Identification of Small Signal Oscillation Mode Parameters from Simulated and Actual PMU Ringdown Data

This paper presents the practical use of Prony Analysis to identify small signal oscillation mode parameters from simulated and actual phasor measurement unit (PMU) ringdown data. A well-known two-area four-machine power system was considered as a study case while the latest PMU ringdown data were collected from a double circuit 275 kV main interconnector on the Irish power system. The eigenvalue analysis and power spectral density were also conducted for the purpose of comparison. The capability of Prony Analysis to identify the mode parameters from three different types of simulated PMU ringdown data has been shown successfully. Furthermore, the results indicate that the Irish power system has dominant frequency modes at different frequencies. However, each mode has good system damping.

An enhanced model for small-signal analysis of the phase-shifted full-bridge converter

This paper presents an in-depth critical discussion and derivation of a detailed small-signal analysis of the Phase-Shifted Full-Bridge (PSFB) converter. Circuit parasitics, resonant inductance and transformer turns ratio have all been taken into account in the evaluation of this topology’s open-loop control-to-output, line-to-output and load-to-output transfer functions. Accordingly, the significant impact of losses and resonant inductance on the converter’s transfer functions is highlighted. The enhanced dynamic model proposed in this paper enables the correct design of the converter compensator, including the effect of parasitics on the dynamic behavior of the PSFB converter. Detailed experimental results for a real-life 36V-to-14V/10A PSFB industrial application show excellent agreement with the predictions from the model proposed herein.1

Small-signal stability assessment of active distribution networks with dynamic loads

This paper investigates small-signal stability of a distribution system with distributed generator and induction motor load, as a dynamic element. The analysis is carried out over a distribution test system with different types of induction motor loads. The system is linearised by the perturbation method. Eigenvalues and participation factors are calculated to see the modal interaction of the system. The study indicates that load voltage dynamics significantly influence the damping of a newly identified voltage mode. This mode has frequency of oscillation between the electromechanical and subsynchronous oscillation of power systems. To justify the validity of the modal analysis time domain simulation is also carried out. Finally, significant parameters of the system that affect the damping and frequency of the oscillation are identified.

A Model-Based Approach for Small-Signal Stability Assessment of Unbalanced Power Systems

In this paper, a modeling technique for small-signal stability assessment of unbalanced power systems is presented. Since power distribution systems are inherently unbalanced, due to its lines and loads characteristics, and the penetration of distributed generation into these systems is increasing nowadays, such a tool is needed in order to ensure a secure and reliable operation of these systems. The main contribution of this paper is the development of a phasor-based model for the study of dynamic phenomena in unbalanced power systems. Using an assumption on the net torque of the generator, it is possible to precisely define an equilibrium point for the phasor model of the system, thus enabling its linearization around this point, and, consequently, its eigenvalue/eigenvector analysis for small-signal stability assessment. The modeling technique presented here was compared to the dynamic behavior observed in ATP simulations and the results show that, for the generator and controller models used, the proposed modeling approach is adequate and yields reliable and precise results.

Performance metrics for small-signal stability assessment of DC-distributed power-system-architecture comparisons

The objective of this paper is to provide performance metrics for small-signal stability assessment of a given system architecture. The stability margins are stated utilizing a concept of maximum peak criteria (MPC) derived from the behavior of an impedance-based sensitivity function. For each minor-loop gain defined at every system interface, a single number to state the robustness of stability is provided based on the computed maximum value of the corresponding sensitivity function. In order to compare various power-architecture solutions in terms of stability, a parameter providing an overall measure of the whole system stability is required. The selected figure of merit is geometric average of each maximum peak value within the system. It provides a meaningful metrics for system comparisons: the best system in terms of robust stability is the one that minimizes this index. In addition, the largest peak value within the system interfaces is given thus detecting the weakest point of the system in terms of robustness.

Simplified small-signal stability analysis for optimized power system architecture

The optimization of power architectures is a complex problem due to the plethora of different ways to connect various system components. This issue has been addressed by developing a methodology to design and optimize power architectures in terms of the most fundamental system features: size, cost and efficiency. The process assumes various simplifications regarding the utilized DC/DC converter models in order to prevent the simulation time to become excessive and, therefore, stability is not considered. The objective of this paper is to present a simplified method to analyze small-signal stability of a system in order to integrate it into the optimization methodology. A black-box modeling approach, applicable to commercial converters with unknown topology and components, is based on frequency response measurements enabling the system small-signal stability assessment. The applicability of passivity-based stability criterion is assessed. The stability margins are stated utilizing a concept of maximum peak criteria derived from the behavior of the impedance-based sensitivity function that provides a single number to state the robustness of the stability of a well-defined minor-loop gain.

Systematic Method to Assess Small-Signal Stability of DC-Distributed Power-System-Architecture

The objective of this paper is to present a simplified method to analyze small-signal stability of a power system and provide performance metrics for stability assessment of a given power-system-architecture. The stability margins are stated utilizing a concept of maximum peak criteria (MPC), derived from the behavior of an impedance-based sensitivity function that provides a single number to state the robustness of the stability of a well-defined minor-loop gain. For each minor-loop gain, defined at every system interface, the robustness of the stability is provided as a maximum value of the corresponding sensitivity function. Typically power systems comprise of various interfaces and, therefore, in order to compare different architecture solutions in terms of stability, a single number providing an overall measure of the whole system stability is required. The selected figure of merit is geometric average of each maximum peak value within the system, combined with the worst case value of system interfaces.

Characteristics of small-signal capacitances of silicon-on-sapphire MOSFETs

The measured inter-electrode capacitances of silicon-on-sapphire (SOS) MOSFETs are presented and compared with simulation results. It is shown that the variations of capacitances with DC bias differ from those of bulk MOSFETs due to change in body potential variation of the SOS device resulting from electron-hole pair generation through impact ionisation.

Power system sensitivity analysis for probabilistic small signal stability assessment in a deregulated environment

Deregulations and market practices in power industry have brought great challenges to the system planning area. In particular, they introduce a variety of uncertainties to system planning. New techniques are required to cope with such uncertainties. As a promising approach, probabilistic methods are attracting more and more attentions by system planners. In small signal stability analysis, generation control parameters play an important role in determining the stability margin. The objective of this paper is to investigate power system state matrix sensitivity characteristics with respect to system parameter uncertainties with analytical and numerical approaches and to identify those parameters have great impact on system eigenvalues, therefore, the system stability properties. Those identified parameter variations need to be investigated with priority. The results can be used to help Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs) perform planning studies under the open access environment.

Eigenvalue sensitivity analysis for probabilistic small signal stability assessment

This paper presents a method to analyze the first order eigenvalue sensitivity with respect to the operating parameters of a power system. The method is based on explicitly expressing the system state matrix into sub-matrices. The eigenvalue sensitivity is calculated based on the explicitly formed system state matrix. The 4th order generator model and 4th order exciter system model are used to form the system state matrix. A case study using New England 10-machine 39-bus system is provided to demonstrate the effectiveness of the proposed method. This method can be applied into large scale power system eigenvalue sensitivity with respect to operating parameters.

A novel grid computing approach for probabilistic small signal analysis

Grid computing is an advanced technique for collaboratively solving complicated scientific problems using geographically and organisational dispersed computational, data storage and other recourses. Application of grid computing could provide significant benefits to all aspects of power system that involves using computers. Based on our previous research, this paper presents a novel grid computing approach for probabilistic small signal stability (PSSS) analysis in electric power systems with uncertainties. A prototype computing grid is successfully implemented in our research lab to carry out PSSS analysis on two benchmark systems. Comparing to traditional computing techniques, the gird computing has given better performances for PSSS analysis in terms of computing capacity, speed, accuracy and stability. In addition, a computing grid framework for power system analysis has been proposed based on the recent study.