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Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems
| Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems |
| Autore | Li Chaoshun |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (212 p.) |
| Soggetto topico |
Research & information: general
Physics |
| Soggetto non controllato |
doubly-fed variable-speed pumped storage
Hopf bifurcation stability analysis parameter sensitivity pumped storage unit degradation trend prediction maximal information coefficient light gradient boosting machine variational mode decomposition gated recurrent unit high proportional renewable power system active power change point detection maximum information coefficient cosine similarity anomaly detection thermal-hydraulic characteristics hydraulic oil viscosity hydraulic PTO wave energy converter pumped storage units pressure pulsation noise reduction sparrow search algorithm hybrid system facility agriculture chaotic particle swarms method operation strategy stochastic dynamic programming (SDP) power yield seasonal price reliability cascaded reservoirs doubly-fed variable speed pumped storage power station nonlinear modeling nonlinear pump turbine characteristics pumped storage units (PSUs) successive start-up ‘S’ characteristics low water head conditions multi-objective optimization fractional order PID controller (FOPID) hydropower units comprehensive deterioration index long and short-term neural network ensemble empirical mode decomposition approximate entropy 1D–3D coupling model transition stability sensitivity analysis hydro power |
| ISBN | 3-0365-5838-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637780603321 |
Li Chaoshun
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Recent Advances and Applications of Machine Learning in Metal Forming Processes
| Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| Autore | Prates Pedro |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (210 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology Mining technology & engineering |
| Soggetto non controllato |
sheet metal forming
uncertainty analysis metamodeling machine learning hot rolling strip edge defects intelligent recognition convolutional neural networks deep-drawing kriging metamodeling multi-objective optimization FE (Finite Element) AutoForm robust analysis defect prediction mechanical properties prediction high-dimensional data feature selection maximum information coefficient complex network clustering ring rolling process energy estimation metal forming thermo-mechanical FEM analysis artificial neural network aluminum alloy mechanical property UTS topological optimization artificial neural networks (ANN) machine learning (ML) press-brake bending air-bending three-point bending test sheet metal buckling instability oil canning artificial intelligence convolution neural network hot rolled strip steel defect classification generative adversarial network attention mechanism deep learning mechanical constitutive model finite element analysis plasticity parameter identification full-field measurements |
| ISBN | 3-0365-5772-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637782503321 |
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Prates Pedro
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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