Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Nguyen, Minh-Tuan; Nguyen, Vu Quoc Huy; Yun, Suk-Jun; Kim, Yong‐Hwa

doi:10.3390/en11051202

Cited by 73 publications

(57 citation statements)

References 39 publications

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“…A specific feature of this type of RNNs is represented by the fact that they incorporate specific loops that facilitate the persistence of information, which is transmitted within the network from one step to the subsequent one. LSTMs have been first introduced in 1997 [38], after that being popularized, used and refined by many other researchers [39][40][41][42][43].…”

Section: The Long Short-term Memory (Lstm) Neural Networkmentioning

confidence: 99%

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Petroşanu¹

2019

Processes

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An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly electricity consumption in the case of medium industrial consumers, therefore assuring an intelligent energy management and an efficient economic scheduling of their resources, having the possibility to negotiate in advance appropriate billing tariffs relying on accurate hourly forecasts, in the same time facilitating an optimal energy management for the dispatch operator. The forecasting method consists of developing first non-linear autoregressive, with exogenous inputs (NARX) artificial neural networks (ANNs) in order to forecast an initial daily electricity consumption, a forecast that is being further processed with custom developed long short-term memory (LSTM) neural networks with exogenous variables support in order to refine the daily forecast as to achieve an accurate hourly forecasted consumed electricity for the whole month-ahead. The obtained experimental results (highlighted also through a very good value of 0.0244 for the root mean square error performance metric, obtained when forecasting the month-ahead hourly electricity consumption and comparing it with the real consumption), the validation of the developed forecasting method, the comparison of the method with other forecasting approaches from the scientific literature substantiate the fact that the proposed approach manages to fill a gap in the current body of knowledge consisting of the need of a high-accuracy forecasting method for the month-ahead hourly electricity consumption in the case of medium industrial consumers. The developed forecasting method targets medium industrial consumers, but, due to its accuracy, it can also be a useful tool for promoting innovative business models with regard to industrial consumers willing to produce a part of their own electricity using renewable energy resources, benefiting from reduced production costs and reliable electricity prices.

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Section: The Long Short-term Memory (Lstm) Neural Networkmentioning

confidence: 99%

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Petroşanu¹

2019

Processes

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“…To effectively solve this problem, deep learning methods that rely on automatic feature extraction are introduced into GIS PD pattern recognition. At present, these deep learning models include LeNet5, AlexNet, one-dimensional convolution, and long short-term memory (LSTM) models [30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

et al. 2019

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Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data.

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“…In recent years, deep neural networks (DNNs) have recently achieved state-of-the-art performance in different fields such as audio processing [8,9], visual object recognition [10], and other domains [11][12][13]. Neural networks (NNs) can extract features from input implicitly and approximate an arbitrary math function.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems

Nguyen¹,

Kim²

2019

Applied Sciences

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Many resource allocation problems can be modeled as a linear sum assignment problem (LSAP) in wireless communications. Deep learning techniques such as the fully-connected neural network and convolutional neural network have been used to solve the LSAP. We herein propose a new deep learning model based on the bidirectional long short-term memory (BDLSTM) structure for the LSAP. In the proposed method, the LSAP is divided into sequential sub-assignment problems, and BDLSTM extracts the features from sequential data. Simulation results indicate that the proposed BDLSTM is more memory efficient and achieves a higher accuracy than conventional techniques.

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Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Cited by 73 publications

References 39 publications

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems

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