Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

Abstract

The power converter is a significant device in a wind power system. The wind turbine will be shut down and off grid immediately with the occurrence of the insulated gate bipolar transistor (IGBT) module open-circuit fault of the power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies for the power converter there are few single and double IGBT module open-circuit fault diagnosis methods producing negative results, including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed: Primarily, by collecting the three-phase current varying with a wind speed of 22 states, including a normal state and 21 failure states of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are utilized as the input of the deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

“…Because of their low maintenance frequency, good grid side performance, strong fault traversal capability, and other advantages, more and more wind power operators tend to use PMSG wind turbines, which have become the mainstream of the current wind power market. 19 The topology of PMSG system is shown in Figure 2, which is composed of fan blades, permanent magnet synchronous generator, back-to-back dual PWM converter, and a transformer. Among them, the wind energy captured by the fan blade is transferred to the permanent magnet synchronous generator in the form of mechanical energy through the drive shaft, and the permanent magnet synchronous generator is used to realize the conversion of mechanical energy and electrical energy.…”

“…When the number of decomposition layers are 4 to 6, the value of the center frequency ω of the last layer increases with the increase of the number of decomposition layers, and at that time, the signal decomposition presents the underdecomposition state; when the value of k is 7, the center frequency of the last layer reaches the maximum for the first time and there will be no decomposition problem; when k value reaches 8, the difference between ω 7 and ω 8 center frequency is small, resulting in overdecomposition causing modal aliasing phenomenon; comprehensive center frequency and according to the relevant literature analysis, it is considered that the decomposition effect is the best when k = 7, so this paper carries out 7-layer decomposition of the three-phase output current signals in 22 states. 19 According to the analysis of relevant literature, it is believed that the decomposition effect is the best at k = 7, so in this paper, three-phase output current signals in 22 states are decomposed in seven layers. 19 The original current signal is decomposed into IMF modal components of different frequency bands.…”

“…However, with the emergence of the demand for large‐capacity wind turbines, PMSG wind turbines are mainly used. Because of their low maintenance frequency, good grid side performance, strong fault traversal capability, and other advantages, more and more wind power operators tend to use PMSG wind turbines, which have become the mainstream of the current wind power market 19 …”

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

“…By comparing the results, the effectiveness of the algorithm is proved. Zhang, Sun et al [40] used a method combining variational mode decomposition method and deep belief network to diagnose open circuit faults of single and double IGBT modules. Experimental results proved that the proposed method has a higher diagnostic accuracy.…”

A Layering Linear Discriminant Analysis-Based Fault Diagnosis Method for Grid-Connected Inverter

“…The signal-based fault diagnosis method depends on the signal distortion caused by the fault [14]. The distinguishing fault features are extracted by signal processing methods such as Fourier transform [15][16][17], wavelet decomposition [18,19], and variational modal decomposition [20,21]. The extracted features are often used as input for an intelligent algorithm, and the intelligent algorithm finally identifies the fault types.…”