A Synchrophasor Data-Driven Method for Forced Oscillation Localization Under Resonance Conditions

Abstract

This paper proposes a data-driven algorithm of locating the source of forced oscillations and suggests the physical interpretation of the method. By leveraging the sparsity of the forced oscillation sources along with the low-rank nature of synchrophasor data, the problem of source localization under resonance conditions is cast as computing the sparse and lowrank components using Robust Principal Component Analysis (RPCA), which can be efficiently solved by the exact Augmented Lagrange Multiplier method. Based on this problem formulation, an efficient and practically implementable algorithm is proposed to pinpoint the forced oscillation source during real-time operation. Furthermore, we provide theoretical insights into the efficacy of the proposed approach by use of physical model-based analysis, in specific by establishing the fact that the rank of the resonance component matrix is at most 2. The effectiveness of the proposed method is validated in the IEEE 68-bus power system and the WECC 179-bus benchmark system.

Inferring Power System Dynamics From Synchrophasor Data Using Gaussian Processes

“…With a slight abuse in notation, we define the D × D matrix Kτ such that [ Kτ ] ij := k ij (τ ) for i, j indexing the eigenstates within the frequency band of interest. Ignoring noise for now, it holds 20) where denotes the element-wise product between two matrices. Note that the data covariance matrix depends linearly on Ã. Matrix Kτ is known from (13).…”

“…The MoM suggests using the sample in lieu of the ensemble covariance to estimate Ã. Vectorizing (20) and the Kronecker product property vec(ABC) = (C ⊗ A) vec(B) yield that…”

“…To account for noise, an additional term dg(σ) should be added on the right-hand side of (20). The M -length vector σ collects the variances for all terms of measurement noise.…”

“…Synchrophasor data can be used to infer more coarse dynamic system information, such as locating the sources of oscillations. The latter task can be accomplished by comparing the arrival time of traveling waves [16] and [17]; by measuring the dissipated energy of power flows [18], [19]; or via robust principal component analysis [20]. But these methods require access to data across the entire power network.…”

A New Robust Identification Method for Transmission Line Parameters Based on ADALINE and IGG Method

“…In the live measurement methods, the increasing number of phasor measurement units (PMU) affords an access to online identify the TL parameters. As the GridEye [3], the PMU can provide high-precision, high-upload frequency voltage and current phasors of the power grid [4], which are applied to state estimation [5], oscillation localization [6], stability evaluation [7], line outage detection [8], frequency response estimation [9] etc. Among them, the identification of TL parameters based on PMU data has also received widespread attention.…”