Spring operation mechanism is widely used in high voltage circuit breakers, and its reliability is related to the ability of the circuit breaker breaking fault current. During the life cycle of spring operating mechanism, stress relaxation, metal fatigue, and any other mechanical defects are easily occurring. And the mechanical performance of the circuit breaker will be influenced by the above defects. Therefore, identifying and predicting the mechanical conditions of the spring operation mechanism can improve the reliability of the circuit breaker. In the present paper, the 252 kV circuit breakers are used as test objects. Firstly, the spring stress relaxation test, the life-cycle test, and the failure simulated test of 252 kV circuit breakers are carried out. Secondly, a multi-body dynamics simulation model of the experimental prototype is established. Thirdly, support vector machine, random forest, and deep neural network are used in the condition identification of the circuit breaker to compare their performances. Then, the prediction model of spring in stress relaxation test is built, however, the model is not suitable for the life-cycle test of repeat close-open operation. Finally, the remaining useful life prediction model is proposed by using Wiener Process.

The optimal allocation of integrated energy system capacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy, which has attracted many attentions. However, the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters. To solve the above problem, the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms. Firstly, an integrated energy system consisting of the photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and energy storage is established. Meanwhile, the minimum economic cost, the maximum wind and PV power consumption rate, and the minimum load shortage rate are considered to be the objective functions. Then, a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-Ⅱ is proposed to solve the optimal allocation problem. According to the optimal result, the economic cost is 6.3 million RMB, and the load shortage rate is 9.83%. Finally, four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method. The comparative results indicate that the proposed method possesses a stronger merit-seeking ability, resulting in a solution satisfaction rate of 87.37%, which is higher than that of the unimproved non-dominated sorting genetic algorithms-Ⅱ.

The construction of a household integrated energy system will reduce greenhouse gas emissions and promote sustainable development. Firstly, a household energy system is proposed, which consists of a photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and hydrogen‐fired gas turbine. The proposed system is modelled as a bi‐objective optimization problem in which the minimum daily system economic cost, and the minimum loss of energy supply probability. Secondly, a novel multi‐objective egret swarm optimization algorithm with strong search capability and fast convergence is proposed. Thirdly, a household load corresponding to a typical day in spring is chosen as the study case. The optimization results show that the daily system economic cost with the optimal number of devices is 97.48 RMB, and the loss of energy supply probability is 8.33% at the lowest. Finally, to validate the efficiency of the proposed method, the proposed method is compared with NSGA‐II (a widely used multi‐objective evolutionary algorithm). The comparison indicates that the proposed method has a better diversity due to the random searchability. As a consequence, the proposed method can be used in the optimization capacity design of the integrated energy system.

Dynamic contact resistance measurement (DRM) has been widely used in evaluating the contact condition of circuit breakers from 20 years ago. With the increasing of the injected current, the contact resistance is lower than it was. It is considered that the contact hardness, contact force, and specific resistivity are affected by the injected current. SF₆ circuit breaker with self-elastic contact is studied in this paper because of its widely used in high-voltage power system. First, the parameters of the dynamic curves are discussed, and the contact resistance in arcing contact part is concerned because of its susceptibility. Second, the mathematical model of contact force is discussed and calculated, and the change of contact force under a different injected current can be ignored. Finally, the mathematical model of contact resistance and injected current is established, when the breaker contact speed is stable. The mathematical model is proven effective by DRM tests.

The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has been widely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth convolution (SPD-Conv) module, aiming to improve the algorithm’s efficacy in recognizing low-resolution and small-size objects. The algorithm's feature extraction network is improved by using a Large Selective Kernel (LSK) attention mechanism, which enhances the ability to extract relevant features. Additionally, the SIoU Loss function is used instead of the Complete Intersection over Union (CIoU) Loss to facilitate faster convergence of the algorithm. Through experimental verification, the improved YOLOv8n model achieves a detection accuracy of 88.8% on the test set. The recognition accuracy of cranes is improved by 2.9%, which is a significant enhancement compared to the unimproved algorithm. This improvement effectively enhances the accuracy of recognizing foreign objects on transmission lines and proves the effectiveness of the new algorithm.