Online identification method of power grid load sensitivity based on adaptive Kalman filter

Hao Huo, Chao Kang, Ningrui Li, Bingbing Liu and Huifeng Zhang

International Journal of Energy Technology and Policy, 2025, vol. 20, issue 1/2, 110-124

Abstract: In order to overcome the problems of large covariance, high deviation, and low sensitivity in traditional load sensitivity identification methods for power grids, this paper proposes an online identification method for power grid load sensitivity based on adaptive Kalman filtering. Firstly, construct a power grid load sensitivity identification architecture consisting of data layer, service layer, and application layer. Secondly, construct a discrete Kalman filter model, determine the time update formula, and design a Kalman filter. Then, the adaptive Kalman filter is used to verify the load node status of the power grid and identify the load data. Finally, based on the data identification results, the relay protection setting value is calculated and used for adaptive online identification of power grid load sensitivity. The experimental results show that the covariance of the method proposed in this paper is stable at 0.01, the sensor acquisition information error remains below 1%, the sensitivity index is high, and it has good robustness.

Keywords: adaptive Kalman filter; grid load; sensitivity; online identification. (search for similar items in EconPapers)
Date: 2025
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