ABSTRACT
Smart devices are playing a vital role in remote sensing and monitoring of devices over a wide range of applications. Machine-to-machine (M2M) communication is leading the world to the next level of technology. This helps the data transmission, monitoring, and control among devices without human intervention. The extensive utility of sensor technology and M2M networking is the future of industries for the production of commercial and non-commercial applications. One such application, the Internet of Things (IoT), is the universally accepted technology of sensors and wide area networking. The extensive use of nonlinear onboard electronic components and power converter units in smart devices generates higher-order current harmonics. This causes the severe effect of low power quality and results in device malfunctioning. The increase in the use of smart devices will be a future challenge for power quality and its effect on associative devices. The main contribution of this chapter is to analyze harmonic distortions due to nonlinear load characteristics in the power management unit of traditional systems and advanced smart IoT devices. It helps us to understand the utility of conventional harmonic mitigation techniques and the need for adaptive features for the latest technology. The conventional compensation techniques are incorporated with machine learning approaches to deal with the uncertainty of load variation on real-time signals. This survey gives a brief insight into the present scope of conventional compensation techniques and futuristic soft computing techniques to mitigate harmonic distortions. Additionally, the survey is intended to call attention to researchers of the future perspective of smart devices and to make the academic community aware of existing practices.
