摘要
In a permanent magnet synchronous generator(PMSG)system,conversion systems are major points of failure that create expensive and time-consuming problems.Fault detection is usually used to achieve a steady system.This paper presents a full analysis of a PMSG system for wind turbines(WT)and proposes a fault detection method using correlation features.The proposed method is motivated by the balance among the three-phase currents both before and after an opencircuit fault occurs in a converter of the PMSG system.It is unnecessary to analyze the output waveforms of a converter during fault detection.In this study,two correlation features of stator currents,the mean and covariation,are extracted to train an artificial neural network(ANN),thereby enhancing the performance of the proposed method under different wind speed conditions.Moreover,additional sensors and the collection of a massive amount of data are not required.Model simulations of an ideal inverter and a PMSG system are conducted using PSCAD software.The simulation results show that the proposed method can detect the locations of faulty switches with a diagnostic rate greater than 99.4%for the ideal inverter,and the PMSG drives settings at different wind speeds.
In a permanent magnet synchronous generator(PMSG) system, conversion systems are major points of failure that create expensive and time-consuming problems. Fault detection is usually used to achieve a steady system. This paper presents a full analysis of a PMSG system for wind turbines(WT) and proposes a fault detection method using correlation features. The proposed method is motivated by the balance among the three-phase currents both before and after an opencircuit fault occurs in a converter of the PMSG system. It is unnecessary to analyze the output waveforms of a converter during fault detection. In this study, two correlation features of stator currents, the mean and covariation, are extracted to train an artificial neural network(ANN), thereby enhancing the performance of the proposed method under different wind speed conditions. Moreover, additional sensors and the collection of a massive amount of data are not required. Model simulations of an ideal inverter and a PMSG system are conducted using PSCAD software. The simulation results show that the proposed method can detect the locations of faulty switches with a diagnostic rate greater than 99.4% for the ideal inverter, and the PMSG drives settings at different wind speeds.
作者简介
Yanhong Tan is a full professor and doctor supervisor with College of Electrical and Information Engineering,Hunan University,Changsha,China.She has published more than 40 papers in conference proceedings and scientific journals.Her areas of specialized research interest include test and diagnosis for mixed-signal circuits and intelligent signals processing.e-mail:tanyho@126.com;corresponding author:Haixia Zhang,is currently working toward the Ph.D.degree with College of Electrical and Information Engineering,Hunan University,Changsha,China.Her areas of specialized research interest include diagnosis and control for converters of wind power system,intelligent signals processing.e-mail:haixia521119@163.com;Ye Zhou received the Ph.D.degree in electrical engineering from Hunan University,Changsha,China in 2013.He is currently a senior engineer in NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing,China.His research interests include security and stability of power systems.e-mail:184971791@qq.com
