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Online Fuzzy Voltage Collapse Risk Quantification

机译：在线模糊电压崩溃风险量化

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摘要

Many voltage stability indicators have been proposed in the past for the voltage collapse assessment. Almost all of them are determined through quite complex analytical tools; therefore, it is difficult for system operators to give them a physical meaning. In order to perform a simple and reliable evaluation of the security margins, it is necessary to make a synthesis of the information given by the various indices. The present work proposes an Artificial Intelligence-based tool for the evaluation of the voltage security. In particular, a Fuzzy Inference Engine is developed and optimized by two different approaches (Neural Networks and Genetic Algorithms). Starting from the state estimation, a given set of mathematical indices is computed to represent a snapshot of the current electric system operating point. The numerical values are then translated into a set of symbolic and linguistic quantities that are manipulated through a set of logical connectives and Inference Methods provided by the mathematical logic. As a result, the Fuzzy Logic gives a MW measure of the distance from the collapse limit, a metric usually appreciated by system operators.rnThe Fuzzy System has been built and optimized by using, as a test system, a detailed model of the EHV Italian transmission network connected to an equivalent of the UCTE network (about 1700 buses).
机译：过去已经提出了许多用于电压崩溃评估的电压稳定性指标。它们几乎都是通过相当复杂的分析工具来确定的。因此，系统操作员很难给他们一个物理意义。为了对安全裕度进行简单而可靠的评估，有必要对各种指标给出的信息进行综合。本工作提出了一种基于人工智能的工具，用于评估电压安全性。特别是，通过两种不同的方法（神经网络和遗传算法）开发并优化了模糊推理引擎。从状态估计开始，计算给定的一组数学指标以表示当前电气系统工作点的快照。然后，将数值转换为一组符号和语言量，这些量通过数学逻辑提供的一组逻辑连接词和推理方法进行操作。结果，模糊逻辑给出了距坍塌极限的距离的MW度量，这是系统操作人员通常会赞赏的度量标准。rn模糊系统是通过使用EHV Italian的详细模型作为测试系统而构建和优化的。传输网络连接到等效的UCTE网络（约1700总线）。

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