Joint Estimation of State and Parameter With Synchrophasors—Part II: Parameter Tracking

Bian, Xiaomeng; Li, X. Rong; Chen, Huimin; Gan, Deqiang; Jia-ju, Qiu

doi:10.1109/tpwrs.2010.2098423

Cited by 44 publications

(26 citation statements)

References 19 publications

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“…Also, there are several published methods for identification and estimation of branch parameter errors using SCADA measurements [16] and considering PMUs measurements [17] in static state estimation. Moreover, simultaneous states and parameters estimation with PMUs has been presented in [18][19] [13,14,20], while the influence of shunt admittances is important on SE solution [21][22]. As a result, all methods of branch parameter errors validation have common limitations as follows:…”

Section: Introductionmentioning

confidence: 99%

Identification and estimation of branch parameter errors: A new three stages method

Jirdehi

Hagh

2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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This paper presents an efficient method to identify and estimate of branch parameter (series and shunt admittances) errors in power system state estimation. The proposed method is composed of three stages. In the stage 1, the modified weighted least square state estimation is solved and the normalized Lagrange multipliers for branch parameter errors are computed. In the stage 2, the errors in branch parameter are identified based on the results of the stage 1. In the stage 3, erroneous branch parameter values can be estimated using a proposed linear approximation approach by eliminating the necessity to augmented state vector. Important advantage of the proposed method is that there is no need to specify and suspect set of parameters, a priori that it is necessary in other recently published methods. Finally, the validity and performance of the proposed procedure is evaluated based on the simulation results of the IEEE-30 and 57 bus test systems and these results are compared with other recently published methods.

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Section: Introductionmentioning

confidence: 99%

Identification and estimation of branch parameter errors: A new three stages method

Jirdehi

Hagh

2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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“…where θ k is the parameter vector of the actuator fault and can generally be assumed as a random walk variable (Bian et al, 2011) …”

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confidence: 99%

Simultaneous State and Parameter Estimation Based Actuator Fault Detection and Diagnosis for an Unmanned Helicopter

Song

et al. 2015

International Journal of Applied Mathematics and Computer Science

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Simultaneous state and parameter estimation based actuator fault detection and diagnosis (FDD) for single-rotor unmanned helicopters (UHs) is investigated in this paper. A literature review of actuator FDD for UHs is given firstly. Based on actuator healthy coefficients (AHCs), which are introduced to represent actuator faults, a combined dynamic model is established with the augmented state containing both the flight state and AHCs. Then the actuator fault detection and diagnosis problem is transformed into a general nonlinear estimation one: given control inputs and the measured flight state contaminated by measurement noises, estimate both the flight state and AHCs recursively in each time-step, which is also known as the simultaneous state and parameter estimation problem. The estimated AHCs can further be used for fault tolerant control (FTC). Based on the existing widely used nonlinear estimation methods such as the unscented Kalman filter (UKF) and the extended set-membership filter (ESMF), three kinds of adaptive schemes (KF-UKF, MIT-UKF and MIT-ESMF) are proposed by our team to improve the actuator FDD performance. A comprehensive comparative study on these different estimation methods is given in detail to illustrate their advantages and disadvantages when applied to unmanned helicopter actuator FDD.

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“…Over the past decades, several methods have been proposed for identification and correction of parameter errors under the SE framework. Earlier proposed approaches and their derivatives can be classified into two categories: those based on residual sensitivity [1][2][3][4][5][6][7][8], and those based on state augmentation [9][10][11][12][13][14][15][16]. The former category of methods identify and estimate parameter errors via analyzing the sensitivity between measurement residuals, which are part of SE outcomes, and parameter errors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The latter category of methods address parameter errors by including the suspicious parameters into the state vector in SE problems, and estimate their values simultaneously with state variables. They either use static normal equations [9,10,13], or use Kalman filter theory to consider information obtained in a time series [11,12,[14][15][16]. The Kalman-filter-based approaches are receiving more attention in recent years due to the investigation of dynamic state estimation and the need of parameter onlinetracking.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Chapter II presented a brief review of the literature related to parameter error detection, identification and correction problem. Among the reviewed approaches, the Largest Normalized Lagrange Multiplier (LNLM) test [17,[20][21][22][23] is advantageous over earlier proposed approaches based on residual sensitivity analysis [1][2][3][4][5][6][7][8] and augmented state estimation [9][10][11][12][13][14][15][16], due to its following features:…”

Section: Introductionmentioning

confidence: 99%

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Reliable and efficient methods for identification of parameter and measurement errors in power networks

Lin¹

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patience, encouragement, and support throughout the years of my Ph.D. studies. He sparked my fascination with power systems engineering, and from there my journey into academia began. He enlightened me what it means to be a true researcher, and taught me many important lessons. He supported me in every aspect through the most difficult times in my Ph.D. years. I learned a great deal from his ways of thinking and philosophy of life. He has been a true mentor to me. The experience with him has profoundly influenced, and will continue to guide me in the years to come. I am also very grateful to Professor Bradley Lehman and Professor Lev-Ari for serving on my Ph.D. committee. They offered an immense depth of knowledge and insightful advice in their areas of expertise, which were indispensable for the completion of my dissertation. My deepest gratitude and love go to my father, who has guided me throughout my life. Without his vision and encouragement, I could never have imagined reaching this place today. As a father, a scholar, and a human being, he has been the greatest and most influential exemplar to me, and has continually motivated me to move forward. I hope I can carry his spirit with me for this day forward. This dissertation is dedicated to him. There are hardly words that can convey my thanks and love to my mother. Among many other things, she taught me how to observe and enjoy the world around us, which is profoundly beneficial to me within both research and life. I am also grateful for her unconditional support rain or shine, without which the successful completion of my degrees would not have been possible. Finally, I would like to thank all my family and friends who accompanied me all these years. viii

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Joint Estimation of State and Parameter With Synchrophasors—Part II: Parameter Tracking

Cited by 44 publications

References 19 publications

Identification and estimation of branch parameter errors: A new three stages method

Identification and estimation of branch parameter errors: A new three stages method

Simultaneous State and Parameter Estimation Based Actuator Fault Detection and Diagnosis for an Unmanned Helicopter

Reliable and efficient methods for identification of parameter and measurement errors in power networks

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