Power System Static State Estimation By the Levenberg-Marquardt Algorithm

Rao, N.D.; Tripathy, S.C.

doi:10.1109/tpas.1980.319662

Cited by 33 publications

(11 citation statements)

References 11 publications

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“…Once the relative frequency ω N1 is determined, the Levenberg-Marquardt Algorithm (LMA) [7,8] could be used to estimate the phase parameter φ X , the model function f E is described in Eq. 8.…”

Section: Estimation Of Phasementioning

confidence: 99%

Research on High Accuracy Zero Cross Time Detection Method of Power-frequency AC Signals

Lei¹,

Guo²,

Zheng³

et al. 2017

dtcse

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Abstract. Zero cross time detection method is very important in the phase measurement of powerfrequency AC signal. The detection uncertainties of most of the CMOS comparator based methods are around 200 µs, which cannot satisfy the high accuracy phase measurement. We proposed a new detecting method with higher accuracy by processing the sampled sequence of the under-test signal to estimate the relative frequency and phase sequentially. We implemented this method on a lowcostSoPC (system on a programmable chip), and used oscillation multiplication technique to improve the detection uncertainty to 1µs. We designed a test bench and tested the implemented detector and the results illustrated the performance of this method.

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Section: Estimation Of Phasementioning

confidence: 99%

Research on High Accuracy Zero Cross Time Detection Method of Power-frequency AC Signals

Lei¹,

Guo²,

Zheng³

et al. 2017

dtcse

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“…Equation (16) exhibits the same form as the basic Levenberg-Marquardt method to solve nonlinear least-squares problems [5], [6]. However, the original Levenberg-Marquardt (L-M) approach preceded the trust region concepts and adjusts λ through the iterative process according to a different rationale, although also based on the effectiveness of the previous trial step results [1], [5], [6].…”

Section: B Basic Trust Region Algorithmmentioning

confidence: 99%

“…They employ a generic orthogonal (QR) factorization to avoid building the normal equation gain matrix, thus reducing the chance of numerical ill-conditioning. In this paper, we show that the Levenberg-Marquardt equation [5], [6] solved as a basic step of the TR approach can be interpreted as the solution of a linearized least-squares problem for which a priori information is taken into account. Moreover, it has been previously shown that state estimators with a priori information processing capability can be implemented through the 3-multiplier version of Givens rotations (G3M) at virtually no computational cost [7].…”

Section: Introductionmentioning

confidence: 99%

Globally convergent state estimation based on givens rotations

Costa

Salgado

Haas

2007

2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability

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This paper proposes the combination of trust region methods and sequential-orthogonal techniques in order to devise globally convergent state estimators. The general nonlinear least squares problem in the context of power system state estimation is formulated so as to include inequality constraints which model the trust region. It is shown that the required changes on the state estimation equations solved in each iteration are equivalent to considering the contribution of properly defined a priori state information to the estimation process. Since a priori information are easily taken into account by the three-multiplier version of Givens rotations, the latter are employed to solve the linearized problem. This imparts numerical robustness to the iterative process in addition to the algorithmic robustness of the trust region approach, thereby improving the state estimator capability to converge even in the presence of severe modeling errors.

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“…Standard estimation algorithms such as Weighted Least Squares (WLS), Levenberg-Marquardt and least absolute values (LAV) [4,5,6] use bus power injection, branch power flow and bus voltage magnitude measurements from a reduced number of buses to compute the bus voltages, but are prone to measurement errors and cannot use bus voltage angle measurements.…”

Section: Introductionmentioning

confidence: 99%

Intelligent monitoring and control in transmission and distribution networks

Ivanov

Gavrilaș

Neagu

2014

2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)

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A fundamental component of a smart grid is transmission and distribution system monitoring and control. Accurate load, power flow and voltage estimation is required for real time generation capacity dispatching and congestion management in wide area power systems or in networks with distributed generation. In electrical networks, bus voltage levels, load, generation and branch power flows are interdependent, and ones can be determined if others are known. This is a problem that can be solved using the approximation capabilities of artificial neural networks (ANNs). This paper explores the possibility of replacing the classic estimation algorithms in voltage and power flow estimation with ANN approaches, using available data from the Romanian power system. I.

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Power System Static State Estimation By the Levenberg-Marquardt Algorithm

Cited by 33 publications

References 11 publications

Research on High Accuracy Zero Cross Time Detection Method of Power-frequency AC Signals

Research on High Accuracy Zero Cross Time Detection Method of Power-frequency AC Signals

Globally convergent state estimation based on givens rotations

Intelligent monitoring and control in transmission and distribution networks

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