Electrical fires are frequently caused by low-voltage AC series arc faults, which can result in significant injuries and property damage. The installation of arc-fault detection devices is mandated or recommended in many regions and countries across the world, yet the current devices’ detection accuracy is insufficient to completely eliminate the risk posed by arc faults. The method based on artificial intelligence is a solution with high detection accuracy, but the AI model is a ‘black box’. When a misjudgment occurs, the cause of the model error cannot be found fundamentally, and the modification and light weight of the model also presents significant difficulties when using the approach. Given the aforementioned issues, this research proposes a novel lightweight low-voltage AC arc-fault detection method based on the explainability approach. By applying the attention mechanism approach and performing a visual analysis, the contribution of arc features to model detection is determined. Model input data optimization and model structure simplification are achieved at the same time as increased model detection accuracy. Ultimately, an experimental prototype for arc-fault detection is designed and validated. Test results demonstrate the effectiveness of the method by demonstrating that the lightweight model maintains 99.69% detection accuracy, even after optimizing the input data by 80% and reducing the model parameters by 51.52%.

With the deep penetration of renewable energy and power electronic equipment, the overcurrent protection of an intelligent miniature circuit breaker faces new challenges. The electronic controller of an intelligent miniature circuit breaker is typically powered by the bus current rather than the phase voltage to ensure a robust overcurrent protection response under all conditions, including severe short-circuit faults. So, the performance of the current transformer serving as an energy harvesting unit and the corresponding direct current to direct current convention circuit is one of the critical issues due to the limited volume of an intelligent miniature circuit breaker. In this research, a finite element model of a current transformer for an intelligent miniature circuit breaker is constructed by COMSOL to evaluate the impact of the core material, the core size, and the number of coil turns on the energy harvesting capability of the current transformer. Meanwhile, the relationship between the output of the power supply and its design parameters is investigated by circuit simulation. As a result, a novel type of current transformer is proposed based on well-designed parameters. Finally, experimental tests have been conducted to verify the hysteresis characteristics, output characteristics, and energy harvesting effect. The results demonstrate that the hysteresis properties of the transformer align with the simulation results. The power supply can work with a minimum current of 8 amperes, which is 23.08% better than before.

With the growing global focus on sustainable development and climate change mitigation, promoting the low carbonization of energy systems has become an inevitable trend. Power load monitoring is crucial to achieving efficient power management, and load identification is the key link. The traditional load identification method has the problem of low accuracy. It is assumed that the technique of fusing harmonic features through color coding can improve the accuracy of load identification. In this paper, the load’s instantaneous reactive power, power factor and current sequence distribution characteristics are used as the mapping characteristics of the R, G and B channels of the two-dimensional V–I trajectory color image of the load using color coding technology. The harmonic amplitude characteristics are integrated to construct the mixed-color image of the load. The void residual shrinkage neural network is selected as the classification training model. The advantages and disadvantages of two residual shrinkage construction units, RSBU-CS and RSBU-CW, are analyzed. A single-load identification model with three RSBU-CWs is built. Different datasets verify the performance of the model. Compared with the test results of the ordinary color image dataset, the accuracy of the mixed-color image dataset is above 98%, and the accuracy of load identification is improved.