Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the EMTP simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the WSCC 9-bus system.Comment: 9 pages, 7 figure

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 × 10−3, being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 × 10−19, showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.N/

Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the fault in relation to the increase in leakage current and thus predict a shutdown might occur. This paper proposes the use of empirical wavelet transform (EWT) to reduce the influence of non-representative variations and combines the attention mechanism with long short-term memory (LSTM) recurrent network for prediction. The Optuna framework has been applied for hyperparameter optimization, resulting in a method called Optimized EWT-Seq2Seq-LSTM with Attention. The proposed model had a 10.17% lower mean square error (MSE) than the standard LSTM and a 5.36% lower MSE than the model without optimization, showing that the attention mechanism and hyperparameter optimization is a promising strategy

Three dimensional flame reconstruction towards the study of fire-induced transmission line flashovers.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2007.The work presented in this thesis focuses on the problem of reconstructing threedimensional models of fire from real images. The intended application of the reconstructions is for use in research into the phenomenon of fire-induced high voltage flashover, which, while a common problem, is not fully understood. As such the reconstruction must estimate not only the geometry of the flame but also the internal density structure, using only a set of a few synchronised images. Current flame reconstruction techniques are investigated, revealing that relatively little work has been done on the subject, and that most techniques follow either an exclusively geometric or tomographic direction. A novel method, termed the 3D Fuzzy Hull method, is proposed, incorporating aspects of tomography, statistical image segmentation and traditional object reconstruction techniques. By using physically based principles the flame images are related to the relative flame density, allowing the problem to be tackled from a tomographic perspective. A variation of algebraic tomography is then used to estimate the internal density field of the flame. This is done within a geometric framework by integrating the fuzzy c-means image segmentation technique and the visual hull concept into the process. Results are presented using synthetic and real flame image sets

Application of artificial intelligence in early fault detection of transmission line-a case study in India

Reliable energy is ensured by the power quality, safety and security. For reliability and economic growth of transmission utilities, it is necessary to maintain continuity of supply, which is challenging under deregulated system. It is essential for utilities to conduct regular maintenance of transmission lines before supply interrupts. To protect line from fault, it is necessary to detect fault on line, its classification and location at the earliest. Various smart techniques along with application of artificial intelligence (AI) in power system are under investigation. This paper tries to find solution by identifying practical common faults occurred on transmission lines, and also suggests the suitable maintenance methodology. It uses the artificial neural network (ANN) method and live line maintenance technique (LLMT) for pre identification of a fault and subsequent predictive maintenance. Paper compares results of combination of ANN with LLMT and cold line maintenance technique (CLMT). Comparison of statistical analysis shows combine model of ANN and LLMT results in minimize outage time, failure rate which can improve system availability and increases revenue

Classification of insulators using neural network based on computer vision

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The characterisation and automatic classification of transmission line faults

Includes bibliographical references.A country's ability to sustain and grow its industrial and commercial activities is highly dependent on a reliable electricity supply. Electrical faults on transmission lines are a cause of both interruptions to supply and voltage dips. These are the most common events impacting electricity users and also have the largest financial impact on them. This research focuses on understanding the causes of transmission line faults and developing methods to automatically identify these causes. Records of faults occurring on the South African power transmission system over a 16-year period have been collected and analysed to find statistical relationships between local climate, key design parameters of the overhead lines and the main causes of power system faults. The results characterize the performance of the South African transmission system on a probabilistic basis and illustrate differences in fault cause statistics for the summer and winter rainfall areas of South Africa and for different times of the year and day. This analysis lays a foundation for reliability analysis and fault pattern recognition taking environmental features such as local geography, climate and power system parameters into account. A key aspect of using pattern recognition techniques is selecting appropriate classifying features. Transmission line fault waveforms are characterised by instantaneous symmetrical component analysis to describe the transient and steady state fault conditions. The waveform and environmental features are used to develop single nearest neighbour classifiers to identify the underlying cause of transmission line faults. A classification accuracy of 86% is achieved using a single nearest neighbour classifier. This classification performance is found to be superior to that of decision tree, artificial neural network and naïve Bayes classifiers. The results achieved demonstrate that transmission line faults can be automatically classified according to cause

Outdoor Insulation and Gas Insulated Switchgears

This book focuses on theoretical and practical developments in the performance of high-voltage transmission line against atmospheric pollution and icing. Modifications using suitable fillers are also pinpointed to improve silicone rubber insulation materials. Very fast transient overvoltage (VFTO) mitigation techniques, along with some suggestions for reliable partial discharge measurements under DC voltage stresses inside gas-insulated switchgears, are addressed. The application of an inductor-based filter for the protective performance of surge arresters against indirect lightning strikes is also discussed