Back to Search
Start Over
An improved particle swarm optimization based maximum power point tracking algorithm for PV system operating under partial shading conditions
- Source :
- Solar Energy. 158:1006-1015
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- Concerns over environment and increased demand for energy have led the world to think about alternate energy sources such as the wind, hydro, solar and fuel cells. Out of these photovoltaic (PV) generation systems (PGS) become increasingly important all over the world due to its availability, cleanness, low maintenance cost, and inexhaustible nature. The probability of partial shading conditions (PSC) is also high for large PGS. Under PSC, the P–V curve of PGS exhibits multiple peaks, which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods. In this paper, an improved particle swarm optimization (PSO) based MPPT algorithm for PGS operating under PSC is proposed. Conventional PSO is modified to meet practical consideration of PGS operating under PSC. Problem formulation, design details, and experimental results are discussed in detail. The proposed technique is independent of system, it is easy to implement, tracking efficiency is high and performance under PSC is good. The effectiveness of the proposed method is validated by analyzing the experimental results obtained from 110 W solar power generation systems.
- Subjects :
- Renewable Energy, Sustainability and the Environment
business.industry
Computer science
020209 energy
Photovoltaic system
Particle swarm optimization
02 engineering and technology
Tracking (particle physics)
Maximum power point tracking
Control theory
0202 electrical engineering, electronic engineering, information engineering
Alternative energy
General Materials Science
Shading
business
Solar power
Energy (signal processing)
Subjects
Details
- ISSN :
- 0038092X
- Volume :
- 158
- Database :
- OpenAIRE
- Journal :
- Solar Energy
- Accession number :
- edsair.doi...........38cd38563668b3da95aa6c344f15f767