Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Abstract

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. Index Terms-Permanent magnet synchronous generator (PMSG), artificial neural network (ANN), mixed logical dynamical (MLD) theory, conversion system.

A novel fault section locating method based on distance matching degree in distribution network

“…According to Fig. 1 and [13][14][15], the instantaneous value of the zero-sequence current transient components of the upstream and downstream are:…”

“…[4][5][6][7][8] realize fault location using line mode head information, but the accuracy of the traveling wave method is not high because of the interference of the branch line in the distribution network [9][10][11]. propose the use of wavelet transform, proxy algorithm and other analysis tools, and the characteristics of the transient information are obtained to achieve fault location by comparison [12][13][14][15]. propose a fault section locating method based on a linear correlation method using the differences of fault transient zero sequence current at two ends of the fault point.…”

Untitled

“…As presented in plenty of previous studies, the category of the wind power generators can be various [1], and the permanent magnet synchronous generator (PMSG) with direct mechanical connection to the wind turbine has become popular with superior performance in terms of energy density and efficiency. In previous literatures, this type of machine was studied for various topics, for instance, machine design [2][3], performance evaluation [4][5], loss calculation [6], sensorless control [7], fault characteristics [8][9], dynamic analysis [10], and power electronics with control [11][12][13][14]. Mostly focusing on the machine topology, such as PM flux direction, stator structure, rotor position and coil windings, the generators can be divided into several types, and it is also aware that the magnetization patterns of the PMs are very important design factor to determine the machine characteristics.…”

Data‐driven design of fault diagnosis for three‐phase PWM rectifier using random forests technique with transient synthetic features

“…Open‐circuit faults in IGBTs are usually caused by over‐current burnout or caused by open‐circuits of driven signals etc. [10, 11]. The open‐circuit faults in IGBTs are less serious than that of short‐circuit faults, which will not cause the system to stop immediately [12, 13].…”