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Solar energy has attracted significant attentions around the globe, while one of its most crucial task is to harvest the maximum available solar power under different weather conditions, also known as maximum power point tracking (MPPT). This paper proposes a novel memetic reinforcement learning (MRL) based MPPT scheme for photovoltaic (PV) systems under partial shading condition (PSC). In order to enhance the searching ability of MRL, the memetic computing structure is incorporated into reinforcement learning (RL). In particular, a virtual population is used for the global information exchange between different agents, such that the learning rate can be dramatically accelerated. Besides, a RL based local search is designed in each memeplex, which can effectively improve the optimum quality. Comprehensive case studies are undertaken, such as start-up test, step change of solar irradiation, ramp change of solar irradiation and temperature, and field atmospheric data of Hong Kong. The PV system responses are then evaluated and compared to that of seven typical MPPT algorithms. • A novel MRL is proposed for maximum power point control of PV systems under PSC. • The memetic computing framework can improve the optimization efficiency of MRL. • A virtual population improves the convergence stability via a global coordination. • MRL can make PV systems generate more energy under different weather conditions. • MRL based MPPT can effectively reduce the power fluctuation of PV systems. [ABSTRACT FROM AUTHOR]

• A maximum power point tracking of thermoelectric generation system is constructed. • The power output feature is analyzed under non-uniform temperature distribution. • A greedy search based data-driven method is used for maximum power point tracking. • The proposed method can rapidly search a high-quality maximum power point. • Both of energy loss and power fluctuation can be reduced by the proposed method. The generation efficiency of thermoelectric generation system is relatively low, thus how maximize its power production is of great importance. This paper designs a novel greedy search based data-driven method for centralized thermoelectric generation system to achieve maximum power point tracking under non-uniform temperature distribution. In order to effectively distinguish the local maximum power points and the global maximum power point under non-uniform temperature distribution, greedy search based data-driven employs a two-layer feed-forward neural network to accurately fit the curve between the power output and the controllable variable based on the real-time updated operation data. Based on the approximation curve, a greedy search is designed to efficiently approach the global maximum power point from a shrinking search space. Cases studies such as start-up test, step variation of temperature, stochastic temperature change, and analyse of sensitivity, are implemented to prove the effectiveness and superiority of the proposed algorithm. Simulation results verify that the proposed method can generate the highest energy under non-uniform temperature distribution condition, e.g., 391.34%, 115.71%, 110.92%, and 109.43% to that of perturb and observe, particle swarm optimization, whale optimization algorithm, and grey wolf optimizer in the stochastic temperature change. Lastly, the implementation feasibility of the proposed method is demonstrated by the hardware-in-the-loop experiment based on dSpace platform. [ABSTRACT FROM AUTHOR]

• An MPPT model of centralized TEG system under NTD condition is constructed. • A novel adaptive compass search is developed for MPPT of centralized TEG system. • An adaptive sequence of exploration directions is used for searching enhancement. • The proposed method can make the centralized TEG system generate more energy. • The proposed method can reduce the power fluctuation of centralized TEG system. This paper proposes a novel adaptive compass search (ACS) for maximum power point tracking (MPPT) of centralized thermoelectric generation (TEG) system under non-uniform temperature distribution (NTD) condition. The centralized TEG system employs only one converter thus the overall costs of converter implementation and maintenance can be significantly reduced compared to that of string-type and modularized configurations. However, multiple maximum power points (MPPs) usually appear for centralized TEG system when exposed under NTD condition. In order to effectively and efficiently seek the global MPP, an adaptive sequence of exploration directions is employed to considerably enhance the global searching ability of ACS through the previous searching results, which is the main improvement against to the original compass search (CS) associated with fixed sequence. Four case studies, including start-up test, step change of temperature, random temperature variation, and sensitivity analysis, are carried out to verify the effectiveness and advantage of ACS in comparison to that of perturb and observe (P&O), particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimizer (GWO), and CS, respectively. Lastly, a dSpace based hardware-in-the-loop (HIL) experiment is undertaken to validate its hardware implementation feasibility. [ABSTRACT FROM AUTHOR]