An experimental evaluation of automatic classification of sequences representing short circuits in transmission lines (original) (raw)
Related papers
2010
Most works in power systems event classification concern classifying an event according to the morphology of the corresponding waveform. An important and even more difficult problem is the classification of the event underlying cause. However, the lack of labeled data is more problematic in this second scenario. This paper proposes a framework based on framebased sequence classification (FBSC), the Alternative Transient Program (ATP) and a public dataset to advance research in this area. As a proof of concept, a thorough evaluation of automatic classification of short-circuits in transmission lines is discussed. Simulations with different preprocessing (e.g., wavelets) and learning algorithms (e.g., support vector machines) are presented. The results can be reproduced at other sites and elucidate several trade-offs when designing the front end and pattern recognition stages of a sequence classifier. For example, when considering the whole event in an off-line scenario, the combination of the raw front end and a decision tree is competitive with wavelets and a neural network.
Transmission line fault classification using Hidden Markov Models
IEEE Access
The maintenance of power quality in electrical power systems depends on addressing the major disturbances that may arise during generation, transmission and distribution. Many studies aim to investigate these disturbances by analyzing the behavior of the electrical signal through the classification of short circuit faults in power transmission lines as a way to assist the administration and maintenance of the electrical system. However, most fault classification methods generate a high computational cost that do not always yield satisfactory results; these methods utilize front ends in data processing before being processed by conventional classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) that are adopted into the Frame Based Sequence Classification (FBSC) architecture that uses the front ends Waveletenergy, Waveletconcat, RAW, Root Mean Square front ends (RMS) and ConcatFrontEnd. An alternative method for classifying faults without having to use front ends employs the UFPAFaults database and the Hidden Markov Model (HMM) algorithm that directly treats the electrical signal in the form of multivariate time series. The results indicate the HMM algorithm as a potential classifier because its comparatively low error rate of 0.03% exceeds the performance of the conventional classifiers ANN, SVM, KNN and RF as used with the FBSC architecture. When the statistical test with a significance of α = 5% is applied, only the ANN and RF classifiers present a result close to what the HMM algorithm provides. Another relevant factor is that the HMM algorithm considerably decreases the computational cost by more than 90% of processing time as compared to the conventional classifiers of the FBSC architecture, thereby validating its potential in the direct classification of faults in electric power system transmission lines. INDEX TERMS Electric power quality, electrical power systems, short circuit, classification of faults, hidden markov model.
Machine Learning Approaches for Power Transmission Lines Fault Classification: A Systematic Review
Journal of Advanced Research in Artificial Intelligence & It's Applications, 2024
The era of computational intelligent power systems has resulted in a proliferation of great number of techniques, technologies and products for the power industry. This may be attributed to the unique ability of computationally intelligent systems to demystify the hidden truths in large volumes of power systems data. In this paper, a succinct systematic review of power transmission line fault classification based on ML approaches is presented. The review accounts for the different Machine Learning (ML) approaches in terms of their nature of classification such as the single or multi-class, the nature of the ML such as the use of purely mathematical or AI, and the nature of the hybridization such as the use of dual or multi-hybridizations and for a period of 2006 to 2020. Specifically, the review studies the aspect of power systems classification that deals with the detection and localization of faults on transmission lines. From the reviews conducted, the most prominent classifiers were found to be the conventional Artificial Neural Networks (ANN) for the detection and localization of power system faults. This shows that the researchers will prefer the predictive ML approaches that are based on the simple intelligent operations that occur in mammalian brains i.e. the conventional Back-Propagation Neural Networks (BPNNs). However, it will be desirous if researchers equally investigate other potential but less popular neural ML model schemes.
Using machine learning algorithms for classifying transmission line faults
DÜMF Mühendislik Dergisi
The faults in transmission lines should be identified for attaining high quality energy in electrical power systems. Savings can be made in both time and energy if the transmission line faults are classified accurately. The present study examined phase-ground, phase-phase-ground, phase-phase, phase-phase-phase and no fault cases. Support Vector Machine (SVM), K-Nearest Neighbours Algorithm (KNN), Decision Tree (DT), Ensemble, Linear discriminant analysis (LDA) classifiers were used for classifying the transmission line faults. These algorithms were compared with regard to parameters such as accuracy, error rate, prediction speed and training time. The accuracy and minimum error of SVM and KNN classifiers were 99.7 % and 0.0011 respectively. DT classifier is faster than the other classifiers with a predicted speed of 29000 obs/sec. Whereas LDA had the shortest training time of 0.76992 sec. The results have indicated that SVM, KNN classifiers have similar performances. In addition, th...
Artificial Intelligence Based Fault detection and classification in Transmission Lines
Iconic Research And Engineering Journals, 2018
Artificial neural networks and wavelet transform have been used to achieve fault Identification and classification on electric power transmission lines. This work proposed an improved solution based on wavelet transform and neural network back-propagation algorithm. The three-phase current and voltage waveforms measured during the occurrence of fault in the power transmission-line are pre-processed first and then decomposed using wavelet multi-resolution analysis to obtain the high frequency details and low frequency approximations. The patterns formed based on high frequency signal components are arranged as inputs of the neural network, whose task is to indicate the occurrence of a fault on the lines. The patterns formed using low frequency approximations are arranged as inputs of the second neural network, whose task is to indicate the exact fault type. The neural networks which can learn was trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for each of the three phases in the Fault location process. An analysis of the learning and generalization characteristics of elements in power system was presented using Neural Network toolbox in MATLAB/SIMULINK environment. Simulation results obtained demonstrated that neural network pattern recognition and wavelet multi-resolution analysis approach are efficient in identifying and classifying faults on transmission lines as satisfactory performance was achieved especially when compared to the conventional methods such as impedance and travelling wave methods.
International Journal of Electrical Power & Energy Systems, 2012
This paper presents a statistical algorithm for classification of faults on power transmission lines. The proposed algorithm is based upon the wavelet transform of three phase currents measured at the sending end of a line and the Classification and Regression Tree (CART) method, a commonly available statistical method. Wavelet transform of current signal provides hidden information of a fault situation as an input to CART algorithm, which is used to classify different types of faults. The proposed technique is simulated using MATLAB/SIMULINK software and it is tested upon the data created with the fault analysis of the 400 kV sample transmission line considering wide variations in the operating conditions. The classification results are also compared with the results obtained using back propagation neural network.
2021
The recent developments in the technology made by organizations have led to a quicker, simpler and a very accurate data analysis. The use of machine learning techniques have been exponentially increasing in the analysis of data in different fields ranging from medicine to defense, education, finance and energy applications. The machine learning techniques reduce further meaningful information processed by data mining. These significant and meaningful information help organizations to establish their future policies to get more advantages in terms of time and cost. In this paper the author has tried to present the best classification method by having a comparative analysis on various methods such as Logistic Regression, Support Vector Machine, Naive Bayes and K-Nearest Neighbors etc. for a particular use case i.e. prediction and classification of transmission line faults. The author has made this analysis by utilizing both Python and MATLAB Simulink. This will surely help the researc...
Autonomous Neural Models for the Classification of Events in Power Distribution Networks
Journal of Control, Automation and Electrical Systems, 2013
This paper presents a method for automatic classification of faults and transients in power distribution networks, based on voltage oscillographies of the distribution networks feeders. For signal preprocessing, the Discrete Wavelet Transform was used with the performances of several families of wavelet functions being compared. In the classification stage, three neural models were assessed: Multi-Layer Perceptrons, Radial Basis Function Networks, and Support Vector Machines. The models were trained autonomously, i.e., using automatic model selection and complexity control. Promising results were obtained using a set of simulations generated using the Alternative Transients Program. Initial results obtained for real data acquired from a set of oscillograph loggers installed in a distribution network are also presented.
The Characterisation and Automatic Classification of Transmission Line Faults: PhD Thesis
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.