Enhancing Voltage Regulation in Synchronous Generators: A Novel Approach With Fractional-Order PID Controllers and Wound Healing Algorithm
Voltage regulation in automatic voltage regulator (AVR) systems has been one of the most challenging engineering problems owing to uncertain load conditions. One of the primary study topics is to obtain an effective and reliable control mechanism for attaining a specific voltage level in AVR systems...
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10750814/ |
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| Summary: | Voltage regulation in automatic voltage regulator (AVR) systems has been one of the most challenging engineering problems owing to uncertain load conditions. One of the primary study topics is to obtain an effective and reliable control mechanism for attaining a specific voltage level in AVR systems. Especially with the method proposed in the study, parameters such as overshoot percentage, peak value, and settling time of the system are optimized and the system is made more stable. In this study, a fractional-order PID controller (FOPID) was used to achieve an optimal design for an automatic voltage regulator. The developed wound healing algorithm (WHA) was used to estimate the tuning parameters of the FOPID controller to achieve the ideal transient response and boost the stability of the AVR system. The terminal voltage of the synchronous generator’s maximum percent overshoot in the step response, the rising time, the settling time, and the steady state error have all been attempted to be decreased utilizing the configuration parameters that have been supplied. Software that performed this was produced using the graphical user interface (GUI) of Matlab’s programming language. Comparisons are made using several metaheuristic algorithms to determine this recommended technique’s results and advantages. The proposed algorithm-based FOPID tuning technique provides 62.01%, 71.61%, 12.2%, 26.6%, 65.23%, 26.32%, 30.36%, 44.37%, and 0.17% reduced overshoot than the Particle Swarm Optimization (PSO), Water Cycle (WCA), Grey Wolf Optimization (GWO), Local Unimodal Sampling (LUS), Differential Evolution (DE), Salp Swarm Optimization (SSA), Water Wave Optimization (WWO), Grasshopper Optimization (GOA), and Sine-Cosine Optimization (SCA) algorithms, respectively, thus validating its competence and effectiveness. |
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| ISSN: | 2169-3536 |