Joost Rommes

Gramian-Based Reduction Method Applied to Large Sparse Power System Descriptor Models

This paper presents an efficient linear system reduction method that computes approximations to the controllability and observability gramians of large sparse power system descriptor models. The method exploits the fact that a Lyapunov equation solution can be decomposed into low-rank Choleski factors, which are determined by the alternating direction implicit (ADI) method. Advantages of the method are that it operates on the sparse descriptor matrices and does not require the computation of spectral projections onto deflating subspaces of finite eigenvalues, which are needed by other techniques applied to descriptor models. The gramians, which are never explicitly formed, are used to compute reduced-order state-space models for large-scale systems. Numerical results for small-signal stability power system descriptor models show that the new method is more efficient than other existing approaches.

Efficient computation of transfer function dominant poles using subspace acceleration

This paper describes a new algorithm to compute the dominant poles of a high-order scalar transfer function. The algorithm, called the subspace accelerated dominant pole algorithm (SADPA), is more robust than existing methods in finding both real and complex dominant poles and faster because of subspace acceleration. SADPA is able to compute the full set of dominant poles and produce good modal equivalents automatically, without any human interaction

Efficient Computation of Multivariable Transfer Function Dominant Poles Using Subspace Acceleration

This paper describes a new algorithm to compute the dominant poles of a high-order multiple-input multiple-output (MIMO) transfer function. The algorithm, called the Subspace Accelerated MIMO Dominant Pole Algorithm (SAMDP), is able to compute the full set of dominant poles efficiently. SAMDP can be used to produce good modal equivalents automatically. The general algorithm is robust, applicable to both square and nonsquare transfer function matrices, and can easily be tuned to suit different practical system needs

Efficient Methods for Large Resistor Networks

Large resistor networks arise during the design of very-large-scale integration chips as a result of parasitic extraction and electro static discharge analysis. Simulating these large parasitic resistor networks is of vital importance, since it gives an insight into the functional and physical performance of the chip. However, due to the increasing amount of interconnect and metal layers, these networks may contain millions of resistors and nodes, making accurate simulation time consuming or even infeasible. We propose efficient algorithms for three types of analysis of large resistor networks: 1) computation of path resistances; 2) computation of resistor currents; and 3) reduction of resistor networks. The algorithms are exact, orders of magnitude faster than conventional approaches, and enable simulation of very large networks.

Computing Rightmost Eigenvalues for Small-Signal Stability Assessment of Large-Scale Power Systems

Knowledge of the rightmost eigenvalues of system matrices is essential in power system small-signal stability analysis. Accurate and efficient computation of the rightmost eigenvalues, however, is a challenge, especially for large-scale descriptor systems. In this paper we present an algorithm, based on Subspace Accelerated Rayleigh Quotient Iteration (SARQI), for the automatic computation of the rightmost eigenvalues of large-scale (descriptor) system matrices. The effectiveness and robustness of the algorithm is illustrated by numerical experiments with realistic power system models, and we also show how SARQI can be used to compute eigenvalues closest to any damping ratio and repeated eigenvalues. The algorithm can be used for stability analysis in any other field of engineering.

Computing Large-Scale System Eigenvalues Most Sensitive to Parameter Changes, With Applications to Power System Small-Signal Stability

This paper describes a new algorithm, named the sensitive pole algorithm, for the automatic computation of the eigenvalues (poles) most sensitive to parameter changes in large-scale system matrices. The effectiveness and robustness of the algorithm in tracing root-locus plots is illustrated by numerical results from the small-signal stability analysis of realistic power system models. The algorithm can be used in many other fields of engineering that also study the impact of parametric changes to linear system models.

Passivity-Preserving Model Reduction Using Dominant Spectral-Zero Interpolation

In this paper, the dominant spectral-zero method (dominant SZM) is presented, a new passivity-preserving model-reduction method for circuit simulation. Passivity is guaranteed via spectral-zero interpolation, and a dominance criterion is proposed for selecting spectral zeros. Dominant SZM is implemented as an iterative eigenvalue-approximation problem using the subspace-accelerated dominant-pole algorithm. Passive circuits are reduced automatically irrespective of how the original system equations are formulated (e.g., circuit models containing controlled sources or susceptance elements). Dominant SZM gives comparable and often more accurate reduced models than known techniques such as PRIMA, modal approximation, or positive real balanced truncation.

Simulation of Mutually Coupled Oscillators Using Nonlinear Phase Macromodels

Design of integrated RF circuits requires detailed insight in the behavior of the used components. Unintended coupling and perturbation effects need to be accounted for before production, but full simulation of these effects can be expensive or infeasible. In this paper, we present a method to build nonlinear phase macromodels of voltage-controlled oscillators. These models can be used to accurately predict the behavior of individual and mutually coupled oscillators under perturbation at a lower cost than full circuit simulations. The approach is illustrated by numerical experiments with realistic designs.

SparseRC: Sparsity Preserving Model Reduction for RC Circuits With Many Terminals

A novel model order reduction (MOR) method, SparseRC, for multiterminal RC circuits is proposed. Specifically tailored to systems with many terminals, SparseRC employs graph-partitioning and fill-in reducing orderings to improve sparsity during model reduction, while maintaining accuracy via moment matching. The reduced models are easily converted to their circuit representation. These contain much fewer nodes and circuit elements than otherwise obtained with conventional MOR techniques, allowing faster simulations at little accuracy loss.

Computation of Transfer Function Dominant Zeros With Applications to Oscillation Damping Control of Large Power Systems

This paper demonstrates that transfer function zeros are equal to the poles of a new inverse system, which is valid even for the strictly proper case. This is a new finding, which is important from practical as well as theoretical viewpoints. Hence, the dominant zeros can be computed as the dominant poles of the inverse transfer function by the recent efficient SADPA and SAMDP algorithms, which are applicable to large-scale systems as well. The importance of computing dominant zeros and the performance of the algorithms are illustrated by examples from large practical power system models. These examples constitute new practical uses for single-input single-output (SISO) and multi-input multi-output (MIMO) zeros, which may be of benefit to power system simulation studies and field tests related to oscillation damping control.