TY - GEN
T1 - Multiple signal processing techniques based power quality disturbance detection, classification, and diagnostic software
AU - Godoy, Ruben Barros
AU - Pinto, Joào Onofre Pereira
AU - Galotto, Luigi
PY - 2007
Y1 - 2007
N2 - This work presents the development steps of the software PQMON, which targets power quality analysis applications. The software detects and classifies electric system disturbances. Furthermore, it also makes diagnostics about what is causing such disturbances and suggests line of actions to mitigate them. Among the disturbances that can be detected and analyzed by this software are: harmonics, sag, swell and transients. PQMON is based on multiple signal processing techniques. Wavelet transform is used to detect the occurrence of the disturbances. The techniques used to do such feature extraction are: Fast Fourier Transform, Discrete Fourier Transform, Periodogram, and statistics. Adaptive Artificial Neural Network is also used due to its robustness in extracting features such as fundamental frequency and harmonic amplitudes. The probable causes of the disturbances are contained in a database, and their association to each disturbance is made through a cause-effect relationship algorithm, which is used to diagnose. The software also allows the users to include information about the equipments installed in the system under analysis, resulting in the direct nomination of any installed equipment during the diagnostic phase. In order to prove the effectiveness of software, simulated and real signals were analyzed by PQMON showing its excellent performance.
AB - This work presents the development steps of the software PQMON, which targets power quality analysis applications. The software detects and classifies electric system disturbances. Furthermore, it also makes diagnostics about what is causing such disturbances and suggests line of actions to mitigate them. Among the disturbances that can be detected and analyzed by this software are: harmonics, sag, swell and transients. PQMON is based on multiple signal processing techniques. Wavelet transform is used to detect the occurrence of the disturbances. The techniques used to do such feature extraction are: Fast Fourier Transform, Discrete Fourier Transform, Periodogram, and statistics. Adaptive Artificial Neural Network is also used due to its robustness in extracting features such as fundamental frequency and harmonic amplitudes. The probable causes of the disturbances are contained in a database, and their association to each disturbance is made through a cause-effect relationship algorithm, which is used to diagnose. The software also allows the users to include information about the equipments installed in the system under analysis, resulting in the direct nomination of any installed equipment during the diagnostic phase. In order to prove the effectiveness of software, simulated and real signals were analyzed by PQMON showing its excellent performance.
KW - Artificial neural networks
KW - Classification
KW - Diagnostic
KW - Disturbance of power quality
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=51149109384&partnerID=8YFLogxK
U2 - 10.1109/EPQU.2007.4424176
DO - 10.1109/EPQU.2007.4424176
M3 - Conference contribution
AN - SCOPUS:51149109384
SN - 8469100572
SN - 9788469100578
T3 - 2007 9th International Conference on Electrical Power Quality and Utilisation, EPQU
BT - 2007 9th International Conference on Electrical Power Quality and Utilisation, EPQU
T2 - 2007 9th International Conference on Electrical Power Quality and Utilisation, EPQU
Y2 - 9 October 2007 through 11 October 2007
ER -