Partial discharge detection for stator winding insulation of motors using artificial neural network

In this article, the insulation fault detection of high-voltage motors by the artificial neural network algorithm is used. The proposed method can evaluate the status of operating motor without interrupting the normal operation. According to the measurement of partial discharge information, this research establishes the relationship of stator failures and pattern features. This study uses common high-voltage motor stator fault types to experimentally produce four types of stator test models with insulation defects; these models are compared with a healthy motor model. Through the learning of the artificial neural network, the experimental results show that the artificial neural network–based stator fault diagnosis system proposed in this article has a recognition rate as high as 90% when the conjugate gradient algorithm is used, and there are 20 neurons in the hidden layer.

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Design of partial discharge location identifier software for high voltage generator using artificial neural network

Proceedings of the Joint International Conference on Electric Vehicular Technology and Industrial, Mechanical, Electrical and Chemical Engineering (ICEVT & IMECE) ◽

10.1109/icevtimece.2015.7496664 ◽

2015 ◽

Cited By ~ 6

Author(s):

Berkah Suprayogi ◽

Umar Khayam

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

High Voltage ◽

Partial Discharge ◽

Voltage Generator ◽

Discharge Location ◽

Artificial Neural ◽

High Voltage Generator

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Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

Measurement and Control ◽

10.1177/0020294020964826 ◽

2020 ◽

pp. 002029402096482

Author(s):

Sulaiman Khan ◽

Abdul Hafeez ◽

Hazrat Ali ◽

Shah Nazir ◽

Anwar Hussain

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Research Work ◽

Recognition Rate ◽

Recognition Task ◽

Convolution Neural Network ◽

Medium Size ◽

Support Vector ◽

Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

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An overview of application of artificial neural network to partial discharge pattern classification

Proceedings of 5th International Conference on Properties and Applications of Dielectric Materials ◽

10.1109/icpadm.1997.617594 ◽

2002 ◽

Cited By ~ 1

Author(s):

Kyu-Bock Cho ◽

Joo-Young Oh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Pattern Classification ◽

Partial Discharge ◽

Discharge Pattern ◽

Artificial Neural

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Fuzzy logic and artificial neural network-based thermography approach for monitoring of high-voltage equipment

International Journal of Electrical Engineering Education ◽

10.1177/0020720915570541 ◽

2015 ◽

Vol 52 (1) ◽

pp. 81-96 ◽

Cited By ~ 3

Author(s):

Mileta D Žarković ◽

Zlatan Stojković

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

High Voltage ◽

Artificial Neural

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Dynamic prediction of Indian stock market: an artificial neural network approach

International Journal of Ethics and Systems ◽

10.1108/ijoes-11-2020-0184 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Himanshu Goel ◽

Narinder Pal Singh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Stock Market ◽

Institutional Investors ◽

Time Series Data ◽

Gradient Algorithm ◽

Series Data ◽

Content Type ◽

Artificial Neural ◽

Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

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