When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms-such as perturb and observe (P&O) and hill-climbing (HC)-are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithmssuch as an artificial neural network (ANN) and fuzzy logic control (FLC)-have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB R is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.INDEX TERMS Adaptive jaya (Ajaya), maximum power point tracking (MPPT), metaheuristic algorithms, conventional algorithms, photovoltaic (PV).

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This article presents a high-gain DC-to-DC converter with a single switch, called the cubic converter, which provides very high voltage gain compared to the existing topologies such as the quadratic converter and conventional boost converter. The operation of the proposed converter at a lower duty ratio ensures lesser conduction losses. Various mathematical approaches are employed to confirm the higher voltage gain and improved efficiency of the converter. The proposed cubic boost converter (CBC) is compared with the quadratic boost converter (QBC) and other converters discussed in the literature. A generalized n th -order boost converter is also derived. To test the effectiveness of the QBC and CBC circuits, the Hardware-In-the-Loop (HIL) validation is performed using Typhoon HIL 402 real-time emulator machine. Moreover, the proposed topology is tested and compared with other topologies for maximum power point tracking (MPPT) of a solar photovoltaic (PV) array to show its effectiveness in a real-world scenario. A detailed comparison between conventional boost, QBC and CBC is presented for dynamic partial shading conditions in real-time mode using Typhoon HIL 402 real-time emulator machine.INDEX TERMS DC-dc converter, high voltage gain, renewable energy, boost converter.

Due to its clean and abundant availability, solar energy is popular as a source from which to generate electricity. Solar photovoltaic (PV) technology converts sunlight incident on the solar PV panel or array directly into non-linear DC electricity. However, the non-linear nature of the solar panels’ power needs to be tracked for its efficient utilization. The problem of non-linearity becomes more prominent when the solar PV array is shaded, even leading to high power losses and concentrated heating in some areas (hotspot condition) of the PV array. Bypass diodes used to eliminate the shading effect cause multiple peaks of power on the power versus voltage (P-V) curve and make the tracking problem quite complex. Conventional algorithms to track the optimal power point cannot search the complete P-V curve and often become trapped in local optima. More recently, metaheuristic algorithms have been employed for maximum power point tracking. Being stochastic, these algorithms explore the complete search area, thereby eliminating any chance of becoming trapped stuck in local optima. This paper proposes a hybridized version of two metaheuristic algorithms, Radial Movement Optimization and teaching-learning based optimization (RMOTLBO). The algorithm has been discussed in detail and applied to multiple shading patterns in a solar PV generation system. It successfully tracks the maximum power point (MPP) in a lesser amount of time and lesser fluctuations.

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).

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