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Using Image Processing Tools in Renewable Energy Systems

This book is beneficial for the researchers to work and focus on the hybrid techniques of GIS, remote sensing, image processing, and its implementation on the setup and applications of renewable energy resources. Due to population growth and modernization, the demand and supply system is unbalanced, which directly affects the environmental condition. Hence, it is required to utilize natural resources wisely so that our demand can be fulfilled without harming the environment. The hybrid techniques are fast and provide financial benefits in terms of a site visit and its proper selection. Designers can plan the complete project virtually with its benefits and losses.

Book includes, the different problems in rural electrification faced by electricity boards in India; the effect of uncertainty in renewable energy planning and operation; computing techniques for the integration of different renewable energy resources are illustrated ; the layout and configurations of the integrated renewable energy system are introduced and various computing technologies that are used to evaluate the performance and sizing of the integrated renewable energy system are presented and discussed in detail.

It also includes the automatic generation control of hydropower plants consisting of three areas was examined; optimization algorithm (WOA) has been utilized for the automatic generation control (AGC) methodology; the modelling of a traditional controller called proportional-integral-derivative controller for the automatic generation control in the system of a single area. Image processing software tools such as ArcGIS, QGIS, AutoCAD, Autodesk 3DS Max, Blender, City Engine 2019, ERDAS IMAGINE, Google Open Street Map have been utilized for calculating the sub-indexes and a methodology for conducting similar assessments using a variety of image processing tools has been demonstrated and various strategies have been designed for land use characterizations which are commonly known as supervised and unsupervised classification.

Source: https://benthamscience.com/

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