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.

Faulted line detection is a key step of intelligent fault diagnosis of distribution systems, laying the foundation for the further fault location and service restoration. A novel single line-to-ground (SLG) faulted line detection method based on the feature fusion framework is proposed. In the proposed framework, one-dimensional convolutional neural network is employed as a powerful tool to extract more effective features. In addition, there is an imbalance phenomenon between data of the faulted line and healthy lines when a data-driven model is used in the faulted line detection. The proposed framework offers an avenue for overcoming it and improves the accuracy of detection. Considering the limited data of SLG faults in actual power systems, prior knowledge of SLG fault detection is integrated into the data-driven model, which proves useful in reducing dependence on the training data quantity. The experiments verified the superior performance of the proposed feature fusion framework-based method.

Incipient faults in power distribution systems potentially lead to catastrophic failures. Detection of incipient faults contributes to proactive fault management and predictive maintenance, which effectively improves power supply reliability. Since the faults are infrequent and transient, few samples can be procured in real applications. In this paper, a detection method based on human-level concept learning (HLCL) is proposed to address this problem. The method contains two steps: human-level waveform decomposition (HLWD) and hierarchical probabilistic learning (HPL). HLWD, inspired by human perception, decomposes waveform into primitives: segments of general shape and residuals, which identify a waveform. HPL learns waveform through a generative process based on primitives, where the probability for an abnormal event to be an incipient fault can be hierarchically calculated. Experiments on simulation data and field data, which contain subcycle incipient faults, multicycle incipient faults, permanent faults and transient disturbances, indicate that the proposed method outperforms other three generally used classifiers.

The combination of power intelligent inspection and edge computing plays a crucial role in the construction of power Internet of Things and transparent power grid. However, as a consequence of the low computing power of edge devices, the detection speed of the model running on edge devices tends to be slow. Accordingly, a fast and accurate method of power line edge intelligent inspection is proposed in this article. First, for key component detection of power lines, RepVGG, diverse branch block (DBB), efficient channel attention (ECA), and improved spatial pyramid pooling (SPP) are utilized to improve YOLOv5, giving rise to RepYOLO. Additionally, to solve the problem of low accuracy in pin defect detection, a two-stage cascaded method is proposed. The first stage localizes various connection fittings in the input image, and the second stage recognizes normal pins and missing pins in these connection fittings. Finally, C++ language combined with TensorRT is employed to optimize and accelerate the model on the NVIDIA Jetson Xavier NX embedded platform, fulfilling efficient power line edge intelligent inspection. Experimental results show that the TensorRT optimized RepYOLO algorithm is 4 times the inference speed of YOLOv5 with a 1.2% increase in accuracy. Moreover, TenosrRT optimized two-stage cascaded method can achieve accurate and real-time pin defect detection on edge devices.

The reliability of distribution systems is often challenged under unfavorable weather conditions, where weather-related failures occur with high probability. Predicting the number of weather-related failures in distribution systems can provide guiding information for operation and maintenance decisions, improving the risk management capability of utility companies. This paper proposes a novel Bayesian Neural Network (BNN) based model to predict weather-related failures caused by wind, rain and lightning. Superior prediction performance of the BNN based model is verified by contrast experiments with other advanced prediction models under four different evaluation metrics. BNN based prediction model presents remarkable robustness, especially in the prediction of high failure levels. In addition, compared to most previous used prediction models without any prediction confidence feedback, BNN based prediction model has the capability of uncertainty estimation. The confidence interval of prediction results can be obtained, which provides sufficient information for guiding risk management of utility companies. An effective operation and maintenance guiding scheme based on the analysis of prediction uncertainty is proposed, which fully excavates the interpretability of the proposed model and enrich the application value of the model.

Analogous deployment of phase measurement units (PMUs), the increase of data quantum and deregulation of energy market, all call for robust state evaluation in large scale power systems. Implementing model based estimators is impracticable as the complexity scale of solving the high dimension power flow equations. In this paper, we first represent massive streaming PMU data as big random matrix flow. Motivated by exploiting the variations in the covariance matrix of the massive streaming PMU data, a novel power state evaluation algorithm is then developed based on the multiple high dimensional covariance matrix test. The proposed test statistic is nonparametric without assuming a specific parameter distribution for the PMU data and of a wide range of data dimensions and sample size. Besides, it can jointly reveal the relative magnitude, duration and location of an system event. For the sake of practical application, we reduce the computation of the proposed test statistic from O(εn _g ⁴ ) to O(ηn _g ² ) by principal component calculation and redundant computation elimination. The novel algorithm is numerically evaluated utilizing the IEEE 30-, 118-bus system and a Polish 2383-bus system and a real 34-PMU system. The case studies illustrate and verify the superiority of proposed state evaluation indicator.