Power System Static-State Estimation, Part III: Implementation

Schweppe, Fred C.

doi:10.1109/tpas.1970.292680

Cited by 456 publications

(319 citation statements)

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“…Nonlinear least squares (NLS) for state estimation as originally suggested by Schweppe [1,8] remains the most common approach to this day. Given an Nbus power system, we define the voltage phasors as z ∈ C N .…”

Section: State Estimation Via Nonlinear Least Squaresmentioning

confidence: 99%

“…Starting from an initial guess u 0 ∈ C N , solving (SEP) using the Gauss-Newton method with a polar parameterization of u yields the classic Schweppe algorithm [8]. Convergence can be guaranteed (under mild assumptions) by adjusting the Gauss-Newton step sizes using backtracking line search, or by adopting a trustregion strategy, as in the Levenberg-Marquardt algorithm; see e.g.…”

Section: State Estimation Via Nonlinear Least Squaresmentioning

confidence: 99%

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Spurious Critical Points in Power System State Estimation

Zhang

Lavaei

Baldick

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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The power systems state estimation problem computes the set of complex voltage phasors given quadratic measurements using nonlinear least squares (NLS). This is a nonconvex optimization problem, so even in the absence of measurement errors, local search algorithms like Newton / Gauss-Newton can become "stuck" at local minima, which correspond to nonsensical estimations. In this paper, we observe that local minima cease to be an issue as redundant measurements are added. Posing state estimation as an instance of the quadratic recovery problem, we derive a bound for the distance between the true solution and the nearest spurious local minimum. We use the bound to show that critical points of the nonconvex least squares objective become increasing rare and far-away from the true solution with the addition of redundant information.

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Section: State Estimation Via Nonlinear Least Squaresmentioning

confidence: 99%

Section: State Estimation Via Nonlinear Least Squaresmentioning

confidence: 99%

Spurious Critical Points in Power System State Estimation

Zhang

Lavaei

Baldick

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…State estimation was first applied to power systems by Schweppe and Wildes in the late 1960's [3]. Since then various methods have been used for power system state estimation.…”

Section: Introductionmentioning

confidence: 99%

Power system static state estimation using Kalman filter algorithm

Saikia

Mehta

2016

Int. J. Simul. Multisci. Des. Optim.

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-State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS) method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates.

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“…State estimation can be classified into two types: static state and dynamic [2]. Static State Estimation (SSE) offers a snapshot of power system operating point reproduced by state variables [3].The state estimation process used the SCADA Data to evaluate the best estimate of the state of electrical power system. SCADA system receives the measurement through Remote Terminal Units (RTU) which gathered from the network.…”

Section: Introductionmentioning

confidence: 99%

Power System Dynamic State Estimation based on Kalman Filter

Taimah¹,

Merzah²

2016

IJCA

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This paper presented the application of Kalman Filtering technique in estimating the dynamic variables for the multimachine power systems. The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are both appropriate tools to be applied in power system dynamic state estimation studies. EKF and UKF are implemented using a second-order swing equation and a classical generator model to estimate the dynamic state (generator rotor angle and generator rotor speed) and comparing the result which obtained from the two estimation algorithm (EKF and UKF) with the result from the fourth order Runge-Kutta method in order to show the statistical performance and estimation accuracy of each algorithm. The algorithms are mathematically demonstrated using the "IEEE 14-bus test system. The results show that the UKF method gives an accurate performance in the dynamic state estimation for multi-machine power system than the EKFmethod. It gives minimum mismatch between estimated state and actual state.

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Power System Static-State Estimation, Part III: Implementation

Cited by 456 publications

References 0 publications

Spurious Critical Points in Power System State Estimation

Spurious Critical Points in Power System State Estimation

Power system static state estimation using Kalman filter algorithm

Power System Dynamic State Estimation based on Kalman Filter

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