Tracking Power Systems Events: PMU, Reporting Rate, Interpolation

Abstract

Fast dynamics and transient events are becoming more and more frequent in power systems, due to the high penetration of renewable energy sources and the consequent lack of inertia. In this scenario, Phasor Measurement Units (PMUs) are expected to track the monitored quantities. Such functionality is related not only to the PMU accuracy (as per the IEC/IEEE 60255-118-1 standard) but also to the PMU reporting rate (RR). High RRs allow tracking fast dynamics, but produce many redundant measurement data in normal conditions. In view of an effective tradeoff, the present paper proposes an adaptive RR mechanism based on a real-time selection of the measurements, with the target of preserving the information content while reducing the data rate. The proposed method has been tested considering real-world datasets and applied to four different PMU algorithms. The results prove the method effectiveness in reducing the average data throughput as well as its scalability at PMU concentrator or storage level.

Inertia Estimation of a Power System Area Based on Iterative Equation Error System Identification

“…If not specified otherwise, in all of the simulations reported in this section, random Gaussian noise as described in [115] is added to the measurements proceeding from the PMUs. The sampling time used for input and output signals is 10 ms, which can be easily achieved by commercially available PMUs [93].…”

“…The reported results are accurate, the method is applicable to both SGs and CIGs, and the trajectories of the estimation reach the steady-state value fairly quickly. However, the time step used for all the simulations is 1 ms, whereas the PMUs reporting rates are on the order of tens of frames per second [93]. Since this method still relies on the RoCoP, it is worth mentioning that this index performs better the higher the branch impedance among frequency measurements is.…”

“…Results are accurate and converging times are rather quick. However, reported results are obtained supposing sampling times of 1 ms, whereas phasor measurement units (PMUs) can achieve sampling times on the order of tens of frames per second [93]. Furthermore, the RoCoP index suffers from intrinsic accuracy problems if the impedances of the branches connecting the bus to be monitored are high [55].…”

“…Inertia Estimation of a Power System Area Based on Iterative Equation Error System Identification and active power measurements.The area inertia estimator based on the iterative EE SI approach is coded in MAT-LAB and makes use of the bus frequency and active power measurements imported from PowerFactory. Measurements are exported using a sampling time of 10 ms, which can be easily achieved by commercially available PMUs[93]. All measurements are filtered using the lowpass function of MATLAB, with a cutoff frequency of 2 Hz.…”

A recursive system identification inertia estimator for traditional and converter-interfaced generators

“…The reported results are accurate, the method is applicable to both SGs and CIGs, and the trajectories of the estimation reach the steady-state value fairly quickly. However, the time step used for all the simulations is 1 ms, whereas the phasor measurement units (PMU) reporting rates are on the order of tens of frames per second [14]. Since this method still relies on the RoCoP, it is worth mentioning that this index performs better the higher the branch impedance among frequency measurements is.…”