Data-Driven Estimation of Voltage-to-Power Sensitivities Considering Their Mutual Dependency in Medium Voltage Distribution Networks
Voltage-to-power sensitivities play a key role in the control and operation of distribution networks. To estimate these sensitivities without network information, data-driven estimation methods have been studied. However, conventional methods do not consider mutual dependency (MD) of the sensitiviti...
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| Veröffentlicht in: | IEEE transactions on power systems 2022-07, Vol.37 (4), p.3173-3176 |
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| container_title | IEEE transactions on power systems |
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| creator | Chang, Jae-Won Kang, Moses Oh, Seaseung |
| description | Voltage-to-power sensitivities play a key role in the control and operation of distribution networks. To estimate these sensitivities without network information, data-driven estimation methods have been studied. However, conventional methods do not consider mutual dependency (MD) of the sensitivities and thus the estimation can be inaccurate. Thus, this paper proposes a new data-driven estimation method of the sensitivities, which considers MD of the sensitivities in medium voltage distribution networks. In the proposed method, via MD analysis, the sensitivities are estimated by solving nonlinear least square problems and thus the accurate estimation can be achieved. The effectiveness of the proposed method is verified using the real-time platform. |
| doi_str_mv | 10.1109/TPWRS.2022.3162745 |
| format | Article |
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| subjects | Artificial neural networks Distribution networks Electric potential Electric power distribution Estimation Medium voltage Medium voltage distribution network mutual dependency Networks nonlinear least square Real-time systems Sensitivity Sensitivity analysis Voltage voltage-to-power sensitivity |
| title | Data-Driven Estimation of Voltage-to-Power Sensitivities Considering Their Mutual Dependency in Medium Voltage Distribution Networks |
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