Inverter fault diagnosis based on Fourier transform and evolutionary neural network

Abstract

The fault diagnosis of the inverter is fundamental to energy intelligence. Due to the complex characteristics of the inverter (e.g., high-dimensional decision and poor stability), it is challenging to solve the problem using traditional fault diagnosis methods. Recently, artificial intelligence (AI)-based approaches have emerged as the most promising methods. However, they often require to set hyperparameters manually, which hinders further AI-based applications in fault diagnosis of inverters. To fill the gap, we propose an inverter fault diagnosis method using fast Fourier Transform (FFT) and evolutionary neural network. This method combines the amplitude of low-frequency harmonic component of the three-phase inverter output current which is obtained by FFT and the average value in a period of three-phase inverter output current into the fault eigenvector. This method uses an evolutionary neural network trained by combining genetic algorithm (GA), ant colony optimization (ACO) algorithm and Back-propagation (BP) algorithm to realize fault diagnosis. This method can effectively resist noise interference and reduce the number of independent variables in the part of feature extraction, so that it can simplify the network model. In addition, this method can avoid the network training from trapping in local optima in the part of fault classification, with high accuracy and fast response speed. The experimental results show that the proposed algorithm and method of fault feature extraction can effectively detect and locate the insulated-gate bipolar transistor (IGBT) with open circuit (OC) fault in three-phase inverter, and can be applied to online monitoring.

Three-Phase Inverter Fault Diagnosis Based on an Improved Deep Residual Network

“…This results in a global field of view of the previous W × H, making the perceptual area wider. Equation (12) demonstrates this process:…”

“…Additionally, several studies have combined various techniques for more comprehensive fault diagnosis. Yang et al [12] combined the use of Fast Fourier Transform (FFT) with evolutionary neural networks, while Yan et al [13] incorporated feature engineering with deep neural networks for fault diagnosis in specific motor drives.…”

A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters

“…To further enhance the non-linear handling capability of ELM, kernels of different types are integrated into ELM to form the hybrid kernel ELM (HKELM). However, the generalization and exploration capability of HKELM is strongly affected by the kernel types and HKELM parameters [23]. To further enhance the performance and stability of HKELM in anomaly detection, this paper combines HKELM with an advanced swarm-based algorithm named northern goshawk optimization (NGO).…”