Automatic registration for infrared, and visible images of power equipment has become a challenging work in intelligent diagnosis system of the power grid. Existing registration methods usually fail in accurately aligning power equipment infrared, and visible images because of resolutions, spectrums, and viewpoints differences. To solve this problem, we propose a novel main orientation of feature points named contour angle orientation (CAO), and describe an automatic infrared, and visible image registration method named CAO-Coarse to Fine (CAO-C2F). CAO is based on the contour feature of images, and invariant to images viewpoints, and scales differences. C2F is a feature matching method to obtain correct matches. Our proposed CAO-C2F method includes four steps. First, feature points in contours are extracted by the curvature scale space (CSS) corner detector based on local, and global curvature. Second, the CAO of each feature point is computed as the main orientation. Third, modified scale-invariant feature transform (SIFT) descriptors on the main orientations are extracted, and matched by bilateral matching. Finally, accurate matches are obtained by applying the C2F method. Registration experiments on a self-established images dataset show our proposed CAO-C2F method accurately aligns images, and outperforms other state-of-arts in terms of precision, recall, and root-mean-square error.

Intelligent perception is the cornerstone of digitalisation of power grid. Due to the closest connection with users, the information perception ability of distribution network directly affects the reliability of power supply. Distribution network has a wide range of geographical distribution and equipment types. The application of intelligent perception is with high complexity and dynamics under the limitation of both technology and management. Therefore, the study first sorts out the current application status of intelligent perception technology and equipment in distribution network from three application fields including distribution automation, metering systems, and inspection. Then, the constraints of existing intelligent perception applications and the key points of further research are pointed out. Finally, the development trends of intelligent perception technology in distribution network are presented from the three aspects of basic technology, perception equipment, and advanced applications. With the improvement of sensing, communication, energy acquisition, chips and perception equipments, the depth and breadth of advanced applications in distribution network will be expanded. The research results can provide references for the follow‐up research and practice of intelligent perception in distribution networks.

It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

Faults with line breaks occur more and more frequently and are difficult to detect by relay protection device in small-current grounding distribution networks. However, faults with line breaks are more likely to cause security accidents such as fires, overvoltages and electric shocks. Considering the low cost and high reliability of collecting data on the low-voltage (LV) side in distribution networks, this paper proposes a method for distinguishing between faults with line breaks and short-circuit faults on the medium-voltage (MV) side. Voltage amplitude and phase on the LV side of distribution transformer (DT) are analyzed through symmetrical component method. The amplitudes are used to identify two-phase faults with line breaks (TPFs-LBs) and two-phase short-circuit faults (TPFs-SCs); while the phases are selected to distinguish single-line-to-ground faults with line breaks (SLGFs-LBs) from single-line-to-ground faults (SLGFs). The effects of network parameters, operation conditions and external factors on the proposed method have been discussed. Simulation results verified the effectiveness of the proposed method.

This paper explores a novel idea for power equipment monitoring and finds that random matrix theory is suitable for modeling the massive data sets in this situation. Big data analytics are mined from those data. We extract the statistical correlation between key states and those parameters. In particular, the (empirical) eigenvalue spectrum distribution and the (theoretical) single ring law are derived from large-dimensional random matrices whose entries are modeled as time series. The radii of the single ring law are used as statistical analytics to characterize the measured data. The evaluation of key state and anomaly detection are accomplished through the comparison of those statistical analytics.

The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods.