A New Hybrid Fault Diagnosis Method for Wind Energy Converters

Abstract

Fault diagnostic techniques can reduce the requirements for the experience of maintenance crews, accelerate maintenance speed, reduce maintenance cost, and increase electric energy production profitability. In this paper, a new hybrid fault diagnosis method based on multivariate empirical mode decomposition (MEMD), fuzzy entropy (FE), and an artificial fish swarm algorithm (AFSA)-support vector machine (SVM) is proposed to identify the faults of a wind energy converter. Firstly, the measured three-phase output voltage signals are processed by MEMD to obtain three sets of intrinsic mode functions (IMFs). The multi-scale analysis tool MEMD is used to extract the common modes matching the timescale. It studies the multi-scale relationship between three-phase voltages, realizes their synchronous analysis, and ensures that the number and frequency of the modes match and align. Then, FE is calculated to describe the IMFs’ complexity, and the IMFs-FE information is taken as fault feature to increase the robustness to working conditions and noise. Finally, the AFSA algorithm is used to optimize SVM parameters, solving the difficulty in selecting the penalty factor and radial basis function kernel. The effectiveness of the proposed method is verified in a simulated wind energy system, and the results show that the diagnostic accuracy for 22 fault modes is 98.7% under different wind speeds, and the average accuracy of 30 running can be maintained above 84% for different noise levels. The maximum, minimum, average, and standard deviation are provided to prove the robust and stable performance. Compared with the other methods, the proposed hybrid method shows excellent performance in terms of high accuracy, strong robustness, and computational efficiency.

Algorithm for Correlation Diagnosis in Multivariate Process Quality Based on the Optimal Typical Correlated Component Pair Group

“…In addition to the traditional diagnostic methods based on mathematical model analysis, in recent years, with the development of artificial intelligence technology, intelligent diagnostic methods have been applied to the field of multivariate process quality diagnosis, and the diagnostic methods based on artificial neural networks (ANNs) [21][22][23], Bayesian networks [24][25][26], support vector machines (SVMs) [27][28][29], etc. have been widely applied.…”

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

“…Multivariate Empirical Mode Decomposition (MEMD), Fuzzy Entropy (FE), and Artificial Fish Swarm Algorithm (AFSA)-Support Vector Machine (SVM) [103].…”

“…DSDEMAE uses multi-scale approximate entropy to extract the features of the fault signal, and then uses Dempster-Shafer theory and Deng entropy to fuse the features of different scales, which effectively deal with the uncertainty and conflict between different features [100]. In [103], MEMD is used to decompress the three-phase output voltage signal synchronously and extract the common mode with the same time scale. Then, FE is used to calculate the complexity of each mode as the fault feature.…”

A multivariate process quality correlation diagnosis method based on grouping technique

“…In addition to the traditional diagnostic methods based on mathematical model analysis, in recent years, with the development of artificial intelligence technology, intelligent diagnostic methods are applied to the field of multivariate process quality diagnosis, and the diagnostic methods based on artificial neural network (ANN) 25 – 28 , Bayesian network 29 – 32 , support vector machine (SVM) 33 – 35 , etc. have been widely applied.…”