Load Estimation for Electric Power Distribution Networks

TL;DR

This paper introduces a novel method using ANFIS to generate representative load curves from historical data, improving real-time load estimation accuracy in distribution networks.

Contribution

The paper presents a new approach employing ANFIS for creating representative load curves, enhancing load estimation in distribution management systems.

Findings

The proposed method accurately estimates load curves with low MAPE.

ANFIS-based RLC improves pseudo-measurements for real-time load estimation.

Validation on an 11kV network demonstrates effectiveness.

Abstract

Distribution Load Estimation (DLE) is a key function of Distribution Management System (DMS). In this paper a novel method for presenting historical load data in the form of Representative Load Curves (RLC) is presented. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is used in this regard to estimate the RLC. Accurate RLCs provide better pseudo-measurements for real-time load estimation in distribution networks. The performance of the proposed method is demonstrated on an 11kV radial distribution network with the aid of the MATLAB software. The mean absolute percent error (MAPE) criterion is used to quantify the accuracy of the estimated RLC.