Machine learning applications in power system fault diagnosis: Research advancements and perspectives

This paper presents a hybrid technique using a signal processing method and an intelligent scheme for power system fault diagnostics. The protection of a power line is critical for maintaining a sustainable power supply. Moreover, electrical protection schemes are required to distinguishes faults accordingly. In the present work, the discrete wavelet transform method is utilized to break down the fault current signal into sub-signal bands. Furthermore, the neural network (NN) algorithm scheme is applied to diagnose different faults which may occur in power grid network. The performance of the protection scheme relies mostly on the ability of the scheme to accurately classify fault. In the present work, the particle swarm optimization (PSO) method is implemented to evaluate the input parameters of the NN classifier. The presented results show that the ANN classified the faults with an accuracy margin of 99%.

A Wavelet-Neural Network-Based Technique for Fault Diagnostics in Power System

Critical Review of Fault Detection, Fault Classification and Fault Location Techniques for Transmission Network

With rapidly developing of the distribution networks the rate of the earth fault increases sharply. Aiming to fault location for distribution networks, many techniques are proposed and applied in distribution networks throughout the world. However, until now the technology for precise fault point location has not been successfully implemented in engineering practice. Traveling wave based methods as common techniques are widely applied in transmission line protection for locating fault point. However, these methods face severe challenge in fault location for distribution networks. The main reason is that the intermittent arc fault easily results in failure of detecting inceptive travelling wave and this intermittent arc is a common earth fault in distribution networks compared with transmission networks. In this paper, a simplified distribution line is built by making reference to the two parallel lossless transmission lines system. Then, the intermittent arc effects on traveling wave based method are modeled and discussed. Finally, the reason why these travelling wave based methods are hard to locate fault point precisely is illustrated.

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Modeling and simulation of intermittent arc effects on traveling wave based fault location techniques for distribution network

Power distribution systems (PDS) are increasingly complex and spread over long distances and in different locations. Given their radial configuration, the loads could be inserted at the same distances from the substation. This leads to multiple estimation of fault location (FL) and yields time consuming for iterative FL algorithms. In this article, we provide a novel practical approach to fault localization in order to defeat these limitations. The central idea of the proposed approach is to divide the multilateral distribution system into a possible number of mono-lateral sub systems (MLS) using a proposed communicating sensor. Next, we apply two different fault locator algorithms only to the defective MLS. The first variant of the approach is based on the impedance method, while the second variant is non-parametric used only when there is lack in the line data. To test the proposed technique in practice, we used the IEEE 13 Node test feeder, and a real Algerian PDS. The results obtained clearly show the contribution of the dedicated method for locating faults in the PDS.

A novel fault location approach for radial power distribution systems

In this paper, a method for detection and classification of faults in an electrical power distribution system is presented. Digsilent Power Factory software was used to model a section of a 66 kV power system. Fault incidents were instantiated on the model. The signal obtained from fault incidences were subsequently fed as input to discrete wavlet transform in order to obtained fault features and subsequently the features were then used as inputs for a support vector machine (SVM) and artificial neural network (ANN) for fault classification and detection. In addition, a Gaussian Process Regression (GPR) technique was employed for estimation of fault locations along the distribution line. Fault detection, classification and location estimation scheme were developed in MATLAB. The method showed that most faults on electric power distribution network can be classified with a good accuracy and minimum fault estimation error. The method is further validated on a real world power system. A hybrid method is thus proposed for detection, classification and estimation of fault location in a distribution network.

A Support Vector Machine Based Fault Diagnostic Technique In Power Distribution Networks

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A Support Vector Machine Based Fault Diagnostic Technique In Power Distribution Networks