Recognition Algorithm of Transient Overvoltage Characteristic Based on Symmetrical Components Estimation The recognition of transient overvoltage characteristics is the premise of disturbance compensation of the transient overvoltage. Based on that, the recognition algorithm of transient overvoltage characteristics based on symmetrical components estimation was proposed. The generation mechanism of the transient overvoltage in gas insulated switchgear (GIS) was analyzed. Then, the transient overvoltage was measured via the capacitive sensor method. The threephase voltage of ultrahigh voltage grid was asymmetrical when the transient overvoltage appeared. At present, the asymmetrical threephase voltage was decomposed into the superposition of a symmetrical positivesequence component, a negativesequence component, and a zerosequence component via the symmetrical components estimation to build the superposition model. The model was decomposed via the trigonometric identity and the modified neural network of the least mean square learning rule was used to estimate the parameter vector of the characteristic quantity of the transient overvoltage in real time. The feasibility of the proposed algorithm was verified via comparing the simulation of the proposed algorithm and the algorithm based on dp transformation. The experimental results show that the proposed algorithm has the advantages of a small operand, high detection precision, and fast action.

Partial Discharge Classification in Power Electronics Applications using Machine Learning A study of machine learning (ML) methods for classification of data from partial discharges (PDs) is described. A novel set of features are suggested and tested using an extensive set of machine learning based algorithms. The aim is to classify PDs occurring within insulation systems and power electronics devices (PED). Due to the increased use of pulse width modulation waveform (PWM) in PEDs, an increased insulation degradation has been observed due to a more intense PD exposure. This study aims to develop suitable tools to detect types of defects to facilitate diagnostics as well as to improve isolation system design. To evaluate the performance of ML based classification, several algorithms have been developed to detect and classify PDs from different kind of material defects with the aim to address the reason behind the appearance of partial discharges. Experiments with different PD source locations and volume of the defect and voltage rise time were investigated on an artificial cavity test object. Relevant signal features found important are for example the maximum magnitude, duration, the distance from polarity shift, the time distance between PDs and the absolute value of the area of the detected PD waveform. It has been observed that forming such PD features based on their time occurrence results in an accurate and generalized solution. With these features the best results were achieved with the deep learning LSTM architecture reaching a test accuracy of 98.3%. For industry applications, feature engineering is useful to reduce amount of data necessary to be analyzed by the neural network or ML algorithm.