One of primary grid management challenge is to ensure that these changing power system operating conditions stay within safe limits at all times including potential and probable future contingencies. In this field, one of the most promising enabling technologies is synchrophasor measurements, which require the estimation of the parameters of sinusoids, namely amplitude, frequency and phase. In the context of synchrophasor measurements many algorithms have been proposed, which are based on the computation of Discrete ourier Transform, on the digital phase-locked loop, the Kalman filter, estimation of sinusoid parameters in noisy environment. In this paper we propose a phasor estimation algorithm based on the high signal-to-noise ratio (SNR) assumption. We show that such an assumption reduces the computational complexity of the algorithm in terms of products per observation window. Amplitude can be estimated as sample-mean of the sequence of magnitude of the observed samples, thus requiring just sums, and frequency and phase can be estimated sequentially from the observed phases only. Monte-Carlo simulations show that the proposed algorithm achieves the Cramer-Rao Lower Bounds for amplitude, frequency and phase and estimated total vector error is well below the limits prescribed in standard in both static and dynamic conditions.
|Titolo:||Phase-based estimation of synchrophasors|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|