Optimal Measurement Allocation Algorithms for Parametric Model Identification of Power Systems

Abstract

In this paper, we present an optimization algorithm that selects the optimal sets of points for placing phasor measurement units (PMUs) on the transmission lines of a multimachine power system for the purpose of identifying the best model fit for its wide-area swing dynamics. Alternatively, the method can also be viewed as a way to select the optimal set of points at which phasor values should be computed using measurements available from PMUs such that these computed values, also referred to here as pseudo-measurements, can generate the best estimate of the swing model, especially when the measurements are noisy. We pose the identification problem as an equivalent parameter estimation problem for the admittance of each tieline and the inertia of each machine using phasor measurements of voltage magnitude, phase angle and frequency, corrupted with high-frequency measurement noise. We then formulate the Cramer-Rao bounds (CRBs) for the estimates of these unknown parameters, and show that the bounds are functions of the PMU locations and of the contribution of each measurement variable in the combined output. We finally state the condition for finding the optimal PMU location that guarantees the tightest CRB, and, therefore, the most accurate swing model for the system.Index Terms-Cramer-Rao bound (CRB), parameter estimation, phasor measurment unit (PMU) allocation, pseudomeasurements, swing dynamics.

A unified framework for wide area measurement system planning

“…The proposed mechanism can also provide estimated system parameters given measurement redundancy. Other analyses take power stability analysis [24,25,26] and oscillation monitoring [27] into consideration. Another widely investigated integration considers power system cyber-security.…”

Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art

“…Ensuring the network observability in both normal condition and controlled islanding mode was accommodated in the OPP problem as well [196]. Other sorts of technical constraints were also added to the OPP problem with an intended specific functionality such as fault location observability [197], bad data detection in SE [198], [199] parameter error identification [200], reducing the variances of the SE errors and increasing the local redundancy [201], enhancing topology error processing [202], defending against data injection attacks [164], generating the best estimate of the swing model [203], minimizing the error of SE [204]- [206], and optimizing a useful metric which accounts for three important requirements in power system state estimation: convergence, observability, and performance [207]. PMUs could also be strategically placed in power networks to offer the detection and identification of parameter error on a single-edge cutset (critical branch) or double-edge cutsets (critical branch pairs) [208].…”