Household electricity network monitoring based on IoT with of automatic power factors improvement using neural network method

Abstract

The development of installed capacity in power plants in 2018 was 41.696 MW or increased from the previous year in 2017 amounted to 39.652 MW (PLN Statistics, 2018). This has an impact on the reduced availability of fuel due to overexploitation. The highest energy sold per customer group in 2018 was the household customer group that was 41.7% higher than the industrial sector customer group by 32.8% (PLN Statistics, 2018). At present the use of electronic equipment for household needs is increasingly diverse. Many equipment that is often used in daily life is electronic equipment that is inductive load. Inductive load causes the value of the power factor to fall so that the power usage (Watt) becomes less than optimal. To overcome the problem caused by the large number of inductive loads a reactive power compensator is needed which is to use a capacitor. In this final project, a system designed to be able to measure and correct power factors automatically uses the Neural Network method and can monitor power online based on IoT. The results of testing the power factor improvement system were 97.8% successful in the trained electric load and 94.8% in the untrained electrical load.

Cloud-Based Automated Power Factor Correction and Power Monitoring

“…The implemented model suggested a successful process management idea while minimizing the need for correction nodes while achieving the same production outcomes. Nugroho et al [22] was used the Neural Network approach to automatically reckoning and rectifying power factor, and power may be monitored online via IoT. Overall, 97.8% of the appliance's load trained and 94.8% of the untrained ones benefited from using this power factor enhancement technology.…”

“…(6) In most endeavours, in order to correct the lagging power factor, solid-state relays are used [11,15,16]. (7) Treat each household or industrial load individually (each home or industry needs an algorithm for correction power factor) [22].…”