Marco Adonis
Dr Marco Adonis, Head: Department Electrical, Electronics & Computer Engineering, Cape Peninsula University of Technology, Cape Town, South Africa.
Dr Marco Adonis, Head: Department Electrical, Electronics & Computer Engineering, Cape Peninsula University of Technology, Cape Town, South Africa.
The challenge for smart cities is to connect as many of its inhabitants to technology enabling solutions that improve their lives. Smart homes provide all users a means of interacting and impacting their environment. In developing economies this proves challenging and these challenges are daunting and overwhelming since system costs are always a foreboding factor. The chapter addresses these challenges by providing a low-cost solution for a home energy saving measure. It introduces an overview of enabling technologies for a smart home by considering energy management, energy saving, load management and monitoring and control of living spaces. By leveraging the application of the Internet of Things (IoT) and load management strategies, the realisation of a smart home is made possible. This chapter presents a broad overview of the design and development of a web-enabled smart home solution. Web development and control systems together form the backbone of automation for modern home automation technologies such as the Internet of Things and embedded systems. The developed web-enabled home automation incorporates elements of web developed software application and digital control systems. The web-enabled interface energy saving measure is a networked system that uses web-enabled applications for enabling energy efficiency by incorporating load management, remote power consumption, monitoring and control.
Part of the book: Sustainable Cities
Vigilant fault diagnosis and preventive maintenance has the potential to significantly decrease costs associated with wind generators. As wind energy continues the upward growth in technology and continued worldwide adoption and implementation, the application of fault diagnosis techniques will become more imperative. Fault diagnosis and preventive maintenance techniques for wind turbine generators are still at an early stage compared to matured strategies used for generators in conventional power plants. The cost of wind energy can be further reduced if failures are predicted in advance of a major structural failure, which leads to less unplanned maintenance. High maintenance cost of wind turbines means that predictive strategies like fault diagnosis and preventive maintenance techniques are necessary to manage life cycle costs of critical components. Squirrel-Cage Induction Generators (SCIG) are the prevailing generator type and are more robust and cheaper to manufacturer compared to other generator types used in wind turbines. A statistical model was developed using SCADA data to estimate the relationships between winding temperatures and other variables. Predicting faults in stator windings are challenging because the unhealthy condition rapidly evolves into a functional failure.
Part of the book: Fault Detection and Diagnosis