Bolt defect inspection is an important work in transmission line inspection. Due to the small size of bolts in the transmission line inspection images, existing algorithms are difficult to extract valuable features and achieve poor performance on bolt defect detection. This paper proposed an ultra-small bolt defect detection model(UBDDM) based on a deep convolutional neural network(DCNN), including an ultra-small object perception module(UOPM) and a local bolt detection module(LBDM). In this paper, UOPM is first constructed to realize coarse region recognition for the salient region of bolts in the inspection images, and the high-resolution image blocks are obtained from the original image according to the recognition results. Then, LBDM is constructed to intelligently identify the bolt defects from the high-resolution image blocks. Considering that the features of ultra-small targets are difficult to extract, feature extraction networks are constructed based on ResNet-50, and the hybrid attention mechanism and multi-scale feature fusion are introduced to further improve the network’s ability to extract shallow features. This method uses two-stage detection to realize end-to-end bolt defect detection but only needs to provide a single-stage target detection label, which greatly reduces the workload of data labeling. Experimental results show that the proposed method achieves excellent performance on bolt defect detection in inspection images.

The icing monitoring of transmission lines is of great significance to prevent freezing disasters of transmission lines and ensure the security and stability of power system. Measurement methods based on image recognition is gradually applied for icing monitoring, but fails to distinguish the icing thickness under the extreme meteorological conditions. Therefore, this paper built an ice monitoring system based on edge intelligence to achieve the ice thickness identification at the icing monitoring terminal, and proposed a lightweight vision identification method based on discriminative driven channel pruning for icing monitoring terminals with limited computing resources. In this method, the lightweight convolutional neural network MobileNetV3 is utilized for feature extraction, and the multi-scale target detection network SSD is used to extract the high-dimensional feature information for ice monitoring. Then, the channel pruning driven by discrimination is used to further compress model, so as to obtain a lightweight identification method suitable for ice monitoring terminals with limited resources. The effectiveness and superiority of the proposed method are verified in comparison to other CNN-based target detection methods and traditional ice monitoring methods. The experimental results showed that the proposed method has 74.5% recognition accuracy for the ice images collected under extreme meteorological conditions. Moreover, the model size is only 11MB, which can be deployed in ice thickness monitoring terminals with limited computing resources.

In keeping with China’s dual carbon goals, optimal low-carbon power system dispatch has become a necessary component of the greening of the power system. However, typically, research considers only the economics of such efforts. Based on our power flow analysis of the power grid and the correlation properties of carbon emission flow, an optimal power flow calculation model targeting the total carbon emission rate of the power system’s power generation cost, active network loss, and load and network loss was constructed. Next, the NSGA-III algorithm was used to solve the model, and the decision was to coordinate and optimize the output schemes of various types of power plants, such as wind, water, and thermal. The modified IEEE39 node simulation system was built with Matlab software (MATLAB R2020b). The results of the calculation showed that, compared to the traditional method of determining the optimal power flow, the proposed method reduced the system carbon emissions by 20% while the power generation cost increased by less than 2%, which proves the effectiveness and practicability of the proposed method.

In complex power systems, when power equipment fails, multiple concurrent failures usually occur instead of a single failure. Concurrent failures are so common and hidden in complex systems that diagnosis requires not only analysis of failure characteristics, but also correlation between failures. Therefore, in this paper, a concurrent fault diagnosis method is proposed for power equipment based on graph neural networks and knowledge graphs. First, an electrical equipment failure knowledge map is created based on operational and maintenance records to emphasize the relevance of the failed equipment or component. Next, a lightweight graph neural network model is built to detect concurrent faults in the graph data. Finally, a city’s transformer concurrent fault is taken as an example for simulation and validation. Simulation results show that the accuracy and acquisition rate of graph neural network mining in Knowledge Graph is superior to traditional algorithms such as convolutional neural networks, which can achieve the effectiveness and robustness of concurrent fault mining.

With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy.

Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two‐dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods.