How PTI uses a python optimization model for Phasor Measurement Unit (PMU) selection in electrical networks

Python is currently one of the most powerful programming languages leveraged. Python was also chosen because ETAP has a strong interface with it through the ETAP DataHub and Python server.
By Mr. Rafael Franco Manrique, Technology Innovation Specialist, PTI SA

This case study presents the development of an optimization algorithm in Python to determine the minimum number and optimal locations of Phasor Measurement Units (PMUs) required for full system observability in electrical networks.

PTI S.A., founded in Cali in 2000, is a Latin American engineering company specialized in smart‑grid transformation, power automation and control, and digital‑twin‑based validation. With 24+ years of experience and operations in Colombia (Cali, Bogotá, Barranquilla, Medellín), Peru (Lima) and Chile (Santiago), we deliver end‑to‑end solutions, from design and studies to implementation and commissioning, grounded in a strong culture of innovation, sustainability, and operational excellence.
PTI is the exclusive representative and integration partner for ETAP Software in Colombia & Panama, and an OPAL‑RT real‑time simulation partner. Our Digital Twin Lab enables predictive validation using SIL/HIL methods to de‑risk projects, accelerate deployment, and ensure regulatory compliance for utilities, renewable developers, and industrial customers across the region.

Location: Cali, Valle del Cauca, Colombia

Year: 2023

Optimizing Phasor Measurement Unit placement and number in the electrical network

Challenges

  • Establish a robust mathematical formulation for PMU placement and full observability.
  • Define a selection algorithm capable of identifying the minimum number of PMUs required.
  • Model the test system using ETAP and integrate it with ETAP-Py for automated calculations.
  • Capture and analyse phasor measurements (voltage and current) at high sampling rates.
  • Generate a Digital Twin of the network and simulate observability conditions under different scenarios.
  • Provide professional, decision-ready reports to utilities and system operators.

Which solutions did they choose?

Selected applications

PTI combined ETAP’s modelling environment with ETAP-Py, ETAP’s Python integration interface, to build a Digital Twin of the electrical network, perform observability analysis, and implement a Python-based optimization routine. The result is a fast, accurate, and low-cost methodology for PMU placement that enhances system monitoring capabilities without requiring additional physical instrumentation. PTI relied on several ETAP tools and modules:

  • ETAP-Py™: Python integration for automated power-flow calculation, matrix formulation, and optimization routines.
  • ETAP Digital Twin: Full electrical network representation supporting PMU placement analysis.
  • ETAP SCADA & Visualization: Displaying real-time results, PMU locations, and system observability.
These tools allowed PTI to execute the complete workflow directly within ETAP.

Why do they use ETAP?

Main customer benefits

ETAP provides a strong and stable interface with Python via the ETAP DataHub™ and Python Server, enabling automated calculations, custom algorithms, and seamless data exchange. The combination of ETAP modelling and Python scripting provides a flexible simulation and optimization environment for advanced research and utility applications.

1. Digital Twin with PMU Behaviour Integrated

Engineers extended the ETAP network model by adding PMU behaviour directly through Python - without requiring physical hardware.

2. Zero-Cost Implementation of the Optimization Algorithm

The methodology relies entirely on Python and ETAP-Py, enabling new simulations without any additional investment.

3. Reduced Computational Effort

The optimized algorithm identifies PMU locations quickly and efficiently within the ETAP environment, reducing modelling time significantly.

4. Improved Accuracy and Error-Free PMU Placement

The Python routine calculates optimal PMU locations without errors introduced by measurement equipment or manual modelling.

5. High-Quality Analytics and Reporting

The methodology provides comprehensive visualizations of PMU placement, observability matrices, and system coverage.

6. Scenario Replication and Expansion

Engineers can duplicate and adjust multiple observability scenarios to evaluate different network configurations.

What do they think about ETAP?

Customer perspectives

Python is currently one of the most powerful programming languages leveraged. Python was also chosen because ETAP has a strong interface with it through the ETAP DataHub and Python server.
— Mr. Rafael Franco Manrique, PTI S.A.

The purpose of the proposed methodology is to integrate this optimization algorithm with ETAP-Py to minimize the number of PMUs, optimize their location, and enhance electrical system observability. With this tool, network operators can analyze and monitor the electrical power system.
— Mr. Rafael Franco Manrique, PTI S.A.



Videos

Development of a Python Optimization Model for the Analysis and Planning of PMU Locations in Electrical Networks​

The proposed methodology aims to seamlessly integrate an optimization algorithm with etapPY, minimizing the number of PMUs required, strategically placing them within the electrical network, and enhancing overall system observability to empower network operators with advanced analytical tools for comprehensive monitoring and analysis.


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