Due to the oscillating behaviour of the pantograph and the catenary during the movement of the locomotive, the contact force changes to wide limits, resulting in electrical sparks with consequences: premature wear of the contact wire, electromagnetic disturbances, energy loss. Suitable control of the contact force at speeds exceeding 160 km/h may diminish these effects. Due to the particularities of the pantograph-catenary system and many random external factors, existing control methods do not always give the best results. The authors’ research has shown that the contact force signal contains a chaotic component that can be offset by chaos theory methods. Their own studies have demonstrated theoretically and by simulation that this approach can largely eliminate the separating the pantograph from the catenary and will lessen excessive forces. Experimental research, conducted by the authors and presented in this article, has been done in laboratory conditions on a specially built test stand and confirms that this new approach to active pantograph control produces superior results to existing methods.
This paper addresses one of the current areas of interest in electrical engineering, which is controlled switching of high voltage circuit breakers. During their operation, the problem of controlled switching of high voltage circuit breakers in commutation regimes was studied. Several types of switching were analyzed, considered representative of the transient regime, depending on the type of load, on the defect that may occur on the power supply lines, as well as depending on the position of this defect (near or far). The study carried out in the paper includes simulations of the controlled connection/disconnection operations in a transient regime, assuming the existence of different kinds of defects. To perform the study and simulations in the transient regime, a model, implemented in Matlab, was used for a time interval located around the origin of the time axis. The study included the dependence of the SF6 circuit breaker switching process on the following parameters: the DC voltage supply, ambient temperature and oil pressure in the circuit breaker actuator. The validity of the theory presented in this paper, in addition to being validated by simulations, is proven by the fact that the protection system currently in use at the power station of an 800 MW power plant, at the 400 kV power line, is based on the principles presented in this paper. The theory presented in the paper has been implemented in industry for nearly two years, and the results confirm that the theory presented in the paper is fully applicable in high voltage power stations.
Electricity has become an important concern in today’s society. This is due to the fact that the electric grid now has a greater number of non-linear components. The AC-powered locomotive is one of these non-linear components. The aim of this paper was to model and predict the reactive power produced by an AC locomotive. This paper presents a study on the modelling and prediction of reactive power produced by AC-powered electric locomotives. Reactive power flow has a significant impact on network voltage levels and power efficiency. The research was conducted by using intelligent techniques—more precisely, by using the adaptive neuro fuzzy inference system (ANFIS). Several approaches to the ANFIS structure were used in the research. Of these, we mention the ANFIS-grid partition, ANFIS subtractive clustering and ANFIS fuzzy c-means (FCM) clustering. Thus; for modelling and predicting reactive power, ANFIS was trained, then tested. For the training of ANFIS, experimental data obtained from measurements performed in a train supply sub-station were used. The measurements were taken over a period of time when the locomotives were far away from the station, close to the station, and at the station, respectively. The currents and voltages from the supply substation, respectively the active, reactive, and distorted powers, were measured on the data acquisition board. With the measured data of the reactive power, the modelling with ANFIS was performed, and a prediction of the variation in the reactive power was made. The paper analysed the results of the modelling by comparing between several types of ANFIS architectures. The values of RMSE, RMS and the training time of ANFIS were compared for several structures of ANFIS.
Because of the large number of parameters that interact in their function, determining dynamic regime parameters as well as the mode of function of amplifying stages is an extremely complex problem. This paper describes a LabVIEW application for studying the functioning of an ampli-fier in various connections. The user selects the generator's parameters, the type of connection and its parameters, as well as the electric charge characteristics. The application can determine both the stage characteristics and the Bode characteristics. The amplifier's stability zone, as well as its gain and phase, are determined based on these characteristics. An important advantage of this application is that the design of the amplifier stage can be made starting from some param-eters that the amplifier can establish, from which the values of components can be determined. In order to validate the simulation results using the LabVIEW application, a specialized program Multisim was used, as well as experimental measurements using the Electronics Explorer Board. Both Multisim and Electronics Explorer Board can determine Bode characteristics. In both simu-lations and experimental amplifiers, the same schemes with the same transistor was used. The application can be used for educational purposes as well as to design the amplifier's stage to achieve specific parameters.
Harmonic generation in power system networks presents significant issues that arise in power utilities. This paper describes a machine learning technique that was used to conduct a research study on the harmonic analysis of railway power stations. The research was an investigation of a time series whose values represented the total harmonic distortion (THD) for the electric current. This study was based on information collected at a railway power station. In an electrified substation, measurements of currents and voltages were made during a certain interval of time. From electric current values, the THD was calculated using a fast Fourier transform analysis (FFT) and the results were used to train an adaptive ANN—GMDH (artificial neural network–group method of data handling) algorithm. Following the training, a prediction model was created, the performance of which was investigated in this study. The model was based on the ANN—GMDH method and was developed for the prediction of the THD. The performance of this model was studied based on its parameters. The model’s performance was evaluated using the regression coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). The model’s performance was very good, with an RMSE (root-mean-square error) value of less than 0.01 and a regression coefficient value higher than 0.99. Another conclusion from our research was that the model also performed very well in terms of the training time (calculation speed).
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