Generator Controller Tuning Considering Stochastic Load Variation Using Analysis of Variance and Response Surface Method

TL;DR

This paper introduces a novel method for tuning generator controllers in power systems with stochastic loads, using statistical analysis and response surface modeling to optimize system response quality.

Contribution

It combines Analysis of Variance and Response Surface Methodology to improve generator controller tuning considering load variability, outperforming benchmark parameters.

Findings

Improved system response with optimized controller tuning.

Statistically significant variables identified for tuning.

Enhanced performance demonstrated on IEEE14 system.

Abstract

This article proposes a method for generator controller tuning in a power system affected by stochastic loads. The method uses the Analysis of Variance to detect the controllers with significant effect over the quality of the system response. Such quality is measured with an objective function defined as a weighted average of the Integral Absolute Error of each controller. The significant variables are then varied over a specified region in order to characterize the objective function through a regression model, which is then optimized. The method was applied to the system IEEE14 and the results were compared with benchmark parameters, showing better performance.