• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.228-229
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• 2006

The RCF analysis method is used to analyze small signal stability of power systems including GTO controlled FACTS equipment such as STATCOM. To apply the RCF analysis method in power system small signal stability problems, the state transition equations of power system equipments and power systems with STATCOM are presented. In eigenvalue analysis of power systems by the RCF analysis method, the STATCOM is modelled into the equivalents voltage source model and the PWM switching circuit model. As a result of simulation, the RCF analysis method is very powerful to calculate the oscillation modes exactly after the switching operations, and useful to analyze the small signal stability of power systems with periodically operated switching device such as the STATCOM.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1605-1614
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• 2013

This paper presents a probabilistic methodology for small signal stability analysis of power system with correlated wind sources. The approach considers not only the stochastic characteristics of wind speeds which are treated as random variables with Weibull distributions, while also the wind speed spatial correlations which are characterized by a correlation matrix. The approach based on the 2m+1 point estimate method and Cornish Fisher expansion, the orthogonal transformation technique is used to deal with the correlation of wind farms. A case study is carried out on IEEE New England system and the probabilistic indexes for eigenvalue analysis are computed from the statistical processing of the obtained results. The accuracy and efficiency of the proposed method are confirmed by comparing with the results of Monte Carlo simulation. The numerical results indicate that the proposed method can actually capture the probabilistic characteristics of mode properties of the power systems with correlated wind sources and the consideration of spatial correlation has influence on the probability of system small signal stability.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1832-1842
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• 2014

A novel probabilistic small signal stability analysis (PSSSA) method considering load correlation is proposed in this paper. The superiority Latin hypercube sampling (LHS) technique combined with Monte Carlo simulation (MCS) is utilized to investigate the probabilistic small signal stability of power system in presence of load correlation. LHS helps to reduce the sampling size, meanwhile guarantees the accuracy and robustness of the solutions. The correlation coefficient matrix is adopted to represent the correlations between loads. Simulation results of the two-area, four-machine system prove that the proposed method is an efficient and robust sampling method. Simulation results of the 16-machine, 68-bus test system indicate that load correlation has a significant impact on the probabilistic analysis result of the critical oscillation mode under a certain degree of load uncertainty.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.3
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• pp.127-137
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• 2003

Contingency analysis is one of the most important tasks encountered by planning and operation of lafe scale power systems. This paper describes a new contingency analysis methods for small signal security assessment based on the eigen-sensitivity/perturbation of the electromechanical oscillation modes. The eigen-sensitivity/perturbation with respect to line suceptances and controller parameters can he used to find possible sources of the system instability, and to select contingency for small signal stability. Also, the contingency selection to identify critical generators for MW changes can be obtained by computing the relative movement of the system oscillation modes. The proposed algorithm has been successfully tested on the KEPCO systems which is comprised of 791-bus, 1575-branch and program PSS/E

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.344-349
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• 2005

In conventional small signal stability analysis, the system is assumed to be invariant and the state space equations are used to calculate the eigenvalues of the state matrix. However, when a system contains switching elements such as FACTS equipments, it becomes a non-continuous system. In this case, a mathematically rigorous approach to system small signal stability analysis is performed by means of eigenvalue analysis of the system's periodic transition matrix based on the discrete system analysis method. In this paper, the RCF (Resistive Companion Form) method is used to analyze the small signal stability of a non-continuous system including switching elements. Applying the RCF method to the differential and integral equations of the power system, generator, controllers and FACTS equipments including switching devices should be modeled in the form of state transition equations. From this state transition matrix, eigenvalues that are mapped into unit circles can be computed precisely.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.230-231
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• 2006

In this paper, the RCF(Resistive Companion Form) analysis method is used to analyze small signal stability of power systems including GTO controlled FACTS equipment such as SSSC. To apply the RCF analysis method in power system small signal stability problems, state transition equations of power system equipments and power systems with SSSC are presented. In eigenvalue analysis of power systems by the RCF analysis method, SSSC is modelled into the equivalents voltage source model and PWM switching circuit model. As a result of simulation, the RCF method is very powerful to calculate the oscillation modes exactly after the switching operations, and useful to analyze the small signal stability of power systems with periodic switching device such as SSSC.

• KIEE International Transactions on Power Engineering
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• v.4A no.2
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• pp.79-83
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• 2004

A new small signal stability analysis method for eigenvalue analysis is presented. This method utilizes the Resistive Companion Form (RCF) for the computation of the transition matrix over a specified time interval, which corresponds to a single cycle operation of the system. This method is applicable to any system, with or without switching element. An illustrative example of the method is presented and the eigenvalues are compared with those of the conventional state space method (analog) in order to demonstrate the accuracy of the proposed eigenvalue analysis method. Also, the variations of oscillation modes that are caused by the switching operation can be precisely analyzed using this method.

• Smart Structures and Systems
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• v.19 no.5
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• pp.499-512
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• 2017

Traditional structural dynamic analysis and Structural Health Monitoring (SHM) of large scale concrete civil structures rely on manufactured embedding transducers to obtain structural dynamic properties. However, the embedding of manufactured transducers is very expensive and low efficiency for signal acquisition. In dynamic structural analysis and SHM areas, piezoelectric transducers are more and more popular due to the advantages like quick response, low cost and adaptability to different sizes. In this paper, the applicable feasibility assessment of the designed "artificial" piezoelectric transducers called Concrete Piezoelectric Smart Module (CPSM) in dynamic structural analysis is performed via three major experiments. Experimental Modal Analysis (EMA) based on Ibrahim Time Domain (ITD) Method is applied to experimentally extract modal parameters. Numerical modal analysis by finite element method (FEM) modeling is also performed for comparison. First ten order modal parameters are identified by EMA using CPSMs, PCBs and FEM modeling. Comparisons are made between CPSMs and PCBs, between FEM and CPSMs extracted modal parameters. Results show that Power Spectral Density by CPSMs and PCBs are similar, CPSMs acquired signal amplitudes can be used to predict concrete compressive strength. Modal parameter (natural frequencies) identified from CPSMs acquired signal and PCBs acquired signal are different in a very small range (~3%), and extracted natural frequencies from CPSMs acquired signal and FEM results are in an allowable small range (~5%) as well. Therefore, CPSMs are applicable for signal acquisition of dynamic responses and can be used in dynamic modal analysis, structural health monitoring and related areas.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.355-362
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• 2005

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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.122-132
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• 2005

This paper presents a novel approach based on Prony method to analysis of small signal stability in power system. Prony method is a valuable tool in identifying transfer function and estimating the modal parameter of power system oscillation from measured or computed discrete time signal. This paper define the relative residue of time signal and propose the condition to select low frequency oscillation in each generator. This paper describes the application results of proposed algorithm with respect to KEPCO systems. Simulation results show that the proposed algorithm can be used as another tools of power systems analysis.