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

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Jalali neda; Tolou Askari Mohammad; RAZMI HADI

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Paper Information

Journal: IRANIAN ELECTRIC INDUSTRY JOURNAL OF QUALITY AND PRODUCTIVITY (IEIJQP) Year:2022 | Volume:10 | Issue:4 ( 25) Page(s): 14-37

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Title

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

Author(s)

Jalali neda | Tolou Askari Mohammad | RAZMI HADI | Issue Writer Certificate

Keywords

classification of power quality disturbances

power system

deep learning algorithm

fuzzy intelligent algorithm

Abstract

Automatic classification of power quality disturbances is the foundation to deal with the power quality problem. From a traditional viewpoint, 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 presents an automated system for the 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 basic domains. 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 disturbances, a novel deep learning-based method is developed. In this method, the activation function is expressed by a fuzzy approach. This makes the system more flexible. The benefits of the proposed strategy are separating the disturbances of basic frequency and using the nature of power quality signals as a tool for feature extraction. However, in the traditional method, for example, in empirical mode decomposition, the separation of signals from their components is not conveniently possible. To evaluate the proposed algorithm, a 33-bus distribution power network has been applied. The results reveal good agreement in comparison with other assessment tests.

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APA: Copy

Jalali, neda, Tolou Askari, Mohammad, & RAZMI, HADI. (2022). A novel method based on a combination of deep learning algorithm and fuzzy intelligent functions for classifying power quality disturbances in power systems. IRANIAN ELECTRIC INDUSTRY JOURNAL OF QUALITY AND PRODUCTIVITY (IEIJQP), 10(4 ( 25) ), 14-37. SID. https://sid.ir/paper/965177/en

Vancouver: Copy

Jalali neda, Tolou Askari Mohammad, RAZMI HADI. A novel method based on a combination of deep learning algorithm and fuzzy intelligent functions for classifying power quality disturbances in power systems. IRANIAN ELECTRIC INDUSTRY JOURNAL OF QUALITY AND PRODUCTIVITY (IEIJQP)[Internet]. 2022;10(4 ( 25) ):14-37. Available from: https://sid.ir/paper/965177/en

IEEE: Copy

neda Jalali, Mohammad Tolou Askari, and HADI RAZMI, “A novel method based on a combination of deep learning algorithm and fuzzy intelligent functions for classifying power quality disturbances in power systems,” IRANIAN ELECTRIC INDUSTRY JOURNAL OF QUALITY AND PRODUCTIVITY (IEIJQP), vol. 10, no. 4 ( 25) , pp. 14–37, 2022, [Online]. Available: https://sid.ir/paper/965177/en

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