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This work proposes a novel classifier to recognize multiple disturbances for electric signals of power systems. The proposed classifier consists of a series of pipeline-based processing components, including amplitude estimator, transient disturbance detector, transient impulsive detector, wavelet transform and a brand-new neural network for recognizing multiple disturbances in a power quality (PQ) event. Most of the previously proposed methods usually treated a PQ event as a single disturbance at a time. In practice, however, a PQ event often consists of various types of disturbances at the same time. Therefore, the performances of those methods might be limited in real power systems. This work considers the PQ event as a combination of several disturbances, including steady-state and transient disturbances, which is more analogous to the real status of a power system. Six types of commonly encountered power quality disturbances are considered for training and testing the proposed classifier. The proposed classifier has been tested on electric signals that contain single disturbance or several disturbances at a time. Experimental results indicate that the proposed PQ disturbance classification algorithm can achieve a high accuracy of more than 97% in various complex testing cases.

This paper proposes a hierarchical artificial neural network for recognizing high similar large data sets. It is usually required to classify large data sets with high similar characteristics in many applications. Analyzing and identifying those data is a laborious task when the methods adopted are primarily based on visual inspection. In many field applications, data sets are measured and recorded continuously using automatic monitoring equipments. Therefore, a large amount of data can be collected, and manual inspection has become an unsuitable approach to recognizing those data. This proposed hierarchical neural network integrates self-organizing feature map (SOM) networks and learning vector quantization (LVQ) networks. The SOM networks provide an approximate method for computing the input vectors in an unsupervised manner. Then the computation of the SOM may be viewed as the first stage of the proposed hierarchical network. The second stage is provided by the LVQ networks based on a supervised learning technique that uses class information to improve the quality of the classifier from the first stage. The multistage hierarchical network attempts to factorize the overall input vector into a number of small groups, each of which requires very little computation. Consequently, by use of the proposed network, the loss in accuracy can be small.

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This work develops a new approach to recognize multiple disturbances for a power quality (PQ) event in power systems. It is usual that several different types of disturbances simultaneously exist in a PQ event. However, most of the existing methods treat a PQ event as a single type of PQ disturbance. The performance of these methods might be limited and impracticable for application in the real power systems. This work proposes a novel approach integrated the wavelet transform and dynamic structural neural network (DSNN) to identify disturbance waveforms. The proposed neural network has the capability of adapting to multiple disturbances for a PQ event. In the proposed approach, the disturbance waveforms are extracted by the wavelet transform and then fed to the DSNN for identifying the types of disturbances. The distinctive features of the proposed method are that it can estimate the amplitude of the considering event, recognize transient and steady state disturbances which are simultaneous existed in a PQ event.Index Terms-Power quality, wavelet transform, neural networks, and pattern recognition.0-7803-9114