When you're looking to analyze Electromagnetic Interference (EMI) in your PSIM circuit, using the Signal Analyzer along with the Line Impedance Stabilization Network (LISN) block is super helpful. Let's break down some important things you need to know about this:
1. The Real-World Impact
PSIM provides tools like LISN, Signal Analyzer, and EMI filter blocks, making EMI analysis easier. However, achieving accurate results involves considering real-world factors. This includes accounting for parasitic electrical paths (parasitic elements) and how components behave in practical scenarios, which aren't always perfect. That being said, it's crucial for designers to incorporate the parasitic capacitances and inductances within the circuit itself as well as the component parasitics (non-ideal components). Non-ideal switching (LvL2 PSIM models) and gate driving are also an important aspect. Remember, there's no one-size-fits-all tool in EMI simulations, and the 'Garbage In Garbage Out' rule holds true in such applications.
2. Time and Frequency Domains: What You Need to Know
The Signal Analyzer's EMI, DM, and CM noise outputs present Time Domain Results. The resolution of these results primarily depends on the Time Step. Furthermore, to analyze results in the frequency domain using PSIM's FFT tool (we'll get to this shortly), it's important to consider two main principles:
a. The FFT algorithm in PSIM adheres to Nyquist's criteria, which means that the maximum frequency of the FFT analysis is calculated using the following formula:
freq_max = 1/(2*time_step)
In other words, in order to get a higher max frequency, you need to use a finer TimeStep for the simulation.
b. Please also keep in mind that the frequency incremental step of the FFT spectrum is:
delt_freq = 1/total_sim_length
3. FFT and Its Limits
I'd also like to offer some insights regarding the FFT algorithm. It's important to note that while the FFT is a valuable tool, it may not be always ideal for EMI analysis. This is not a PSIM issue but rather an FFT algorithm issue. Some of the reasons why:
- Spectral leakage due to non-smooth signals at the beginning and the end of the simulation (sudden onset and decay of signals)
- Spectral leakage due to the fact that FFT sampling frequency may not be an integer multiplication of measured signal frequency
- Spectral leakage due to the fact that the FFT window may not contain an integer amount of frequency component periods
- Different FFT results based on the chosen time window.
Indeed, windowed frequency analysis algorithms are preferred in literature over the FFT for EMI analysis due to those issues. A simple example of a windowed FFT is the STFT (Short-Time Fourier Transform). You can export the PSIM time domain results as .txt and then leverage Altair Compose and Python to use a ready-to-go python STFT algorithm to delve deeper in those issues.
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Furthermore, PSIM provides some very interesting ready-to-go examples through the EMI Design Suite, to help you get into EMI analysis immediately. Those examples involve the LISN and Signal Analyzer blocks, as well as user-friendly ways to minimize the EMI with the "EMI Filter" block. Feel free to navigate to Design Suites >> EMI Design Suite in order to explore those examples:
Here are some links for additional insights on this topic:
EMI Analysis and Filter Design in PSIM
How to Add EMI Standard Plots to an FFT
Introduction to PSIM Level 2 MOSFET & Comparison with SPICE