A novel method based on a combination of deep learning algorithm and fuzzy intelligent functions in order to classification of power quality disturbances in power systems

Automatic classification of power quality disturbances is the foundation to deal with power quality problem. From the traditional point of view, the identification process of power quality disturbances should be divided into three independent stages: signal analysis, feature selection and classification. However, there are some inherent defects in signal analysis and the procedure of manual feature selection is tedious and imprecise, leading to a low classification accuracy of multiple disturbances. To deal with these problems, this paper carries out an automated system for classification and identification of power quality disturbances. After receiving input signals, the proposed system requires some preprocessing such as changing the range of values by dividing the signals into their basis domain. In the next stage, the RMS value of the signal can be appraised to know the occurrence of the disturbance. If the RMS value of the input signal is not equal to the normal signal, the disturbance is occurring. To identify and classify of disturbances, a novel deep learning-based method has been developed. In this method, the activation function is expressed by fuzzy approach. This make the system more flexible. The benefit of the proposed strategy is separating the disturbances of basis frequency and using the nature of power quality signals as a tool for feature extraction. However, in the traditional method, take empirical mode decomposition as an example, the separation of signals from their components are not conveniently possible. To evaluate the proposed algorithm, the 33-bus distribution power network has been applied. The obtained results are reported good agreement in comparison with other assessment tests.