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Digital Overcurrent Relay Implementation With Non-Standard Inverse Curve Modelling Using Adaptive Neuro Fuzzy Inference System

机译:基于自适应神经模糊推理系统的非标准逆曲线建模的数字过电流继电器实现

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The inverse curve characteristic of overcurrent relay in an industrial power system refers more to the IEC standard. Complex load demands and variation types of relay curve are possible to occur curves intersection. Therefore, in this paper suggest to model an unconventional curve using adaptive neuro fuzzy inference system or ANFIS. Test data made consist of two intersection inverse curve to represent the relay coordination system. New data points of relay are designed by user to obtained the preliminary data. Fault current and tripping time of the relay are used as input and output for ANFIS training. To get the optimum result between data target and design, g-bell with different membership function are implemented in the proposed method. The number of 10-gbell mf obtains more accurate results. The modelling results with a test current of 3.57 amperes obtained a trip time of 5.53 seconds while for prototype testing the trip time is 5.46 seconds, so that between the target modelling results to the prototype relay current testing has better accuracy with the smallest error average value of 3,4,E-03.
机译:工业电力系统中过电流继电器的反曲线特性更多地参考IEC标准。复杂的负载需求和继电器曲线的变化类型可能会发生曲线交点。因此,本文建议使用自适应神经模糊推理系统或ANFIS对非常规曲线进行建模。测试数据由两条相交的反曲线组成,代表了继电器协调系统。用户设计继电器的新数据点以获得初步数据。继电器的故障电流和跳闸时间用作ANFIS训练的输入和输出。为了在数据目标和设计之间获得最佳结果,该方法实现了具有不同隶属度函数的g-bell。 10 gbell mf的数量可获得更准确的结果。测试电流为3.57安培的建模结果的跳闸时间为5.53秒,而原型测试的跳变时间为5.46秒,因此目标建模结果与原型继电器电流测试之间的精度更高,误差平均值最小3,4,E-03。

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