Dynamic State Estimation of Electric Power Systems Using Kalman Filtering Techniques

Angel, Basil; Duraisamy, Maharajan

doi:10.1088/1742-6596/2335/1/012053

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…where, (√(𝑛 + 𝜆)𝑷 𝑘,𝑘 ) 𝑖 represents the 𝑖 𝑡ℎ element of the second order matrix (𝑛 + 𝜆)𝑷 𝑘,𝑘 , which is obtained through Cholesky decomposition [22]. The weighting factors of the covariance and the mean values, u 𝑐 0 and u 𝑚 0 , are calculated as follow [13].…”

Section: Estimation Of the Sps And Calculation Of The Weight Coeffici...mentioning

confidence: 99%

“…α is usually chosen between 0 and 1 so that the predicted covariance is positive and the covariance mean value has good accuracy. The optimal value of β is equal to 2. 𝜎 stands for the deviation of sample points from the mean value [13].…”

Section: Estimation Of the Sps And Calculation Of The Weight Coeffici...mentioning

confidence: 99%

“…However, the goal of parameter estimation is to find the exact value of parameters that have been currently set on the system (some parameters may be changed and re-adjusted by operators during the operational period). The unscented Kalman filter (UKF) has been also used for state estimation problems [11][12][13][14]. The method can also be utilized to estimate system parameters.…”

Section: Introductionmentioning

confidence: 99%

“…) are obtained [13]. The new sample points as well as their mean (𝑥 ̂𝑘+1,𝑘 ) and covariance (𝑷 𝑥𝑘+1,𝑘 ) are calculated as follows:…”

mentioning

confidence: 99%

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An Improved Unscented Kalman Filter Algorithm for Dynamic Systems Parameters Estimation

doroudi,

mohseni,

Karrari

2024

Preprint

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The high capabilities of unscented Kalman filter (UKF) for estimating the state variables of a dynamic system have led to their use for parameter estimation as well. In order to use the UKF to estimate the unknown parameters of a dynamic system, the parameters must be assumed to be in the form of virtual state variables. This paper first shows that this assumption causes some serious challenges. Then, trying to solve this problem, a modified UKF algorithm will be presented. Eventually, using the proposed algorithm, the parameters of a power plant turbine-governor system as a typical dynamic system are estimated and the efficacy of the method is investigated. The results show that the proposed method has good performance and is superior to the conventional algorithm. Purpose – This paper proposes a modified UKF algorithm to estimate the parameters of a dynamic system Design/methodology/approach – In this paper, by changing the point of view to system modeling, an improved version of the UKF-based method was presented. In the proposed version of the UKF algorithm, unlike the traditional one, the whole of the measurement signal samples is used as input in each stage of the estimation process. By doing this, throughout the entire simulation time i.e. within the entire time in which the measured signals exist, the unknown parameters are considered constant. Findings – The effectiveness of the proposed method is demonstrated through an illustrative example in parameter estimation of a TGOV1 Turbine-governor system as a case study. The proposed approach overcomes the shortcomings of the conventional method and shows high efficiency. It can be a useful substitute for the conventional UKF method. Originality/value – The proposed method is an evolutionary method whose evolution principles do not random behavior. It is based on Kalman filter rules and relations and enjoys all the advantages of this filter. It looks similar to a smoothing approach whose practical result is to filter out (in the mean sense) estimates with little physical meaning that normally arise when the number of state-variables is increased, that ultimately might lead the filter to diverge.

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Section: Estimation Of the Sps And Calculation Of The Weight Coeffici...mentioning

confidence: 99%

Section: Estimation Of the Sps And Calculation Of The Weight Coeffici...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…) are obtained [13]. The new sample points as well as their mean (𝑥 ̂𝑘+1,𝑘 ) and covariance (𝑷 𝑥𝑘+1,𝑘 ) are calculated as follows:…”

mentioning

confidence: 99%

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An Improved Unscented Kalman Filter Algorithm for Dynamic Systems Parameters Estimation

doroudi,

mohseni,

Karrari

2024

Preprint

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show abstract

“…The EKF excels when dealing with noisy measurements and when highly accurate state estimation of the system is required, although its implementation requires a dynamic model of the system and can result in a significant computational burden. On the other hand, WLS is suitable for situations where measurements come with different levels of precision, although its ability to maintain accuracy in the presence of high noise levels may be more limited compared with the EKF [14].…”

Section: Introductionmentioning

confidence: 99%

The Efficiency of the Kalman Filter in Nodal Redundancy

Moyano,

Vargas

2024

Energies

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The growing integration of distributed energy resources underscores the critical importance of having precise insights into the dynamics of an electrical power system (EPS). Consequently, an estimator must align with the EPS dynamics to enhance the overall reliability, safety, and system stability. This alignment ensures that operators can make informed decisions during system operations. An initial step in gaining insight into the system’s state involves examining its state vector, which is represented by voltage phasors. These results are derived through the application of a distributed state-estimation process in large-scale systems. This study delved into the effectiveness of Bayesian filters, with a particular emphasis on the extended Kalman filter (EKF) algorithm in the context of distributed state estimation. To analyze the outcomes, the nodal partitioning process was incorporated within the distributed state-estimation framework. The synergy between the EKF algorithm and the partitioning method was evaluated using the IEEE118 test system.

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STPGTN–A Multi-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data

Zhang¹,

Weng²

2023

Computer Modeling in Engineering &Amp; Sciences

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Dynamic State Estimation of Electric Power Systems Using Kalman Filtering Techniques

Cited by 3 publications

References 19 publications

An Improved Unscented Kalman Filter Algorithm for Dynamic Systems Parameters Estimation

An Improved Unscented Kalman Filter Algorithm for Dynamic Systems Parameters Estimation

The Efficiency of the Kalman Filter in Nodal Redundancy

STPGTN–A Multi-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data

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