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Power quality pollution to power system and locomotive's discontinued current collection are caused by two phase traction power supply system in electrical railway. Cost-effective co-phased traction power systems (CTPS) consisted of balanced transformer, active power flow controller device (APFC) and passive compensation device (PCD) was presented to reduce power quality pollution and guarantee locomotive's current collection continuously by canceling neutral section. Two schemes of CTPS were compared, parameter calculating method of PCD and control strategy of APFC are analyzed. CTPS and its control strategy have been verified based on MATLAB simulation.Keywords-co-phased traction power system, traction power supply system, hybrid compensation, power quality, neutral section, balanced transformer, single phase load I.

The excessive use of power electronics makes power quality problems in power grids increasingly prominent. The estimation of the harmonic parameters of harmonic sources in the power grid and the division of harmonic responsibilities are of great significance for the evaluation of power quality. At present, methods for estimating harmonic parameters and harmonic responsibilities need to provide the amplitude and phase information of the current and voltage of the point of common coupling (PCC). However, in practical engineering applications, the general power quality monitor only provides the amplitude information of the voltage and current of the measured point and the phase difference information between them. Missing phase information invalidates existing methods. Based on the partial least squares regression method, the present work proposes a method for estimating harmonic parameters in the case of monitoring data without phase. This method only needs to measure the amplitude information of the harmonic voltage and current of the PCC and the phase difference between them, then use the measurable data to estimate the harmonic parameters and the harmonic responsibility of each harmonic source. It provides a new way to effectively solve the problem that the measured data of the project has no phase information. The feasibility and effectiveness of the proposed method are proved by simulation data and measured engineering data.

The denoising and detection of transient disturbances are two important subjects for power quality monitoring and analysis. To effectively denoise and detect transient disturbances under noisy conditions, an improved iterative adaptive kernel regression method is proposed in this paper. The proposed method has advantages in that it does not need to estimate the noise variance or a filter threshold, and has both denoising and detection capabilities for transient disturbances. Simulation results demonstrate that the proposed method provides excellent denoising effects, which can not only suppress noise effectively but also preserve disturbance features of sudden change points well. Additionally, it provides good detection and location performance for single and combined transient disturbances, even under strong noise conditions. Finally, the effectiveness of the proposed method is further verified by using real disturbance data.

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In railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics and improve the power quality of the traction network. As a result of the coupling characteristics of the traction power supply system, the identification results of harmonic impedance may be inaccurate and controversial. In this context, an identification method based on a data evolution mechanism is proposed. At first, a harmonic impedance model is established and the equivalent circuit of the traction network is established. According to the harmonic impedance model, the proposed method eliminates the outliers of the measured data from trains by the Grubbs criterion and calculates the harmonic impedance by partial least squares regression. Then, the data evolution mechanism based on the sample coefficient of determination is introduced to estimate the reliability of the identification results and to divide results into several reliability levels. Furthermore, in the data evolution mechanism through adding new harmonic data, the low-reliability results can be replaced by the new results with high reliability and, finally, the high-reliability results can cover all frequencies. Moreover, the identification results based on the simulation data show the higher reliability results are more accurate than the lower reliability results. The measured data verify that the the data evolution mechanism can improve accuracy and reliability, and their results prove the feasibility and validation of the proposed method.

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