Abstract
This paper investigates the impact of various weather conditions on the reliability performance of power distribution networks. Especially, a hybrid approach based on multilayer perceptrons (MLPs) and parametric models is proposed to forecast the daily numbers of sustained and momentary power interruptions in the distribution management area using chronological weather data. First, the parametric regression models are implemented to analyze the relationship between power interruptions and different weather characteristics including temperature, wind speed, rain precipitation, air pressure, and lightning. The selected weather characteristics and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to forecast the daily numbers of power interruptions. In addition, a modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated forecasting model. Finally, the real power interruption data collected from a Florida electric utility is used to verify the applicability and effectiveness of the proposed hybrid approach.
Original language | English |
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Title of host publication | 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020 |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9781728157481 |
DOIs | |
State | Published - Sep 2020 |
Event | 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020 - Nanjing, China Duration: Sep 20 2020 → Sep 23 2020 |
Publication series
Name | Asia-Pacific Power and Energy Engineering Conference, APPEEC |
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Volume | 2020-September |
ISSN (Print) | 2157-4839 |
ISSN (Electronic) | 2157-4847 |
Conference
Conference | 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020 |
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Country/Territory | China |
City | Nanjing |
Period | 09/20/20 → 09/23/20 |
Funding
This work was supported by State Grid Corporation technology project 5100-201958522A-0-0-00.
Keywords
- ELM
- distribution system
- hybrid forecast forecast
- regression model
- weather parameter