Seung-Mook Baek

Power System Control with an Embedded Neural Network in Hybrid System Modeling

Output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to nonsmooth nonlinearities arising from the saturation limits, these values cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures can been used. A feedforward neural network (with a structure of multilayer perceptron neural network) is applied to identify the dynamics of an objective function formed by the states and, thereafter, to compute the gradients required in the nonlinear parameter optimization. Moreover, its derivative information is used to replace that obtained from the trajectory sensitivities based on the hybrid system model with the differential-algebraic-impulsive-switched structure. The optimal output limits of the PSS tuned by the proposed method are evaluated by time-domain simulation in both a single-machine infinite bus system and a multimachine power system.

A Practical Power System Stabilizer Tuning Method and its Verification in Field Test

This paper deals with parameter tuning of the Power System Stabilizer (PSS) for 612 MVA thermal power plants in the KEPCO system and its validation in a field test. In this paper, the selection of parameters, such as lead-lag time constants for phase compensation and system gain, is optimized using linear and eigenvalue analyses. This is then verified through the time-domain transient stability analysis. In the next step, the performance of PSS is finally verified by the generators on-line field test. After the field test, measured and simulated data are also compared to prove the effectiveness of the models used in the simulations.

Parameter Optimization of PSS Based on Estimated Hessian Matrix from Trajectory Sensitivities

This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear parameters such as the saturation limits of the PSS cannot be tuned by the conventional methods based on linear approaches. To implement the systematic optimal tuning for the output limits of the PSS, a feedforward neural network (FFNN) is applied to the hybrid system model based on the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is firstly designed to identify the trajectory sensitivities obtained from the DAIS structure. Thereafter, it estimates the second-order derivatives of an objective function J, which is used during iterations of optimization process. The performance of the optimal output limits tuned by the proposed method is evaluated by applying a large disturbance to a power system.

A Tuning Method for the Power System Stabilizer of a Large Thermal Power Plant and Its Application to Real Power System : PART II - Field Tests and Verification of PSS Performance

【This paper, as the second part of the paper, dealt with the field test and test results to validate PSS(Power System Stabilizer) parameters which are previously tuned in Part 1 paper. In Part 1 of the paper, the selection of parameters such as lead-lag time constants for phase compensation and system gain was optimized by using linear & eigenvalue analyses and they were verified through the time-domain transient stability analysis. In part 2, the performance of PSS was finally verified by the generators on-line field test. Through the comparisons of simulation results and measured data before and after tuning of the PSS, the models of generator and its controllers including AVR, Governor and PSS used in the simulation are verified and confirmed.】

Hessian Matrix Estimation in Hybrid Systems Based on an Embedded FFNN

This paper describes the Hessian matrix estimation of nonsmooth nonlinear parameters by the identifier based on a feedforward neural network (FFNN) embedded in a hybrid system, which is modeled by the differential-algebraic-impulsive-switched (DAIS) structure. After identifying full dynamics of the hybrid system, the FFNN is used to estimate second-order derivatives of an objective function J with respect to the nonlinear parameters from the gradient information, which are trajectory sensitivities. Then, the estimated Hessian matrix is applied to the optimal tuning of a saturation limiter used in a practical engineering system.

2009 Special Issue: RMP model based optimization of power system stabilizers in multi-machine power system

This paper describes the nonlinear parameter optimization of power system stabilizer (PSS) by using the reduced multivariate polynomial (RMP) algorithm with the one-shot property. The RMP model estimates the second-order partial derivatives of the Hessian matrix after identifying the trajectory sensitivities, which can be computed from the hybrid system modeling with a set of differential-algebraic-impulsive-switched (DAIS) structure for a power system. Then, any nonlinear controller in the power system can be optimized by achieving a desired performance measure, mathematically represented by an objective function (OF). In this paper, the output saturation limiter of the PSS, which is used to improve low-frequency oscillation damping performance during a large disturbance, is optimally tuned exploiting the Hessian estimated by the RMP model. Its performances are evaluated with several case studies on both single-machine infinite bus (SMIB) and multi-machine power system (MMPS) by time-domain simulation. In particular, all nonlinear parameters of multiple PSSs on IEEE benchmark two-area four-machine power system are optimized to be robust against various disturbances by using the weighted sum of the OFs.

New Modeling of Switching Devices Considering Power Loss in Electromagnetic Transients Program Simulation

This paper presents the modeling of insulated-gate bipolar transistor (IGBT) in electromagnetic transients program (EMTP) simulation for the reliable calculation of switching and conduction losses. The conventional approach considering the physical property of switching devices requires many attribute parameters and large computation efforts. In contrast, the proposed method uses the curve fitting and interpolation techniques based on typical switching waveforms and a userdefined component with variable resistances to capture the dynamic characteristics of IGBTs. Therefore, the simulation time can be efficiently reduced without losing the accuracy while avoiding the extremely small time step, which is required in simulation by the conventional method. The EMTP based simulation includes turn-on and turn-off transients of IGBT, saturation state, forward voltage of free-wheeling diode, and reverse recovery characteristics, etc. The effectiveness of proposed modeling for the EMTP simulation is verified by the comparison with experimental results obtained from practical implementation in hardware.

Field Implementation of Voltage Management System (VMS) into Jeju Power System in Korea

This paper presents the results of field tests on Voltage Management System (VMS) using hybrid voltage control, which utilizes coordinated controls of various reactive power resources such as generators, FACTS and switched shunt devices to regulate the pilot bus voltage in a voltage control area. It also includes the results of performance test on RTDS-based test bed in order to validate the VMS before installing it in Jeju power system. The main purpose of the system is adequately to regulate the reactive power reserve of key generators in a normal condition with coordination of discrete shunt devices such as condensers and reactors so that the reserves can avoid voltage collapse in emergency state in Jeju system. Field tests in the automatic mode of VMS operation are included in steady-states and transient states. Finally, by the successful operation of VMS in Jeju power system, the VMS is proved to effectively control system voltage profiles in steady-state condition, increase system MVAR reserves and improve system reliability for pre- and post-contingency.

Nonlinear controller optimization of a power system based on reduced multivariate polynomial model

This paper describes the design of a nonlinear controller in a power system by using the reduced multivariate polynomial (RMP) optimization algorithm with the one-shot training property. The RMP model is applied to estimate its Hessian matrix in addition to identifying the trajectory sensitivities obtained from hybrid system modeling for the power system. In this paper, the saturation limiter of the power system stabilizer (PSS), which is an important nonlinear controller to improve low-frequency oscillation damping performance, is tuned optimally by using Hessian matrix estimated by the RMP model. The performance of the optimal output limits determined by the proposed method is evaluated by applying the large disturbance such as a three-phase short circuit to a power system.

A survey on operation of wind turbine generators in stand-alone microgrid

This paper reviews the various operation techniques of wind turbines within the stand-alone microgrid. First, the paper focuses on the feature and operation of the stand-alone microgrid when it includes wind turbines. Then, various methods to operate and control wind turbines within the stand-alone microgrid are introduced. In addition, main considerations are explained for the operation of a wind turbine generator (WTG) in a stand-alone microgrid. Finally, the technical challenges for this issue are discussed.