Low-order black-box models for control system design in large power systems

Abstract

The paper studies two multi-input multi-output (MIMO) procedures for the identification of low-order state-space models of power systems, by probing the network in open loop with low-energy pulses or random signals. Although such data may result from actual measurements, the development assumes simulated responses from a transient stability program, hence benefiting from the existing large base of stability models. While pulse data is processed using the eigensystem realization algorithm, the analysis of random responses is done by means of subspace identification methods. On a prototype Hydro-Qu6bec power system, including SVCs, DC lines, series compensation, and more than 1100 buses, it is verified that the two approaches are equivalent only when strict requirements are imposed on the pulse length and magnitude. The lmh-order equivalent models derived by randomsignal probing allow for effective tuning of decentralized power system stabilizers (PSSs) able to damp both local and very slow inter-area modes.

Control Coordination Within a VSC HVDC Link for Power Oscillation Damping: A Robust Decentralized Approach Using Homotopy

“…Therefore, an identification routine was used to accurately estimate linearized models of the power system, capturing the critical dynamics in the frequency range of interest. Here, numerical algorithm for sub-space statespace system identification (N4SID) technique [28], [29] was used to derive linear models by measuring simulated outputs (i.e tie-line flow, bus phase angle of voltages) in response to injected pseudo random binary sequence (PRBS) signals [30] at the VSC HVDC reference control inputs.…”

Spectral estimation of low-frequency oscillations in the Nordic grid using ambient synchrophasor data under the presence of forced oscillations

“…This method was later extended to incorporate the Autoregressive Moving Average (ARMA) model [5] and the Autoregressive model with spectral analysis [6]. State space based tool for mode identification is proposed in [7] and discussed in [6]. Adaptive tuning of autoregressive models by adaptive filtering techniques enables continuous modes tracking.…”

MANGO ? Modal Analysis for Grid Operation: A Method for Damping Improvement through Operating Point Adjustment

“…The linear dependency of columns in the Δp matrix of Equation (21) is much more complex than that of the Δp matrix in Equation (17). For example, in Δp the only constraint is Equation (18).…”

“…Mode estimation methods based on measurement data have been extensively studied. A sample of papers includes [15], [16], [17], [18], [19], [20], [21], [22], and [23]. Measurement-based models usually take much less effort to build than those required for a model-based method.…”