| Citation:Dongliang Xu,Junjun Xu,Cheng Qian,Zaijun Wu,Qinran Hu.A pseudo-measurement modelling strategy for active distribution networks considering uncertainty of DGs[J].Protection and Control of Modern Power Systems,2024,V9(5):1-15[Copy] |
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| Abstract: |
| Active distribution networks utilize advanced sensors, communication, and control technologies to achieve flexible and intelligent power distribution management. Reliable state estimation (SE) is crucial for distribution management systems to monitor these networks. Historically, the scarcity of measurement resources has hindered the application of SE technology in distribution networks. Establishing a dependable pseudo-measurement model for active distribution networks can significantly enhance the feasibility of SE. This paper proposes a pseudo-measurement model that aligns with the actual operating status of the distribution network, considering the uncertainty in output from distributed generations (DGs) such as wind turbines and photovoltaics. Firstly, it analyzes and models the uncertainty of high-penetration DG output, establishing a reliable output model that incorporates the physical characteristics of wind and photovoltaic output. Secondly, it proposes a pseudo-measurement modeling method based on support vector machine (SVM), where the kernel function of the SVM is weighted according to the information entropy of fluctuations in historical operating data. This weighting ensures that the established pseudo-measurement model better reflects the actual operating status of the active distribution network. Finally, a mathematical model for optimizing pseudo-measurement selection is developed, with the minimum state estimation error as the objective function and the observability of the active distribution network system as the constraint. Case studies demonstrate the accuracy and effectiveness of this approach. |
| Key words: Distribution network, pseudo-measure-ment, uncertainty of DGs, state estimation, entropy weighting method-support vector machine (EWM-SVM). |
| DOI：10.23919/PCMP.2023.000189 |
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| Fund:This work is supported by the National Natural Science Foundation of China (No. 52377086). |
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