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 multienergy interaction characteristic of regional integrated energy systems can greatly improve the efficiency of energy utilization. This paper proposes an energy prediction strategy for multienergy information interaction in regional integrated energy systems from the perspective of horizontal interaction and vertical interaction. Firstly, the multienergy information coupling correlation of the regional integrated energy system is analyzed, and the horizontal interaction and vertical interaction mode are proposed. Then, based on the long short‐term memory depth neural network time series prediction, parallel long short‐term memory multitask learning model is established to achieve horizontal interaction among multienergy systems and based on user‐driven behavioral data to achieve vertical interaction between source and load. Finally, uncertain resources composed of wind power, photovoltaic, and various loads on both sides of source and load integrated energy prediction are achieved. The simulation results of the measured data show that the interactive parallel prediction method proposed in this article can effectively improve the prediction effect of each subtask.

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.

Icing monitoring system within internet of things is developed to provide multimodal data for assessing the degree of icing on transmission lines. Nevertheless, existing methods relying solely on either sensor data or images demonstrate limited precision and inferior fault tolerance. This paper proposes a correlation-based multimodal feature fusion approach to integrate both sensor data and imaging data, thereby enhancing the monitoring of icing severity on transmission lines and addressing the aforementioned problems. The inherent correlation characteristics present in both sensor data and images, as well as the correlation between these two modalities are thoroughly analyzed, to gain a comprehensive understanding of icing characteristics in multimodal data. Specifically, the squeeze-excitation module along with convolutional neural networks are combined to extract the temporal and spatial correlation inherent in sensor data, as well as the spatial and channel correlation inherent in images. Subsequently, the covariance matrix is used to capture the correlation between these two modalities. Moreover, with the assistance of the weight determination structure, this correlation is mapped to the fusion weights. The four-stage training strategy is introduced to guide the network training. The essentiality of the staged training approach and the necessity of correlation extraction in perceiving icing severity are validated in experiments.