Abstract
Bad data in a power system fail to accurately reflect its state of operation. Cleaning bad data is thus necessary for the situational analysis of a data-based power system. Methods for the identification and modification of bad data based on state estimation may fail to converge in iterative calculations, and yield poor results when the errors are significant. State estimation also involves non-linear computation, which incurs a significant computational overhead and makes it difficult to efficiently handle large volumes of data. To solve these problems, this paper proposes a method for the detection and identification of bad data based on constraints on spatial–temporal correlation. In the spatial domain, we fix the equivalent power balance condition through the topology of the network. In sequential distribution, we establish data constraints based on the similarity of data among temporal sections. We then check the data in combination with the spatial–temporal correlation constraints. The proposed method was applied to an IEEE 14-bus system and a provincial grid, and the results show that it can reliably detect and modify bad data to significantly improve their quality. The proposed approach avoids non-linear calculations, is computationally efficient, and can quickly detect and modify bad data.
Highlights
With the continual development of power grids, the complexity of their polytropic processes and operations has sharply increased
This paper proposes a method for the detection and identification of bad data based on constraints on spatial–temporal correlation
COMPARISON OF DATA CLEANING EFFECTS OF DIFFERENT METHODS To test the feasibility of the method proposed in this paper, we used the IEEE 14-bus system as test system and state estimation as comparative method
Summary
With the continual development of power grids, the complexity of their polytropic processes and operations has sharply increased. A collection of these data is generally known as the power grid operation dataset. Bad data are detected based on the difference between the measured and the estimated data [1214]. Traditional data mining-based methods to analyze the operational behavior of systems necessitate mechanism modeling [20], and computational complexity is a bottleneck for them when rapidly processing large volumes of data. We apply the correlation between the data against the spatiotemporal constraints of the system operation and organize the constraints on the data Based on these constraints, we propose a method to identify and clean bad data. The principle of the method is to directly detect and modify bad data in combination with the equivalent power balance relationship as reflected by the equivalent topology and the power constraint-based relationship between the timing sections. The results of experiments on the grid yielded a substantial improvement in the quality of the dataset after data cleaning
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