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首页> 外文会议>2013 48th International Universities' Power Engineering Conference >ANN-based classification system for different windows of voltage dips in a power network
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ANN-based classification system for different windows of voltage dips in a power network

机译：基于ANN的电网中电压暂降不同窗口的分类系统

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Voltage dip has been identified as one of the common types of power quality disturbance in electrical power networks and drew a lot of attention in voltage quality research presently. It is regarded as the most costly power quality problem. It can be caused by starting electrical motor, switching of generators or bulk loads, transformer energizing and short circuits in the power networks. In recent time power utilities and customers have made an effort to improve the reliability of power network, but it has been so difficult to control the external factors that cause voltage dip. As a result voltage dip must be classified and diagnosed accurately so that proper mitigation measures can be implemented. Classification of voltage dips plays an important role in voltage dip mitigation, investigations, assessment and equipment immunity specifications. In this context, this paper develops a technique using artificial neural network (ANN) for voltage dip classification, which is based on the voltage dip windows defined by the South African utility ESKOM. The test is carried out on IEEE 9-bus system through simulation in DigSILENT Power Factory 14.0 software and the ANN model is trained, tested and validated in Matlab environment using neural network Toolbox.
机译：电压骤降​​已被确定为电力网络中常见的电能质量扰动类型之一，目前引起了电压质量研究的广泛关注。它被认为是最昂贵的电能质量问题。这可能是由于启动电动机，切换发电机或大负载，变压器通电以及电网中的短路引起的。近年来，电力公司和客户已经在努力提高电力网络的可靠性，但是很难控制引起电压骤降的外部因素。结果，必须对电压跌落进行分类和准确诊断，以便可以采取适当的缓解措施。电压暂降的分类在缓解电压暂降，研究，评估和设备抗扰性规范中起着重要作用。在这种情况下，本文基于南非公用事业公司ESKOM定义的电压骤降窗口，开发了一种使用人工神经网络（ANN）进行电压骤降分类的技术。该测试通过在DigSILENT Power Factory 14.0软件中进行仿真在IEEE 9总线系统上进行，并且使用神经网络Toolbox在Matlab环境中对ANN模型进行了训练，测试和验证。

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