基于混合量测的电力系统自适应抗差动态状态估计方法

doi:10.19595/j.cnki.1000-6753.tces.241387

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Abstract

State estimation is a key function of the energy management system in the power grid dispatching control center, which can be used to estimate the operating status of the entire power network and enable real-time monitoring of the power system. Currently, the availability of phasor measurement units (PMUs) alone is insufficient to meet the observability requirements for state estimation. To address this, integrating conventional measurement systems, like supervisory control and data acquisition (SCADA), into a hybrid measurement setup can effectively enhance measurement redundancy. Meanwhile, the Kalman filter algorithm used for dynamic state estimation in power systems demands high model accuracy, yet its tracking capability and estimation precision are compromised under the influence of unknown time-varying noise. To mitigate the impact of unknown measurement noise and accurately obtain system states, this paper proposes a hybrid measurement state estimation method based on the improved adaptive robust extended Kalman filter (IAREKF) algorithm.
Firstly, the power measurements of SCADA are converted into equivalent current phasor measurements by the measurement transformation strategy, which linearizes the measurement equations and enhances computational efficiency. Simultaneously, a spatio-temporal pseudo-measurement generation method is devised that incorporates power transfer distribution factors and temporal correlations, thereby supplementing missing state information and enabling the fusion of hybrid measurements. Secondly, the measurement noise covariance is estimated by analyzing the statistical characteristics of the measurement devices. An equivalent weight function is constructed based on the standardized innovation to modify the measurement noise covariance, thereby enhancing the robustness of the algorithm. Finally, an adaptive forgetting factor method is employed to refine the Sage-Husa noise estimator. Quantify the overall filtering performance of the current state estimation utilizing the ratio of the trace of the innovation variance matrix to the trace of the innovation covariance matrix as a metric. An adaptive forgetting factor is determined using this metric, adjusting the level of discounting applied to outdated data during the estimation of the process noise covariance matrix. This method enhances the precision of the adaptive estimation of process noise, thereby improving the algorithm's dynamic tracking performance of the system state.
Simulation results show that in the IEEE 39-bus power system, under conditions of unknown model and noise parameters, the proposed method achieves a mean absolute error (MAE) of 3.477×10-4/p.u. for voltage phase angle estimation, and MAE of 3.057×10-2/° for voltage phase angle estimation. Compared to the generalized maximum likelihood iterated extended Kalman filter (GMIEKF) and adaptive H? extended Kalman filter (AHEKF) algorithms, the MAE reductions are 37.51% and 42.21% for voltage magnitude estimation, and 14.97% and 21.46% for voltage phase angle estimation, respectively. Under the influence of bad data, the MAE reductions are 37.26% and 24.84% for voltage magnitude estimation, and 12.95% and 6.55% for voltage phase angle estimation, demonstrating excellent robustness. During system transients, the proposed method maintains high estimation accuracy, showcasing its superior dynamic tracking capability.
The following conclusions can be obtained through simulation analysis: 1) Facing the differences in characteristics among various measurement systems, the measurement transformation technology is adopted to achieve the fusion of hybrid measurements. Pseudo-measurements are constructed based on the spatio-temporal characteristics of the system to increase the frequency of state estimation to synchronize with PMU sampling, enabling a more rapid acquisition of the system's dynamic changes. 2) The measurement noise covariance is estimated by analyzing the characteristics of measurement devices. Furthermore, a robust factor is constructed based on the standardized innovation to adjust the measurement noise covariance matrix, ensuring robustness against bad data. 3) An adaptive forgetting factor-based process noise estimation algorithm is proposed. Compared to the traditional Sage-Husa noise estimation method, this algorithm can more quickly forget previously invalid statistical information, leading to a more accurate estimation of process noise. Consequently, it achieves higher estimation accuracy under system disturbances or abrupt changes.

Key words： State estimation hybrid measurements Kalman filter robust estimation

Received: 05 August 2024

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TM711

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	Lin Junjie
	Hong Hongbin
	Song Wenchao
	Jiang Changxu
	Lu Chao

Cite this article:

Lin Junjie,Hong Hongbin,Song Wenchao等. Power System Adaptive Robust Dynamic State Estimation Based on Hybrid Measurements[J]. Transactions of China Electrotechnical Society, 0, (): 20241387-20241387.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.241387 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/20241387

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