PSO-Based Modeling and Analysis of Electrical Characteristics of Photovoltaic Module Under Nonuniform Snow Patterns

Abstract

In this paper, a novel universal multi-zone approach of photovoltaic (PV) modeling is proposed to determine the electrical characteristics of PV modules covered with nonuniform snow patterns under partial shading conditions. A precise estimation of the penetrating light into the snow layer on the surface of PV modules is obtained through the theory of Giddings and LaChapelle based on the physical and optical properties of the accreted snow. The single-diode-five-parameter equivalent circuit model of the PV unit is considered as the platform for the modeling approach. Original contributions are brought through: (1) the use of a contour-based discretization methodology that can separate any nonlinear PV characteristics to the multiple linear ones; (2) a swarm-based optimization methodology that is adapted to instantaneously update and evaluate the output characteristics of PV modules and (3) a power loss equation to represent the performance of non-uniformly-covered snowy PV panels. The proposed model was successfully tested using three different commercial types of PV technologies commonly used in North America. The accuracy of the proposed modeling approach for power loss determination was validated by processing real data of a 12-MW grid-connected PV farm. Due to the high extent of snow impact on the PV losses, the proposed model of PV modules could be regarded as a basis not only for analyzing PV plant performance, but also for optimizing the power converter design.

Hybrid optimization of photovoltaic power prediction with PSO-CNN-GRU considering seasonal partitioning

“…PSO may generate solutions that violate constraints when dealing with constrained problems [6] . To address this issue, penalty functions are introduced to improve particle swarm optimization.…”

Downscaling Surface Albedo to Higher Spatial Resolutions With an Image Super-Resolution Approach and PROBA-V Satellite Images

“…As an indicator of the radiative surface energy and its responsiveness to the land surface characteristics, surface albedo varies both in space and in time. In its nascent stage, the harvesting of this solar radiative surface energy through innovations in solar technology, such as bifacial solar panels [7], has produced increases in solar energy yields of up to 40% when compared with the equivalent monofacial panels from simulation studies [8], [9]. At the same time, curbing the effects of radiative surface energy through greening of urban environments and cooling measures is set to reduce building cooling energy requirements, which currently amounts to 22% of electricity consumption [10], [11].…”