Driven by the high penetration of renewable energy sources and power electronic devices, modern power systems have become increasingly complex, intensifying the demand for accurate and intelligent fault detection. This paper analyzes a total of 81 references to explore the integrated application of artificial intelligence (AI) technologies across all stages of fault data processing, modeling, and optimization. The application potential of AI in fault data processing is firstly analyzed in terms of its performance in mitigating class imbalance, extracting feature information, handling data noise and classification. Then, the modeling of fault detection is classified into rule-driven, data-driven and hybrid-driven methods to evaluate their applicability in scenarios such as transmission lines and distribution networks. The accuracy of fault detection models is also investigated by studying the hyperparameter optimization (HPO) methods. The results indicate that the utilization of AI-driven imbalance handling enhances model accuracy by a range of 16.2% to 26.2%, while deep learning-based feature extraction techniques sustain accuracy levels exceeding 98.5% under a signal-to-noise ratio (SNR) of 10 dB. With a 99.96% detection accuracy, hybrid-driven models applied in fault detection perform the best. For the optimization of fault detection models, heuristic algorithms provide 6.92–19.375% improvement over the baseline models. The findings suggest that AI-driven methodologies in data processing demonstrate notable noise resilience and other benefits. For modeling fault detection, data-driven and hybrid-driven models are presently extensively employed for detecting short-circuit faults, predicting transformer gas trends, and identifying faults in complex and uncertain scenarios. Conversely, rule-driven models are better suited for scenarios possessing a comprehensive experience library and are utilized with less frequency. In the optimization of fault detection models, heuristic algorithms occupy a pivotal position, whereas hyperparameter optimization incorporating reinforcement learning (RL) is better suited for real-time fault detection. The discoveries presented in this paper facilitate the seamless integration of AI with fault detection in modern power systems, thereby advancing their intelligent evolution.

When exposed to sunlight, crystalline silicon solar cells (CSSC) will not only generate electric energy but are also heated by solar radiation. Such a self-heating effect makes the working temperature of CSSC 20–40 °C higher than that of the ambient temperature, which degrades their efficiency and reliability. The elevated operating temperatures of CSSC are mainly derived from absorbing photons that cannot be converted to electrons. Therefore, it is important to prevent CSSC from absorbing useless solar light to have a better cooling effect. In this paper, photonic structures based spectrum-selective mirror is designed to cool the operating temperatures of CSSC passively. The mirror could make CSSC absorb about 93% of the sunlight in the wavelength range of 0.3 to 1.1 µm and only absorb about 4% of the sunlight in the wavelength range of 1.1 to 2.5 µm. Meanwhile, the design has good compatibility with the radiative cooling strategy. By applying selective-absorptive and radiative cooling strategies, the operating temperature of CSSC could be decreased about 23.2 K and 68.1 K under different meteorological conditions. Moreover, unlike the single radiative cooling strategy, the spectrum-selective mirror also has effective cooling effects in high wind speed meteorological conditions.

Network reconfiguration is an effective way to avoid severe, large-scale power outages and to improve the resilience of active distribution networks (ADNs). Furthermore, the rapid development of distributed energy resources (DERs) provides new perspectives for network reconfiguration. In this paper, the effect of network reconfiguration and DER collaboration on ADN’s resilient restoration are studied. The applications of DERs are fully explored. In order to achieve a better resilient performance, a detailed multiperiod model considering both reconfiguration and multiple DERs is established. Some linearization techniques are used to simplify the proposed model. Then, we build a rolling horizon optimization framework to solve the model. The framework eliminates the adverse effect of prediction errors and speeds up the calculation. By introducing predictions into strategy determination, the framework achieves a better restoration effect than the traditional greedy method. The proposed framework is tested on a 33-bus system. The simulations verify the efficiency of our work.

Cogeneration is becoming increasingly popular in building and community energy systems with demands on electricity and heat, which is suitable for residential and industrial use in remote areas. This paper considers a stand-alone cogeneration energy hub. The electrical and thermal energies are produced by a combined heat and power (CHP) unit, photovoltaic panels, and a solar thermal collector. Since solar units generate no electricity and heat during the night, energy storage units which shift demands over time can promote the usage of solar energy and reduce the fuel cost of the CHP unit. This paper proposes a method to retrieve the optimal operation cost as an explicit function in the capacity parameters of electric and thermal energy storage units, reflecting the value of energy storage in the cogeneration energy hub. The capacity parameter set is divided into a collection of polyhedrons; on each polyhedron, the optimal value is an affine function in the capacity parameters. Furthermore, the optimal sizes of system components are discussed. The capacity of the CHP unit is determined from a linear program, ensuring supply adequacy; the capacities of solar generation and energy storage units are calculated based on the cost reduction and the budget. Case studies demonstrate the effectiveness of the proposed method.

This study constructed a two-dimensional alkaline water electrolyzer model based on the two-phase flow Euler–Euler model. In the model, the micro-nano surface electrodes with different structure types and graphic parameters (distance, height, and width) were used and compared with the vertical flat electrode to evaluate their influence on electrolysis performance. The simulation results show that the performance of the micro-nano surface electrode is much better than that of the vertical flat electrode. The total length of micro-nano structural units relates to the contact area between the electrode and the electrolyte and affects the cell voltage, overpotential, and void fraction. When rectangular structural units with a distance, height, and width of 0.5 µm, 0.5 µm, and 1 µm are used, the total length of the corresponding micro-nano surface electrode is three times that of the vertical flat electrode, and the cathode overpotential decreases by 65.31% and the void fraction increases by 54.53% when it replaces the vertical flat electrode.