Design of Electricity Theft Monitoring System Based on RDBN Deep Learning Algorithm
Electricity theft not only leads to an increase in non-technical losses in the power grid,but also has the potential to impact the operational safety of power grid equipment and pose risks to the safety of individuals involved in power theft due to improper handling.The current power grid faces challenges in detecting electricity theft,including difficulty in inspections and low detection efficiency.In response to these issues,a power theft monitoring system is proposed.The associated monitoring device can be flexibly installed on power supply lines,utilizing a current transformer for power supply.It real-time collects current from the power line and transmits the data to a cloud server using a 4G module.The upper-level software employs the real-valued deep belief network(RDBN)algorithm for data analysis.Through simulation and experimental testing,the RDBN algorithm achieves a recognition accuracy of 98.15％for power theft states.The monitoring system can acquire and analyze monitoring data in real-time,mark suspicious power theft lines,reduce inspection difficulty,and improve detection efficiency.
non-technical lossesdetecting electricity theftcurrent transformer for power supplyreal-valued deep belief network(RDBN)
万方数据
维普
