Corona fault detection in switchgear with extreme learning machine

Ishak, Sanuri; Koh, S. P.; Tan, Jian; Tiong, Sieh Kiong; Chen, Chai Phing

doi:10.11591/eei.v9i2.2058

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…The novel feature extraction method was developed for improving the classification performance of the remotely sensed data using SL [18]. Using back propagation algorithm [19], MLP-ANN [20] and extreme learning machine (ELM) [21], the fault detection and classification method was developed for rotating machinery. The butterfly optimization algorithm utilizing the Levy flight (BOALF) and modified butterfly optimization algorithm (BOARN) was proposed for detecting the pneumonia diseases [22].…”

Section: Supervised Learning and Unsupervised Learningmentioning

confidence: 99%

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Sagiraju

Shashi

2021

IJEECS

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Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).

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Section: Supervised Learning and Unsupervised Learningmentioning

confidence: 99%

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Sagiraju

Shashi

2021

IJEECS

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“…Finally, a depth detection model has utilized based on long short term memory that in turn differentiated between malicious behaviors that were observed from the behavior chains [19]- [22]. With this, the malware detection accuracy was said to be improved with a minimum false positive rate [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Classification of malware using multinomial linked latent modular double q learning

Kudrekar

Vinayakamurthy

2022

IJEECS

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In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detection that extracts the behavior based on the network pattern. The behavior is extracted which are grouped to perform on the path protocol for analyzing repeated behaviors. Finally, the modular double q learning malware classification model is the grouped behaviors for significant malware detection. The effectiveness of proposed MLLD-M DQL method is compared with existing models. The results obtained by the proposed method show that the model combined with the machine learning (ML) significantly determined malware detection time and also reduced the false positive rate (FPR). The results showed that the false positive rate is significantly reduced by 42% for the proposed method better when compared to the existing behavior based malware detection model that obtained 62% of FPR.

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“…The common electrical faults of the switchgear are the corona [27][28][29][30][31][32][33][34], tracking [27,35], and arcing [34,[36][37][38], as shown in Figure 1. Furthermore, the switchgear might be experiencing more than one specific fault at the same time.…”

Section: Introductionmentioning

confidence: 99%

“…The major dictating factor for its presence is a high potential in the electrical field. Machine learning has been widely applied for various purposes in switchgear systems for fault diagnosis [30,[44][45][46][47][48][49][50][51][52][53][54][55] as well as prediction [56][57][58] and maintenance [44]. To be precise, the focus of machine learning is based on neural networks [46,47,[50][51][52]54,56], support vector machine (SVM) [45,49], and extreme learning machine (ELM) [30], and have been widely used in switchgear system fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has been widely applied for various purposes in switchgear systems for fault diagnosis [30,[44][45][46][47][48][49][50][51][52][53][54][55] as well as prediction [56][57][58] and maintenance [44]. To be precise, the focus of machine learning is based on neural networks [46,47,[50][51][52]54,56], support vector machine (SVM) [45,49], and extreme learning machine (ELM) [30], and have been widely used in switchgear system fault diagnosis. Literature studies state that by using extreme learning machine (ELM) the learning speed can be instantly quicker than conventional feed-forward neural network (FFNN) learning algorithms, while also achieving improved generalization performance [59][60][61][62][63][64][65].…”

Section: Introductionmentioning

confidence: 99%

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Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

et al. 2021

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Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.

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Corona fault detection in switchgear with extreme learning machine

Cited by 6 publications

References 25 publications

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Classification of malware using multinomial linked latent modular double q learning

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

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