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A comprehensive Python tool for analyzing TSOC power system operational data from Excel files

Project description

TSOC Data Analysis

Author: Sustainable Power Systems Lab (SPSL), https://sps-lab.org, contact: info@sps-lab.org

A comprehensive Python tool for analyzing TSOC power system operational data from Excel files. Provides a powerful command-line interface (CLI) and modular Python API for load analysis, generator categorization, wind power analysis, reactive power calculations, and representative operating point extraction.

License Python Documentation

📖 Full Documentation

For complete installation instructions, detailed usage examples, configuration options, and troubleshooting, visit:

https://tsoc-data-analysis.sps-lab.org/

Quick Installation

pip install tsoc-data-analysis

Quick Start

Python API

from tsoc_data_analysis import execute, extract_representative_ops

# Load and analyze data
success, df = execute(month='2024-01', data_dir='raw_data', output_dir='results')
if success:
    # Extract representative points
    rep_df, diagnostics = extract_representative_ops(
        df, max_power=450, MAPGL=200, output_dir='results'
    )

Key Features

  • Month-based data filtering for efficient processing
  • Load calculations (Total Load, Net Load) with statistics
  • Wind power analysis with generation profiles
  • Generator categorization (Voltage Control vs PQ Control)
  • Reactive power analysis with comprehensive calculations
  • Data validation with advanced gap filling and anomaly detection
  • Representative operating points extraction using K-means clustering
  • Comprehensive logging and error handling

Requirements

  • Python 3.7+
  • pandas>=1.3.0, numpy>=1.20.0, matplotlib>=3.3.0, seaborn>=0.11.0
  • openpyxl>=3.0.0, scikit-learn>=1.0.0, scipy>=1.7.0
  • psutil>=5.8.0, joblib>=1.1.0

Documentation Sections

Support

For detailed information, examples, and troubleshooting, please visit the full documentation.

License

Licensed under the Apache License 2.0. See LICENSE for details.

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