Dishonest Gauss Newton method based power system state estimation on a GPU

Rahman, Mohammad Azizur; Venayagamoorthy, G.K.

doi:10.1109/psc.2016.7462826

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…In [296] the Dishonest Gauss Method in the WLS algorithm is used, where the Jacobin update is not executed at every iteration. The algorithm was implemented on a Tesla K20c GPU, fragmenting the original by vectorizing multiplication and multi-threading addition processes.…”

Section: State Of the Artmentioning

confidence: 99%

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

Al-Shafei¹,

Zareipour²,

Cao³

2022

Preprint

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Section: State Of the Artmentioning

confidence: 99%

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

Al-Shafei¹,

Zareipour²,

Cao³

2022

Preprint

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show abstract

“…After OPF applications, GPU usage in dynamic state estimations [191][192][193][194][195][196][197][198][199], power quality [202][203][204][205][206][207][208][209], and dynamic models [210][211][212][213][214][215] appear. Related to the dynamic state estimation of power systems, a lateral two-level dynamic state estimator based on the extended Kalman Filter method is implemented in a CPU-GPU platform [194].…”

Section: Dynamic State Estimation Power Quality and Dynamic Modelsmentioning

confidence: 99%

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

et al. 2021

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The power system expansion and the integration of technologies, such as renewable generation, distributed generation, high voltage direct current, and energy storage, have made power system simulation challenging in multiple applications. The current computing platforms employed for planning, operation, studies, visualization, and the analysis of power systems are reaching their operational limit since the complexity and size of modern power systems results in long simulation times and high computational demand. Time reductions in simulation and analysis lead to the better and further optimized performance of power systems. Heterogeneous computing—where different processing units interact—has shown that power system applications can take advantage of the unique strengths of each type of processing unit, such as central processing units, graphics processing units, and field-programmable gate arrays interacting in on-premise or cloud environments. Parallel Heterogeneous Computing appears as an alternative to reduce simulation times by optimizing multitask execution in parallel computing architectures with different processing units working together. This paper presents a review of Parallel Heterogeneous Computing techniques, how these techniques have been applied in a wide variety of power system applications, how they help reduce the computational time of modern power system simulation and analysis, and the current tendency regarding each application. We present a wide variety of approaches classified by technique and application.

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“…Therefore, the kernels can be executed sequentially without any delay [21]. The details of the GPU implementation can be found in [22]. For simulation, three sets of magnitudes are chosen to build the Jacobian matrix, |V i | = 0.9 pu, 1.0 pu, and 1.2 pu.…”

Section: Setup Of the Experimentmentioning

confidence: 99%

Convergence of the Fast State Estimation for Power Systems

Rahman

Venayagamoorthy

2017

SAIEE Afr. Res. J.

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Power system state estimation is a fundamental computational process that requires both speed and reliability. To meet the needs, some variants of the constant Jacobian methods have been used in the industry over the last several decades. The variants work very well under normal operating conditions with nominal values of the states. However, the convergence of the methods are not analysed mathematically and it may contain pitfalls. In this study, the convergence of the constant Jacobian methods are analysed and it is shown that the methods fail under high variations of the states. To increase the reliability of the processes, a multi-Jacobian method is proposed. Through simulation, a special case is shown for IEEE 68, and IEEE 118-bus systems where the Jacobian calculated with the nominal value fails, and the proposed multi-Jacobian method succeeds.

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Dishonest Gauss Newton method based power system state estimation on a GPU

Cited by 12 publications

References 9 publications

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

Convergence of the Fast State Estimation for Power Systems

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