Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Identification and classification of voltage and current disturbances in por systems is an important task in por system monitoring and protection. Most por quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. New intelligent system technologies using wavelet transform, expert systems and artificial neural networks provide some unique advantages regarding fault analysis.  presents new approach aimed at automating the analysis of por quality disturbances including sag, sll, transient, fluctuation, interruption and normal waveform. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a porful combination of neural network and fuzzy logic. The system is modelled using VHDL follod by extensive testing and simulation to verify the correct functionality of the system. Then, the design is synthesized to APEX EP20K200EBC652-1X FPGA, tested and validated. Comparisons, verification and analysis made from the results obtained from the application of this system on software-generated and utility sampled disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.17%.
['F. Choong', 'Mamun Bin Ibne Reaz', 'Faisal Mohd-Yasin']
I am writing a research paper on a idea Power quality disturbance detection using artificial intelligence: a hardware approach
[{"from": "human", "value": "I am writing a research paper on a idea Power quality disturbance detection using artificial intelligence: a hardware approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Identification and classification of voltage and current disturbances in por systems is an important task in por system monitoring and protection. Most por quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. New intelligent system technologies using wavelet transform, expert systems and artificial neural networks provide some unique advantages regarding fault analysis.  presents new approach aimed at automating the analysis of por quality disturbances including sag, sll, transient, fluctuation, interruption and normal waveform. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a porful combination of neural network and fuzzy logic. The system is modelled using VHDL follod by extensive testing and simulation to verify the correct functionality of the system. Then, the design is synthesized to APEX EP20K200EBC652-1X FPGA, tested and validated. Comparisons, verification and analysis made from the results obtained from the application of this system on software-generated and utility sampled disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.17%."}]