A feeder loss estimation algorithm based on k-medoids clustering and ensemble learning

Shuangwei Li; Hui Fu; Mingming Shi; Jing Wang; Juntao Fei; Huiyu Miu; Bingjie Zhang

doi:10.1117/12.2652850

20 October 2022 A feeder loss estimation algorithm based on k-medoids clustering and ensemble learning

Shuangwei Li, Hui Fu, Mingming Shi, Jing Wang, Juntao Fei, Huiyu Miu, Bingjie Zhang

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123501X (2022) https://doi.org/10.1117/12.2652850
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

Line loss is an important indicator for evaluating the economic operation of the power system. With the development of smart grid, it has accumulated a large amount of line loss data, which allows us to use a data-driven approach for line loss estimation. In this paper, an algorithm based on K-Medoids clustering and ensemble learning (KMC-EL) is proposed for feeder loss estimation. Firstly, considering the difference between feeders, an unsupervised learning algorithm, which is called the K-Medoids clustering, is used for feeders clustering. And then, for each type of feeder, an ensemble learning algorithm based on bagging, boosting and weighted integrated algorithm are proposed to estimate line loss. Compared with the traditional algorithms, the KMC-EL model has a lower MSE value, which means it has a better generalization ability and can be applied in different feeder loss estimation scenarios.

Citation Download Citation

Shuangwei Li, Hui Fu, Mingming Shi, Jing Wang, Juntao Fei, Huiyu Miu, and Bingjie Zhang "A feeder loss estimation algorithm based on k-medoids clustering and ensemble learning", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123501X (20 October 2022); https://doi.org/10.1117/12.2652850

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KEYWORDS

Evolutionary algorithms

Statistical analysis

Transformers

Machine learning

Neural networks

Electroluminescence

Fusion energy

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