A blocked sparse matrix formulation for the solution of equality-constrained state estimation

Nucera, R.R.; Gilles, M.

doi:10.1109/59.131065

Cited by 63 publications

(12 citation statements)

References 15 publications

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“…In this work, standard IEEE 14 bus [31] system is considered for solving the proposed SE problem. For this problem, the voltage magnitudes at each bus and the phase angles at all buses except reference bus are selected as the state variables.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Power system state estimation using teaching learning-based optimization algorithm

Salkuti

2020

TELKOMNIKA

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The main goal of this paper is to formulate power system state estimation (SE) problem as a constrained nonlinear programming problem with various constraints and boundary limits on the state variables. SE forms the heart of entire real time control of any power system. In real time environment, the state estimator consists of various modules like observability analysis, network topology processing, SE and bad data processing. The SE problem formulated in this work is solved using teaching leaning-based optimization (TLBO) technique. Difference between the proposed TLBO and the conventional optimization algorithms is that TLBO gives global optimum solution for the present problem. To show the suitability of TLBO for solving SE problem, IEEE 14 bus test system has been selected in this work. The results obtained with TLBO are also compared with conventional weighted least square (WLS) technique and evolutionary based particle swarm optimization (PSO) technique.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Power system state estimation using teaching learning-based optimization algorithm

Salkuti

2020

TELKOMNIKA

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“…It is always assumed that the parameters and observability of the systems are already determined in advance. It is to be noted that the measurements are never simultaneous, they are sequential, however at a very close interval and therefore the static state estimator assumes it to be snap-shot measurement [13], i.e. all measurements are assumed to be taken simultaneously.…”

Section: Static State Estimation Of Power Systemsmentioning

confidence: 99%

Optimal Power System Control Using Artificial Neural Networks

2019

ISDE

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Electric power system is a highly complex and non linear system. Its analysis and control in real time environment requires highly sophisticated computational skills. Computations are reaching a limit as far as conventional computer based algorithms are concerned. It is therefore required to find out newer methods which can be easily implemented on dedicated hardware. It is a very difficult task due to complexity of the power system with all its interdependent variables, thus making the neural networks one of the better options for the solution of different issues in operation and control. In this project an attempt has been made to implement ANN's for observability determination, State Estimation, Economic Load Dispatch and for Reactive Power Optimization. A Hopfield neural network model has been developed to test Topological Observability of Power System and it is tested on two different test systems. The results so obtained, are comparable with those results of conventional root based observability determination technique. Further a Hopfield model has been developed to determine State Estimation of power system. State Estimation of 6 bus system and IEEE 14 bus system is attempted using this Hopfield neural network. Results obtained by developed model are compared with those of conventional Non Linear WLS State Estimation. Next use of ANN for Economic Load Dispatch problem has been developed. Economic Load dispatch has been studied using various test system data (like 3, 6, 20 & 30 units) and the results are compared with conventional Lambda iterative technique and Particle Swarm Optimization techniques. Next Reactive Power Optimization problem has been attempted using ANN. The performance of so developed ANN is tested on Ward Hale 6 bus system and IEEE 30 bus system data and the results obtained are compared with those of the results obtained by GA and Particle Swarm Optimization technique.

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“…Fast decoupled state estimation (FDSE) algorithms are used widely for transmission networks [9], [10] [11] due to their high efficiency. FDSE has become a standard method for SE in energy management systems (EMSs) [12].…”

Section: Introductionmentioning

confidence: 99%

“…These problems can be addressed by normal equations with constraints [14] [15] or Hachtel's augmented matrix method [16]. A fast decoupled formulation for SE based on Hachtel's sparse tableaux approach is proposed in [12]. To improve the numerical stability of FDSE, an orthogonal row processing algorithm can be used with Givens transformations.…”

Section: Introductionmentioning

confidence: 99%

Fast Decoupled State Estimation for Distribution Networks Considering Branch Ampere Measurements

et al. 2018

IEEE Trans. Smart Grid

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Fast decoupled state estimation (FDSE) is proposed for distribution networks, with fast convergence and high efficiency. Conventionally, branch current magnitude measurements cannot be incorporated into FDSE models; however, in this paper, branch ampere measurements are reformulated as active and reactive branch loss measurements and directly formulated in the proposed FDSE model. Using the complex per unit normalization technique and special chosen state variables, the performance of this FDSE can be guaranteed when it is applied to distribution networks. Numerical tests on seven different distribution networks show that this method outperforms Newton type solutions and is a promising method for practical application.

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A blocked sparse matrix formulation for the solution of equality-constrained state estimation

Cited by 63 publications

References 15 publications

Power system state estimation using teaching learning-based optimization algorithm

Power system state estimation using teaching learning-based optimization algorithm

Optimal Power System Control Using Artificial Neural Networks

Fast Decoupled State Estimation for Distribution Networks Considering Branch Ampere Measurements

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