Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation: Preprint
Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Electricity
- DOE Contract Number:
- DE-AC36-08GO28308
- OSTI ID:
- 1605534
- Report Number(s):
- NREL/CP-5D00-75247; MainId:18808; UUID:8c1bab3e-5ef4-e911-9c29-ac162d87dfe5; MainAdminID:7286
- Resource Relation:
- Conference: To be presented at the 2020 IEEE Power and Energy Society General Meeting (IEEE PES GM), 2-6 August 2020, Montreal, Canada; Related Information: 79002
- Country of Publication:
- United States
- Language:
- English
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