A Hybrid Approach for Weather-based Power Interruption Forecasting using Multilayer Perceptrons and Parametric Models

Longfei Wei, Xiaolei Yang, Guangyi Liu, Renchang Dai, Aditya Sundararajan, Arif I. Sarwat

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728157481
DOIs
StatePublished - Sep 2020
Event12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020 - Nanjing, China
Duration: Sep 20 2020Sep 23 2020

Publication series

NameAsia-Pacific Power and Energy Engineering Conference, APPEEC
Volume2020-September
ISSN (Print)2157-4839
ISSN (Electronic)2157-4847

Conference

Conference12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020
Country/TerritoryChina
CityNanjing
Period09/20/2009/23/20

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

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