Table of Content

25 February 2021, Volume 41 Issue 1

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Forecast of Daily Maximum Load of Distribution Network Based on Hausdorff Shape Clasification

ZHOU Wangfeng, LI Yong, GUO Yixiu, QIAO Xuebo, DENG Wei, LUO Weicheng

2021, 41(1):  1-5,10.  doi:10. 3969/j. issn. 1008-0198. 2021. 01.001

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The accurate prediction of daily maximum load and its occurrence time are the focus of economic dispatch and safe operation of distribution network. This paper studies the internal law of daily maximum value and its occurrence time of various types of load,and proposes a forecasting method of daily maximum load and its occurrence time combined with Hausdorff load shape classification and holiday correction in the same period of last year. Firstly， the shape characteristics of daily load are analyzed, and the load types are classified by Hausdorff distance algorithm. Then, the corection effect of holidays in the same period of last year in the prediction of daily maximum load and its occurrence time are analyzed, and it is used as the forecast input together with the recent daily load, temperature and other data. Finally, based on ElasticNet regression algorithm, the daily maximum load and its occurrence time prediction model are constructed for each type of load. Taking the load data of a certain station area in Hunan province as an example, the daily maximum load and its occurrence time in the Spring Festival of the station area are predicted and the accuracy and effectiveness of the method are verified in an example.

Application of Coal Consumption Prediction Based on Least-Squares Procedure in Economy Evaluation of Peak-regulating

XIAO Zhibo, WANG Lei, YUAN Xu, SIMA Boyong, JIANG Lei

2021, 41(1):  6-10.  doi:10.3969/i. issn. 1008-0198. 2021.01.002

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In this paper, the coal consumption data matching each load rate of the 300MW generating unit of a power plant in the past year are screened and preprocessed ，and the coal consumption prediction model of the generator unit is given by using the curve fitting method based on the least square principle. Then，the actual benefit difference per hour under two conditions of participating in peak - regulating and no participating in peak- regulating is taken as the objective function ，and the minimum compensation price is calculated ，which provides a reference for the decision - making of power plant peak load regulation bidding. Finally, the influence of coal consumption and peak - regulating load ratio on the economy of the power plant is analyzed.

Service Restoration of Distribution Network with DG Based on Dual-population CSO Algorithm

LUO Chunhui, QU Gangju, TANG Tao, LIU Junyao, JIANG Jiadong, LUO Weiyuan

2021, 41(1):  11-17.  doi:10.3969/j.issn.1008-0198. 2021. 01. 003

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To guarantee both the speediness and global optimization of service restoration of distribution network simultaneously, a hierarchical response fault recovery method based on reconfiguration is proposed, and the crisscross optimization ( CSO) algorithm with dual dual-population is applied to seek the reconstruction scheme. Firstly， partial reconstruction using heuristic rules for non-islanding operation is implemented for fast restoration of distribution network connectivity with fewer switching times. Secondly，the optimal solution is searched in the feasible region by CSO algorithm with dual dual-population for global reconstruction while the safety indicators are over-limit, and if the safety indicators are still over-limit, part of non-critical load is shed in the order of the importance index of the load until the disribution network is back to a new safe operation state. Finally, the feasibility and effectiveness of the proposed method are verified by simulation results of improved PG& E distribution system.

Probabilistic Recognition of Voltage Sag Sources Based on Gradient Boosting Decision Tree

2021, 41(1):  18-24.  doi:1008- 0198( 2021 )01-0018-07

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In this paper, a method based on gradient boosting decision tree for voltage sag sources probabilistic recognition is proposed. The residuals are continuously fitted through the training of multiple decision trees， and the sag sources are recognized according to the recognition probability. The effectiveness and accuracy of the proposed method are verified by using various types of sag source waveform obtained by simulation, and compared with the traditional support vector machine method. Compared with the support vector machine, this method has higher accuracy under the same sample number, and the various types of sag source identification probability information can better reflect the credibility of the model identification， which is helpful for decision makers to assist decision making.