Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller
This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic re...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023064071 |
| _version_ | 1797669918238310400 |
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| author | Peter Anuoluwapo Gbadega Yanxia Sun |
| author_facet | Peter Anuoluwapo Gbadega Yanxia Sun |
| author_sort | Peter Anuoluwapo Gbadega |
| collection | DOAJ |
| description | This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation. |
| first_indexed | 2024-03-11T20:51:58Z |
| format | Article |
| id | doaj.art-9d82bc3b39a6443a9b2a2041e1b07d84 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T20:51:58Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-9d82bc3b39a6443a9b2a2041e1b07d842023-10-01T05:58:50ZengElsevierHeliyon2405-84402023-09-0199e19199Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controllerPeter Anuoluwapo Gbadega0Yanxia Sun1Corresponding author.; Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South AfricaDepartment of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South AfricaThis paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.http://www.sciencedirect.com/science/article/pii/S2405844023064071Automatic generation controlSuperconducting magnetic energy storage (SMES)Multi-source power generationRenewable energy sourcesProportional-integral-derivative (PID) controllerEnhanced whale optimization algorithm |
| spellingShingle | Peter Anuoluwapo Gbadega Yanxia Sun Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller Heliyon Automatic generation control Superconducting magnetic energy storage (SMES) Multi-source power generation Renewable energy sources Proportional-integral-derivative (PID) controller Enhanced whale optimization algorithm |
| title | Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller |
| title_full | Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller |
| title_fullStr | Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller |
| title_full_unstemmed | Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller |
| title_short | Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller |
| title_sort | multi area load frequency regulation of a stochastic renewable energy based power system with smes using enhanced woa tuned pid controller |
| topic | Automatic generation control Superconducting magnetic energy storage (SMES) Multi-source power generation Renewable energy sources Proportional-integral-derivative (PID) controller Enhanced whale optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023064071 |
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