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| Power Quality Characteristics Identification of Industrial Users Based on Data Correlation Analysis |
| Zhang Yi1, Li Ke1, Shao Zhenguo1, Lin Nan1, Yu Junhong2 |
1. College of Electrical Engineering and Automation Fuzhou University Fuzhou 350108 China;
2. Fuzhou Power Supply Company of State Grid Fujian Electric Power Company Fuzhou 350009 China |
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Abstract The type and number of interference source users at the point of common coupling (PCC) are increasing. This makes the index and spatial-temporal characteristics of power quality (PQ) disturbance more complex. However, it is difficult for the PQ monitoring devices to monitor all terminal users of each feeder. Recently, some methods were presented to identify the PQ characteristics of multiple users, but most of them failed to accommodate long-term PQ phenomena and uncertain user operating conditions. To address these issues, this paper proposes a industrial users PQ characteristics identification method. Using derivative dynamic time warping (DDTW) to calculate the correlation of the existing multi-source data, it accurately identifies the PQ characteristics for industrial users.
Firstly, the over-limit and fluctuation periods of PQ monitoring index data are defined as disturbance periods. According to national standard limits and probability distribution, the over-limit and fluctuation values of PQ time series and their periods are extracted. Secondly, the screening rules of key harmonic indicators are set up to obtain the disturbance time series data of key harmonic indicators, voltage deviation and negative sequence voltage imbalance, so that the characteristics identification is more targeted. Thirdly, the user power data during the disturbance period are extracted, and the correlation between them and the monitoring indicators are calculated by using DDTW. Finally, the power quality characteristics of users are identified according to the correlation degree of different indicators.
The simulation results show that the proposed method can select the user groups most related to PQ problem, which is consistent with the simulation settings. In addition, compared with the traditional DTW algorithm, this shows that DDTW overcomes ill-conditioned matching of time series data, so as to effectively filter out the data with strong correlation in time series.To compare with the identification method based on mutual information, the power quality monitoring data with an interval of 3 minutes is averaged every 5 as a group, which is aligned with the power data of 15 minutes, and then the maximum mutual information decision method is used to identify the user power quality characteristics. The result shows that the maximum mutual information method obtains more types of characteristics and some errors.
The following conclusions can be drawn from the simulation and real measurement analysis: (1) Through the national standard limits and probability distribution method, it effectively determines key indicators for long time scale series. (2) The DDTW algorithm is used to match the power quality index of the monitoring point and the power time series data of the user side. In this way, it is more practical to select the representative indicators that are greatly affected by the power fluctuation. (3) The proposed method only needs long-term statistical data in the existing relevant systems. It is convenient to realize online identification of user power quality characteristics.
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Received: 06 April 2022
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2023, Vol. 38 
