Developing PMU Models in MATLAB Environment

Developing PMU (Phasor Measurement Unit) models in MATLAB environment serves critical applications in power system monitoring, protection, and control. PMUs are typically employed for real-time acquisition of voltage/current magnitudes and phases, achieving high-precision measurements through synchronized timing signals (e.g., GPS synchronization).

To implement a PMU model, begin by defining signal sampling frequency and synchronization mechanisms. In MATLAB, utilize signal processing toolbox functions to simulate synchronized sampling processes. Implement interpolation algorithms (e.g., cubic spline using `interp1`) or digital filtering techniques (using `fir1` or `designfilt`) to enhance measurement accuracy. Code implementation should include anti-aliasing filters and sample rate conversion modules.

The model must incorporate phasor calculation modules, typically employing Discrete Fourier Transform (DFT) or Fast Fourier Transform (FFT) for fundamental frequency component extraction. MATLAB's built-in `fft` function can be applied with proper windowing techniques (Hanning window via `hann` function) to minimize spectral leakage. Implement frequency tracking algorithms using phase-locked loops (PLL) with `pll` function from Control System Toolbox.

For PMU output simulation, construct standardized data frames containing timestamp labels (using `datetime` or `posixtime` functions), phasor magnitudes, phase angles, and frequency/rate-of-change data. Model communication delays through transport lag blocks (`tf` with delay) or buffer mechanisms (`buffer` function). Utilize MATLAB's Simulink for dynamic system validation and Power System Toolbox for scenario-based performance testing under various grid conditions.