- Merkebu Zenebe Degefa
- WP2 Lead
- SINTEF Energi AS
Instantaneous Frequency Identification in Microgrids
Challenge and objective
- With power electronic equipment and DG, the power system has become more prone to harmonic pollution.
- As isolated systems categorized by low inertia such as microgrids are more common, the presence of nonlinear distortion is becoming an increasing problem.
- Commonly used methods for surveillance are thus not suited for the rising non-linearity caused by the harmonics, and there is a need for alternative surveillance methods.
- This thesis has explored the use of adaptive data analysis as an alternative surveillance method for harmonic detection in the power system.
- Empirical Mode Decomposition (EMD) and its real-time extension, Online EMD, have been used in conjunction with Hilbert-Transform (HT) and Fast Fourier Transform (FFT) for instantaneous frequency and ampl. identification.
- The methods proved to be powerful tools for harmonic detection when supported with techniques to handle mode mixing on more complex signals.
- The use of masking signals turned out to be a highly effective mode mixing separation technique
Impact for distribution system innovation
- Measurement based techniques can identify special characteristics of equipment or systems when modelbased techniques may fail due to parameter uncertainty.
- Possible to identify the connected equipment and sources based on their response to disturbances.
Reference in CINELDI
- E. Westad: "Instantaneous Frequency Identification in Microgrids Through Adaptive Data Analysis", MSc-thesis, NTNU, June 2020.