Novel MPPT algorithm with MIWO for PV Power Generation System under Partial Shading

Ramakant Upadhyay, Assistant Professor, Department of Computer Science Engineering, Sanskriti University, Email Id-ramakantupadhyay@sanskriti.edu.in, Mathura, Uttar Pradesh, India
Chetan Chaudhary, Assistant Professor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Email Id-ccnitj@gmail.com, Uttar Pradesh, India
Mohit Kumar Sharma, Assistant Professor, Electrical Engineering, Vivekananda Global University, Email Id-mohit.kumar.sharma@vgu.ac.in, Jaipur, India
Kuldeep Singh Kulhar, Professor, Civil Engineering, Vivekananda Global University, Email Id-k.singh@vgu.ac.in, Jaipur, India

^* Corresponding author: ramakantupadhyay@sanskriti.edu.in

Abstract

A Modified Invasive Weed Optimization (EIWO) method has been created and combined with the perturb & observe (P&O) algorithm in order to maximize the power output of a photovoltaic (PV) system amidst sudden weather variations and instances of partial shading. Extracting the maximum power from photovoltaic systems under partial shading conditions poses a challenge, as traditional P&O algorithms are unable to accurately identify the maximum power point (MPP) due to the presence of multiple MPPs. Evolutionary optimization techniques have the capability to attain the global maximum power point under such circumstances. The MIWO-based P&O algorithm adjusts the reference voltage in order to optimize the operation of the PV system at the maximum power point based on prevailing weather conditions. The boost converter is utilized to optimize power output from the PV system and supply it to DC loads. Rapid weather changes are considered for case studies. MATLAB results through OPAL-RT platform by using Hardwrein-Loop (HIL) are presented to validate the performance of the proposed method. Small signal analysis also reported in this paper for better analysis. The robustness of the loop design is also analyzed. Simulations for the small signal model and robustness studies are conducted for a specific set of system parameters to validate the findings.