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MNFIS and other soft computing based MPPT techniques: A comparative analysis

  • Tennessee Technological University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Maximum Power Point Tracking (MPPT) is the process of searching the voltage space for the optimal power generation and tracking the optimum as it changes. This paper presents a performance analysis of soft computing algorithms applied to this endeavor and a deployment recommendation based on performance goals. Specifically, fuzzy logic (FL) and artificial neural networks (ANN) were tested with direct and indirect converter control and compared against multiple metrics for fitness. Along the way a novel algorithm was also developed, deemed the Modified Neuro-Fuzzy Inference System (MNFIS). This algorithm incorporates the strengths of both FL and ANN MPPT while mitigating the weaknesses of either.

Original languageEnglish
Title of host publication2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages146-150
Number of pages5
ISBN (Electronic)9781509056057
DOIs
StatePublished - 2017
Event44th IEEE Photovoltaic Specialist Conference, PVSC 2017 - Washington, United States
Duration: Jun 25 2017Jun 30 2017

Publication series

Name2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017

Conference

Conference44th IEEE Photovoltaic Specialist Conference, PVSC 2017
Country/TerritoryUnited States
CityWashington
Period06/25/1706/30/17

Keywords

  • Artificial Neural Network
  • Fuzzy Logic
  • Machine Learning
  • Maximum Power Point Tracking
  • Modified Neuro Fuzzy Inference System

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