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Oscillation detection, characterization and source localization
Zaphiro Technologies SA —Renens, VD
- Analyse de données
- Niveau Master
- Esprit d'analyse
8 h
Incipient Fault Detection Using Waveform Analysis
Zaphiro Technologies SA —Renens, VD
- Niveau Master
- Esprit d'analyse
- Stage
8 h
Senior Software Engineer - Data Science Platform
Compagnie Financière Tradition —Lausanne, VD
- Télétravail
- Niveau Master
- JavaScript
- Linux
- Travail à domicile
- Congé parental payé
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Oscillation detection, characterization and source localization

Zaphiro Technologies SA
Renens, VD

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Détails de l'emploi

Stage
Il y a 8 heures

Qualifications

MATLAB
Analyse de données
Niveau Master
Esprit d'analyse
Python

Description complète du poste

Project type

Internship
MSc thesis

Contact point

[email protected]

Project description and objectives

Power system oscillations—whether caused by faults, control interactions, or generator dynamics—can threaten grid stability and reliability. Phasor Measurement Units (PMUs) provide high-resolution, time-synchronized measurements that enable real-time monitoring and analysis of such oscillations.

This project aims to develop a data-driven framework using PMU data to detect, characterize, and identify the source of oscillations in a power system. The project will explore algorithms for oscillation detection (e.g., Prony analysis, spectral methods, machine learning), estimate modal characteristics (frequency, damping, mode shape), and propose approaches to pinpoint the most likely source or contributing element of the oscillation.

Objectives:

Develop algorithms for real-time oscillation detection using PMU data.
Characterize oscillation modes in terms of frequency, damping ratio, and energy contribution.
Implement source identification techniques to locate the origin of oscillations.
Evaluate performance using synthetic or real-world PMU datasets.

Tasks

Literature Review: Study existing methods for oscillation detection and modal analysis using PMUs. Review signal processing and machine learning approaches relevant to the problem.
Data Acquisition and Preprocessing: Obtain PMU data (from open datasets or simulations such as IEEE test systems). Preprocess data for noise filtering and synchronization.
Oscillation Detection: Implement algorithms to detect sustained or growing oscillations (e.g., FFT, Prony, or AR-based methods). Compare detection accuracy and computational performance.
Oscillation Characterization: Extract modal parameters such as frequency, damping ratio, and mode shape. Visualize time-domain and frequency-domain characteristics.
Source Identification: Apply energy-based or participation factor-based methods to locate oscillation sources. Validate using test scenarios or benchmark systems.
Evaluation and Reporting: Analyze results and compare algorithm performance. Prepare a final report and presentation summarizing findings, challenges, and recommendations.

This project, could be developed in full or partly during a single student project.

Required skills

Strong background in power systems and power system dynamics.
Familiarity with phasor measurement units (PMUs) and synchrophasor data.
Knowledge of signal processing (e.g., FFT, Prony, wavelet, spectral analysis).
Programming proficiency in MATLAB, Python, or similar environments.
Understanding of machine learning techniques (optional but beneficial).
Ability to perform data analysis and visualization.

Other benefits and/or compensation

Depending on the final project type, scope and deliverables, Zaphiro may consider providing additional adequate compensation.

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Oscillation detection, characterization and source localization

Incipient Fault Detection Using Waveform Analysis

Senior Software Engineer - Data Science Platform

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