The article presents the results of measurements of power quality parameters in the system supplying railroad automation equipment and railroad traffic control devices, in the 3 kV DC traction system. Measurements wer...
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power quality event detection is a critical aspect of ensuring the reliability and efficiency of electrical power systems. This paper proposes a novel approach of event detection by integrating the discrete wavelet tr...
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This paper explores the critical issue of circuit failures in nuclear power plant, with a specific focus on mitigating the risk of hot short circuit malfunctions through circuit design optimization. Beginning with an ...
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Combined digital measuring current and voltage transformers (DCVTs), developed at Ivanovo State powerengineering University (ISPEU), are innovative products. The researchers have accumulated some operating experience...
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Large scale wind turbines replace traditional generators and are integrated into the power grid. The increase in wind power penetration rate leads to a decrease in the overall inertia of the power system. To alleviate...
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Considering the long-term memory characteristics exhibited by user groups implementing the same electricity pricing strategy on time series data of electricity consumption, as well as the dynamic changes in user elect...
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ISBN:
(数字)9798350377033
ISBN:
(纸本)9798350377040;9798350377033
Considering the long-term memory characteristics exhibited by user groups implementing the same electricity pricing strategy on time series data of electricity consumption, as well as the dynamic changes in user electricity consumption behavior, a long short-term memory network (LSTM)-based electricity price default user detection model was constructed. First, an autocorrelation analysis was conducted on the time series of electricity consumption for different electricity price categories to illustrate the long-term memory of users' electricity consumption patterns. Second, the time series data of electricity consumption was converted into a tensor form, and a classification model based on LSTM was constructed. At the same time, L1 regularization was applied to the model, and the L1 norm of the LSTM layer weight parameters was added as a regularization term in the loss function, making the model more focused on features that have a key impact on the prediction results. The experimental results showed that the model proposed in this paper could deeply analyze user electricity consumption data, accurately identify abnormal users in data sets with abnormal electricity price labels, and provide solid support for monitoring the implementation of electricity prices.
Multiple DC transmission feeding in receiving power grid brings huge risk to the stable operation of the power grid. It is of great significance to plan the DC feeding point economically and reasonably under the premi...
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With the continuous advancement of the construction of new electric power system, the distribution network automation system is more and more networked and intelligent, and massive distribution terminals are connected...
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Digital substation is composed of intelligent primary equipment and networked secondary equipment in layers. Based on high-speed network communication platform, information sharing and interoperability among intellige...
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With the development of artificial intelligence, the application of artificial intelligence technology in the field of electrical automation control has gradually become widespread, promoting the improvement of electr...
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