The Experts below are selected from a list of 13338 Experts worldwide ranked by ideXlab platform

Aleksey Kudreyko - One of the best experts on this subject based on the ideXlab platform.

  • Reliability Analysis of Power Distribution Network Based on PSO-DBN
    IEEE Access, 1
    Co-Authors: Hongtao Shan, Wenjun Zhang, Aleksey Kudreyko
    Abstract:

    The main problem dealt with in this paper is to find a method to improve the performance of the reliability analysis of Power Distribution Networks. With the help of deep learning, which has the characteristics of large-scale parallel processing and self-learning, a deep belief Network (DBN) simulation model for Power Distribution Network reliability analysis is established. After training RBM layer by layer and extracting feature information from complex data, DBN model parameters are adaptively adjusted by particle swarm optimization (PSO) algorithm. The results of Power Distribution Network reliability analysis based on PSO-DBN model is compared with those of Monte Carlo model. In order to evaluate the performance of the proposed model, the coefficient R2, the mean absolute error and the root mean square error are used to evaluate the model. The results show that the reliability analysis model based on PSO-DBN is more accurate, and the reliability analysis efficiency of the trained PSO-DBN model is higher, which to some extent proves the superiority of applying deep neural Network to the reliability analysis of Distribution Network.

Hongtao Shan - One of the best experts on this subject based on the ideXlab platform.

  • Reliability Analysis of Power Distribution Network Based on PSO-DBN
    IEEE Access, 1
    Co-Authors: Hongtao Shan, Wenjun Zhang, Aleksey Kudreyko
    Abstract:

    The main problem dealt with in this paper is to find a method to improve the performance of the reliability analysis of Power Distribution Networks. With the help of deep learning, which has the characteristics of large-scale parallel processing and self-learning, a deep belief Network (DBN) simulation model for Power Distribution Network reliability analysis is established. After training RBM layer by layer and extracting feature information from complex data, DBN model parameters are adaptively adjusted by particle swarm optimization (PSO) algorithm. The results of Power Distribution Network reliability analysis based on PSO-DBN model is compared with those of Monte Carlo model. In order to evaluate the performance of the proposed model, the coefficient R2, the mean absolute error and the root mean square error are used to evaluate the model. The results show that the reliability analysis model based on PSO-DBN is more accurate, and the reliability analysis efficiency of the trained PSO-DBN model is higher, which to some extent proves the superiority of applying deep neural Network to the reliability analysis of Distribution Network.

Wenjun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Reliability Analysis of Power Distribution Network Based on PSO-DBN
    IEEE Access, 1
    Co-Authors: Hongtao Shan, Wenjun Zhang, Aleksey Kudreyko
    Abstract:

    The main problem dealt with in this paper is to find a method to improve the performance of the reliability analysis of Power Distribution Networks. With the help of deep learning, which has the characteristics of large-scale parallel processing and self-learning, a deep belief Network (DBN) simulation model for Power Distribution Network reliability analysis is established. After training RBM layer by layer and extracting feature information from complex data, DBN model parameters are adaptively adjusted by particle swarm optimization (PSO) algorithm. The results of Power Distribution Network reliability analysis based on PSO-DBN model is compared with those of Monte Carlo model. In order to evaluate the performance of the proposed model, the coefficient R2, the mean absolute error and the root mean square error are used to evaluate the model. The results show that the reliability analysis model based on PSO-DBN is more accurate, and the reliability analysis efficiency of the trained PSO-DBN model is higher, which to some extent proves the superiority of applying deep neural Network to the reliability analysis of Distribution Network.

Subrata Paul - One of the best experts on this subject based on the ideXlab platform.

  • A comprehensive review on Power Distribution Network reconfiguration
    Energy Systems, 2017
    Co-Authors: Sivkumar Mishra, Debapriya Das, Subrata Paul
    Abstract:

    Reconfiguration of radial Distribution Networks is becoming a viable solution for improving the performance of Distribution Networks. Configurations may be varied with manual or automatic switching operations so that all of the loads are supplied and reduce Power loss, increase system security, and enhance Power quality. Reconfiguration also relieves the overloading of the Network components. The change in the Network configuration is performed by opening sectionalizing (normally closed) and closing tie (normally open) switches of the Network. These switchings are performed in such a way that the radiality of the Network is maintained and all of the loads are energized. Several researchers have attempted to solve the Power Distribution Network reconfiguration problem using various techniques. This paper presents a comprehensive survey on Network reconfiguration to bring out a clear idea for future research.

Zhao Wenzhong - One of the best experts on this subject based on the ideXlab platform.

  • Review of Reactive Power Compensation Techniques of Rural Power Distribution Network
    Low Voltage Apparatus, 2020
    Co-Authors: Zhao Wenzhong
    Abstract:

    To solve the current problems between the economic operation on rural Power Distribution Network and the lack of actual investment,according to the reactive Power compensation technique development experience at home and abroad,the problems of 10(6) kV reactive Power compensation of rural Power Distribution Network were mainly analyzed.A utility program was proposed to reduce Network losses and improve operational efficiency of the rural Power Distribution Network reactive Power compensation.The program had a certain reference value on rural economic operation of Power Distribution Network.