CANONICAL PIECEWISE-LINEAR ANALYSIS FOR NONLINEAR DC FAULT CIRCUITS

This paper uses canonical piecewise-linear analysis method to analyze nonlinear DC fault circuitsand solve for the values of the test port voltages which are selected beforehand .The method needs lessmemory storages,obtains the results in finite steps and has high efficiency in computation.It can be appliedto the circuits containing multiport nonlinear elements.It is a good method of pre-test analysis for fault cir-cuits in simulation-before-test aproach in analogue circuit diagnosis.

fault500 kV变电站35 kV串联电抗器故障分析及建议

某500kV变电站主变35kV侧发生一起电容器组串联电抗器短路故障,通过对事故现场检查,结合故障录波器记录,对本次事故发生的过程进行了分析并得出结论。针对本次故障暴露出的问题,从串联电抗器安装位置、在线监测等方面提出了事故防范措施,对于变电站电容器组安全运行具有一定参考价值。

Testing Cross-Talk Induced Delay Faults in Digital Circuit Based on Transient Current Analysis

The delay fault induced by cross-talk effect is one of the difficult problems in the fault diagnosis of digital circuit. An intelligent fault diagnosis based on IDDT testing and support vector machines （SVM） classifier was proposed in this paper. Firstly, the fault model induced by cross-talk effect and the IDDT testing method were analyzed, and then a delay fault localization method based on SVM was presented. The fault features of the sampled signals were extracted by wavelet packet decomposition and served as input parameters of SVM classifier to classify the different fault types. The simulation results illustrate that the method presented is accurate and effective, reaches a high diagnosis rate above 95%.

Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.

Data-driven fault diagnosis method for analog circuits based on robust competitive agglomeration

The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.

Analysis on the fault characteristics of three-phase short-circuit for half-wavelength AC transmission lines

Half-wavelength AC transmission(HWACT) is an ultra-long distance AC transmission technology, whose electrical distance is close to half-wavelength at the system power frequency. It is very important for the construction and operation of HWACT to analyze its fault features and corresponding protection technology. In this paper, the steady-state voltage and current characteristics of the bus bar and fault point and the steady-state overvoltage distribution along the line will be analyzed when a three-phase symmetrical short-circuit fault occurs on an HWACT line. On this basis, the threephase fault characteristics for longer transmission lines are also studied.

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

According to statistic data,machinery faults contribute to largest proportion of High-voltage circuit breaker failures,and traditional maintenance methods exist some disadvantages for that issue.Therefore,based on the wavelet packet decomposition approach and support vector machines,a new diagnosis model is proposed for such fault diagnoses in this study.The vibration eigenvalue extraction is analyzed through wavelet packet decomposition,and a four-layer support vector machine is constituted as a fault classifier.The Gaussian radial basis function is employed as the kernel function for the classifier.The penalty parameter c and kernel parameterδof the support vector machine are vital for the diagnostic accuracy,and these parameters must be carefully predetermined.Thus,a particle swarm optimizationsupport vector machine model is developed in which the optimal parameters c andδfor the support vector machine in each layer are determined by the particle swarm algorithm.The validity of this fault diagnosis model is determined with a real dataset from the operation experiment.Moreover,comparative investigations of fault diagnosis experiments with a normal support vector machine and a particle swarm optimization back-propagation neural network are also implemented.The results indicate that the proposed fault diagnosis model yields better accuracy and e-ciency than these other models.

Improved RBF network application in analog circuit fault isolation

One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.

A Fault Current Limiting Hybrid DC Circuit Breaker

Due to the low impedance characteristic of the high voltage direct current(HVDC)grid,the fault current rises extremely fast after a DC-side fault occurs,and this phenomenon seriously endangers the safety of the HVDC grid.In order to suppress the rising speed of the fault current and reduce the current interruption requirements of the main breaker(MB),a fault current limiting hybrid DC circuit breaker(FCL-HCB)has been proposed in this paper,and it has the capability of bidirectional fault current limiting and fault current interruption.After the occurrence of the overcurrent in the HVDC grid,the current limiting circuit(CLC)of FCL-HCB is put into operation immediately,and whether the protected line is cut off or resumed to normal operation is decided according to the fault detection result.Compared with the traditional hybrid DC circuit breaker(HCB),the required number of semiconductor switches and the peak value of fault current after fault occurs are greatly reduced by adopting the proposed device.Extensive simulations also verify the effectiveness of the proposed FCL-HCB.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

Robust Fault Diagnosis of Analog Circuits with Tolerances

A method for robust analog fault diagnosis using hybrid neural networks is proposed. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of element tolerances and reduce testing time. The proposed approach is based on the fault dictionary diagnosis method and backward propagation neural network (BPNN) and the adaptive resonance theory (ART) neural network. Simulation results show that the method is robust and fast for fault diagnosis of analog circuits with tolerances.

A Neural-based L1-Norm Optimization Approach for Fault Diagnosis of Nonlinear Resistive Circuits

This paper deals with fault isolation in nonlinear analog circuits with tolerance under an insufficient number of independent voltage measurements.A neural network-based L1-norm optimization approach is proposed and utilized in locating the most likely faulty elements in nonlinear circuits.The validity of the proposed method is verified by both extensive computer simulations and practical examples.One simulation example is presented in the paper.

Research method of circuit fault diagnosis based on FCM

Using fuzzy C cluster mean (FCM), fuzzy theory and neural network, a fault diagnosis method was proposed, which was based on fuzzy C-means clustering algorithm of neural network that was applied in non-linear analog circuits and in diagnoses the ARNIC 429 reception circuit of aviation aircraft avionics. The C cluster algorithm can make the amount of the fuzzy rule automatically and can create an initial fuzzy rule database of fault diagnosis. A type of fuzzy neural network and a fault tree were generated. The algorithm avoids the disadvantage that gets into the part of optimum circumstance. A validate application was implemented, which proves that the method is effective. Therefore, the method is superior to the traditional methods in fault diagnosis, and the efficiency is heavily improved.

The simulation research for transient current variation in short circuit fault of urban rail transportation

Urban rail transit is one of the most important way for urban residents. However, frequent power failure, especially the short fault hinders the safe and stable operation of rail transit. The research of the transient variation of line electrical parameters in short circuit fault is the basis of researches for technology of line protection and short circuit fault location. Based on Matlab/Simulink, a 24-pulse rectifier circuit model is established, the resistance and inductance value of the catenary and rail network are calculated. The short Circuit fault simulation model of DC traction power supply system is established. The short-circuit fault of the traction network at close and distant points are simulated, the transient variation values of fault current with the different fault distance are analyzed. The simulation results show that the transient current peak of the nearby short circuit is oscillatory and convergent due to the nonlinear devices, which proves the accuracy of the model and provides a reference for the precise configuration of the line protection equipment.

THE EFFECTIVE RANGE OF K-FAULT DIAGNOSIS OF-LINEAR CIRCUITS

In view of K-fault testability,the topological construction of a practical circuitis far from ideal.In order to improve the testability of a circuit,we may increase the numberof accessible nodes or use the multi-excitation method.Effectiveness of these methods and thefeasibility of choosing accessible nodes are discussed in detail.The conditions for multi-excitationtestability are presented.

Evolutionary Design of Fault-Tolerant Digital Circuit Based on Cartesian Genetic Programming

In many areas, reliability of the digital circuits has become the key factor to restrict circuit development. Fault-tolerant design is the commonly used method to improve the reliability of digital circuits. The current fault-tolerant design methods are based on triple modular redundancy( TMR) or multiple modular redundancy( MMR). These redundancy designs rely on the experience of the designers,and the designed circuits have poor adaptabilities to a complex environment. However, evolutionary design of digital circuits does not rely on prior knowledge. During the evolution, some novel and optimal circuit topologies can be found, and the evolved circuits can feature strong adaptive capacities. Based on Cartesian genetic programming( CGP), a novel method for designing fault-tolerant digital circuits by evolution is proposed,key steps of the evolution are introduced,influences of function sets on evolution are investigated,and as a preliminary result,an evolved full adder with high fault-tolerance is shown.

A Method on Analog Circuit Fault Diagnosis with Tolerance

In this paper, it is proved that the direction of the node-voltage difference vector, which is the difference between the node-voltage vector at faulty state and the one at the nominal state, is determined only by the location of the faulty clement in linear analog circuits. Considering that the direction of the node-voltage sensitivity vector is the same as the one of the node-voltage difference vector and also considering that the module of the node-voltage sensitivity vector presents the weight of the parameter of faulty element deviation relative to the voltage difference, fault dictionary is set up based on node-voltage sensitivity vectors. A decision algorithm is proposed concerned with both the location and the parameter difference of the faulty element. Single fault and multi-fault can be diagnosed while the circuit parameters deviate within the tolerance range of 10 %.

Research on Parameter Collaborative Configuration of DC Circuit Breaker and DC Fault Current Limiter

Fault current suppression is the key technology to ensure the safe operation of the DC power distribution system. In order to realize the parameter collabora-tive configuration of the DC circuit breaker and the DC current limiter and improve the fault current suppression capability, the fault current suppression mechanism of the DC power distribution system is revealed based on the circuit model. Then, based on the mathematical model of the DC breaker, the characteristic parameters of DC breaking are extracted, and then the influence of different characteristic parameters on the breaking characteristics of fault current is studied. Finally, the mathematical model of the collaborative process between DC circuit breaker and DC current limiter is established. The charac-teristic parameters of fault current collaborative suppression are extracted. The coupling effects of different characteristic parameters on the fault current col-laborative suppression are studied. The principle of collaborative configuration of DC circuit breaker and DC current limiter is proposed, and the collaborative suppression ability of DC circuit breaker and DC current limiter to fault current is fully exploited to ensure the safe and reliable operation of the DC power distribution system.

充电桩充电模块功率器件故障诊断研究综述

大功率直流充电桩是未来电动汽车充电设施的发展方向,充电模块是直流充电桩最重要以及故障率最高的部件,其中功率器件开路故障较为常见。为保证充电模块安全可靠运行,需要对充电模块功率器件开路故障进行准确识别和定位。文中首先对充电模块拓扑结构和故障类型进行分析,然后分别对基于解析模型、基于信号处理和基于知识的功率器件开路故障诊断方法进行总结,分别介绍了各类方法的基本思想、研究进展和优缺点,最后总结并展望充电模块功率器件开路故障诊断方法未来的研究和发展方向。