Hybrid integration of multilayer perceptrons and parametric models for reliability forecasting in the smart grid

TL;DR

This paper presents a hybrid MLP and parametric model framework for forecasting power interruptions in smart grids based on weather data, improving reliability prediction accuracy for utility operations.

Contribution

It introduces a novel combined modeling approach integrating parametric regression and neural networks for reliability forecasting in smart grids.

Findings

The integrated model accurately predicts daily power interruptions.

Weather parameters significantly influence reliability performance.

The hierarchical learning algorithm enhances model training efficiency.

Abstract

The reliable power system operation is a major goal for electric utilities, which requires the accurate reliability forecasting to minimize the duration of power interruptions. Since weather conditions are usually the leading causes for power interruptions in the smart grid, especially for its distribution networks, this paper comprehensively investigates the combined effect of various weather parameters on the reliability performance of distribution networks. Specially, a multilayer perceptron (MLP) based framework is proposed to forecast the daily numbers of sustained and momentary power interruptions in one distribution management area using time series of common weather data. First, the parametric regression models are implemented to analyze the relationship between the daily numbers of power interruptions and various common weather parameters, such as temperature, precipitation, air pressure, wind speed, and lightning. The selected weather parameters and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to predict the daily numbers of power interruptions. A modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated model using realtime reliability data from an electric utility in Florida and common weather data from National Climatic Data Center (NCDC). In addition, the sensitivity analysis is implemented to determine the various impacts of different weather parameters on the daily numbers of power interruptions.