menu
{ "item_title" : "Applications of Machine Learning in Power Electronics for Integration of Renewable Energy Sources", "item_author" : [" Dr Kumar K "], "item_description" : "Applications of Machine Learning in Power Electronics for Integration of Renewable Energy Sources explores how AI techniques enhance renewable energy systems through optimized power electronics. This essential guide introduces machine learning applications for improving control, predictive maintenance, and fault detection across renewable sources like solar and wind. With real-world case studies and practical insights, the book equips engineers, researchers, and students with tools to tackle challenges in renewable integration, power quality, and system reliability. An invaluable resource for both beginners and experts, this book sheds light on AI-driven innovations shaping a sustainable energy future.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/889/610/9798896101857_b.jpg", "price_data" : { "retail_price" : "14.99", "online_price" : "14.99", "our_price" : "14.99", "club_price" : "14.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Applications of Machine Learning in Power Electronics for Integration of Renewable Energy Sources|Dr Kumar K

Applications of Machine Learning in Power Electronics for Integration of Renewable Energy Sources : Intelligent Algorithms for Efficient Energy Integra

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

Applications of Machine Learning in Power Electronics for Integration of Renewable Energy Sources explores how AI techniques enhance renewable energy systems through optimized power electronics. This essential guide introduces machine learning applications for improving control, predictive maintenance, and fault detection across renewable sources like solar and wind. With real-world case studies and practical insights, the book equips engineers, researchers, and students with tools to tackle challenges in renewable integration, power quality, and system reliability. An invaluable resource for both beginners and experts, this book sheds light on AI-driven innovations shaping a sustainable energy future.

This item is Non-Returnable

Details

  • ISBN-13: 9798896101857
  • ISBN-10: 9798896101857
  • Publisher: Notion Press
  • Publish Date: November 2024
  • Dimensions: 11 x 8.5 x 0.2 inches
  • Shipping Weight: 0.43 pounds
  • Page Count: 74

Related Categories

You May Also Like...

    1

BAM Customer Reviews