A distributed multi-area power system state estimation method based on generalized loss function

Chen, Tengpeng; Liu, Fangyan; Li, Po; Sun, Lu; Amatratunga, Gehan

doi:10.1088/1361-6501/ace643

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…However all the above DSE methods validate the results in a Gaussian noise environment. However, in reality, the uncertainty in energy prediction is difficult to describe accurately in terms of Gaussian distributed noise [18], where problems such as anomalous system inputs exist in power systems [19].…”

Section: Introductionmentioning

confidence: 99%

A dynamic state estimation method for integrated energy system based on radial basis kernel function

Chen,

Luo,

Foo

et al. 2024

Meas. Sci. Technol.

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For state estimation of dynamic electro-thermal gas coupled systems, measurements usually assume that the measurement noise obeys a Gaussian distribution. The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are some of the widely used estimation methods in state estimation. However, the measurement noise does not always follow Gaussian distribution in practice. When the measurement noise is non-Gaussian, the performance of these methods may not be satisfactory. In this paper, we propose an unscented Kalman filtering method based on minimising radial basis kernel function criterion (MRBFC-UKF), which explores the optimal values of the shape parameters of the kernel function instead of using the widely used Gaussian and exponential kernal functions. Simulations are run dynamically in an integrated energy system which comprises an IEEE 14-bus, a 20-node natural gas network and a 32-node local thermal network. The results show that the proposed method has good robustness and accuracy, and can effectively cope with the presence of unexpected bad data inputs.

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Section: Introductionmentioning

confidence: 99%

A dynamic state estimation method for integrated energy system based on radial basis kernel function

Chen,

Luo,

Foo

et al. 2024

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

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“…Reference [21] introduces a state estimation method based on floating-point operations, aiming to reduce the computational load of distribution system state estimation and achieve faster response times. [22] presents a state estimation method based on weighted least squares and a generalized loss function. [23] introduces a multi-source mode estimator using PMU measurement units, employing a data fusion strategy to enhance estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

Karim Sarmadi,

Salehi Dobakhshari

2024

Acta IMEKO

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This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.

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A Comprehensive Review of Hybrid State Estimation in Power Systems: Challenges, Opportunities and Prospects

Kamyabi,

Lie,

Madanian

et al. 2024

Energies

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Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly from a dynamic point of view. To maintain the stability of power systems, operators need to continuously monitor and analyze the grid’s state. Since modern power systems are large-scale, non-linear, complex, and interconnected, it is quite challenging and computationally demanding to monitor, control, and analyze them in real time. State Estimation (SE) is one of the most effective tools available to assist operators in monitoring power systems. To enhance measurement redundancy in power systems, employing multiple measurement sources is essential for optimal monitoring. In this regard, this paper, following a brief explanation of the SE concept and its different categories, highlights the significance of Hybrid State Estimation (HSE) techniques, which combine the most used data resources in power systems, traditional Supervisory Control and Data Acquisition (SCADA) system measurements and Phasor Measurement Units (PMUs) measurements. Additionally, recommendations for future research are provided.

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A distributed multi-area power system state estimation method based on generalized loss function

Cited by 3 publications

References 48 publications

A dynamic state estimation method for integrated energy system based on radial basis kernel function

A dynamic state estimation method for integrated energy system based on radial basis kernel function

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

A Comprehensive Review of Hybrid State Estimation in Power Systems: Challenges, Opportunities and Prospects

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