Multiple signal processing techniques based power quality disturbance detection, classification, and diagnostic software

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.