Abstract

This study presents a new technique for fast detecting and diagnosing of power grids faults. Discrete Wavelet transform (DWT) has a major disadvantage of noise sensitivity. The proposed technique solves the problems of DWT, where a high-precision classification of noisy and faulty signals could be obtained. Fusion between voltage and power readings is done to provide a more reliable and accurate decision to determine the exact location of the fault. In this technique, the learner classifier is used,and the system is trained for multiple situations where most faults may occur. All simulations were carried out and performed on the standard IEEE 14 bus system to check the efficiency and performance of the technique proposed. Simulation results demonstrate, as will be discussed, a strong effectiveness of the suggested approach relative to others. The main feature of the proposed technique is that it can differentiate between faulty and noisy signals and recognize the fault's location quickly and with high reliability.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.