⚡ Transformer Performance Analysis

Python-Based Analysis of Transformer Parameters, Efficiency, Losses, and Voltage Regulation

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📖 Project Overview

This project presents a comprehensive transformer performance analysis using Python. The analysis focuses on extracting equivalent circuit parameters from Open Circuit Test and Short Circuit Test data, evaluating transformer efficiency, calculating voltage regulation, and analyzing power losses under varying load conditions.

The project demonstrates both electrical engineering fundamentals and engineering computation skills through numerical modeling, visualization, and technical reporting.

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🎯 Objectives

• Determine transformer equivalent circuit parameters.
• Analyze core and copper losses.
• Calculate transformer efficiency under different loading conditions.
• Evaluate voltage regulation for various power factors.
• Visualize transformer performance characteristics.
• Create a reusable Python-based engineering analysis workflow.

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⚙️ Engineering Topics Covered

1. Open Circuit Test Analysis

Determination of shunt branch parameters:

• Core Loss (Pcore)
• Core Loss Resistance (Rc)
• Magnetizing Reactance (Xm)
• Magnetizing Current (Im)

Equations

Rc = Voc² / Poc

Ic = Poc / Voc

Im = √(Ioc² - Ic²)

Xm = Voc / Im

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2. Short Circuit Test Analysis

Determination of series branch parameters:

• Equivalent Resistance (Req)
• Equivalent Reactance (Xeq)
• Copper Loss (Pcu)

Equations

Zeq = Vsc / Isc

Req = Psc / Isc²

Xeq = √(Zeq² - Req²)

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3. Voltage Regulation Analysis

Voltage Regulation (%) =
((Vnl - Vfl) / Vfl) × 100

Analysis is performed under:

• 0.8 Lagging Power Factor
• 1.0 Unity Power Factor
• 0.8 Leading Power Factor

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4. Efficiency Analysis

η =
Output Power /
(Output Power + Core Loss + Copper Loss)

Evaluated at:

• 25% Load
• 50% Load
• 75% Load
• 100% Load
• 125% Load

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5. Loss Analysis

• Core Loss
• Copper Loss
• Total Loss

The relationship between transformer loading and losses is investigated through graphical analysis.

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📂 Project Structure

transformer-performance-analysis/
│
├── README.md
├── requirements.txt
├── .gitignore
│
├── data/
│   ├── raw/
│   │   └── transformer_test_data.csv
│   │
│   └── processed/
│       └── calculated_results.csv
│
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_transformer_calculations.ipynb
│   └── 03_performance_analysis.ipynb
│
├── src/
│   ├── transformer_parameters.py
│   ├── efficiency_analysis.py
│   ├── voltage_regulation.py
│   ├── load_analysis.py
│   └── visualization.py
│
├── results/
│   ├── figures/
│   │   ├── efficiency_curve.png
│   │   ├── voltage_regulation_curve.png
│   │   ├── load_vs_losses.png
│   │   ├── efficiency_heatmap.png
│   │   └── transformer_equivalent_circuit.png
│   │
│   └── reports/
│       └── transformer_performance_report.pdf
│
├── docs/
│   ├── methodology.md
│   ├── transformer_theory.md
│   └── project_summary.md
│
└── tests/
    ├── test_efficiency.py
    ├── test_voltage_regulation.py
    └── test_transformer_parameters.py

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📊 Sample Dataset

Rated_kVA,100
Rated_Voltage_HV,11000
Rated_Voltage_LV,415

OC_Voltage,415
OC_Current,3.2
OC_Power,620

SC_Voltage,450
SC_Current,9.1
SC_Power,980

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🖥️ Python Modules

transformer_parameters.py

calculate_rc()
calculate_xm()
calculate_req()
calculate_xeq()

efficiency_analysis.py

calculate_efficiency()
maximum_efficiency()

voltage_regulation.py

calculate_voltage_regulation()

load_analysis.py

calculate_losses()
load_profile_analysis()

visualization.py

plot_efficiency_curve()
plot_voltage_regulation()
plot_losses()
plot_heatmap()

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📈 Expected Outputs

Efficiency Curve

Shows transformer efficiency variation across loading levels.

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Voltage Regulation Curve

Illustrates voltage regulation under different power factor conditions.

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Load vs Losses

Compares core losses, copper losses, and total losses.

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Efficiency Heatmap

Visual representation of efficiency as a function of load factor and power factor.

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Equivalent Circuit

Transformer equivalent circuit derived from OC and SC test results.

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🔬 Methodology

Transformer Test Data
            │
            ▼
Open Circuit Analysis
            │
            ▼
Short Circuit Analysis
            │
            ▼
Equivalent Circuit Parameters
            │
            ▼
Loss Calculation
            │
            ▼
Voltage Regulation
            │
            ▼
Efficiency Analysis
            │
            ▼
Visualization & Reporting

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📌 Key Findings

• Transformer efficiency reaches its maximum near the optimal load point.
• Copper losses increase proportionally to the square of load current.
• Voltage regulation is highly dependent on power factor.
• Core losses remain approximately constant regardless of load level.
• Performance visualization improves engineering decision-making and operational assessment.

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🛠 Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Jupyter Notebook
• Electrical Power Systems Analysis

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🚀 How to Run

git clone https://github.com/yourusername/transformer-performance-analysis.git

cd transformer-performance-analysis

pip install -r requirements.txt

python src/transformer_parameters.py

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👨‍💻 Author

Electrical Engineering Portfolio Project demonstrating:

• Power Systems Engineering
• Transformer Analysis
• Engineering Computation
• Python Programming
• Technical Documentation

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T## Results

Efficiency Curve

Transformer efficiency under varying load conditions.

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Voltage Regulation Curve

Voltage regulation performance across different loading levels.

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Transformer Loss Analysis

Comparison between core losses and copper losses.

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Transformer Parameter Heatmap

Visual representation of transformer parameter distribution.ransformer Performance Analysis | Electrical Engineering + Python Portfolio Project