06/06/2017
FYP Title: Electric Power Sensing
Session: Fall 2013 - Abasyn University Islamabad Campus
Group Members: Umar Farooq 0342, Usman Khan 0337, Waseem Abbas 0379.
Abstract: Electrical energy is a precious resource. Its optimal usage requires insight of energy consumption patterns. The energy meters, presently in use, only provide a lump sum KWh reading. However, it doesn’t offer insight to instantaneous load demand, usage of active/reactive power etc. This project presents a monitoring system which can either be provided as smart IoT (Internet of Things) enabled socket or may be installed in the distribution board of the residential, commercial or industrial concern. It would measure all the voltage, current, frequency, power factor, active power, reactive power, apparent power, energy of single phase and three phase system. It uploads these measurements to cloud using wireless link in real time, cloud services not only logs the uploaded KPIs but also present to user via insightful energy dashboards e.g. load analysis, energy consumption pattern in different time of the day etc.
This project is perfect for real time monitoring of main circuit breaker or main supply of whole house but if needs real time monitoring appliance by appliance of whole house then this project method is costly and difficult to install. For this purpose we need a smart socket with each appliance this method is called SDSPS (Single Device Single Point Sensing). SPSDS failed because in future load can be change.
The solution is SDMPS (Single Device Multiple Device Sensing) that measures power consumption appliance by appliance of whole house from main wire. This project is based on machine learning. The SDMPS module is installed with main circuit breaker that provides instantaneous updates to user on mobile phone. The user can see appliances ON-OFF status and load patterns in different time of the day appliance by appliance.
Keywords: Load monitoring, Metering, IoT, Cloud, Machine learning
