PMU based generator parameter identification to improve the system planning and operation

Abstract

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

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“…The method requires a priori additional information of the state variable to estimate the parameters accurately. Other effort integrates hybrid dynamic simulation, trajectory sensitivity, parameter correlation analysis and minimum variance criterion to solve the estimation problem [11]. It is non-recursive and computationally exhaustive.…”

“…In this paper, the value of is set such that the fourth order error for a Gaussian is minimized [21]. Consequently, each sigma points is passed through the model of state evolution to obtain the predicted-state sigma points given as (11) Subsequently, the weighted mean and covariance of the predicted sigma points defined as and , respectively, are calculated using ( 12):…”

Parameter Identification Method for Turbine Speed Governor System Based on Particle Swarm Optimization

“…In order to formulate the state estimation equation for a generator system, it is necessary to employ a mathematical model, which will represent the generator and turbine in the conditions under study. Inference [2] proposed a system identification procedure, as shown in Fig. 1.…”

“…As a novel intelligent optimization method, PSO was originally developed in 1995. Power system have applied this method to solve optimization problems in many areas, such as economic dispatch, reactive power and voltage control, state estimation, as well as load flow and optimal power flow [2]. A variety of PSO algorithms have evolved since the technique's first development.…”

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