Identification of Parameters for Power System Stability Analysis Using Kalman Filter

Namba,; Nishiwaki,; Yokokawa,; Ohtsuka,; Ueki,

doi:10.1109/mper.1981.5511693

Cited by 4 publications

(6 citation statements)

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“…The intention has been to follow to some extent the standards [15,36,37], present important literature in the area [24,30] or developments that, in the author's opinion, represent a landmark [24][25][26][27][28] in the evolution of parameter estimation field. However, this does not mean that other works [38,39,40] cannot be more effective or even provide much better results than the ones mentioned here.…”

Section: Other Methodscontrasting

confidence: 59%

Review of synchronous generator parameters estimation and model identification

Ghomi

Sarem

2007

2007 42nd International Universities Power Engineering Conference

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In this article the review of synchronous generator parameter estimation and model identification presented. Synchronous generators play a very important role in the stability of power systems. A valid model for synchronous generators is essential for a reliable analysis of stability and dynamic performance. Almost three-quarters of a century after the first publications in this area, the subject is still a challenging and attractive research topic. There are many propositions for the determination of synchronous machine parameters. Among the modern techniques used are neural networks, finite elements, on-line and off-line statistical methods, frequency response, load rejection and others.

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Section: Other Methodscontrasting

confidence: 59%

Review of synchronous generator parameters estimation and model identification

Ghomi

Sarem

2007

2007 42nd International Universities Power Engineering Conference

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“…Equation (8) implies that the measurement device that can track the dynamic voltage/current phase at a node of the power system, namely the synchrophasor, can be used to track the real-time local RoCoF with a proper algorithm; however, RoCoF estimation techniques face great challenges in accuracy and robustness. This section provides a comprehensive review of the existing real-time RoCoF tracking algorithms of synchrophasors, which can be divided into three main categories: (i) DFTbased [64][65][66][67][68][69][70][71][72][73][74][75][76], (ii) Kalman filter techniques [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92], and (iii) other methods [93][94][95][96][97][98][99][100]. The details for each category are provided in the subsection.…”

Section: Real-time Rocof Tracking Techniquesmentioning

confidence: 99%

“…The Kalman filter technique is widely used in the field of the power system state estimation [77][78][79]. The real-time RoCoF tracking techniques based on the Kalman filter can effectively avoid the impact of harmonics and noise of the input signal fed into the synchrophasor through solving the optimal estimation problem of the continuous prediction-correction operation [78,80,81].…”

Section: Kalman Filter Techniquesmentioning

confidence: 99%

Review of RoCoF Estimation Techniques for Low-Inertia Power Systems

Deng

Wang

et al. 2023

Energies

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As the traditional generation is gradually replaced by inverter-based resources, a lack of rotational inertia is now a common issue of modern power systems, which leads to an increasingly larger rate of change of frequency (RoCoF) following contingencies and may result in frequency collapse. As a crucial index of the frequency security and stability of power systems, the accurate estimation of the RoCoF can be a foundation for the development of advanced operations and control techniques of the future power system. This paper firstly analyzes the role of the RoCoF in typical blackouts occurring in recent years and discusses the physical and numerical nature of the RoCoF; then, by introducing the frequency spatial distribution of the power system, the paper discusses the concept of the “center” RoCoF that can present the frequency security and stability of the entire system. The estimation and prediction techniques of the maximal power system RoCoF following a contingency and the existing real-time tracking techniques of the power system RoCoF are comprehensively reviewed. Finally, the open questions and related research topics of the RoCoF estimation are discussed.

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“…The literature has some accounts of error criteria for parameter identification of synchronous machines, such as extended Kalman filter (EKF) [31], Levenberg-Marquardt algorithm [14], recursive least squares (RLS) [15], [16], Prony method [17], among others.…”

Section: Algorithm For Parameter Estimationmentioning

confidence: 99%

Online parameter identification of synchronous machines using Kalman filter and recursive least squares

Alves

Nøland

Marafioti

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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This paper investigates and implements a procedure for parameter identification of salient pole synchronous machines that is based on previous knowledge about the equipment and can be used for condition monitoring, online assessment of the electrical power grid, and adaptive control. It uses a Kalman filter to handle noise and correct deviations in measurements caused by uncertainty of instruments or effects not included in the model. Then it applies a recursive least squares algorithm to identify parameters from the synchronous machine model. Despite being affected by saturation effects, the proposed procedure estimates 8 out of 13 parameters from the machine model with minor deviations from data sheet values and is largely insensitive to noise and load conditions.

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Identification of Parameters for Power System Stability Analysis Using Kalman Filter

Cited by 4 publications

References 0 publications

Review of synchronous generator parameters estimation and model identification

Review of synchronous generator parameters estimation and model identification

Review of RoCoF Estimation Techniques for Low-Inertia Power Systems

Online parameter identification of synchronous machines using Kalman filter and recursive least squares

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