Next-generation power grids is the main direction of future smart grid development, and the accurate prediction of power loads is an important basic task of smart grids. To improve the accuracy of load prediction in smart power systems, this work characterized the load dataset based on an Autoformer, a prediction model with an autocorrelation mechanism; adds a feature extraction layer to the original model; optimized the model parameters in terms of the number of coding layers, decoding layers, learning rate, and batch size; and achieved cycle-flexible load prediction. The experimental results show that the model performs better in prediction, with an MAE, MSE, and coefficient of determination of 0.2512, 0.1915, and 0.9832, respectively. Compared with other methods, this method has better load prediction results.
In industrial scenarios such as substations, video-based visual smoke detection has been adopted as a new environmental monitoring method to assist or replace smoke sensors. However, in industrial applications, visual smoke detection algorithms are required to maintain a low false detection rate while minimizing the missed detection rate. To address this, this study proposes a smoke detection algorithm based on spatial and frequency domain methods, which perform smoke detection in both domains. In the spatial domain, in addition to extracting smoke motion characteristics, this study designed a method for extracting smoke mask characteristics, which effectively ensures a low missed detection rate. In the frequency domain, this study combined filtering and neural network modules to further reduce the false detection rate. Finally, a fusion domain post-processing strategy was designed to obtain the final detection results. In experiments conducted on a test dataset, the smoke detection algorithm achieved a false detection rate of 0.053 and missed detection rate of 0.113, demonstrating a good balance between false alarms and missed detections, which is suitable for smoke detection in substation industrial scenes.
In the process of government and enterprise evolution, as information technology deepens from informatization into digitization, the data generated by various applications are becoming increasingly multimode, multisource, and massive, thereby posing new challenges to data management. To address these challenges, many new technologies and concepts have emerged in the field of data management. Data Fabric is a method that integrates distributed data storage, processing, and applications into a whole, providing a set of visual interfaces for management. First, we analyzed the technical architecture, characteristics, value, and complete process of managing and applying the multimode data of Data Fabric. Subsequently, we proposed anomaly monitoring methods based on time series indicators as well as log data for multimode and multisource data, whereby the processing speed improved by 33.3% and 42.2%, and F1 score improved by 12.2 pps (percentage points) and 14.8 pps, respectively, using Data Fabric technology. This further demonstrates the efficiency and application value of Data Fabric technology in the newly proposed methods.
Advanced metering infrastructure is an important component in the construction of new power systems; however, advanced measurement systems rely on network information infrastructures and present major security issues. This study evaluates the network security posture of advanced metering systems by combining a hidden Markov model with an artificial immunity algorithm. First, a counting algorithm is used to obtain the security observation data in the power network. Subsequently, the Markov model is used to describe the change in the network security state, whereby the artificial immunity algorithm calculates the transfer probability matrix between different states. The state transfer matrix is then modified based on the state assessment error. Upon calculating the probability of being at different security states at different times and combining it with the risk loss vector, the final value of safety posture is obtained. The experiments demonstrate that the method proposed in this study has a good assessment effect and can capture the safety defects in the system more accurately, ensuring the safe operation of the advanced measurement system by accurately identifying the relevant safety defects in the system for a safe, smooth, and reliable operation of the new grid environment.
Power theft seriously endangers power equipment and personal safety, and causes significant economic losses for energy suppliers. Hence, it is important for these suppliers to accurately identify instances of power theft to reduce losses and increase efficiency. In this paper, based on the residual network (ResNet) structure, a 2D convolutional neural network is combined with a depthwise separable convolution enhanced self-attentive (DSCAttention) mechanism to improve the number of correctly-classified electricity theft users. In addition, electricity theft data often contains missing values, outliers, and positive and negative sample imbalance. Each of the above problems are treated separately using the zero-completion method, quantile transformation, and hierarchical splitting method, respectively. The proposed model has been extensively tested using real power theft data sets. The results show that the area under curve (AUC) index of the proposed model reaches a value of 91.92%, while mean average precision values MAP@100 and MAP@200 are measured reaching 98.58% and 96.77%, respectively. Compared with other electricity theft classification models, the proposed model performs the electricity theft classification task better. The method in this paper can be extended to electricity theft intelligent identification.