A hybrid renewable energy source (HRES) consists of two or more renewable energy sources, suchas wind turbines and photovoltaic systems, utilized together to provide increased system efficiency and improved stability in energy supply to a certain degree. The objective of this study is to present a comprehensive review of wind-solar HRES from the perspectives of power architectures, mathematical modeling, power electronic converter topologies, and design optimization algorithms. Since the uncertainty of HRES can be reduced further by including an energy storage system, this paper presents several hybrid energy storage system coupling technologies, highlighting their major advantages and disadvantages. Various HRES power converters and control strategies from the state-of-the-art have been discussed. Different types of energy source combinations, modeling, power converter architectures, sizing, and optimization techniques used in the existing HRES are reviewed in this work, which intends to serve as a comprehensive reference for researchers, engineers, and policymakers in this field. This article also discusses the technical challenges associated with HRES as well as the scope of future advances and research on HRES.

AC series arc is dangerous and can cause serious electric fire hazards and property damage. This article proposed a convolutional neural network -based arc detection model named ArcNet. The database of this research is collected from eight different types of loads according to IEC62606 standard. The two most common types of arcs, including arcs from a loose connection of cables and those caused by the failure of the insulation, are generated in testing and included in the database. Using the database of raw current, experimental results indicate ArcNet can achieve a maximum of 99.47% arc detection accuracy at 10 kHz sampling rate. The model is also implemented in Raspberry Pi 3B for classification accuracy. A tradeoff study between the arc detection accuracy and model runtime has been conducted. The proposed ArcNet obtained an average runtime of 31 ms/sample of 1 cycle at 10 kHz sampling rate, which proves the feasibility of practical hardware deployment for real-time processing.

Thermal stress has been identified as one of the major failure causes in power modules. Generated from the power loss, thermal stress accelerates the degradation of semiconductor devices and downgrades the system reliability. This paper presents a finite control set model predictive control (FCS-MPC) oriented to reduce the power loss over the mission profile and relieve the thermal stress in power modules. Conventional control approaches including the switching frequency regulation, the reactive power injection and the DC bus voltage adaption show an effective progress. However, the increased control loops and complicated modulation schemes limit the system performance and practical implementation. In the proposed FCS-MPC, a secondary problem formulation is defined to reduce the power loss for the thermal stress reduction in power modules. It is simply integrated with the primary problem formulation in order to achieve the power flow control and power loss reduction simultaneously. An energy-based loss model is proposed for the loss prediction. The impact of the weightings between primary and secondary problem formulations is investigated and a most efficient weighting curve with a weighting-zones strategy is presented to design the proposed FCS-MPC. The proposed FCS-MPC is validated in the simulation studies and experiments. A 2.5-kW grid-tied inverter prototype is developed for the hardware testing and validation.

Photovoltaic (PV) systems are increasingly used for renewable energy generation but remain vulnerable to series arc faults, which can cause serious safety risks, fire hazards, and system failures. Detecting these faults in DC circuits is challenging due to their subtle electrical signatures and the presence of noise from system and environmental sources. Traditional detection methods often fall short in terms of accuracy, prompting growing interest in artificial intelligence (AI)-based solutions. This review article provides a comprehensive analysis of AI-based techniques for series arc fault detection in PV systems, covering key aspects such as data preprocessing, feature extraction, model optimization, and hardware implementation. It presents a structured comparison of existing methods, including their strengths and limitations, through descriptive discussion and summary tables. The review also includes a simplified flowchart to illustrate the typical AI-based DC arc fault detection process. Key challenges are discussed, along with future directions such as hybrid models, transfer learning, and implementation in resource-constrained edge devices. This work aims to support continued research and development by helping researchers and engineers better understand the strengths and limitations of current approaches and identify practical ways to improve arc fault detection for enhanced PV system safety and reliability.

In this paper, two types of Silicon (Si) IGBT and Silicon Carbide (SiC) hybrid switch (Si/SiC HyS) based three-level active neutral-point-clamped (3L-ANPC) inverter are proposed for high efficiency and low device cost. The proposed Si/SiC HyS-based 3L-ANPC inverters are compared with the full Si IGBT, full SiC MOSFET, and Si with SiC devices-based hybrid 3L-ANPC solutions on the inverter efficiency, power capacity, and device cost. It is shown that compared with the full Si IGBT 3L-ANPC solution, the inverter efficiency improvement by Si/SiC HyS is 2.4% and 1.8% at light load condition and heavy load condition, respectively. Compared to the full SiC MOSFET solution and 2-SiC MOSFET hybrid scheme, the device cost of 2-Si/SiC HyS-based 3L-ANPC is reduced by 78% and 50% with 0.28% and 0.21% maximum inverter efficiency sacrifices. The testing results show that the proposed Si/SiC HyS-based 3L-ANPC inverter is a cost-effective way to realize high inverter efficiency. Between the two proposed Si/SiC HyS-based 3L-ANPC inverters, the 2-Si/SiC HyS-based 3L-ANPC inverter has lower device cost which makes it more suitable for cost-sensitive and high efficiency applications. While the 4-Si/SiC HyS-based 3L-ANPC inverter has higher output power capacity, making it a better candidate for high power density, high power capacity, and high efficiency applications.

As a key device to facilitate power flow in the multi-terminal direct current (MTDC) transmission system, DC power flow controller (DCPFC) can effectively expand the power flow regulation area by implementing its multiport output functionality. In order to solve the coupling issue between the transmission line current and the capacitor voltage of the inter-line multiport DCPFC (M-DCPFC), an improved M-DCPFC with a partial power rated auxiliary DC/DC converter unit is proposed in this paper. It provides the advantages of power flow reversal, high adjustment flexibility and low port-expansion cost. Meanwhile, a generic control strategy is developed for the M-DCPFC with random number of the ports. Moreover, from the perspective of the transmission system, the proposed M-DCPFC is modeled as the equivalent series voltage source. Its power flow adjustment capability and its impact on the local voltage source converter (VSC) stations are investigated in detail in this paper. Lastly, to validate the effectiveness of the proposed M-DCPFC, it is first tested in the simulations of the MATLAB/Simulink and then deployed on an experiment platform. Both simulation and experimental results show that the proposed M-DCPFC can achieve stable power flow control in different conditions.