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Time-current characteristic measurement of overcurrent relay in power system using multilayer perceptron network

机译:利用多层Perceptron网络电力系统过电力继电器的时间流特性测量

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Protection in power system is very important to ensure the systems are in a good condition without any failure. It is necessary that the protection system can operate at the shortest time to clear the fault as soon as possible. Overcurrent relay protection is depending on their time-current characteristic curve. In this particular characteristic curve, the required relay operating time can be determined in order to remove high fault current in the system. If fault current are not remove in correct possible time, it will cause damage of expensive equipment and loss of life as well. Therefore, until now, researchers are always come out with several methods to solve this problem. In this paper, artificial neural network (ANN) using multilayer perceptron network (MLP) is applied to determine an accurate operating time. The MLP network is trained by Levenberg-Marquardt algorithm as for its fastest convergence rate and with a minimum error. Experimental results proved that the proposed method produced acceptable results in determining an accurate operating time. Sum squared error (SSE) produced during training and testing is 1.04% and 0.73%, respectively. In addition, the result reveals that the relay operating time obtained by neural network is 1ms which is considerably fast time for the system to isolate fault.
机译:电力系统的保护非常重要,确保系统处于良好状态而没有任何故障。保护系统必须在最短的时间内运行,以尽快清除故障。过电流继电保护是根据其时间流特性曲线。在该特定特征曲线中,可以确定所需的继电器操作时间以便在系统中移除高故障电流。如果无法在可能的时间内拆卸故障电流,它将导致昂贵的设备损坏和生命损失。因此,直到现在,研究人员总是用几种方法来解决这个问题。在本文中,应用了使用多层Perceptron网络(MLP)的人工神经网络(ANN)来确定准确的操作时间。 MLP网络由Levenberg-Marquardt算法接受训练,如其最快的收敛速度和最小误差。实验结果证明,所提出的方法在确定准确的操作时间方面产生可接受的结果。培训和测试期间生产的SUM平方误差(SSE)分别为1.04%和0.73%。此外,结果表明,神经网络获得的继电器运行时间是1ms,其为系统隔离故障的时间很快。

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