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At present, the method of obtaining the voltage of transmission lines by using the inverse problem of the electric field is generally faced with the difficulties of solving data equations. Aiming at the above problems, the method that solves transmission wire voltage through spatial electric field integration based on D-dot sensor is proposed. In this paper, with the ground as the reference potential and the vertical line of the transmission line to the ground as the integral path, D-dot electric field sensor is applied to measure the instant electric field values of several nodes on the integral path and the transmission wire voltage is directly solved combined with Gauss–Legendre numerical integration algorithm. The Gauss–Legendre algorithm is verified by the electric field distribution data on the integration path obtained by simulation. Furthermore, the improvement of the Gauss–Legendre algorithm is realized through optimizing the integral interval so that the location of integral nodes is more reasonable. Finally, the measurement system based on LabVIEW is designed and the experiment platform of the analog transmission line is constructed. Experiment results show that the measurement method of transmission wire voltage through the improved Gauss–Legendre algorithm based on D-dot sensor owns high accuracy with an error of less than 1%. [ABSTRACT FROM AUTHOR]

An accurate model of nonlinear load is important for the evaluation of power quality (PQ). Among different PQ disturbance sources, alternating current electric arc furnace (AC EAF) is one of most complicated and serious loads. To provide effective operation prediction of AC EAF, a data-driven modeling approach based on time- and frequency-domain voltage-current (v -i) characterization is proposed in this paper. With the prediction of the proposed model in the time domain, the dynamic and short-term behavior of AC EAF can be observed. And the quasistationary and long-term features of AC EAF would be extracted via the frequency-domain phase of the proposed model. From the comparison on the field measurement data, the performance of the proposed model can be verified in the applications of PQ studies. [ABSTRACT FROM AUTHOR]

A method of predicting the residual breakdown electrical field strength of an epoxy-mica paper insulation system is described. This method was developed by using data on service?aged stator bars from generators as well as accelerated aging model bars. Thermal aging, mechanical aging, and combined thermal and mechanical aging were done individually as accelerated aging. The partial discharge characteristics, dissipation factor, and breakdown voltage were measured on the model bars and service?aged bars from generators. From these results, the residual breakdown electrical field strength was predicted based on the partial discharge characteristics and dissipation factor. First, the dominant type of deterioration (thermal or mechanical aging) on a bar?s insulation was determined by the pattern of voltage?partial discharge magnitude characteristics. Then the residual breakdown electrical field strength was predicted by using a predicting equation for both thermal and mechanical aging. Each predicting equation uses dissipation factor tip-up (i.e. difference in dissipation factor values between 2 kV and rated voltage) and partial discharge inception electrical field strength. The accuracy of this method was confirmed using data measured on 112 service-aged stator bars from generators and 10 accelerated aged model bars. The 95% confidence interval level included 94% of the measured residual breakdown electrical field strength values. This result shows that the method enables precise predictions of the residual breakdown electrical field strength of an epoxy-mica paper insulation system. [ABSTRACT FROM AUTHOR]