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Phasor measurement unit (PMU), which could provide high-frequency phasors including the amplitude and phase angle synchronized with Global Positioning System (GPS)/BeiDou Navigation Satellite System (BDS) signal, plays a vital role in dynamic monitoring of power systems. However, if the GPS/BDS signal could not be received, the time of PMU, which depends on the crystal oscillator (CO), would cause measurement deviation due to frequency shifts of CO, and then the PMU data would engage deviations and deteriorate the relevant applications. In recognizing the above problems, this article proposes a method to detect the frequency deviation of the crystal oscillator (FDCO) based on the correlation coefficient, according to its relation with the characteristic of the voltage phase angle difference (VPAD) between the two ends of the line. In detail, firstly, the quality of the VPAD of PMU between the two ends of the line is analyzed and the related problem is stated. Secondly, the characteristics of PMU data, when FDCO exists, are derived and analyzed, especially the VPAD between the line, and the suitability of the data for detecting the FDCO is discussed. Thirdly, the ideas and specific methods of detecting the FDCO in case of malfunction of GPS/BDS signal are proposed. Furthermore, the validity of the proposed method with simulation data and modified measured data is presented. Finally, the conjectures on the reasons for the approximately constant VPAD deviation in the previous section are discussed.

Accurate transmission line parameters are the basis of power system calculations. Aiming at obtaining accuracy online transmission line parameters in the case of large random noises even bad data in Phasor Measurement Unit (PMU) measurements, which occurs frequently in the practice, a new adaptive robust identification method combining adaptive linear neuron (ADALINE) and traditional robust IGG (Institute of Geodesy & Geophysics, Chinese Academy of Sciences) method is proposed. In detail, first, the identification model of transmission line parameters is presented based on the multi-period PMU measurements at both ends of the transmission line. Then, a parameter solving model based on ADALINE is established. Furthermore, to fully use measurement information, the adaptive robust ADALINE (ARA) are proposed, which applies the robust IGG weight function (Scheme I) to ADALINE to realize “three segments” robust identification. In addition, to improve the robustness, the expectation and variance of the equation residual sequence are estimated adaptively with the median principle to adjust the threshold for the IGG function to assure robustness (TAR), which is independent of the known information for the error of the measurement equipment. The cases based on PSCAD simulated data and measured data show the effectiveness and engineering practicality of the proposed method.

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.