Monitoring and alarm play an important role in preventing trip-out caused by wildfires for high-voltage transmission lines. This paper proposes a multi-Doppler weather radar-based method for monitoring and alerting of wildfires near transmission lines. Firstly, characteristic parameters are mathematically proposed to distinguish wildfire's radar echoes. Then, the four-site neighbourhood algorithm and the multithreading method are used to extract all possible wildfire echo units detected by various Doppler weather radars, and a cumulative probability is calculated to identify the wildfire. Finally, a regional block search method is used to quickly locate wildfire-affected transmission towers. The membership function of spread time in the fuzzy set is proposed to determine the alarm level, which takes into account the influences of environment, topography, and vegetation on wildfire spread rate. The application to a provincial power grid demonstrates that the proposed method has an accuracy of 82.4%, a missing alarm rate of 27.4%, and a delay of fewer than 15 min. In addition, the joint observation of wildfires by multi-Doppler weather radars and satellites indicates a promising application prospect for transmission line wildfire fighting.

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Aiming to solve the partial discharge problem caused by defects in composite insulators, most existing live detection methods are limited by the subjectivity of human judgment, the difficulty of effective quantification, and the use of a single detection method. Therefore, a composite insulator defect diagnosis model based on acoustic–electric feature fusion and a multi-scale perception multi-input of stacked auto-encoder (MMSAE) network is proposed in this paper. Initially, during the withstanding voltage experiment, the electromagnetic wave spectrometer and ultrasonic detector were used to collect and process the data of six types of composite insulator samples with artificial defects. The electromagnetic wave spectrum, ultrasonic power spectral density, and n-S map were then obtained. Then, the network architecture of MMSAE was built by integrating a stacked auto-encoder and multi-scale perception module; the feature extraction and fusion methods of the electromagnetic wave spectrum and ultrasonic signal were investigated. The proposed method was used to diagnose test samples, and the diagnostic results were compared to those obtained using a single input source and the artificial neural network (ANN) method. The results demonstrate that the detection accuracy of acoustic–electric feature fusion is greater than that of a single feature; the accuracy of the proposed method is 99.17%, which is significantly higher than the accuracy of the conventional ANN method. Finally, composite insulator defect diagnosis software based on PYQT5 and Keras was developed. Ten 500 kV aging composite insulators were used to validate the effectiveness of the proposed method and design software.