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首页> 外文期刊>Expert systems with applications >Bio-inspired Fuzzy Logic Based Tuning Of Power System Stabilizer
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Bio-inspired Fuzzy Logic Based Tuning Of Power System Stabilizer

机译：基于生物启发式模糊逻辑的电力系统稳定器调整

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摘要

In this paper, bacteria foraging optimization (BFO) - a bio-inspired technique, is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. A comparative performance study of these four variants of PSSs is also made. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. A comparison between the results of the BFO and that of genetic algorithm (GA) is conducted in this study. The comparison reveals that BFO is more effective than GA in finding the optimal transient performance. For on-line, off-nominal operating conditions Sugeno fuzzy logic (SFL) based approach is adopted. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.
机译：在本文中，细菌觅食优化（BFO）是一种受生物启发的技术，用于调整单输入和双输入电源系统稳定器（PSS）的参数。对常规PSS（CPSS）和三个双输入IEEE PSS（PSS2B，PSS3B和PSS4B）进行了优化调整，以获得最佳的瞬态性能。还对PSS的这四个变体进行了比较性能研究。结果表明，双输入PSS的瞬态性能优于单输入PSS。此外，还探讨了在双输入PSS中，PSS3B提供了出色的瞬态性能。本研究对BFO的结果与遗传算法（GA）的结果进行了比较。比较表明，BFO在寻找最佳瞬态性能方面比GA更有效。对于在线，非标称运行条件，采用基于Sugeno模糊逻辑（SFL）的方法。通过实时测量系统的运行状况，SFL可以非常快速地自适应地产生在线，名义外的最佳稳定器参数。

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