Publisher Correction: Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

Xu, Biao; Zhang, Guiyuan; Li, Ké; Li, Bing; Chi, Hongyuan; Yao, Yao; Fan, Zhun

doi:10.1186/s41601-023-00290-1

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…Further research is needed in this direction in the future. Additionally, the research did not consider the specific impact and role of the identified parameters on the cell itself [36][37][38].…”

Section: Discussionmentioning

confidence: 99%

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

He¹,

Zhou²,

Liu³

et al. 2023

Energies

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An accurate parameter extraction of the proton exchange membrane fuel cell (PEMFC) is crucial for establishing a reliable cell model, which is also of great significance for subsequent research on the PEMFC. However, because the parameter identification of the PEMFC is a nonlinear optimization problem with multiple variables, peaks, and a strong coupling, it is difficult to solve this problem using traditional numerical methods. Furthermore, because of insufficient current and voltage data measured by the PEMFC, the precision rate of cell parameter extraction is also very low. The study proposes a parameter extraction method using a generalized regression neural network (GRNN) and meta-heuristic algorithms (MhAs). First of all, a GRNN is used to de-noise and predict the data to solve the problems in the field of PEMFC, which include insufficient data and excessive noise data of the measured data. After that, six typical algorithms are used to extract the parameters of the PEMFC under three operating conditions, namely high temperature and low pressure (HTLP), medium temperature and medium pressure (MTMP), and low temperature and high pressure (LTHP). The last results demonstrate that the application of GRNN can prominently decrease the influence of data noise on parameter identification, and after data prediction, it can greatly enhance the precision rate and reliability of MhAs parameter identification, specifically, under HTLP conditions, the V-I fitting accuracy achieved 99.39%, the fitting accuracy was 99.07% on MTMP, and the fitting accuracy was 98.70%.

show abstract

Section: Discussionmentioning

confidence: 99%

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

He¹,

Zhou²,

Liu³

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…The operation and scheduling of active distribution networks (ADNs) have undergone great challenges due to intrinsic intermittence and volatility from renewable energy resources (Xiang et al, 2017;Li et al, 2023). This has led to the necessity to fully utilize support and adjust the capability of flexibility resources such as distributed energy storage and electric vehicles for reliable power supply (Xu et al, 2022;Lu et al, 2023). Considering the properties of large quantities, decentralized locations, and diverse stakeholders for heterogeneous flexibility resources, the traditional centralized control strategy faces various challenges in the form of system reliability, mass communication, and information privacy (Hu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Opinions on fast distributed optimization for large-scale scheduling of heterogeneous flexibility resources

Tan,

Gao,

Liu

2024

Front. Energy Res.

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Study of generalized electric spring modeling based on Hamilton’s principle and its stability

Wang,

Zhao,

Chen

et al. 2024

J. Power Electron.

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Publisher Correction: Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

Cited by 5 publications

References 1 publication

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

Opinions on fast distributed optimization for large-scale scheduling of heterogeneous flexibility resources

Study of generalized electric spring modeling based on Hamilton’s principle and its stability

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