Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling

TL;DR

This paper presents a multiobjective optimization approach for solar-powered irrigation systems, incorporating Fuzzy Type-2 noise modeling and Bacterial Foraging Algorithm to handle environmental uncertainties and improve system design.

Contribution

It introduces a novel combination of Fuzzy Type-2 modeling with Bacterial Foraging Algorithm for optimizing solar irrigation systems under noisy conditions.

Findings

Successful construction of Pareto frontiers for system design

Effective handling of environmental noise factors in optimization

Identification of optimal system configurations under uncertainty

Abstract

Optimization is becoming a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar insolation and ambient temperature fluctuations) when their system interacts with the environment. In this chapter, the sizing and design optimization of the solar powered irrigation system was considered. This problem is multivariate, noisy, nonlinear and multiobjective. This design problem was tackled by first using the Fuzzy Type II approach to model the noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the context of a weighted sum framework) was employed to solve this multiobjective fuzzy design problem. This method was then used to construct the approximate Pareto frontier as well as to identify the best solution option in a fuzzy setting. Comprehensive analyses and discussions were performed on the generated numerical results with respect to the implemented solution methods.