Power quantity data correction method based on improved DBSCAN density clustering algorithm

Jing Yang; Zhidong Deng; Kunpeng Liu; Longzhu Zhu; Lihua Gong

doi:10.1117/12.3029333

22 May 2024 Power quantity data correction method based on improved DBSCAN density clustering algorithm

Jing Yang, Zhidong Deng, Kunpeng Liu, Longzhu Zhu, Lihua Gong

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131761X (2024) https://doi.org/10.1117/12.3029333
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

In order to improve the correction accuracy and stability of power quantity data, reduce errors, and improve the management efficiency and operation quality of power system, a power quantity data correction method based on improved DBSCAN density clustering algorithm was proposed. The principal component method is used to process the collected power quantity data, divide the power quantity data into different time interval levels, collect missing values according to the recorded power consumption data, and filter out the irregular power consumption detection error information in the power system environment. The pre-processing is completed by selecting the Romanjofs reference, and the remaining data is detected by the T-distribution detection method to determine whether it is gross error. The power quantity error data obtained by DBSCAN clustering algorithm is summarized, the distance between classification attribute data is calculated, and the original DBSCAN is extended instead of the European distance, so that it can process classification attribute data, set the correction period of the correction matrix, and constantly control the collection process of power quantity error data by using the positive period parameter. Finally, the correction of power error data is realized. The experimental results show that the power consumption of the proposed method reaches the maximum value of 48.53KWH at the end of the experiment, the average correct rate is about 97%, the correction correct rate is the highest, and the actual correction effect is the best.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jing Yang, Zhidong Deng, Kunpeng Liu, Longzhu Zhu, and Lihua Gong "Power quantity data correction method based on improved DBSCAN density clustering algorithm", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131761X (22 May 2024); https://doi.org/10.1117/12.3029333

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KEYWORDS

Data corrections

Data processing

Data modeling

Mathematical optimization

Matrices

Tunable filters

Data mining

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