Yung-Tien Chen

Cornell University, Ithaca, NY, United States

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Publications (11)22.55 Total impact

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    ABSTRACT: Load modeling has a significant impact on power system dynamic analysis. Currently, static load models are commonly used in the power industry to model dynamic behaviors of reactive loads. Dynamic and composite load models are recommended to possibly improve modeling accuracy for reactive power. In this paper, the performance of six load models proposed in the literature for modeling dynamic behaviors of reactive loads are evaluated and compared. The issues of estimation accuracy and model complexity are compared to evaluate the estimation performance of each model. Numerical results indicate that static load models do not adequately model dynamic behaviors of reactive loads. A first-order induction motor model can satisfactorily capture the dynamic behaviors of reactive loads, while composite load models can accurately capture the dynamic behaviors of reactive loads. In addition, the issue of the incorporation of dynamic load models increasing the dimension of system representation is addressed.
    International Journal of Electrical Power & Energy Systems - INT J ELEC POWER ENERG SYST. 01/2008; 30(9):497-503.
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    ABSTRACT: Accurate load modeling is essential for power system dynamic simulation. In this paper, four dynamic load models are identified based on multiple online measurement data from the Taiwan Power System. The performances in modeling real and reactive power behaviors by dynamic and selected static load models are evaluated. Parameter variation with respect to different loading conditions is analyzed. A simple and efficient method is presented to estimate a representative parameter set for different loading conditions. The cross-validation technique is applied to validate the four dynamic load models in order to obtain a better estimate of their performance. Numerical studies indicate that linear dynamic load models studied in this paper give better results than two nonlinear dynamic load models in modeling reactive power behaviors during disturbance while they are comparable in modeling real power behaviors
    IEEE Transactions on Power Systems 09/2006; · 2.92 Impact Factor
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    ABSTRACT: Load representation has a significant impact on power system analysis and control results. In this paper, composite load models are developed based on on-line measurement data from a practical power system. Three types of static-dynamic load models are derived: general ZIP-induction motor model, exponential-induction motor model and Z-induction motor model. For dynamic induction motor model, two different third-order induction motor models are studied. The performances in modeling real and reactive power behaviors by composite load models are compared with other dynamic load models in terms of relative mismatch error. In addition, numerical consideration of ill-conditioned parameters is addressed based on trajectory sensitivity. Numerical studies indicate that the developed composite load models can accurately capture the dynamic behaviors of loads during disturbance
    Power Engineering Society General Meeting, 2006. IEEE; 01/2006
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    ABSTRACT: Load modeling is well known to have a significant impact on voltage stability analysis while it is not clear the degree of influence each load has on voltage stability analysis. In this paper the problems associated with quantifying the degree of influence and load ranking on voltage stability analysis are addressed. A computational method based on load margin for this numerical quantification is presented. A load ranking scheme is developed and applied to Taiwan power system to rank the degree of influence each nodal load of Taipower transmission network on load margin of voltage stability. Physical insights of top-ranked nodal loads are provided. Furthermore, the robustness of load ranking under different loading conditions, different contingencies and different power transfer patterns is investigated. Some applications of load ranking results are described
    Power Engineering Society General Meeting, 2006. IEEE; 01/2006
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    ABSTRACT: We developed, using on-line field measurements, a dynamic ZIP-motor load model which attempts to capture the dynamic behavior of loads. The dynamic ZIP-motor load model consists of two parts: a static part and a dynamic part, where the static load is represented by a combination of constant impedance, constant current and constant power while the dynamic load is represented by an induction motor model. A procedure for building the model structure of the dyanmic ZIP-motor load model is developed. An effective solution algorithm for estimating the associated model parameters is presented. A notable feature of the algorithm is that the model parameters are estimated based upon pseudo-gradient information instead of exact gradient calculation. The proposed algorithm is easy to implement and can be used to identify nonlinearities associated with ZIP-motor models, such as models accounting for magnetizing and leakage reactances. The results developed in this paper are tested and evaluated using real field measurements from a real power system. It is shown numerically in the case study that the developed dynamic load model can accurately capture the dynamic behaviors of loads.
    International Journal of Electrical Power & Energy Systems 10/1997; · 3.43 Impact Factor
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    ABSTRACT: Experience with the identification and tuning of exciter constants for a generating unit at the second nuclear power plant of the Taiwan Power Company is reported. A field test is first performed on the excitation system with the generator open-circuited. Since the field test results differ from the computer simulation results using manufacturer's constants, the authors first modify the manufacturer's constants based on their previous experience to reach a preliminary set of parameters for the excitation system. Then a hybrid nonlinear simulation-sensitivity matrix method is developed to further refine the excitation system parameters. The exciter constants are tuned in order to give better dynamic response before a power system stabilizer is applied to the generator. Field tests are then performed in order to compare the dynamic response of the generator without and with power system stabilizer
    IEEE Transactions on Power Systems 06/1996; · 2.92 Impact Factor
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    ABSTRACT: Experiences with the model parameter identification of the excitation system (EXS) and the power system stabilizer (PSS) for Mingtan#6 pumped storage generation unit of Taiwan Power System are presented in this paper. The input-output data corresponding to each block of the EXS and PSS were obtained when the finalization tests of this unit were performed. The generalized least squares (GLS) approach is introduced and employed to identify the desired parameters of the noisy excitation system and PSS models. In this method, the reduction technique of biased estimates due to the nonwhite (correlated) identification residual is also applied to improve the accuracy of identification. The results of the parameter estimation are satisfactory. The GLS parameter identification method using the measured data at finalization tests is then suggested from the viewpoint of economy and engineering
    IEEE Transactions on Power Systems 06/1995; · 2.92 Impact Factor
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    ABSTRACT: In this paper, a sixth-order synchronous generator model identification technique from online measurements is considered. An algorithm is devised to identify the generator model. A constrained conjugate gradient method is incorporated into the algorithm to guarantee rapid convergence to the final solution. Using the algorithm, a complete generator model is derived from online measurements recorded by a plant transient recording system during a system disturbance. In addition, the algorithm does not greatly rely upon the accuracy of the initial estimates, allowing the initial estimates to deviate reasonably far from the true parameters. Detailed numerical studies of the Taipower system using raw and filtered data are included
    IEEE Transactions on Energy Conversion 07/1994; · 3.35 Impact Factor
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    ABSTRACT: Accurate generator modeling allows for more precise calculations of power system control and stability limits. In this paper, a procedure using a set of measured data from an online plant transient recording and analysis system to develop the synchronous generator model for the Taipower system is described. A continuous-time transfer function matrix is derived for a popular sixth-order synchronous generator model. In order to accommodate the nature of online digital measurements, the transfer function matrix is transformed into a simple discrete-time linear regression model. A measure of discrepancy between the generator model outputs and the online measurements from generators is employed. A modified conjugate gradient method suitable for identifying generator parameters is developed to minimize the measure of discrepancy, from which a set of accurate generator parameter values can be obtained. The merits of the modified conjugate gradient method include its computational efficiency and numerical reliability. The proposed procedure allows simultaneous estimation of all generator parameter values
    IEEE Transactions on Energy Conversion 07/1994; · 3.35 Impact Factor
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    ABSTRACT: Time domain stability simulation results using derived load models were compared with corresponding data obtained from transient disturbance recorders to validate the accuracy of the load models. Observations of real and reactive power load responses at primary substations and real and reactive power flows on major trunk lines indicated that using a composite (dynamic and static) load model provides a more accurate representation than other models, and closely matches the actual recorded values from the Taipower System. A severe fault, during which enormous voltage and frequency excursions occurred in the system, was extensively examined considering load rejection due to large voltage drops
    IEEE Transactions on Power Systems 03/1994; · 2.92 Impact Factor
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    ABSTRACT: The authors consider the problem of finding minimal neural networks (in terms of number of neurons and synapses) subject to desired learning and generalization capabilities. An algorithm which automatically determines the number of neurons and the location of synaptic connections is proposed. A new neural network model is introduced to facilitate solving the optimal architecture problem. The synaptic connections are pruned based on testing hypotheses that the corresponding weights be smaller than cutting thresholds. Simulation results are demonstrated for designing neural networks for: (1) a 7-segment electronic display; and (2) a power system load modeling problem. Optimal architecture (in the sense of achieving the lower bound on the number of neurons) are obtained for (1), and a 50%-60% save-up of synapses with the desired learning/generalization capabilities is obtained for (2)
    Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of; 08/1991