Correction to: Neural network with adaptive evolutionary learning and cascaded support vector machine for fault localization and diagnosis in power distribution system

Abstract Nowadays, electrical power system is considered as one of the most complicated artificial systems all over the globe, as social and economic development depends on intact, consistent, stable and economic functions. Owing to diverse random causes, accidental failures occur in electrical power systems. Considering this issue, this article aimed to propose the use of deep belief network (DBN) in detecting and classifying fault signals such as transient, sag and swell in the transmission line. Here, wavelet-decomposed fault signals are extracted and the fault is diagnosed based on the decomposed signal by the DBN model. Further, this article provides the performance analysis by determining the types I and II measures and root-mean-square-error (RMSE) measure. In the performance analysis, it compares the performance of the DBN model to various conventional models like linear support vector machine (SVM), quadratic SVM, radial basis function SVM, polynomial SVM, multilayer perceptron SVM, Levenberg-Marquardt neural network and gradient descent neural network models. The simulation results validate that the proposed DBN model effectively detects and classifies the fault signal in power distribution system when compared to the traditional model.

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A Hybrid Framework for Fault Classification and Location in Power Distribution System Using Wavelet and Support Vector Machine

Lecture Notes in Electrical Engineering - Innovation in Electrical Power Engineering, Communication, and Computing Technology ◽

10.1007/978-981-15-2305-2_27 ◽

2020 ◽

pp. 339-349

Author(s):

Satyajit Panigrahy ◽

A. K. Chandel

Keyword(s):

Support Vector Machine ◽

Power Distribution ◽

Distribution System ◽

Fault Classification ◽

Support Vector ◽

Power Distribution System ◽

Hybrid Framework

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Comparative Study in Determining Features Extraction for Islanding Detection using Data Mining Technique: Correlation and Coefficient Analysis

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i3.pp1112-1124 ◽

2017 ◽

Vol 7 (3) ◽

pp. 1112 ◽

Cited By ~ 1

Author(s):

Aziah Khamis ◽

Yan Xu ◽

Azah Mohamed

Keyword(s):

Data Mining ◽

Power Distribution ◽

Distribution System ◽

Harmonic Distortion ◽

Rate Of Change ◽

Support Vector ◽

Islanding Detection ◽

Data Mining Technique ◽

Detection Scheme ◽

Power Distribution System

A comprehensive comparison study on the datamining based approaches for detecting islanding events in a power distribution system with inverter-based distributed generations is presented. The important features for each phase in the island detection scheme are investigated in detail. These features are extracted from the time-varying measurements of voltage, frequency and total harmonic distortion (THD) of current and voltage at the point of common coupling. Numerical studies were conducted on the IEEE 34-bus system considering various scenarios of islanding and non-islanding conditions. The features obtained are then used to train several data mining techniques such as decision tree, support vector machine, neural network, bagging and random forest (RF). The simulation results showed that the important feature parameters can be evaluated based on the correlation between the extracted features. From the results, the four important features that give accurate islanding detection are the fundamental voltage THD, fundamental current THD, rate of change of voltage magnitude and voltage deviation. Comparison studies demonstrated the effectiveness of the RF method in achieving high accuracy for islanding detection.

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Intelligent Techniques for the Analysis of Power Quality Data in Electrical Power Distribution System

Handbook of Research on Industrial Informatics and Manufacturing Intelligence - Advances in Civil and Industrial Engineering ◽

10.4018/978-1-4666-0294-6.ch024 ◽

2012 ◽

pp. 563-575

Author(s):

Zahir Javed Paracha ◽

Akhtar Kalam

Keyword(s):

Neural Network ◽

Power Quality ◽

Power Distribution ◽

Distribution System ◽

Electrical Power ◽

Power Industry ◽

Quality Data ◽

Power Utility ◽

Power Distribution System ◽

The Real

This chapter is about the intelligent techniques for the analysis of power quality problems in electrical power distribution system. The problems related with electrical power industry are becoming more widespread, complex, and diversified. The behaviour of power distribution systems can be monitored effectively using artificial intelligence techniques and methodologies. There is a need of understanding the power system operations from power utility perspectives and application of computational intelligence methods to solve the problems of the power industry. The real power quality (PQ) data is taken from a power utility in Victoria Australia. Principal Component Analysis Technique (PCAT) is used to reduce the large number of PQ data attributes of the power distribution system. After the pre-processing of PQ data using PCAT, intelligent computational techniques will be used for the analysis of power quality data. Neural network techniques will be employed to estimate the values of PQ parameters of the power distribution system. The Feed Forward Back Propagation (FFBP) neural network and Recurrent Neural Networks (RNN) are used for intelligent estimation of PQ data. The results obtained through these intelligent techniques are compared with the real data of power utility in Victoria, Australia for stability, reliability and enhanced power systems performance.

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An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities

Energies ◽

10.3390/en13123242 ◽

2020 ◽

Vol 13 (12) ◽

pp. 3242 ◽

Cited By ~ 2

Author(s):

Muhammad Salman Saeed ◽

Mohd Wazir Mustafa ◽

Usman Ullah Sheikh ◽

Touqeer Ahmed Jumani ◽

Ilyas Khan ◽

...

Keyword(s):

Decision Tree ◽

Power Distribution ◽

Distribution System ◽

Research Work ◽

Machine Learning Algorithms ◽

Support Vector ◽

Chi Square ◽

Power Distribution System ◽

Energy Theft ◽

C5.0 Decision Tree

Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers’ consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson’s chi-square feature selection algorithm is adopted to select the most relevant features among the extracted ones. Finally, the Boosted C5.0 Decision Tree (DT) algorithm is used to classify the honest and the fraudster consumers based on the outcomes of the selected features. To validate the superiority of the proposed NTL detection approach, its performance is matched with that of few state-of-the-art machine learning algorithms (one of most exciting recent technologies in Artificial Intelligence), like Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Gradient Bossting (XGBoost). The proposed NTL detection method provides an accuracy of 94.6%, Sensitivity of 78.1%, Specificity of 98.2%, F1 score 84.9% and Precision of 93.2% which are significantly higher than that of the same for the above-mentioned algorithms.

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