Modeling and Control of a Based Extreme Learning Machine as Distributed Setpoint for the HEPP Cascade System in a Nickel Processing Plant

Abstract

The aim of this research is to model the cascade system of hydropower plants in order to predict the set point power value of each generator. The model simulates several input data variables to obtain an accurate prediction of the set point value. Various historical data are used in this study to evaluate the relationship between input and output variables. This paper presents an Extreme Learning Machine (ELM) method for modeling system models and generating set point values for each generator in a hydroelectric power plant (HEPP) cascade system in a nickel processing plant (NPP). The issue of coordination time between the production and utility departments is addressed. The research aims to use the ELM method to auto-generate setpoint values.  The MATLAB application serves as a simulator for generating the expected Extreme Learning Machine (ELM) model.  As a result, this allows for automatic changes to the set point of each generator in the cascade system. The ELM method yields a MAPE value of 13.94%, indicating accurate predictions.