Maximum power point tracking (MPPT) is required in PV power systems for the highest solar energy harvest. This article proposes a self-tuning scheme to improve the MPPT performance in terms of high accuracy and speed. The scheme adopts the reinforcement learning (RL) and Beta parameter for the highest MPPT performance. The tracking speed and accuracy are significantly improved since the RL algorithm is enhanced for high convergence speed, meanwhile, the guiding variable β is introduced to constrain the exploration space. Simulation and experimental test are applied to validate the superior performance of the proposed solution following the EN50530 dynamic test procedure.

The mismatch problem is commonly happened in photovoltaic (PV) systems due to partial shading conditions(PSCs). Distributed maximum power point tracking (DMPPT) architectures can be used to solve such problem. Reinforcement learning (RL) method which is one of the advanced artificial intelligence(AI) methods is proposed to improve the tracking speed. However, the drawbacks such as the lack of limited adaptability and exploration-exploitation trade-off theory make the RL method low in efficiency. Therefore, this article combines the Beta method and $\varepsilon$ - greedy algorithm with the RL method to address this problem. The simulation and experimental tests have been carried out and the result shows the efficiency of the proposed RL method is up to 96.85%, which verifies the superiority of the proposed scheme.

Photovoltaic (PV) power generation is considered to be a clean energy source. Solar modules suffer from nonlinear behavior, which makes the maximum power point tracking (MPPT) technique for efficient PV systems particularly important. Conventional MPPT techniques are easy to implement but require fine tuning of their fixed step size. Unlike conventional MPPT, the MPPT based on reinforcement learning (RL-MPPT) has the potential to self-learn to tune step size, which is more adaptable to changing environments. As one of the typical RL algorithms, the Q-learning algorithm can find the optimal control strategy through the learned experiences stored in a Q-table. Thus, as the cornerstone of this algorithm, the Q-table has a significant impact on control ability. In this paper, a novel Q-table of reinforcement learning is proposed to maximize tracking efficiency with improved Q-table update technology. The proposed method discards the traditional MPPT idea and makes full use of the inherent characteristics of the Q-learning algorithm such as its fast dynamic response and simple algorithm principle. By establishing six kinds of Q-tables based on the RL-MPPT method, the optimal discretized state of a photovoltaic system is found to make full use of the energy of the photovoltaic system and reduce power loss. Therefore, under the En50530 dynamic test standard, this work compares the simulation and experimental results and their tracking efficiency using six kinds of Q-table, individually.

A new type of hybrid magnet dual stator field modulation machine with different split ratios of stators (DSR-HMDSFMM) is proposed in this article, which has the merit of high torque density. The split teeth of stator sever as modulators to modulate air gap flux density based on the magnetic gearing effect for high torque production. The total split tooth number of inner stator is equal to that of outer stator because the pole pair numbers of armature reaction and permanent magnet (PM) excitation on rotor are equal. Furtherly, the inner and outer stators with different split ratios and same split tooth number are employed to increase torque production because armature windings with high winding factor and high turn number can be used. In addition, the employment of hybrid magnet excitation can increase space utilization and enhance the air gap flux density with low harmonic distortion. Therefore, the proposed DSR-HMDSFMM can achieve high torque production with the same volume and PM usage. In order to find the optimal split ratio match of stators, several DSR-HMDSFMMs with different combinations of split ratios are proposed and calculated. The configuration and operating principle are illustrated, together with electromagnetic performances of the proposed machines analyzed using finite element analysis (FEA). The FEA-predicted results show that the proposed HMDSFMM exhibits high air-gap flux density with low harmonic distortion and high torque density, which is promising for the direct-drive applications.

This article proposes a novel embedded communication strategy for in-home energy routers (IHERs). It is based on the power/signal dual modulation (PSDM) principle and applied in the small-scale community energy local area network (E-LAN). The proposed communication strategy multiplexes power converters as data transmitters, and thus high cooperation and synchronization between power flow and information flow are achieved. In addition, system reliability is enhanced since communication module has practically the same reliability as the power module. A typical IHER’s structure and operation principles are presented, based on which the detailed design of embedded communication is proposed. For distributed power management of community E-LAN, both intra-IHER communication and inter-IHERs communication are involved, and their channels are modeled mathematically. IHER interconnection interface converters (IICs) play a significant role in power exchange between IHERs, and their operation modes are analyzed in detail. Finally, an experimental prototype is built to validate the feasibility of the proposed method.

In order to solve the frequency modulation (FM) problem caused by load in the virtual synchronous generator (VSG) system under an islanding state, this paper proposes a frequency modulation method based on active disturbance rejection control (ADRC). Firstly, design a nonlinear function using the design rules of nonlinear functions. Then, the extended state observer (ESO) and nonlinear feedback control law (NLSEF) of ADRC are designed based on the nonlinear function. By introducing ADRC into the power–frequency control loop of VSG, error-free frequency modulation of VSG is achieved. Finally, the superiority of the proposed method was verified through experimental comparison.