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Fuzzy Theory-Based Partial Discharge Technique for Operating State Diagnosis of High-Voltage Motor

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Abstract

This study developed an operating state diagnostic system based on partial discharge detection for analyzing faults in high-voltage motor stator windings. First, the partial discharge signal of the stator windings is measured with a high-frequency current sensor, and then phase-resolved partial discharge technology is used to convert it into three-dimensional graphics. A systematic analysis process involving the use of fractal theory architecture for fault extraction from the signal characteristics was designed for identifying faults and obtaining the fractal dimension and the lacunarity of the characteristic parameters. Similarity function defined extension theory was used for building a failure signature database for each fault. Finally, to design and build fuzzy membership function and inference systems for analyzing the operation status of the motor, the partial discharge energy value and fault type were used as indicators in fuzzy algorithms. The experimental results prove that the fuzzy diagnostic system proposed in this paper is combined with the important information of fault type and discharge quantity, where fault type is successfully recognized, and a more reliable high-voltage motor operating state monitoring technique is created. The findings are expected to monitor the operating state of high-voltage motor more effectively, thus, reducing additional maintenance costs resulted from heavy accidents.

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Acknowledgments

The research was supported by the Ministry of Science and Technology of the Republic of China, under Grant No. MOST 103-2221-E-011-077-MY2.

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Correspondence to Hong-Chan Chang.

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Chang, HC., Lin, SC., Kuo, CC. et al. Fuzzy Theory-Based Partial Discharge Technique for Operating State Diagnosis of High-Voltage Motor. Int. J. Fuzzy Syst. 18, 1092–1103 (2016). https://doi.org/10.1007/s40815-016-0210-0

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  • DOI: https://doi.org/10.1007/s40815-016-0210-0

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