Virtual Laboratory

Radar Principles: Measure the Transformer Hum and its Frequency
Undergraduate Electrical Engineering Course

1. Objective

Upon successful completion of this virtual laboratory experiment, the student will be able to:

  1. Understand the physical origin of acoustic noise (hum) in power transformers due to magnetostriction and electromagnetic forces.
  2. Measure the fundamental frequency of transformer hum and identify its harmonic components using spectral analysis techniques.
  3. Apply Fast Fourier Transform (FFT) based spectral analysis methods that are foundational to modern radar signal processing.
  4. Analyze the relationship between line frequency (50/60 Hz), the resulting vibration frequency (100/120 Hz), and higher-order harmonics.
  5. Draw parallels between low-frequency spectral analysis of transformer hum and high-frequency radar signal analysis (Doppler shift, pulse compression, spectral estimation).
  6. Develop proficiency in interpreting spectrum analyzer displays and identifying peak frequencies with proper measurement techniques.
Key Principle: The frequency measurement and spectral analysis techniques practiced in this laboratory are directly applicable to radar systems, where precise frequency determination (e.g., Doppler frequency shift) is essential for target velocity measurement.

2. Theory

2.1 Origin of Transformer Hum

Power transformers produce a characteristic audible hum during operation. This acoustic noise originates primarily from two physical phenomena:

Magnetostriction — The Dominant Cause

Transformer cores are constructed from laminated silicon steel. When subjected to an alternating magnetic field, these ferromagnetic materials experience minute dimensional changes — a phenomenon called magnetostriction. The core material expands and contracts twice during each full cycle of the AC supply because the magnetic flux reaches peak magnitude twice per cycle (positive and negative peaks).

fhum = 2 × fline

Therefore, for a 50 Hz power system, the fundamental hum frequency is 100 Hz. For a 60 Hz system, it is 120 Hz. These frequencies fall within the human audible range (20 Hz – 20 kHz).

Electromagnetic Forces — Secondary Contribution

Current-carrying windings experience Lorentz forces proportional to the product of current and flux density. Since both current and flux vary sinusoidally at the line frequency, the force varies at twice the line frequency, again producing 100 Hz (or 120 Hz) vibration. Additionally, loose laminations or clamping structures can resonate at harmonic frequencies.

2.2 Harmonic Content of Transformer Hum

Real transformer hum is not a pure sinusoid. Due to the non-linear B-H characteristic of the core material and saturation effects, the vibration waveform contains significant harmonic components:

Harmonic 50 Hz System 60 Hz System Typical Relative Amplitude
Fundamental (2nd electrical harmonic) 100 Hz 120 Hz 1.00 (0 dB)
2nd harmonic (4th electrical) 200 Hz 240 Hz 0.10 to 0.30 (-20 to -10 dB)
3rd harmonic (6th electrical) 300 Hz 360 Hz 0.05 to 0.15 (-26 to -16 dB)
4th harmonic (8th electrical) 400 Hz 480 Hz 0.02 to 0.08 (-34 to -22 dB)
Note: Odd multiples of the line frequency (50, 150, 250 Hz...) are generally absent or strongly suppressed in a balanced three-phase transformer because the magnetostrictive strain is an even-symmetric function of flux density.

2.3 Frequency Measurement Techniques

Method A: Direct Time-Domain Measurement

Using an oscilloscope, measure the period T between consecutive peaks of the acoustic waveform. The frequency is:

f = 1 / T

Accuracy limitation: Dependent on time-base calibration and visual interpolation. Typical accuracy: ±1% to ±2%.

Method B: Frequency Counter

A digital frequency counter counts the number of zero-crossings (or positive peaks) within a known gate time tg:

f = N / tg

Accuracy limitation: ±1 count error (±1/tg Hz). For tg = 1 s, resolution is 1 Hz.

Method C: FFT-Based Spectral Analysis Radar Relevance

The Fast Fourier Transform converts a time-domain signal x(t) into its frequency-domain representation X(f):

X[k] = Σn=0N-1 x[n] · e-j2πkn/N

Key parameters:

  • Frequency resolution: Δf = fs / N (where fs = sampling rate, N = number of samples)
  • Maximum detectable frequency: fmax = fs / 2 (Nyquist criterion)
  • Spectral leakage: Minimized by applying window functions (Hanning, Hamming, Blackman)

Radar Connection: FFT is the backbone of modern pulse-Doppler radar, synthetic aperture radar (SAR), and frequency-modulated continuous-wave (FMCW) radar for target detection and velocity estimation.

2.4 Connection to Radar Principles

Why study transformer hum in a radar course? The fundamental signal processing chain is identical:

  1. Signal Acquisition: Microphone (acoustic) ↔ Radar antenna (electromagnetic)
  2. Amplification & Conditioning: Audio preamp ↔ RF low-noise amplifier (LNA)
  3. Analog-to-Digital Conversion: Audio ADC ↔ Radar receiver ADC
  4. Spectral Analysis (FFT): Identifying hum peaks ↔ Identifying Doppler-shifted target returns
  5. Frequency Estimation: Peak detection algorithms ↔ Constant false alarm rate (CFAR) detection

Transformer hum provides a safe, low-frequency, easily observable platform to master these techniques before applying them to GHz-range radar signals.

2.5 The Doppler Analogy

In radar, a moving target shifts the frequency of the returned echo by the Doppler frequency:

fd = (2vr · fc) / c

where vr is radial velocity, fc is carrier frequency, and c is the speed of light. Precise measurement of small frequency shifts (often Hz or sub-Hz) requires the same FFT-based spectral estimation techniques used to resolve closely-spaced harmonics in transformer hum analysis.

3. Procedure

3.1 Equipment Setup (Virtual)

  1. Launch the Virtual Lab simulation (Section 4 below).
  2. Familiarize yourself with the control panel: Line Frequency, Harmonic Content, Noise Level, and Window Function selectors.
  3. Set the default parameters: Line Frequency = 50 Hz, 2nd Harmonic = 20%, 3rd Harmonic = 10%, Noise = Low, Window = Hanning.

3.2 Experiment A: Fundamental Frequency Measurement

  1. Time-Domain Observation: Observe the waveform on the upper oscilloscope display. Count the number of complete cycles in the 100 ms time window. Calculate the frequency.
  2. Spectral Observation: Examine the FFT spectrum on the lower display. Identify the tallest peak — this is the fundamental hum frequency.
  3. Record: Note the measured frequency and compare with the theoretical value (2 × line frequency).
  4. Repeat: Change the line frequency to 60 Hz and record the new fundamental hum frequency.

3.3 Experiment B: Harmonic Analysis

  1. Keep line frequency at 50 Hz.
  2. Systematically vary the 2nd, 3rd, and 4th harmonic amplitudes from 0% to 30%.
  3. For each setting, record the relative amplitude of each harmonic peak in the FFT display.
  4. Plot a graph of harmonic amplitude (%) vs. measured spectral peak height (dB).
  5. Observation: Verify that the harmonic frequencies occur at integer multiples of the fundamental hum frequency (100 Hz, 200 Hz, 300 Hz...).

3.4 Experiment C: Windowing and Spectral Leakage

  1. Set line frequency to 50 Hz and all harmonics to 0% (pure fundamental).
  2. Switch the FFT window function between Rectangular, Hanning, Hamming, and Blackman.
  3. Observe the main lobe width and side lobe levels for each window.
  4. Record: Which window provides the narrowest main lobe? Which provides the lowest side lobes? What is the trade-off?

3.5 Experiment D: Noise and Signal-to-Noise Ratio (SNR)

  1. Set line frequency to 50 Hz with moderate harmonics (2nd = 15%, 3rd = 8%).
  2. Gradually increase the noise level from "None" to "High".
  3. Observe at what noise level the 3rd harmonic peak becomes indistinguishable from the noise floor.
  4. Calculate: Estimate the SNR (dB) at which the 3rd harmonic is no longer detectable.
Safety Note (Real-World Context): When performing this experiment with physical equipment, always maintain safe distance from high-voltage terminals. Use a directional microphone or accelerometer with proper isolation. Never open energized transformer enclosures.

4. Virtual Laboratory Simulation

🔊

Power Transformer Model
Core vibration frequency = 2 × Line Frequency

Time Domain — Simulated Transformer Vibration Waveform (0–100 ms)
Frequency Domain — FFT Spectrum (0–1000 Hz)

Simulation Guide

  • The top display shows the time-domain vibration waveform as it would appear on an oscilloscope.
  • The bottom display shows the FFT magnitude spectrum, with frequency (Hz) on the horizontal axis and magnitude (dB) on the vertical axis.
  • Use the Auto Measure Peak button to automatically detect and display the dominant frequency component.
  • Click Play Audio to hear a synthesized version of the hum (headphones recommended).

5. Guidelines for Report Writing

5.1 Report Structure

Your laboratory report must be organized into the following sections:

  1. Title Page: Experiment title, student name, ID, date, course name, and instructor.
  2. Abstract: A concise summary (150–200 words) of the experiment objectives, methods, key results, and conclusions.
  3. Introduction & Objectives: State the purpose of the experiment and its relevance to radar signal processing.
  4. Theoretical Background: Explain magnetostriction, harmonic generation, FFT principles, and the radar-Doppler analogy. Include relevant equations.
  5. Equipment & Setup: Describe the virtual simulation environment and parameter settings for each experiment.
  6. Procedure: Present a clear, step-by-step account of Experiments A through D. Include screenshots of the virtual instrument displays.
  7. Results & Data: Present all measured data in properly labeled tables. Include plotted graphs (harmonic amplitude vs. spectral peak, window comparison chart).
  8. Analysis & Discussion:
    • Compare measured hum frequencies with theoretical predictions. Calculate percent error.
    • Discuss the effect of each window function on spectral resolution and leakage.
    • Analyze the relationship between harmonic percentage settings and measured FFT peak heights. Is it linear in the dB scale?
    • Explain the practical implications of noise on weak harmonic detection.
    • Draw explicit connections to radar: How does this experiment relate to Doppler radar signal analysis?
  9. Conclusion: Summarize key findings, state whether objectives were met, and suggest improvements or extensions.
  10. References: Cite textbooks, IEEE papers, and technical datasheets using a consistent format (IEEE or APA).

5.2 Grading Rubric

Criterion Weight Expectations
Theoretical Understanding 20% Correct equations, clear explanations, radar connection established
Experimental Procedure 15% Clear, reproducible steps; all four experiments completed
Data Presentation 20% Tables, graphs, screenshots are labeled, accurate, and well-organized
Analysis & Discussion 25% Critical thinking, error analysis, meaningful radar analogies
Report Quality 10% Grammar, formatting, professional appearance
Quiz Performance 10% Post-lab quiz score (Section 6)
Tip: When discussing the radar connection, specifically address: (1) how FFT resolution affects Doppler velocity precision, (2) how windowing reduces radar clutter leakage, and (3) how SNR thresholds determine minimum detectable target cross-section.

6. Post-Laboratory Quiz

Answer all questions. After submitting, review the correct answers and explanations to reinforce your understanding.

1. What is the primary physical cause of transformer hum?

Correct Answer: b) Magnetostriction of the core laminations

Explanation: Magnetostriction is the reversible dimensional change in ferromagnetic materials when subjected to a magnetic field. In transformers, the core laminations expand and contract twice per AC cycle, generating the characteristic hum at twice the line frequency. While electromagnetic forces (Lorentz) also contribute, magnetostriction is the dominant mechanism, typically accounting for 60–80% of the total acoustic energy.

2. For a power system operating at 60 Hz, what is the fundamental frequency of transformer hum?

Correct Answer: c) 120 Hz

Explanation: Because magnetostrictive strain is proportional to the square of flux density (or follows an even-symmetric curve), the core experiences maximum compression/extension at both the positive and negative peaks of the AC cycle. Thus, the mechanical vibration occurs at twice the electrical line frequency: fhum = 2 × 60 Hz = 120 Hz.

3. In the FFT spectrum of transformer hum, which harmonic family is typically dominant?

Correct Answer: d) Even harmonics of the line frequency (100, 200, 300 Hz...)

Explanation: The magnetostriction curve is approximately an even function of flux density B. Since B(t) = Bmaxsin(ωt), the strain ε ∝ B² contains only DC and 2ω components. Therefore, the resulting vibration spectrum contains the fundamental hum (2f) and its integer multiples (4f, 6f, 8f...), which correspond to even harmonics of the line frequency. Odd line-frequency harmonics are suppressed in balanced three-phase units.

4. What does the Nyquist criterion state regarding FFT-based spectral analysis?

Correct Answer: b) The sampling rate must be at least twice the maximum frequency present

Explanation: The Nyquist-Shannon sampling theorem states that to avoid aliasing, a signal must be sampled at a rate fs strictly greater than twice its maximum frequency component (fs > 2fmax). In practice, fs ≥ 2.5fmax is often used to allow for non-ideal anti-aliasing filter roll-off. This principle is equally critical in radar digital receivers to prevent target ambiguity.

5. Which FFT window function provides the narrowest main lobe but highest side lobes?

Correct Answer: a) Rectangular

Explanation: The rectangular window (equivalent to no windowing) has a main lobe width of 2 bins (zero-to-zero) but exhibits side lobes at only -13 dB relative to the peak. This creates significant spectral leakage. Hanning, Hamming, and Blackman windows progressively widen the main lobe (2, 2, 3 bins respectively at -3dB) but suppress side lobes to -31 dB, -41 dB, and -57 dB, reducing leakage. This trade-off is fundamental in radar for resolving closely-spaced targets vs. detecting weak targets near strong clutter.

6. In radar, the Doppler frequency shift is used primarily to determine:

Correct Answer: c) Target radial velocity

Explanation: The Doppler effect causes the frequency of the radar echo to shift proportionally to the radial velocity component of the target: fd = 2vrfc/c. By measuring this frequency shift using the same FFT-based spectral analysis techniques practiced in this lab, radar systems determine whether a target is approaching (positive shift) or receding (negative shift), and at what speed. Range is determined by time-of-flight, not Doppler.

7. If the FFT frequency resolution is 2 Hz and a transformer hum peak appears at bin 51, what is the measured frequency?

Correct Answer: b) 102 Hz

Explanation: The frequency corresponding to FFT bin k is calculated as f = k × Δf, where Δf is the frequency resolution. Here, f = 51 × 2 Hz = 102 Hz. This is consistent with a 50 Hz line frequency system where the fundamental hum is 100 Hz; the small discrepancy (2 Hz) illustrates the quantization error inherent in finite-length FFT analysis. In radar, this same bin-to-frequency mapping is used to convert spectral peaks to velocity estimates.

8. What happens to the FFT spectrum when the analysis window captures a non-integer number of signal periods?

Correct Answer: d) Spectral leakage spreads energy across multiple bins

Explanation: When the FFT window length is not an exact integer multiple of the signal period, the periodic extension implicit in the FFT creates discontinuities at the window boundaries. These discontinuities introduce additional frequency components, causing the signal energy to "leak" into adjacent bins. This is why window functions (Hanning, Hamming, etc.) are applied — they taper the signal to zero at the edges, minimizing the discontinuity and reducing leakage, though at the cost of slightly reduced frequency resolution.

9. In a 50 Hz power system, the 3rd harmonic of the transformer hum appears at:

Correct Answer: a) 300 Hz

Explanation: The fundamental hum frequency in a 50 Hz system is 100 Hz (2 × 50 Hz). Harmonics are integer multiples of this fundamental. Therefore, the 3rd harmonic is 3 × 100 Hz = 300 Hz. Note that this corresponds to the 6th electrical harmonic of the line frequency. The 150 Hz option (3rd electrical harmonic) is a common distractor but is typically absent in transformer hum spectra due to the even-symmetric nature of magnetostriction.

10. Which parameter directly improves the frequency resolution of an FFT spectrum?

Correct Answer: c) Increasing the total observation time (more samples at the same rate)

Explanation: FFT frequency resolution is given by Δf = fs/N = 1/T, where T is the total observation time. Resolution improves only by increasing T. Option (a) increases fs and N proportionally, leaving Δf unchanged. Option (b) worsens resolution. Option (d) changes the window shape but not the underlying resolution limit. In radar, longer coherent integration times (equivalent to more samples) directly improve Doppler resolution, enabling finer velocity discrimination.

11. A radar operating at 10 GHz detects a Doppler shift of +2 kHz. Is the target approaching or receding?

Correct Answer: b) Approaching, because the received frequency increased

Explanation: A positive Doppler shift (higher received frequency) indicates the target is moving toward the radar, compressing the wavelength of the reflected wave. Conversely, a negative shift indicates recession. The magnitude can be used to calculate radial velocity: vr = fd·c / (2fc) = (2000)(3×10⁸) / (2×10×10⁹) = 30 m/s. This frequency-domain measurement principle is identical to identifying the dominant peak in the transformer hum spectrum.

12. Why is transformer hum analysis considered a good pedagogical analog for radar signal processing?

Correct Answer: d) Both rely on identical spectral analysis and frequency measurement techniques

Explanation: While transformer hum (≈100 Hz acoustic) and radar echoes (MHz–GHz electromagnetic) differ vastly in physical nature and frequency, the signal processing pipeline is conceptually identical: signal acquisition → amplification → ADC → FFT spectral analysis → peak detection → frequency/velocity estimation. Transformer hum provides a safe, audible, low-frequency platform to master FFT windowing, resolution, leakage, and SNR concepts before applying them to radar Doppler processing, where these same principles determine target detection and tracking performance.