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首页> 外文会议>2013 International Conference on Circuits, Power and Computing Technologies >A fast and efficient back propagation algorithm to forecast active and reactive power drawn by various capacity Induction Motors
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A fast and efficient back propagation algorithm to forecast active and reactive power drawn by various capacity Induction Motors

机译：一种快速高效的反向传播算法，可预测各种容量感应电动机的有功和无功功率

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摘要

Power system operators/planners are always face problem regarding reactive power compensation. Reactive power plays an important role in maintaining voltage stability and system reliability. In this paper, a new algorithm based on back propagation neural network is used by using suitable number of layers and various constants is presented, for forecasting the active and reactive power consumed by various capacities Induction Motor. Firstly, Database of active power (P) and reactive power (Q) for different voltages and frequencies are generated through real time experiment on various capacities Induction Motor. Then, Back propagation Neural Network (BPNN) is designed to predict the P and Q drawn by in induction motor for different voltages and frequency condition. Back Propagation technique is used for training. These trained BPNN models are used to predict P & Q for many unseen operating conditions and the results are found to be coming fast and very accurate.
机译：电力系统运营商/规划人员始终面临有关无功补偿的问题。无功功率在维持电压稳定性和系统可靠性方面起着重要作用。在本文中，通过使用适当的层数并使用各种常数来使用基于反向传播神经网络的新算法，以预测各种容量感应电动机所消耗的有功和无功功率。首先，通过对不同容量的感应电动机进行实时实验，生成了不同电压和频率的有功功率（P）和无功功率（Q）的数据库。然后，设计了反向传播神经网络（BPNN）来预测感应电动机在不同电压和频率条件下得出的P和Q。反向传播技术用于训练。这些训练有素的BPNN模型可用于预测许多看不见的运行条件下的P＆Q，并且发现结果很快且非常准确。

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