Enhanced Power Line Communication with Adaptive Neuro-Fuzzy Inference System
Keywords:AI, Neuro-Fuzzy, Fuzzy Logic, Neurocomputing Power-Line-Communication, PLC, Smart grid
This chapter deals with the use of Adaptive Neuro-Fuzzy Inference System (ANFIS) in treating signals for effective communication, especially in Power Line Communications (PLC) which has become an emerging trend for smart grid and IoT nowadays. A communication channel established through existing power line has been used to measure the level of noise and interference that affect PLC in narrow and wideband frequency ranges. At the completion of the experiment, results showed an extensive amount of noise that deteriorate the state of the received signal, especially for the narrow band and therefore prompted the need to redesign robust filters to cancel noise. An ANFIS based communication model was further developed in MATLAB to mimic the PLC transmission problem considering two different scenarios of interferences. Results showed that the signals that passed through the ANFIS system were recovered with considerable accuracy for which the two minimal estimated RMSE were respectively 0.717 and 0.847 for the two scenarios tested. This further re-emphasizes the importance and reliability of the ANFIS system.
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