Power Quality Events Classification using ANN with Hilbert Transform
Power Quality Events Classification using ANN with Hilbert Transform
复制标题
使用 ANN 和希尔伯特变换进行电能质量事件分类
DOI：
10.23956/ijermt.v6i6.274
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Tanuj Manglani
中科院分区：
文献类型：
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作者：
Tarun Kumar Chheepa;Tanuj Manglani
随着智能电网的发展，电能质量问题日益突出。城市发展涉及到计算机、微处理器控制的电子负载和电力电子设备的使用。这些设备是电能质量干扰的来源。PQ问题的特点是系统电压和电流的幅度和频率与其标称值的变化。为了确定控制动作，需要适当的分类机制对不同的PQ事件进行分类。在本文中，我们提出了一种混合方法来执行这项任务。采用不同的神经网络拓扑，即级联前向反向支撑神经网络（CFBNN）、Elman反向支撑神经网络（EBPNN）、前馈反向支撑神经网络（FFBPNN）、前馈分布式时滞神经网络（FFDTDNN）、层递归神经网络（LRNN）、非线性自回归外源性神经网络（NARX）、径向基函数神经网络（RBFNN），并应用Hilbert变换对PQ事件进行分类。对这些神经网络拓扑进行了有意义的比较，发现径向基函数神经网络（RBFNN）是执行分类任务最有效的拓扑。在输入特征中加入不同程度的加性高斯白噪声（AWGN）来进行分类器的比较。
With the evolution of Smart Grid, Power Quality issues have become prominent. The urban development involves usage of computers, microprocessor controlled electronic loads and power electronic devices. These devices are the source of power quality disturbances. PQ problems are characterized by the variations in the magnitude and frequency in the system voltages and currents from their nominal values. To decide a control action, a proper classification mechanism is required to classify different PQ events. In this paper we propose a hybrid approach to perform this task. Different Neural topologies namely Cascade Forward Backprop Neural Network (CFBNN), Elman Backprop Neural Network (EBPNN), Feed Forward Backprop Neural Network (FFBPNN), Feed Forward Distributed Time Delay Neural Network (FFDTDNN) , Layer Recurrent Neural Network (LRNN), Nonlinear Autoregressive Exogenous Neural Network (NARX), Radial Basis Function Neural Network (RBFNN) along with the application of Hilbert Transform are employed to classify the PQ events. A meaningful comparison of these neural topologies is presented and it is found that Radial Basis Function Neural Network (RBFNN) is the most efficient topology to perform the classification task. Different levels of Additive White Gaussian Noise (AWGN) are added in the input features to present the comparison of classifiers.