Handbook of Metrology and Applications


Book Description

​This handbook provides comprehensive and up-to-date information on the topic of scientific, industrial and legal metrology. It discusses the state-of-art review of various metrological aspects pertaining to redefinition of SI Units and their implications, applications of time and frequency metrology, certified reference materials, industrial metrology, industry 4.0, metrology in additive manufacturing, digital transformations in metrology, soft metrology and cyber security, optics in metrology, nano-metrology, metrology for advanced communication, environmental metrology, metrology in biomedical engineering, legal metrology and global trade, ionizing radiation metrology, advanced techniques in evaluation of measurement uncertainty, etc. The book has contributed chapters from world’s leading metrologists and experts on the diversified metrological theme. The internationally recognized team of editors adopt a consistent and systematic approach and writing style, including ample cross reference among topics, offering readers a user-friendly knowledgebase greater than the sum of its parts, perfect for frequent consultation. Moreover, the content of this volume is highly interdisciplinary in nature, with insights from not only metrology but also mechanical/material science, optics, physics, chemistry, biomedical and more. This handbook is ideal for academic and professional readers in the traditional and emerging areas of metrology and related fields.




Monitoring and Control of Electrical Power Systems using Machine Learning Techniques


Book Description

Monitoring and Control of Electrical Power Systems using Machine Learning Techniques bridges the gap between advanced machine learning techniques and their application in the control and monitoring of electrical power systems, particularly relevant for heavily distributed energy systems and real-time application. The book reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for monitoring power quality. This reference introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices and discusses monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms. Covers advanced applications and solutions for monitoring and control of electrical power systems using machine learning techniques for transmission and distribution systems Provides deep insight into power quality disturbance detection and classification through machine learning, deep learning, and spatio-temporal algorithms Includes substantial online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub