In view of the problems that traditional crowbar protection is difficult to simultaneously suppress rotor current and DC bus voltage as well as absorb reactive power during low-voltage ride-through. This paper proposes a control strategy of multi-stage crowbar switching and reactive power compensation. During the fault period, the crowbar resistance is adjusted in real time by grading the crowbar, so as to achieve the purpose of suppressing the rotor overcurrent and DC bus overvoltage. At the same time as the multi-stage crowbar is put into operation, the GSC adopts the reactive power compensation strategy, so that the fan can provide certain reactive power support to the power grid. On this basis, the change of rotor flux linkage after the crowbars at all levels are put into operation is analyzed, analytical expressions for the stator and rotor short-circuit currents upon the activation of each crowbar stage are derived, and the resistance value for each stage is subsequently set. Then, the short-circuit current of GSC is analyzed according to the reactive power compensation strategy, and then the influence of multi-stage crowbar protection access and GSC additional reactive power compensation strategy on DFIG reactive power characteristics is further analyzed. Finally, the effectiveness of the proposed control strategy and the correctness of the short-circuit current expression are verified by simulation.

The cascaded H-bridge topology exhibits distinct advantages in the field of photovoltaic （PV） power generation with its modular design, smaller filtering inductance, and simplified layout. However, the three-phase cascaded H-bridge inevitably inherits the inherent issue of the single-phase full-bridge inverter, namely, double-line frequency voltage ripple on the DC-link. This issue leads to an output voltage deviation of photovoltaic array from its maximum power point, thereby reducing the power generation efficiency of system. To address this issue, this paper presents an innovative control method based on adaptive third-harmonic injection, which effectively avoids over-modulation risks under varying power factor angles. The method can calculate the optimal third-harmonic injection based on the real-time operating conditions of the system, thereby achieving optimal suppression of DC-link voltage ripple. Finally, a full-scale experimental platform is constructed, and experimental results confirm the effectiveness and feasibility of the proposed method.

To address the issues of decreased performance in traditional current differential protection and poor robustness of existing dynamic state estimation, a protection method for PV station transmission line, based on recursive maximum entropy dynamic state estimation, is proposed. This method is unaffected by power source characteristics and can handle measurement data containing significant outliers. Firstly, the adaptability of traditional current differential protection in PV station transmission line is analyzed, revealing the limitations of traditional current differential protection. Then, based on Kirchhoff's law, a dynamic state estimation model for AC transmission lines is constructed. On this basis, considering the impact of large outliers on measurement data, the nonlinear modeling capability of the maximum correntropy Gaussian kernel function and the smooth decision boundary characteristics are utilized to ensure that the dynamic state estimation results are not disturbed by outliers. Finally, a centralized PV station grid-connected model is built on the Matlab/Simulink platform, and the superiority, sensitivity, reliability, and robustness of the proposed protection method under different faults are verified.

To address the issue of reduced electrical efficiency caused by uneven surface temperatures of solar cells in high concentration photovoltaic （HCPV） systems, a manifold microchannel heat sink with an array of inclined ribs is proposed to reduce the temperature difference on the surface of the solar cells. Computational fluid dynamics （CFD） is used to simulate and analyze the impact of different rib inclination angles on the heat dissipation performance of the manifold microchannel. The results show that compared with the structure without ribs, the temperature difference on the battery surface is significantly reduced when the inclination angle of the ribs in the inlet manifold is 11.54°. The maximum reduction rate is 61.86% under various flow rates. Under high flow conditions, the maximum temperature on the battery surface is reduced by 11.90 K, and the PEC range under various flow rates is 1.06 to 1.13.

To address the high energy consumption and significant carbon reduction challenges in steel plate degreasing for industrial equipment manufacturing, this study proposes a novel PV/T-coupled ground-source heat pump heating system for degreasing （PV/T-GSHPHD） and its corresponding operational strategy. A comprehensive energy conversion model was established for system components, and an optimization configuration model was developed to enhance economic efficiency while ensuring soil thermal balance through typical scenario clustering analysis. The optimal system components and their respective capacities were determined based on these models. Results show that the optimal PV/T-GSHPHD system configuration consists of a PV/T module, a ground-source heat pump, a heat collection tank, and a thermal storage tank, with soil heat supplementation achieved via the PV/T module. Compared to a conventional electric boiler heating system, the PV/T-GSHPHD achieves 69.1% reduction in annual heating costs and 83.3% reduction in environmental costs, demonstrating significant economic and environmental advantages. The analysis of operating characteristics of the system on typical days indicates that the PV/T-GSHPHD effectively reduces grid peak loads year-round, with only 13% of its equivalent electricity sourced from the grid during summer, thereby alleviating regional electricity supply pressures. This study highlights the technical and economic viability of PV/T-GSHPHD as a sustainable alternative for industrial degreasing heating applications.

To improve the accuracy of power generation prediction in high penetration photovoltaic microgrids, this paper proposes an improved Markov chain displacement time series prediction method （MFM） to optimize the problems of insufficient feature extraction and inaccurate prediction in the conjugate gradient method （CG） - deep belief network （DBN） combined prediction model （CG-DBN）. Firstly, using Pearson correlation coefficient to analyze the influencing factors of high penetration photovoltaic microgrid power generation; Secondly, taking advantage of the inefficiency of Markov chain displacement time series prediction method, the residual correction process is applied to the CG-DBN prediction model to construct a short-term prediction model for the power generation of MFM-CG-DBN high penetration photovoltaic microgrids; Finally, the MFM-CG-DBN short-term prediction model is used to simulate the power generation data of high penetration photovoltaic microgrids under three types of weather conditions： sunny, cloudy, and rainy. The simulation results show that the proposed short-term prediction model has higher prediction accuracy than the traditional CG-DBN short-term prediction model, and can meet the demand for high penetration photovoltaic microgrid power generation prediction.

A numerical model based on the finite difference method is developed for deep borehole heat exchangers used in medium-deep ground source heat pump systems, and the heat transfer characteristics under dynamic thermal load conditions are analyzed. Using this model along with actual meteorological data from Qingdao and validation via field experiments, the effect of fluid velocity, geothermal gradient, and geological parameters on the thermal performance of the deep borehole heat exchanger are investigated. The results indicate that while the inlet and outlet temperatures of the exchanger fluctuate in response to dynamic thermal loads, the outlet temperature exhibits greater stability. Although increasing fluid velocity enhances heat extraction, it also leads to higher heat loss caused by reverse heat transfer in shallow rock and soil layers. Higher geothermal gradients significantly improve heat extraction performance, and heat exchange efficiency is found to be greater in deeper regions compared to shallow zones.

A panel heating terminal with photovoltaic direct drive electric heating phase change heat storage wall panel heating terminal is proposed, which uses photovoltaic DC to drive the electric heating film to produce heat and store the heat in the phase change material at the same time. The heat transfer model is established by using the multi physical field coupling software COMSOL, and the effect of adding flat fins （fin spacing, length and thickness） on the heat transfer process of phase change heat storage wall panel under natural convection conditions is discussed. The results show that properly reducing the fin spacing can effectively shorten the complete melting time of PCM, improve the average heat storage rate, and extend the heat release time, but the effect is not obvious. When the fin spacing is less than 25 mm, the total heat storage decreases significantly; Under the same fin spacing, the greater the proportion of fin length to the radial length of PCM, the higher the heat storage rate; When the thickness is 1 mm, the average heat storage rate reaches the maximum of 103.3 kJ/h, which increases by 27.2% compared with the structure without fins.

For the purpose of achieving a scientifically rigorous and methodologically sound clustering partition of photovoltaic clusters, this study incorporates the synergistic effects of meteorological factors and geographical positioning on the clustering of photovoltaic power stations. Employing output characteristics as the basis for subgroup delineation, the paper introduces a K-medoids clustering algorithm based upon an enhanced Manhattan distance metric. The two pivotal dimensions in photovoltaic cluster analysis encompass the robust identification of meteorological features and the precise quantification of inter-station similarity. Accordingly, an integrated approach combining the coefficient of variation method with the rank-sum ratio method is utilized to derive meteorological input features, thereby augmenting the efficacy of feature discrimination. Moreover, the refined Manhattan distance is embedded within the K-medoids framework to comprehensively elucidate the dynamic attributes inherent in the dataset. Three internally validated clustering metrics are leveraged to optimize cluster cardinality, effectively mitigating the dispersion and systematic bias endemic to conventional partitioning approaches while enhancing overall clustering robustness. Subsequent predictive validation is performed based on the established clusters. To resolve the challenge of parameter calibration within the forecasting model, metaheuristic optimization techniques are integrated. Specifically, an improved grey wolf optimization （IGWO） algorithm is deployed to fine-tune the hyperparameters of both the iTransformer and extreme gradient boosting （XGBoost） architectures. The power forecasting results verify the operational efficacy and superior predictive accuracy of the proposed clustering methodology.

Under conditions of partial shading caused by cloud cover, dust accumulation, building obstructions, and vessel movement during navigation, photovoltaic arrays deployed on ship decks exhibit multi-peak characteristics in their power-voltage output curves. This renders traditional maximum power point tracking （MPPT） algorithms inadequate for optimal power extraction. To address this issue, this study proposes a hybrid MPPT control method combining improved frilled lizard optimization algorithm with variable step perturbation and observation method （IFLO-IP&O） for shipboard photovoltaic systems. The methodology involves three key innovations： First, the integration of non-standard Circle chaotic mapping, Lévy flight strategy, and triangular random walk strategy into the Frilled Lizard Optimization algorithm creates an enhanced global search capability to locate the neighborhood of the global maximum power point （GMPP）. Second, a variable-step perturbation mechanism enables precise tracking of GMPP. Third, an adaptive switching logic seamlessly integrates these two algorithms into a unified framework, which is then applied to shipboard photovoltaic systems. Simulation results demonstrate that the proposed IFLO-IP&O hybrid method achieves superior performance in convergence speed, tracking accuracy, and oscillation suppression across various illumination scenarios.