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A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost

Author

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  • Shouxiang Wang

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Pengfei Dong

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Yingjie Tian

    (State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China)

Abstract

The estimation of losses of distribution feeders plays a crucial guiding role for the planning, design, and operation of a distribution system. This paper proposes a novel estimation method of statistical line loss of distribution feeders using the feeder cluster technique and modified eXtreme Gradient Boosting (XGBoost) algorithm that is based on the characteristic data of feeders that are collected in the smart power distribution and utilization system. In order to enhance the applicability and accuracy of the estimation model, k -medoids algorithm with weighting distance for clustering distribution feeders is proposed. Meanwhile, a variable selection method for clustering distribution feeders is discussed, considering the correlation and validity of variables. This paper next modifies the XGBoost algorithm by adding a penalty function in consideration of the effect of the theoretical value to the loss function for the estimation of statistical line loss of distribution feeders. The validity of the proposed methodology is verified by 762 distribution feeders in the Shanghai distribution system. The results show that the XGBoost method has higher accuracy than decision tree, neural network, and random forests by comparison of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Absolute Percentage Error (APE) indexes. In particular, the theoretical value can significantly improve the reasonability of estimated results.

Suggested Citation

  • Shouxiang Wang & Pengfei Dong & Yingjie Tian, 2017. "A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost," Energies, MDPI, vol. 10(12), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2067-:d:121659
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    References listed on IDEAS

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    1. Mark Chiang & Boris Mirkin, 2010. "Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 3-40, March.
    2. Jan Schepers & Eva Ceulemans & Iven Mechelen, 2008. "Selecting Among Multi-Mode Partitioning Models of Different Complexities: A Comparison of Four Model Selection Criteria," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 67-85, June.
    3. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    4. Urraca, R. & Martinez-de-Pison, E. & Sanz-Garcia, A. & Antonanzas, J. & Antonanzas-Torres, F., 2017. "Estimation methods for global solar radiation: Case study evaluation of five different approaches in central Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1098-1113.
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    Cited by:

    1. Gheorghe Grigoras & Bogdan-Constantin Neagu, 2019. "Smart Meter Data-Based Three-Stage Algorithm to Calculate Power and Energy Losses in Low Voltage Distribution Networks," Energies, MDPI, vol. 12(15), pages 1-27, August.
    2. Mirosław Kornatka & Anna Gawlak, 2021. "An Analysis of the Operation of Distribution Networks Using Kernel Density Estimators," Energies, MDPI, vol. 14(21), pages 1-12, October.
    3. Kun Li & Junsan Zhao & Yilin Lin, 2023. "Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2477-2511, September.
    4. Ma, Chenjie & Menke, Jan-Hendrik & Dasenbrock, Johannes & Braun, Martin & Haslbeck, Matthias & Schmid, Karl-Heinz, 2019. "Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation," Applied Energy, Elsevier, vol. 256(C).

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