Accurate transmission line parameters are the basis of power system calculations. The measured phasor measurement unit (PMU) phase angle data at both ends of a line may contain large errors caused by synchronization problems, which can seriously affect the accuracy of parameter identification. This paper proposes a robust PMU-based method for calculating transmission line parameters from PMU data at both ends of a line in such a way that a synchronization error between the ends does not degrade the results. Specifically, a π -equivalent model for the transmission line is established, and a least square objective function with nonlinear equations for positive parameter identification is derived. Furthermore, to reduce the impact of noise and biased data, median estimation is used to obtain the final result. Finally, a simulation shows the effectiveness and robustness of the proposed method, and its practicality is demonstrated in a case study using measured PMU data.

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Compared with the traditional supervisory control and data acquisition (SCADA) data, phasor measurement unit (PMU) data is characterized by phase angle measurement and high reporting speed (perhaps 100 Hz). The high reporting speed provides dynamic characteristics of the power system frequency, voltage, and current measurement. PMUs have become one of the important data sources for smart grid monitoring. PMU/WAMS (wide area measurement system) based advanced applications have been widely used in the dispatch centers. Some of the applications, such as line parameter identification and state estimation, depend not only on phase angle data but also on phase angle difference between different locations. Field data can suffer from errors, such as time synchronization error, transducer error, PMU algorithm error, hardware error or malicious attacks, etc. A time synchronization error can directly cause an error in the phase angle difference calculated between the two ends of a transmission line that could degrade a PMU based application. In this paper, a novel method to cluster the phase angle difference data, assess the data quality and screen out the bad PMU phase angle difference data is proposed. First, we develop the hyperplane cluster method to cluster the phase angle difference data. Second, in order to screen out the right data type, this paper compares the virtual reactance parameters of each data type obtained by voltage mean to the line reactance parameter given by the system model. Finally, the performance of the proposed methods has been verified by a simulation. The efficiency of the proposed method has been analyzed. The application of the proposed method using field measured PMU data shows the engineering practicability of the proposed method. In addition, the comparison of the proposed method with other clustering methods is discussed.INDEX TERMS Data screening, hyperplane clustering, measured PMU data, phasor angle.

Smart grids are increasingly dependent on data with the rapid development of communication and measurement. As one of the important data sources of smart grids, phasor measurement unit (PMU) is facing the high risk from attacks. Compared with cyber attacks, global position system (GPS) spoofing attacks (GSAs) are easier to implement because they can be exploited by portable devices, without the need to access the physical system. Therefore, this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference (PAD) deviation. Specifically, this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA, i.e., the step attack and the ramp attack. Then, the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed, which does not require the line parameters. After obtaining the estimated PAD deviations, the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD deviations. Finally, with the case studies, the effectiveness of the proposed method is demonstrated, and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.