Nature-Inspired Optimization Algorithms in Solving Partial Shading Problems: A Systematic Review

TL;DR: In this paper , the authors present the fact that recent advancements in design optimization of Permanent Magnet Synchronous Generator (PMSG) for wind turbine systems are able to deliver tangible benefits to the renewable energy technology (RET) industry as a whole.

Abstract: The efficient operation of PV systems relies heavily on maximum power point tracking (MPPT). Additionally, such systems demonstrate complex behavior under partial shading conditions (PSC), with the presence of multiple maximum power points (MPP). Among the existing MPPT algorithms, the conventional perturb and observe, and incremental conductance stand out for their high simplicity. However, they are specialized in single MPP problems. Thus, due to the existence of multiple MPPs under PSC, they fail to track the global MPP. Compared with the conventional schemes, the modified conventional algorithms, and several existing MPPT variants introduce a trade-off between complexity and performance. To enhance the simplicity of the PV system, it is crucial to adapt the operation of the simple conventional algorithm to scenarios under PSC. To achieve such an adaptation, the power-voltage curve that conventionally admits multiple MPPs under PSC must be converted to an equivalent curve having only a single MPP. To address such a requirement, this paper introduces a novel approach to the fast determination of the MPP. A consistent methodology for reducing the complex multiple MPP problem of PV systems under PSC, to a single MPP objective, is put forward. Thus such reduction enhances the tracking environment for simple conventional MPPT algorithms under partial shading. Studies of the PV array behavior for 735 partial shading patterns revealed an interesting possibility of reducing the classical PV curve to 8.2620% of its actual area. The newly established area is an optimum power region that accommodates a single MPP. To arrive at such a reduction, an intelligent neural network-based predictor, incorporating a cost-effective and reliable solar irradiance estimator is put forward. Unlike existing methods, the approach is free from the direct and expensive measurement of solar irradiance. The predictor relies on the PV array current and voltage only to precisely determine the optimum power region of the PV system.

TL;DR: In this article , the authors present the most recent, up-to-date and state-of-the-art approaches in detecting and diagnosing faults in wind energy conversion systems, including cepstral editing procedure, sensor fault detection and isolation method, induction abrasive particle sensor, novel model-based approach and machine learning based data-driven approach.

TL;DR: A novel meta-heuristic method, Corona-Virus Search Optimizer (CVSO), is proposed, balancing local and global search, and outperforms existing algorithms on 40 test functions and a real-world engineering optimization problem, achieving superior optimization accuracy and convergence rate.

TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.

TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.

TL;DR: The Bat Algorithm as mentioned in this paper is based on the echolocation behavior of bats and combines the advantages of existing algorithms into the new bat algorithm to solve many tough optimization problems.

TL;DR: The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems and the statistical results show that this algorithm is able to provide very promising and competitive results.