Abstract
The traditional power quality disturbances classification methods include three stages, i.e., feature extraction, feature selection, classifier training. These methods suffer from low accuracy and a limited improvement margin. Since deep learning can greatly improve the accuracy of classification, a new classification method was designed in this paper by combining three types of deep learning frameworks, including CNN-GRU, ResNet-GRU, and Inception-GRU. The proposed method omits the two steps of feature extraction and feature selection, achieving “end-to-end” PQDs identification. To improve the performance on real signals, “pre-training and re-training” is applied. Then, a voting method was employed to vote the prediction labels by different algorithms, which further improves the accuracy of classification. Simulation experiments show that for the classification of compound PQDs, the proposed method performs better than the triple-stage methods and single deep learning classification method. Finally, real signals from Power source are test by the twice-trained model, and the five metrics are better than the old methods.
Highlights
With the gradual decline in manufacturing costs of wind turbines and the solar panels, the integration of new energy into the power grid has become inevitable, and the proportion of thermal power plants in power generation is gradually decreasing
REAL SIGNALS TEST In order to verify the effectiveness of the algorithm in this article, the Power source FLUKE6105A provided by the Wuhan Branch of China Electric Power Research Institute, is used to generate the power quality disturbances (PQDs) signals
An ensemble architecture is designed for multi-label PQD classification
Summary
With the gradual decline in manufacturing costs of wind turbines and the solar panels, the integration of new energy into the power grid has become inevitable, and the proportion of thermal power plants in power generation is gradually decreasing. The huge number of trainable parameters in LSTM results in a relatively long training time Another approach to achieve higher accuracy is to obtain more characteristic in PQDs signals. Since the architecture utilized the time and frequency domain information, it achieved an accuracy of 99.86%, better than that of traditional CNN. To overcome the defects of the aforementioned algorithms, the contributions of this paper are summarized as follows: 1) A multi-fusion CNN combing the time information and frequency domain information from FFT is designed. It consists of two sub-modules that take signal and FFT as input. 1D signals are studied in this paper, the 1D convolution operation is similar to that of 2D signals, so the architecture is similar to 2D- CNN
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