Dynamic state estimation of power system with stochastic delay based on neural network

Zhang, Guangdou; Li, Jian; Cai, Dongsheng; Huang, Qi; Hu, Weihao

doi:10.1016/j.egyr.2021.02.009

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…The delays are prevalent in the state estimation problem in power systems [42,43]. Such delays may be a result of the imperfect synchronization or the congestion in communication network.…”

Section: Infinite Bus Systemmentioning

confidence: 99%

A Comparative Study of Particle Filter Resampling Techniques for Power Systems State Estimation

Abolmasoumi

Heydari

Farahani

et al. 2022

Preprint

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The effect of the resampling stage on the overall performance of the particle filter state estimation for power systems is investigated. As each general particle filter algorithm includes three steps of the prediction, update and resampling, the resampling step is essential to avoid the sample degeneracy problem. In this paper, the standard particle filter is applied to the problem of the decentralized state estimation in power systems. Various existing resampling techniques are described, tested, and evaluated in different power systems with different fault scenarios. The resampling methods are compared based on their mean square error, their needed computation time and their performance against various data quality issues such as delays, missing measurements, etc. More efficient resampling methods are then determined for three cases of the power system cases: a single-machine-infinite-bus system, the medium size two-area four-machine system and the large-scale New-England system considering different fault scenarios.

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“…The delays are prevalent in the state estimation problem in power systems [42,43]. Such delays may be a result of the imperfect synchronization or the congestion in communication network.…”

Section: Infinite Bus Systemmentioning

confidence: 99%

A Comparative Study of Particle Filter Resampling Techniques for Power Systems State Estimation

Abolmasoumi

Heydari

Farahani

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, influenced by various stochastic factors, such as weather and seasonal changes, the power outputs of wind and solar energy display significant random fluctuations. Directly connecting them to the grid can lead to profound impacts on the power system, resulting in issues such as voltage fluctuations, abnormal power equipment operation, and grid frequency instability [2]. Enhancing the forecasting accuracy of wind and solar power outputs plays a crucial role in ensuring the safety, stability, and economical operation of the new power system.…”

Section: Introductionmentioning

confidence: 99%

Joint Forecasting Method of Wind and Solar Outputs Considering Temporal and Spatial Correlation

Yuan,

Zhang,

Ming

et al. 2023

Sustainability

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In response to the problem of low forecasting accuracy in wind and solar power outputs, this study proposes a joint forecasting method for wind and solar power outputs by using their spatiotemporal correlation. First, autocorrelation analysis and causal testing are used to screen the forecasting factors. Then, a convolutional neural network–long short-term memory (CNN-LSTM) is constructed and trained to extract features effectively. Finally, the independent, ensemble, and joint forecasting effects are compared, using a certain clean energy base as the research object. Results show that the forecasting accuracy of the ensemble wind and solar power outputs is better than that of independent forecasting. The joint forecasting method can improve the forecasting accuracy of wind power by 20% but slightly affects the forecasting accuracy of solar power.

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Dynamic state estimation of power system with stochastic delay based on neural network

Cited by 6 publications

References 22 publications

A Comparative Study of Particle Filter Resampling Techniques for Power Systems State Estimation

A Comparative Study of Particle Filter Resampling Techniques for Power Systems State Estimation

Joint Forecasting Method of Wind and Solar Outputs Considering Temporal and Spatial Correlation

False data injection in power smart grid and identification of the most vulnerable bus; a case study 14 IEEE bus network

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