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A power system adaptive protection scheme depending on a data mining model

机译：基于数据挖掘模型的电力系统自适应保护方案

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The conditions of any power system always keep on changing over time due to the continuous load variation nature of the system. The conventional relays that had been always used for power system protection face difficulties to comply and offer the required protection because of their fixed settings parameters. Many adaptive solutions have been introduced. They depend on evaluating the system conditions in order to help taking the proper actions, regarding changing the relay settings or protection schemes to suit the new system conditions. Among these solutions, data mining-based solutions are distinctive. The most frequently used data mining algorithm for the evaluation process is the decision trees. This paper introduces a new evaluating data mining model using support vector machines (SVM), as another superior tool. The SVM model proves to have higher correct rate in evaluating and predicting the system conditions than the decision trees model.
机译：由于系统不断变化的负载特性，任何电源系统的状况都会随着时间不断变化。一直用于电力系统保护的常规继电器由于其固定的设置参数而难以遵守并无法提供所需的保护。已经引入了许多自适应解决方案。它们依赖于评估系统状况以帮助采取适当的措施，包括更改继电器设置或保护方案以适应新的系统状况。在这些解决方案中，基于数据挖掘的解决方案是与众不同的。评估过程中最常用的数据挖掘算法是决策树。本文介绍了一种使用支持​​向量机（SVM）作为另一个高级工具的新评估数据挖掘模型。与决策树模型相比，SVM模型在评估和预测系统条件方面具有更高的正确率。

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