To ensure the system stability, generator owners are required to provide their generator models and parameters to ISO/RTO for system analysis. In addition to field testing, there is increasing demand of online approach for parameter identification. The approach of Phasor Measurement Unit (PMU) based generator unit's parameter identification is a new emerging technology to satisfy these requirements. However, for large and complex electrical systems, the estimation of generator parameters based on PMU measurements remains a very challenge task. This paper proposed a systematic identification process to provide solution for the parameter identification problems. Three crucial steps are introduced in the proposed process. Firstly, hybrid dynamic simulation is implemented to obtain the dynamic equivalent circuit of the external system to reduce the computational time. Secondly, trajectory sensitivities and parameter correlation analysis is used to determine key parameters to further improve the computational efficiency. Finally, a minimum variance calibration strategy is proposed to derive the parameters of generator. A sample system is used to illustrate the effectiveness of the proposed approach. Lee has been involved in research on renewable energy, power flow, transient and dynamic stability, voltage stability, short circuits, relay coordination, power quality analysis, demand response, on-line equipment protection, monitoring, and control system, and utility deregulation. He has served as the primary investigator (PI) or Co-PI of over seventy funded research projects. He has published more than one hundred sixty (160) journal papers and conference proceedings. He has provided on-site training courses for power engineers in
This paper proposes a new identification process for solving generator parameter identification problem for industrial plants with co-generation facilities by using data from Phasor Measurement Unit (PMU). First of all, a seamless hybrid dynamic method is proposed to implement in commercially available software to realize the equivalent of an external-system outside the buses of PMU boundary. Then, the trajectory sensitivity is adopted to screen out the key parameters. Finally, a high dimension optimization algorithm, Simultaneous Perturbation Stochastic Approximation (SPSA) is used to compute a set of model parameters that provide a best fit between measurements and simulation response. This approach is based on ISO's view that only PMU measurement data is known, models are unchangeable and condition of system during disturbance is uncertain. The effectiveness and feasibility of the proposed process were demonstrated by processing data from newly installed generator unit in ERCOT system. The experiment showed encouraging results, verifying that the proposed approach was capable of identify parameters with better accuracy.
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