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Jin Zhao
Enhancing Resilience in Power Distribution Systems
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7654764Enhancing Resilience in Power Distribution Systemshttps://www.gandhi.com.mx/enhancing-resilience-in-distribution-systems-9780443236396/phttps://gandhi.vtexassets.com/arquivos/ids/7281818/image.jpg?v=63889072929483000022132459MXNElsevierInStock/Ebooks/<p>Enhancing Resilience in Power Distribution Systems presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems. The book begins by explaining the risks and problems for resilience presented by renewable-based power systems. It goes on to clarify the current state of research and propose several novel methodologies and technologies for analysis and improvement of power system resilience. These methods include deep learning, linear programming, and generative adversarial networks.Packed with practical steps and tools for implementing the latest technologies, this book provides researchers and industry professionals with guidance on the resilient systems of the future. - Breaks down novel methodologies and tools from deep learning to generative adversarial networks - Supports readers in implementing practical steps towards resilient renewable energy - Presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems</p>...7262403Enhancing Resilience in Power Distribution Systems22132459https://www.gandhi.com.mx/enhancing-resilience-in-distribution-systems-9780443236396/phttps://gandhi.vtexassets.com/arquivos/ids/7281818/image.jpg?v=638890729294830000InStockMXN99999DIEbook20259780443236396_W3siaWQiOiI1N2U3MzVmMi0wZjllLTRmYzEtYjFmYi1hYzY5M2FkZTg4OTIiLCJsaXN0UHJpY2UiOjI0NTksImRpc2NvdW50IjoyNDYsInNlbGxpbmdQcmljZSI6MjIxMywiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjUtMDctMjJUMTQ6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9780443236396_<p><em>Enhancing Resilience in Distribution Systems</em> presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems. The book begins by explaining the risks and problems for resilience presented by renewable-based power systems. It goes on to clarify the current state of research and propose several novel methodologies and technologies for analysis and improvement of power system resilience. These methods include deep learning, linear programming, and generative adversarial networks. Packed with practical steps and tools for implementing the latest technologies, this book provides researchers and industry professionals with guidance on the resilient systems of the future.</p><ul><li>Breaks down novel methodologies and tools from deep learning to generative adversarial networks</li><li>Supports readers in implementing practical steps towards resilient renewable energy</li><li>Presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems</li></ul>...(*_*)9780443236396_<p>Enhancing Resilience in Power Distribution Systems presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems. The book begins by explaining the risks and problems for resilience presented by renewable-based power systems. It goes on to clarify the current state of research and propose several novel methodologies and technologies for analysis and improvement of power system resilience. These methods include deep learning, linear programming, and generative adversarial networks.Packed with practical steps and tools for implementing the latest technologies, this book provides researchers and industry professionals with guidance on the resilient systems of the future. - Breaks down novel methodologies and tools from deep learning to generative adversarial networks - Supports readers in implementing practical steps towards resilient renewable energy - Presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems</p>...9780443236396_Elsevier Sciencelibro_electonico_9780443236396_9780443236396Jin ZhaoInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/elsevierrefmonographs-epub-88055d4a-f083-4ec7-8193-a5671ff51aff.epub2025-07-01T00:00:00+00:00Elsevier