A fault warning for inter-turn short circuit of excitation winding of synchronous generator based on GRU-CNN

Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.

The Detection of Inter-Turn Short Circuits in the Stator Windings of Sensorless Operating Induction Motors

This work proposes an alternative strategy to the use of a speed sensor in the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-gain observer gives very similar results to those using the speed sensor. The estimation errors in the cases of speed variation and load torque are almost identical. Variations in stator and rotor resistances influence the performance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.

感应电动机内部故障检测技术综述

针对感应电动机常见的几种内部故障,详细的阐述了不同故障产生的原因,分析了国内外目前所用的检测方法,并指出了故障检测技术的发展趋势。

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit(ITSC)faults in induction motors(IMs)is proposed.Determining the incipient ITSC fault and its severity is challenging for several reasons.The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault.Moreover,the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity.The objective of this study is the early detection of incipient ITSC faults.Furthermore,it aims to determine the percentage of shorted turns in the faulty phase,which acts as an indicator for severe damage to the stator winding.Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform.A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet.The statistical parameters L1 and L2 norms are computed for the detailed coefficients.These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault.Combining L1 and L2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim.An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process.The classifier output defines the percentage of defective turns with a high level of accuracy.The competency of the adopted methodology is validated via simulations and experiments.The results confirm the merits of the proposed method,with a classification test correctness of 95.29%.