In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.
In the context of the rapid development of integrated energy and the digital transformation of power grids, data is playing an increasingly important role in the safe operation of power grids. To deepen the value of data application and ensure the accuracy of data application, this paper proposes a data filling method that combines linear interpolation and LightGBM (Light Gradient Boosting Machine) in response to the missing phenomenon in the source network data collection process. The process can generally be divided into 2 steps: First, linear interpolation is exploited to process short-term missing data. Then the LightGBM can be used to process long-term missing data. In the process of using LightGBM, linear interpolation is used to interpolate the independent variables of the input model. Through the above process, the data for the missing ratio could be obtained, which can then be used to complete all data filling in order from high to low. Through actual data test, this method has better data filling performance.
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