Low Computational Complexity Model Reduction of Power Systems With Preservation of Physical Characteristics

Abstract

A data-driven algorithm recently proposed to solve the problem of model reduction by moment matching is extended to multi-input, multi-output systems. The algorithm is exploited for the model reduction of large-scale interconnected power systems and it offers, simultaneously, a low computational complexity approximation of the moments and the possibility to easily enforce constraints on the reduced order model. This advantage is used to preserve selected slow and poorly damped modes. The preservation of these modes has been shown to be important from a physical point of view and in obtaining an overall good approximation. The problem of the choice of the socalled tangential directions is also analyzed. The algorithm and the resulting reduced order model are validated with the study of the dynamic response of the NETS-NYPS benchmark system (68-Bus, 16-Machine, 5-Area) to multiple fault scenarios.Index Terms-Dynamic equivalents, model reduction of power systems, coherency, model reduction from data.

Frequency-limited pseudo-optimal rational Krylov algorithm for power system reduction

“…Thus, the preservation of their identity in the ROM is critical for a good damping controller design that adds damping to these modes [14]. Moreover, it is shown in [4], [9], [15] that the preservation of these modes also improves the accuracy in the time-domain. We present a MOR algorithm for the power system reduction problem under consideration which not only preserves the specified modes of the original system, but it also ensures superior accuracy within the frequency region specified by the user.…”

“…This is particularly important when the ROM is used for obtaining a damping controller to reduce the local and interarea oscillations. It is argued in [4], [9] that a good time-domain accuracy can be achieved if the slowest and most poorly damped modes of the original model are preserved in the ROM. The lightly damped modes in the frequency range [0, 2] Hz are called electromechanical modes [32] and can easily be captured using Subspace Accelerated Rayleigh Quotient Iteration (SARQI) algorithm [31].…”

Constrained optimal reduced-order models from input/output data

“…If the interest of the designer is to have the best approximation along all the frequencies, then the unconstrained problem should be used. On the other hand if the designer knows that the system is driven by a specific class of input signals (as it is desirable when moment matching is preferred with respect to other reduction methods), for instance like in the case of the reduction of power systems [36], [42], then the constrained problem should be solved.…”

“…The reason that justifies the interest in the constrained problem is that in many applications (e.g. power systems and converters [36], [37]), the system is excited by a specific class of input signals (which are connected with the interpolation points). It is then desirable that the steady-state error for this class of signals be identically equal to zero.…”

Clustering approach to model order reduction of power networks with distributed controllers

“…Literature review Conventional model reduction techniques, including balanced truncation and Krylov subspace methods, have been extended to the dynamic reduction of power systems (see, e.g., [10,23,24,32,42,45,49]). In these papers, the power network systems are modeled in first-order state space representations, and the reduced-order models are constructed within the framework of Petrov-Galerkin projection.…”