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| Interval Harmonic Robust State Estimation Method Based on Multi-Source Measurement Data Fusion |
| Chen Yihuang1,2, Shao Zhenguo1,2, Lin Junjie1,2, Zhang Yan1,2, Chen Feixiong1,2 |
1. Key Laboratory of Energy Digitalization Fuzhou 350108 China;
2. College of Electrical Engineering and Automation Fuzhou University Fuzhou 350108 China |
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Abstract Harmonic state estimation is a key part of power system operation management, which can be used to monitor the harmonic in the power grid and provide an important reference for the stable operation of the power grid. At present, the number of phasor measurement unit (PMU) configurations are difficult to satisfy the observability requirements of state estimation. It is necessary to adapt power quality monitoring device (PQMD) data to improve the redundancy of measurement and make the harmonic state estimation possible. However, the non-synchronized monitoring data characteristics of PQMD make the fused measurement data still have deviations, which will lead to a large error in harmonic estimation state. Fusing PMU and PQMD measurement data and minimizing the asynchronous measurement bias of PQMD measurement data, as well as suppressing the influence of this measurement bias in state estimation, will provide a more effective means for grid harmonic analysis. Therefore, the paper proposes an interval robust harmonic state estimation method based on PMU and PQMD measurement data fusion.
Firstly, the detection period of PQMD is selected with the overlap index, and the reference period is selected with the maximum overlap as the target. The selected reference period is used as the measurement buffer of PMU, and the PQMD measurement data in this period is fused to form an interval mixed measurement set. The harmonic power measurements of PQMD are converted into equivalent harmonic current phasor measurement by the measurement transformation strategy, which is updated with state quantity in iterative solution. Secondly, the projection statistical method is used to calculate the initial weight of the measurement, and the overlap index is introduced into the Huber weight function to adjust the measurement weight. The measurement with low overlap and large residual is given a small weight to suppress the influence of measurement deviation, and further improve the robustness of the algorithm. Finally, the measurement points are preferring according to the weights, and the measurement subset with the least deviation of non-synchronous measurement is obtained. The harmonic state estimation model is solved by iterative reweighted least square method, and the harmonic state range of the whole network is obtained. The simulation results show that when the load fluctuation is 10% and the average overlap degree is 0.85, the estimated error of the proposed method is 1.92% in the upper bound and 3.24% in the lower bound. The error of phase angle upper bound estimation is 2.27%, and the error of lower bound estimation is 4.22%. When the overlap degree is reduced to 0.6, the average error of amplitude and phase angle of the proposed algorithm is less than 6%. When the level of load fluctuation increases to 40%, the average estimation error of state quantity is less than 5%.
The following conclusions can be obtained through simulation analysis: (1) The overlap index is used to quantify the measurement deviation of PQMD to improve the weight matrix in the state estimation, which can effectively suppress the influence of the measurement deviation on state estimation. In addition, the interval mixed measurement subset is obtained by preferring the measurement points according to the weight coefficient, which can further reduce the non-synchronous measurement deviation of the measurement set, improve the reliability of the interval mixed measurement set and the accuracy of state estimation. (2) Converting the interval weight matrix and Jacobian matrix into a definite value can further reduce the conservatism of the solution interval. (3) The proposed algorithm can effectively reduce the estimation error under different measurement deviations and different load fluctuation sizes which has robustness.
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Received: 31 October 2023
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2024, Vol. 39 
