Electric power system static state estimation through Kalman filtering and load forecasting

Blood, Ellery A.; Krogh, Bruce H.; Ilić, Marija

doi:10.1109/pes.2008.4596742

Cited by 57 publications

(32 citation statements)

References 11 publications

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“…From the literature, it can be observed that the DSE or FASE techniques are more focused on TSs, such as the studies done in [67,71] and [78]. In [67], an exponential-weight function is used to increase the robustness of the EKF-based estimator.…”

Section: State Filteringmentioning

confidence: 99%

Distribution system state estimation-A step towards smart grid

Ahmad

Rasool

Özsoy

et al. 2018

Renewable and Sustainable Energy Reviews

141

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Section: State Filteringmentioning

confidence: 99%

Distribution system state estimation-A step towards smart grid

Ahmad

Rasool

Özsoy

et al. 2018

Renewable and Sustainable Energy Reviews

141

View full text Add to dashboard Cite

“…Compute the objective values (forecasting errors) by using the initial solution, P. The mean absolute percentage error (MAPE), given by Equation (10), is used to measure the forecasting errors.…”

Section: Implementation Steps Of Chaotic Quantum Tabu Search (Cqts) Amentioning

confidence: 99%

“…In the last few decades, models for improving the accuracy of load forecasting have included the well-known Box-Jenkins' ARIMA model [6], exponential smoothing model [7], Kalman filtering/ linear quadratic estimation model [8][9][10], the Bayesian estimation model [11][12][13], and regression models [14][15][16]. However, most of these models are theoretically based on assumed linear relationships between historical data and exogenous variables and so cannot effectively capture the complex nonlinear characteristics of load series, or easily provide highly accurate load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Lee

Lin

2016

Energies

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Abstract:Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

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“…In one paradigm (see e.g. [5], [6], [20] and the references therein) called forecasting-aided state estimation, the bus voltages are chosen as state variables and a succession of the quasi steadystates is assumed to evolve in time. Therefore, a dynamic model is adopted to describe the slow time evolution of the quasi steady-state.…”

Section: M)mentioning

confidence: 99%

A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements

Wang

Rahman

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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Abstract-In this paper, a hybrid filter algorithm is developed to deal with the state estimation problem for power systems by taking into account the impact from the phasor measurement units (PMU). Our aim is to include PMU measurements when designing the dynamic state estimators for power systems with traditional measurements. Also, as data dropouts inevitably occur in the transmission channels of traditional measurements from the meters to the control centre, the missing measurement phenomenon is also tackled in the state estimator design. In the framework of extended Kalman filter (EKF) algorithm, the PMU measurements are treated as inequality constraints on the states with the aid of the statistical criterion, and then the addressed state estimation problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved by using the particle swarm optimization (PSO) algorithm together with the penalty function approach. The proposed algorithm is applied to estimate the states of the power systems with both traditional and PMU measurements in the presence of probabilistic data missing phenomenon. Extensive simulations are carried out on the IEEE 14-bus test system and it is shown that the proposed algorithm gives much improved estimation performances over the traditional EKF method.

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Electric power system static state estimation through Kalman filtering and load forecasting

Cited by 57 publications

References 11 publications

Distribution system state estimation-A step towards smart grid

Distribution system state estimation-A step towards smart grid

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements

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