (PSO) [15], artificial bee colony (ABC) [14], the grasshopper optimization algorithm (GOA) [16], the bibliography-based optimization (BBO) [17] algorithm, and the pattern search algorithm (PSA) [18]. The results prove the superiority of the proposed method over all the studied methods under an identical system configuration and operating conditions.…”

Section: Introductionmentioning

confidence: 99%

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Salp Swarm Optimization Algorithm-Based Fractional Order PID Controller for Dynamic Response and Stability Enhancement of an Automatic Voltage Regulator System

et al. 2019

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Owing to the superior transient and steady-state performance of the fractional-order proportional-integral-derivative (FOPID) controller over its conventional counterpart, this paper exploited its application in an automatic voltage regulator (AVR) system. Since the FOPID controller contains two more control parameters (µ and λ ) as compared to the conventional PID controller, its tuning process was comparatively more complex. Thus, the intelligence of one of the most recently developed metaheuristic algorithms, called the salp swarm optimization algorithm (SSA), was utilized to select the optimized parameters of the FOPID controller in order to achieve the optimal dynamic response and enhanced stability of the studied AVR system. To validate the effectiveness of the proposed method, its performance was compared with that of the recently used tuning methods for the same system configuration and operating conditions. Furthermore, a stability analysis was carried out using pole-zero and bode stability criteria. Finally, in order to check the robustness of the developed system against the system parameter variations, a robustness analysis of the developed system was undertaken. The results show that the proposed SSA-based FOPID tuning method for the AVR system outperformed its conventional counterparts in terms of dynamic response and stability measures.

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“…In [31], the authors used local unimodal sampling (LUS) optimization algorithm for obtaining gain values of a PID controller for the AVR problem. A pattern search (PS) optimization based PID controller was proposed in [32]. In [33], authors proposed biogeography-based optimization (BBO) for PID controller parameter tuning for an AVR system.…”

Section: Comparative Studymentioning

confidence: 99%

Voltage and frequency control in conventional and PV integrated power systems by a particle swarm optimized Ziegler–Nichols based PID controller

et al. 2021

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Variations of load demands, expansion of power system by interconnections among different areas and integration of renewable energy sources bring new challenges for stable, reliable and uninterrupted operations of power systems. In this paper, a control technique is proposed to control and optimize the performances of the three models having importance in the present and future energy systems. These are the output variations of an automatic voltage regulation (AVR) system, frequency variations in a load frequency control (LFC) system of a thermal power plant and frequency variations of a PV integrated thermal power plant. The proposed controller is a particle swarm optimized Ziegler–Nichols (ZN) method based proportional-integral-derivative (PID) controller. A particle swarm optimization (PSO) method suffers from the unavailability of prior knowledge of initial values of parameters. Whereas, the classical ZN method leaves the scope for performance improvements of a system. A rejuvenation to the classical ZN method is proposed by integrating PSO. The combined effect optimizes the voltage and the frequency performances, while ensuring system stability. Additionally, different objective functions inspired from energy industry requirements are considered to demonstrate performance improvements of the systems (e.g. maximum overshoot, steady-state error, settling time). The robustness of the proposed method is demonstrated by considering parametric uncertainty in the system. The proposed method is compared with performances of different controllers (e.g. PI, fuzzy PI, fuzzy PID), different iterative soft computing methods (e.g. pattern search, artificial bee colony, different variants of PSO) and classical optimization method (e.g. linear matrix inequality) considering different objective functions and different load disturbances for the aforementioned models. It is also observed that better performances are obtained using a significantly less number of iterations.

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Robust analysis and design of PID controlled AVR system using Pattern Search algorithm

Cited by 32 publications

References 9 publications

Sine-cosine algorithm-based optimization for automatic voltage regulator system

Sine-cosine algorithm-based optimization for automatic voltage regulator system

Salp Swarm Optimization Algorithm-Based Fractional Order PID Controller for Dynamic Response and Stability Enhancement of an Automatic Voltage Regulator System

Voltage and frequency control in conventional and PV integrated power systems by a particle swarm optimized Ziegler–Nichols based PID controller

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