Global Maximum Power Point Tracking of PV Systems under Partial Shading Condition: A Transfer Reinforcement Learning Approach

This paper aims to introduce a novel maximum power point tracking (MPPT) strategy called transfer reinforcement learning (TRL), associated with space decomposition for Photovoltaic (PV) systems under partial shading conditions (PSC). The space decomposition is used for constructing a hierarchical searching space of the control variable, thus the ability of the global search of TRL can be effectively increased. In order to satisfy a real-time MPPT with an ultra-short control cycle, the knowledge transfer is introduced to dramatically accelerate the searching speed of TRL through transferring the optimal knowledge matrices of the previous optimization tasks to a new optimization task. Four case studies are conducted to investigate the advantages of TRL compared with those of traditional incremental conductance (INC) and five other conventional meta-heuristic algorithms. The case studies include a start-up test, step change in solar irradiation with constant temperature, stepwise change in both temperature and solar irradiation, and a daily site profile of temperature and solar irradiation in Hong Kong.

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Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition

Energy ◽

10.1016/j.energy.2019.03.053 ◽

2019 ◽

Vol 174 ◽

pp. 1079-1090 ◽

Cited By ~ 18

Author(s):

Xiaoshun Zhang ◽

Shengnan Li ◽

Tingyi He ◽

Bo Yang ◽

Tao Yu ◽

...

Keyword(s):

Reinforcement Learning ◽

Maximum Power Point Tracking ◽

Maximum Power ◽

Maximum Power Point ◽

Partial Shading ◽

Pv Systems ◽

Point Tracking ◽

Power Point Tracking ◽

Power Point

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A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition

Frontiers in Energy Research ◽

10.3389/fenrg.2021.801571 ◽

2021 ◽

Vol 9 ◽

Author(s):

Dongrui Li ◽

Jinjin Li ◽

Ning Wang

Keyword(s):

Optimization Algorithm ◽

Maximum Power Point Tracking ◽

Maximum Power ◽

Solar Irradiation ◽

Maximum Power Point ◽

Partial Shading ◽

Pv Systems ◽

Point Tracking ◽

Power Point Tracking ◽

Power Point

One of the most critical tasks during the application of photovoltaic (PV) systems is to harvest the optimal output power at various environmental scenarios, which is called maximum power point tracking (MPPT). Though plenty of advanced techniques are developed to achieve this purpose, most of them have corresponding prominent disadvantages, such as inefficient tracking ability, high computation burden, and complex convergence mechanism. Therefore, this work aims to propose a novel and powerful bio-inspired meta-heuristic optimization algorithm called peafowl optimization algorithm (POA), which is inspired by the group food searching behaviors of peafowl swarm. It can effectively achieve a suitable balance between local exploitation and global exploration thanks to its efficient exploratory and exploitative searching operators. Thus, a satisfactory MPPT performance for PV systems under partial shading condition (PSC) can be obtained based on POA. Moreover, two case studies, e.g., start-up test and step change in solar irradiation with constant temperature, are adopted to fairly and comprehensively validate the superiority and effectiveness of POA in contrast with particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), respectively.

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An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions

Energies ◽

10.3390/en14040953 ◽

2021 ◽

Vol 14 (4) ◽

pp. 953

Author(s):

Ali M. Eltamaly

Keyword(s):

Maximum Power Point Tracking ◽

Cuckoo Search ◽

Optimization Techniques ◽

Maximum Power Point ◽

Partial Shading ◽

Swarm Optimization ◽

Pv Systems ◽

Point Tracking ◽

Power Point Tracking ◽

Power Point

The problem of partial shading has serious effects on the performance of photovoltaic (PV) systems. Adding a bypass diode in shunt to each PV module avoids hot-spot phenomena, but causes multi-peaks in the power–voltage (P–V) characteristics of the PV array, which cause traditional maximum power point tracking (MPPT) techniques to become trapped in local peaks. This problem has forced researchers to search for smart techniques to track global peaks and prevent the possibility of convergence at local peaks. Swarm optimization techniques have been used to fill this shortcoming; unfortunately, however, these techniques suffer from unacceptably long convergence time. Cuckoo search (CS) is one of the fastest and most reliable optimization techniques, making it an ideal option to be used as an MPPT of PV systems under dynamic partial shading conditions. The standard CS algorithm has a long conversion time, high failure rate, and high oscillations at steady state; this paper aims to overcome these problems and to fill this research gap by improving the performance of the CS. The results obtained from this technique are compared to five swarm optimization techniques. The comparison study shows the superiority of the improved CS strategy introduced in this paper over the other swarm optimization techniques.

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