Research on fault detection and diagnosis of inverter circuits
Jiajie Ou
Nanjing University of Posts and Telecommunications
DOI: https://doi.org/10.59429/esta.v12i3.11346
Keywords: inverter circuits; fault detection; diagnostic technology; early warning
Abstract
This paper comprehensively discusses the fault detection and diagnosis techniques of inverter circuits. It begins with an introduction to the fundamental principles of inverter circuits and their applications in solar photovoltaic systems, electric vehicles, and uninterruptible power supplies (UPS). Inverter circuits achieve efficient transmission and utilization of electrical energy by converting direct current (DC) into alternating current (AC). However, due to the non-ideal characteristics of switching devices and environmental factors, the output waveform of inverter circuits may become distorted and contain harmonics, impacting system performance. Therefore, a deep understanding of the basic principles of inverter circuits is critical for effective fault detection and diagnosis. Following this, the paper analyzes common fault types and electrical characteristics of inverter circuits, including overheating, short circuits, open circuits, and parameter drift of switching devices. To extract fault features, signal processing methods such as Fourier transform and wavelet transform are widely applied. Meanwhile, machine learning techniques, especially support vector machines (SVMs) and neural network algorithms, are used for accurate identification and early warning of complex failure modes. Regarding fault detection methods, the paper delves into fault detection technologies based on signal processing and machine learning. Signal processing technology analyzes voltage and current signals and employs Fourier and wavelet transforms to extract fault characteristics. Machine learning algorithms then enable precise fault recognition and advance warnings. Finally, this paper proposes a design framework for an inverter circuit fault diagnosis system that includes key technologies such as data acquisition, signal processing, and fault detection and diagnosis algorithms. The research results indicate that the integration of advanced signal processing technology and machine learning algorithms can significantly enhance the accuracy and efficiency of fault detection and diagnosis for inverter circuits, providing a robust guarantee for the stable operation of power electronic systems.
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