Classification of Faults in a Wind Connected Power System Using Artificial Neural Network

Riti P. Nanda, Debasmita Bisoi, Abhimanyu Behera, B. Panigrahi, Arpan K. Satapathy
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引用次数: 6

Abstract

The transmission lines are used to supply electrical power from source to load and distribution network is used to distribute the transmitted power among the load. During the distribution of electrical power, different types of conductors, insulators, circuit breakers and relays are used for the protection. Demand for electrical power is increasing day by day. So to increase electrical power generation, different types of Distribute generators are used like solar, wind, tidal, etc. In this work, the wind farm is connected to the grid via long transmission networks. As the complexity increases, probability of fault occurrence increases, which will hamper the consumer service. Concern to the consumer satisfaction, faults should be cleared first. The detection technique should be accurate and intelligent so that it can clear the fault. Artificial neural network technique is an intelligent tool which can clear the fault. In this work, classification of fault is done using ANN in the model. Modeling is done in MATLAB and SIMULINK. The voltage signal is extracted and was given to the ANN as input. The voltage signal is trained and tested with accuracy.
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基于人工神经网络的风电系统故障分类
输电线路用于从电源向负载供电,配电网用于在负载之间分配传输的功率。在电力分配过程中,使用不同类型的导体、绝缘体、断路器和继电器进行保护。对电力的需求日益增加。因此,为了增加发电量,使用了不同类型的分布式发电机,如太阳能、风能、潮汐能等。在这项工作中,风电场通过长传输网络连接到电网。随着系统复杂性的增加,故障发生的概率也随之增加,这将阻碍用户服务。考虑到消费者的满意,故障首先要清除。检测技术要准确、智能,才能排除故障。人工神经网络技术是一种能够清除故障的智能工具。在本工作中,在模型中使用人工神经网络对故障进行分类。在MATLAB和SIMULINK中进行建模。提取电压信号作为神经网络的输入。对电压信号进行了精确的训练和测试。
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