Research On Power Grid Status Perceiving Technology Based On PLC Channel Characteristics

The development of smart grid can improve the effectiveness and reliability of power system, and it has become one of the national strategic development projects now. Through PLC signals in the power line, grid state perceiving technique can realize the grid situational awareness, the running state of the comprehensive protection of power facilities, real-time perception and intelligent diagnosis, and improve the efficiency of safety monitoring and efficient operation. Channel characteristics are the main parameters reflecting changes in power grid operation, so we can cognitive and measure the operation state of power grid accordingly through detecting and estimating the power line channel characteristics.In this paper we study the power grid status perceiving technology based on detecting and analyzing channel characteristics. After analyzing the power line channel characteristics, the paper expounds the application direction of grid perceiving technology, including realizing the detection of power grid, the positioning of fault, the cognition of power grid topology, and the identification of the user-load. On the basis of the existing technology, this paper studies and improves Non-Intrusive Power Line Parameter Monitoring Method based on channel transfer function, and Online User-load Identification Method based on channel noise characteristics. The former designs a method speculating and recognizing the changes of cable parameters based on real-time detecting channel transfer function, by analyzing the channel parameters reflecting power cable quality changes, including electrical conductivity, relative permittivity, relative permeability, etc. Through simulations the feasibility of this method has been proved. The latter firstly summarizes the power line channel noise characteristics, and makes collection and classification of some noise produced by electricity loads in laboratory environment. It estimates electricity loads in the actual environment through the analysis and comparison of the noises they produce. Based on experiments we verify the feasibility of this method.