Real Power Contingency Ranking Using Wavelet Transform Based Artificial Neural Network (WNN)

S. Sutha; N. Kamaraj

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Real Power Contingency Ranking Using Wavelet Transform Based Artificial Neural Network (WNN)

机译：基于小波变换的人工神经网络（WNN）的有功功率排序

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In deregulated operating regime power system security is an issue that needs due thoughtfulness from researchers in the horizon of unbundling of generation and transmission. Real power contingency ranking is an inherent part of security assessment. The target of contingency ranking and screening is to rapidly and precisely grade the decisive contingencies from a large list of plausible contingencies and rank them according to their severity for further rigorous analysis. In the proposed work, Wavelet Transform Based Artificial Neural Networks (WNN) is used for real power contingency ranking of the system. The results from offline AC load flow calculation are used to train the WNN for estimating the performance index. The effectiveness of the purported method is exhibited by contingency ranking on IEEE 14 bus, IEEE 5 bus systems and comparisons are made with conventional method. Good calculation accuracy, faster analysis times are obtained by using WNN.

机译：在放松管制的运营体制中，电力系统安全是一个问题，需要研究人员在发电和输电捆绑的视野中进行认真考虑。有功事故应急评级是安全评估的固有部分。突发事件排名和筛选的目标是从大量可能的突发事件中快速准确地对决定性突发事件进行分级，并根据其严重性对它们进行排序，以进行进一步的严格分析。在提出的工作中，基于小波变换的人工神经网络（WNN）用于系统的有功功率应急排序。离线交流潮流计算的结果用于训练WNN以估计性能指标。该方法的有效性通过在IEEE 14总线，IEEE 5总线系统上的权变排序来展示，并与常规方法进行比较。使用WNN可获得良好的计算精度和更快的分析时间。

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《International journal of electrical and power engineering》 |2008年第2期|共6页
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S. Sutha; N. Kamaraj;
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中图分类发电、发电厂;
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Real Power Contingency Ranking Using Wavelet Transform Based Artificial Neural Network (WNN)

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