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A new de‐excitation system for advanced brushless excitation based on a synchronous generator is presented. The proposed system uses a discharge resistor in series with a freewheeling diode of a buck chopper. It improves the reliability of the entire power plant and can be used on several excitation systems. It can be used in case of programmed shutdown of the generator and also shows good results in voltage regulation without destroying the efficiency of the excitation system under nominal operation. An analysis of the system has allowed us to suggest a methodology to obtain the appropriate value of the discharge resistance to be used. The validation of the proposed system has been done through simulations in the Matlab/Simulink environment, supported by experimental tests.

This paper is in the field of electric power quality monitoring and presents a new approach for the identification, classification and characterization of the nine voltage dips and swells in electricity networks. The proposed method is based on the study in the complex plane of the signatures of the different voltage dips and swells. In the study of these signatures, the elements taken into account are the root mean square (RMS) values of the phases, the existence or not of an additional phase shift of the voltages and their rotation sense. The informations obtained are synthesized in three variables, and used to the implementation of the method. The results found by the computer simulations carried out by the MATLAB/Simulink software show that the proposed approach uses few parameters, is easy to implement and understand, and makes it possible to efficiently detect, classify and characterize the nine voltage dips and swells.

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29 compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.