Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method

By M. Seyedmahmoudian, R. Rahmani, Saad Mekhilef, Amanullah Maung Than Oo, A. Stojcevski, Tey Kok Soon, A. Ghandhari. IEEE Transactions on Sustainable Energy, 2015

301 citations recorded by the source corpus.

In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/26ee6ace2e7da271b6c7b647c8e77235e7f8b9b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Jubaer Ahmed
2. Zainal Salam
3. N. Rajasekar
4. J. Prasanth Ram
5. Sanchita Basak
6. Saptarshi Sengupta
7. R. Peters

In Renewable Energy, Sustainability and the Environment

1. Jubaer Ahmed
2. Zainal Salam
3. N. Rajasekar
4. J. Prasanth Ram
5. Kashif Ishaque
6. Saad Mekhilef

Citations in the corpus

• A Novel Flower Pollination Based Global Maximum Power Point Method for Solar Maximum Power Point Tracking (2017, methodology): “In addition to the above methods, a new deferential evolution PSO (DEPSO) fusing the properties of metaheuristic differential evolution and PSO method is also attempted for MPPT problem [33].”
• Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “ation purposes whereas Sahu et al. [79] proposed a new fuzzy Proportional–Integral Derivative (PID) controller for automatic generation control of interconnected power systems. Seyedmahmoudian et al. [80] used DEPSO to detect maximum power point under partial shading conditions. The proposed techniq”
• An Enhanced Adaptive P&O MPPT for Fast and Efficient Tracking Under Varying Environmental Conditions (2018, methodology): “This is evident from the recent proliferation of MPPT techniques using particle swarm optimization (PSO) [1], differential evolution (DE) [2], artificial neural network (ANN) [3], ant colony optimization (ACO) [4], artificial bee colony (ABC) [5], grey wolf (GW) [6], fireflies (FF) [7], and cuckoo s”
• A Modified P&O Maximum Power Point Tracking Method With Reduced Steady-State Oscillation and Improved Tracking Efficiency (2016, methodology): “On the other hand, soft computing based MPPT such as artificial neural network [8], fuzzy logic [9], differential evolution [10], particle swarm optimization [11], [12] and cuckoo search [13] tend to be more versatile and flexible.”