A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

Abstract

The necessity for clean and sustainable energy has shifted the energy sector's interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV's power transfer efficiency varies with different load's electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV's surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Natureinspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions. INDEX TERMSMetaheuristic Algorithms, improved Limited Search Strategy (iLSS), Photovoltaic (PV), Partial Shading (PS), Maximum Power Point Tracking (MPPT).

A novel technique using stretching, repulsion, and chimp optimization algorithm to find global maximum power point considering complex partial shading conditions

“…On-grid performance of SRCA PSC- Key points to compare Proposed Sun et al 26 Pervez et al 27 Alshareef 28 Millah et al 29 Deboucha et al 30 Pervez et al The key observation points in the off-grid and on-grid analyses considering CPSCs are given as follows.…”

“…In the same way, some of the modified metaheuristic algorithms have been proposed to improve the tracking time and to reduce the search space for MPPT with better results. [27][28][29] A control-based high speed MPPT using the analog-to-digital converter was proposed for the fast-charging irradiation by mobile PVs. 30 The transient losses of energy were reduced up to four times.…”

“…It was compared with seven other techniques such as the search‐skip‐judge, section‐dividing point (SDP), skipping adaptive perturbation and observation, modified maximum power trapezium (M‐MPT), high‐performance, voltage track optimizer, and plain hybrid M‐MPT and SDP algorithms. In the same way, some of the modified metaheuristic algorithms have been proposed to improve the tracking time and to reduce the search space for MPPT with better results 27–29 . A control‐based high speed MPPT using the analog‐to‐digital converter was proposed for the fast‐charging irradiation by mobile PVs 30 .…”

A novel algorithm for maximum power point tracking using computer vision (CVMPPT)

“…For such conditions, other algorithms like distributed MPPT, PV curve scan, and other algorithms are proposed which have a high level of complexity and in some cases, they require to change the hardware configuration based on shading condition [29][30][31][32][33]. There are other algorithms, among which someone can refer to Low Burden Narrow Search (LBNS) [34], butterfly optimization algorithm [35] and fuzzy logic control (FLC) MPPT [36]. For a complete review of the MPPT methods, one can see [37].…”

Potential Induced Degradation in Photovoltaic Modules: A Review of the Latest Research and Developments

“…Maximum power point tracking (MPPT) algorithms [44,45]: these algorithms optimize the power output of PV systems and can also be used to detect faults by analyzing the output of PV panels and identifying deviations from expected behavior.…”