{"product_id":"9783031990472","title":"Springer Theses: Probabilistic and Machine Learning Approaches","description":"\u003ch1\u003eSpringer Theses: Probabilistic and Machine Learning Approaches\u003c\/h1\u003e \u003ch2\u003eZhao, Yuqi\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp style=\"line-height: 115%;\"\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; color: #44546a; mso-ansi-language: EN-US;\"\u003eThis book tackles the technical challenges of integrating renewable energy sources into power grids to reduce exposure to significant financial and operational risks. It does so by introducing advanced methods for harmonic estimation and forecasting in sparsely monitored and uncertain power networks, leveraging probabilistic and machine learning techniques.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp style=\"line-height: 115%;\"\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; color: #44546a; mso-ansi-language: EN-US;\"\u003eWith a focus on practical applications, the book introduces a Monte-Carlo-based simulation framework to address operational randomness and uncertainties, along with the development of a Norton equivalent model of wind farms for probabilistic harmonic propagation studies. The author also presents cost-effective methods for harmonic estimation in non-radial distribution networks and proposes a sequential artificial\u003c\/span\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; mso-ansi-language: EN-US;\"\u003e-\u003cspan style=\"color: #44546a;\"\u003eneural\u003c\/span\u003e-\u003cspan style=\"color: #44546a;\"\u003enetwork-based approach for probabilistic harmonic forecasting in transmission networks with limited harmonic measurements. By significantly reducing the reliance on extensive power-quality-monitoring installations, these methods provide robust, accurate, and reliable harmonic data and enable more effective and informed decision-making for future power system operations.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; color: #44546a; mso-ansi-language: EN-US; mso-fareast-language: EN-US;\"\u003eTargeted at academic researchers, industrial engineers, and graduate students, \u003cem\u003e\u003cspan style=\"font-family: 'Calibri',sans-serif; font-style: normal;\"\u003ethis book\u003c\/span\u003e\u003c\/em\u003e matches theoretical advance with practical application. It supports the assessment of standard compliance and benchmarking, minimizes the need for power-quality-monitoring installations, accelerates the evaluation of harmonic propagation and mitigation strategies in uncertain, power-electronic\u003c\/span\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ansi-language: EN-US; mso-fareast-language: EN-US;\"\u003es\u003cspan style=\"color: #44546a;\"\u003e-rich networks, and advances the forecasting of potential harmonic issues in future power systems.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2026-01-03\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783031990472\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-031-99048-9\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 209\u003c\/p\u003e ","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":44341322973324,"sku":"9783031990472","price":224.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783031990472.jpg?v=1779569386","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783031990472","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}