The traditional Vine-Copula method employs Vine structure to represent the dependent structure between wind speed of wind turbines in wind farms, which can fully capture the spatial correlation among them, but lacks t...
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The current research on differentiated service methods for electric power customers is difficult to analyze the actual needs of users, and the matching degree with users is low, and it is difficult to satisfy users. I...
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Faults occur from time to time in the magnetic coupling wireless power transmission (MC-WPT) system. When the fault occurs, if the system can detect the changes of various parameters in time, the operator can quickly ...
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With the development of renewable energy, the installed capacity of photovoltaic power is increasing rapidly. Aiming at the long-term planning of photovoltaic grid-connected, photovoltaic power (PV) maintenance planni...
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A photovoltaic system built in Varna region was examined. In order to control the output power of the plant, it is necessary to manage the changing value of the current - ipv. The paper proposes to do this through a c...
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This paper presents an enhanced approach to managing a Double Fed Induction Generator (DFIG) wind turbine with a Supercapacitor (SC) energy storage system. The focus is on achieving constant active power and inertia c...
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This paper designs a UHV converter station ground fault early warning system based on deep learning to improve the accuracy of fault detection, response speed and adaptability to complex environments. The system adopt...
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ISBN:
(纸本)9798350377040;9798350377033
This paper designs a UHV converter station ground fault early warning system based on deep learning to improve the accuracy of fault detection, response speed and adaptability to complex environments. The system adopts an algorithm model combining improved CNN and LSTM to fully mine the spatial features and time series information in the operating data to achieve accurate prediction and early warning of ground faults. In the data processing process, the CNN module is used to extract spatial features in the operating data, such as the key mode of the fault current waveform, and the LSTM module models the time series changes to capture the law of abnormal signal changes over time. Through this collaborative mechanism, the system can effectively improve the sensitivity to abnormal signals, reduce the false alarm rate, and maintain high performance under complex and changeable operating conditions. To verify the performance of the system, this paper conducts model simulation experiments under various environmental conditions based on the historical operating data and fault samples of the real converter station, covering different load levels, temperature and humidity ranges and other working conditions. The experimental results show that the accuracy of the system in ground fault detection reaches 98.7%, the false alarm rate is reduced to 1.3%, and it can maintain stable early warning capabilities under various environmental conditions.
In order to further improve the transportation capacity of belt conveyor and solve the power imbalance problem of multi-motor-driven belt conveyor in practical application, on the basis of analyzing the basic structur...
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In this paper, the common problems of electrical engineering under automation environment are systematically analyzed, and some new solutions are put forward. First of all, this paper reviews the research status of th...
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