Dynamic Equivalence of Active Distribution Network: Multiscale and Multimodal Fusion Deep Learning Method with Automatic Parameter Tuning
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| Publicado en: | Mathematics vol. 13, no. 19 (2025), p. 3213-3234 |
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| Autor principal: | |
| Otros Autores: | , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/math13193213 |2 doi | |
| 035 | |a 3261084064 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Wang, Wenhao | |
| 245 | 1 | |a Dynamic Equivalence of Active Distribution Network: Multiscale and Multimodal Fusion Deep Learning Method with Automatic Parameter Tuning | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Dynamic equivalence of active distribution networks (ADNs) is emerging as one of the most important issues for the backbone network security analysis due to high penetration of distributed generations (DGs) and electricity vehicles (EVs). The multiscale and multimodal fusion deep learning (MMFDL) method proposed in this paper contains two modalities, one of which is a CNN + attention module to simulate Newton Raphson power flow calculation (NRPFC) for the important feature extraction of a power system caused by disturbance, which is motivated by the similarities between NRPFC and convolution network computation. The other is a long short-term memory (LSTM) + fully connected (FC) module for load modeling based on the fact that LSTM + FC can represent a load′s differential algebraic equations (DAEs). Moreover, to better capture the relationship between voltage and power, the multiscale fusion method is used to aggregate load modeling models with different voltage input sizes and combined with CNN + attention, merging as MMFDL to represent the dynamic behaviors of ADNs. Then, the Kepler optimization algorithm (KOA) is applied to automatically tune the adjustable parameters of MMFLD (called KOA-MMFDL), especially the LSTM and FC hidden layer number, as they are important for load modeling and there is no human knowledge to set these parameters. The performance of the proposed method was evaluated by employing different electric power systems and various disturbance scenarios. The error analysis shows that the proposed method can accurately represent the dynamic response of ADNs. In addition, comparative experiments verified that the proposed method is more robust and generalizable than other advanced non-mechanism methods. | |
| 653 | |a Behavior | ||
| 653 | |a Accuracy | ||
| 653 | |a Electrical loads | ||
| 653 | |a Deep learning | ||
| 653 | |a Dynamic response | ||
| 653 | |a Distributed generation | ||
| 653 | |a Parameter identification | ||
| 653 | |a Modelling | ||
| 653 | |a Voltage | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Electric vehicles | ||
| 653 | |a Equivalence | ||
| 653 | |a Power flow | ||
| 653 | |a Error analysis | ||
| 653 | |a Modules | ||
| 653 | |a Machine learning | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Electric power systems | ||
| 653 | |a Electric potential | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Neural networks | ||
| 653 | |a Methods | ||
| 653 | |a Differential equations | ||
| 653 | |a Parameters | ||
| 653 | |a Electric power distribution | ||
| 700 | 1 | |a Liu, Zhaoxi | |
| 700 | 1 | |a Dai Fengzhe | |
| 700 | 1 | |a Quan Huan | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 19 (2025), p. 3213-3234 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3261084064/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3261084064/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3261084064/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |