Ms. Longzhu Zhu Profile

Longzhu Zhu

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Proceedings Article | 22 May 2024 Paper

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, 131761X (2024) https://doi.org/10.1117/12.3029333

KEYWORDS: Data corrections, Data processing, Data modeling, Tunable filters, Matrices, Mathematical optimization, Process control, Data mining

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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.

Proceedings Article | 27 September 2022 Paper

Power big data management analysis and service based on cloud computing

Jing Yang, Bo Peng, Hui Wang, Lihua Gong, Longzhu Zhu

Proceedings Volume 12346, 123461I (2022) https://doi.org/10.1117/12.2653739

KEYWORDS: Clouds, Data processing, Data storage, Data modeling, Data acquisition, Data integration, Data analysis, Computing systems, Information fusion, Analytical research

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To improve the response time of the power big data management and control service, the paper builds a more flexible and diversified management service mode. It creates a stable and reliable processing environment, combines with cloud computing technology, and designs a power big data management analysis and service method. The management and control environment is preprocessed, and the hierarchical processing mechanism is constructed in combination with the cloud computing model. Based on this, the cloud computing integrated power big data management and control model is designed, and the cloud service processing is used to realize the management and control. The final test results show that compared with the traditional multi-dimensional power big data management test group and the traditional positioning power big data management test group, the response time of the management and control service are obtained by the cloud computing power. The Big Data Management test group designed in this paper is relatively short, which indicates that it has a better management effect for power big data and has important practical significance.

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