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.
Highlights
The capacity of power converters in recent years has steadily grown in step with the increased size of large wind turbines; correspondingly, the load capacity of components of the converter have improved and the electrical structure is more complex, which is bound to raise the failure rate greatly [1,2]
The simulation results produced from the proposed method which is addressed to diagnose the open-circuit faults of grid-side converter (GSC) are evaluated
The length of time of each sample is between T and 1.15T, where T is the period of the phase current. 800 samples out of 1000 samples under each state are randomly selected to compose training set, and the remaining samples are used to compose test set
Summary
The capacity of power converters in recent years has steadily grown in step with the increased size of large wind turbines; correspondingly, the load capacity of components of the converter have improved and the electrical structure is more complex, which is bound to raise the failure rate greatly [1,2]. The open-circuit fault diagnosis of wind turbine power converter is crucially significant; its essence is to diagnose the power switching component, such as the IGBT module [14,15]. Qualitative fault diagnosis includes fault tree analysis method and expert system method, whose basic idea is to build a knowledge base by using the effective experience and expertise accumulated by experts in the case of open-circuit fault of the power converter, and to determine the diagnosis results and fault causes according to certain logic reasoning for the fault status. The open-circuit faults are identified calculating the average values of differences between predicted and measured phase currents This method is insensitive to load changes but out of the high power application. [23] proposes an approach, which is based on the absolute normalized Park’s current vector This method can detect multiple open-circuit switch faults.
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