UKF based estimation of synchronous generator electromechanical parameters from phasor measurements

Wehbe, Yasser; Fan, Lingling

doi:10.1109/naps.2012.6336347

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…(16) Using Equation ( 16), besides the approximation introduced in part A, the relationship between rotor angle frequency (𝜔 m ) and electrical frequencies at both sides of the transmission line (𝜔 1 and 𝜔 2 ) is obtained in Equation (17).…”

Section: Rotor Speed Estimation Algorithmmentioning

confidence: 99%

“…IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology calculated using the least squares (LS) technique. Although this approach is useful to estimate the rotor angle, because of considerable error of estimation, it is not a reliable approach for analyses such as coherent generators determination, parameter estimation and transient stability prediction [17].…”

Section: Introductionmentioning

confidence: 99%

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A model‐based method for rotor speed estimation of synchronous generators using wide area measurement system

Hosseini

Afsharnia

2021

IET Generation Trans & Dist

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In this paper a novel method is proposed to estimate the rotor speed of synchronous generators in steady state and dynamic conditions using phasor measurement unit's data. This method uses the synchronous generator model to find the relationship between generator rotor speed and frequencies at different buses of the power system. The principles of the proposed method are first presented through a simple network consisting a generator connected to a transmission line. Afterward the general formulation is developed. To demonstrate the effectiveness of our method, IEEE 9-bus and IEEE 39-bus power systems are used as the benchmark systems. Different tests are carried out under three conditions including, normal condition, presence of measurement errors and uncertainty of the power system parameters. As well, the estimation results are compared with the results of other estimation methods. The results show the high accuracy of the proposed method in different conditions.

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Section: Rotor Speed Estimation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A model‐based method for rotor speed estimation of synchronous generators using wide area measurement system

Hosseini

Afsharnia

2021

IET Generation Trans & Dist

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“…The UKF has earlier been used in power system for dynamic state estimation [17], [18] and parameters estimation using operational data [19]. It has potential to be reformulated to solve dynamic parameters estimation problems [20]. However, the method proposed in this paper employs better sigma point distribution and filtering approach compared to [20], which reflects in a better accuracy of the parameter estimated and consumes less computing power.…”

Section: State Of the Artmentioning

confidence: 99%

“…It has potential to be reformulated to solve dynamic parameters estimation problems [20]. However, the method proposed in this paper employs better sigma point distribution and filtering approach compared to [20], which reflects in a better accuracy of the parameter estimated and consumes less computing power. The UKF offers flexibility to allow information beyond mean and covariance to be incorporated in the estimations [21].…”

Section: State Of the Artmentioning

confidence: 99%

Estimating Dynamic Model Parameters for Adaptive Protection and Control in Power System

Ariff

Pal

Singh

2015

IEEE Trans. Power Syst.

113

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This paper presents a new approach in estimating important parameters of power system transient stability model such as inertia constant and direct axis transient reactance in real time. It uses a variation of unscented Kalman filter (UKF) on the phasor measurement unit (PMU) data. The accurate estimation of these parameters is very important for assessing the stability and tuning the adaptive protection system on power swing relays. The effectiveness of the method is demonstrated in a simulated data from 16-machine 68-bus system model. The paper also presents the performance comparison between the UKF and EKF method in estimating the parameters. The robustness of method is further validated in the presence of noise that is likely to be in the PMU data in reality.

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“…Parameter estimation is addressed in [9]- [10] for multi-machine power systems, using a set of internal measurements which are difficult to obtain in practice. These difficulties are overcome in [11]- [12], where phasor measurement units (PMUs) are used for the parameter estimation. Estimation techniques based on KFs are used in studies related to PV power generation.…”

Section: Introductionmentioning

confidence: 99%

State and Parameter Estimation of Photovoltaic Modules using Unscented Kalman Filters

González-Cagigal¹,

Rosendo-Macías²,

Gómez-Expósito³

2024

RE&PQJ

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This paper presents a parameter estimation technique for the circuit of a photovoltaic module. The proposed method is based on unscented Kalman filter for the joint estimation of the state variables and the parameters involved in the model equations, using external measurements only. A case study is presented where the obtained estimation errors remain lower than 3% in all cases. Additionally, three formulations of the Kalman filter are compared in terms of convergence and accuracy. This technique may contribute to the optimal operation of photovoltaic power plants and the maximization of the investor revenue.

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UKF based estimation of synchronous generator electromechanical parameters from phasor measurements

Cited by 12 publications

References 11 publications

A model‐based method for rotor speed estimation of synchronous generators using wide area measurement system

A model‐based method for rotor speed estimation of synchronous generators using wide area measurement system

Estimating Dynamic Model Parameters for Adaptive Protection and Control in Power System

State and Parameter Estimation of Photovoltaic Modules using Unscented Kalman Filters

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