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Incipient Fault Detection Using Waveform Analysis
Zaphiro Technologies SA —Renens, VD
- Niveau Master
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Incipient Fault Detection Using Waveform Analysis

Zaphiro Technologies SA
Renens, VD

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Détails de l'emploi

Stage
Il y a 13 heures

Qualifications

MATLAB
Niveau Master
Esprit d'analyse
Python
Debugging

Description complète du poste

Project type

Internship
MSc thesis

Contact point

[email protected]

Project description and objectives

This project focuses on identifying incipient faults in power systems through detailed waveform analysis. Incipient faults are low magnitude, developing anomalies that may precede permanent faults. Early identification can significantly improve system reliability and reduce maintenance costs. The project aims to investigate state-of-the-art methods waveforms for detecting such conditions, and propose an improved detection technique, and validate its effectiveness through simulations.

Tasks

Semester Project:

Perform a literature survey on waveform-based incipient fault detection methods in both transmission and distribution systems.
Identify key challenges and gaps in existing techniques.
Develop an algorithm to detect and characterize incipient faults

Thesis will also include:

Implement the algorithm in MATLAB or Python.
Validate the proposed method through simulation studies and performance assessment.
Validate the developed method through case studies in Power Factory and evaluate the performance.

Required skills

Background in electrical or power systems engineering.
Knowledge of power system protection and fault analysis.
Experience with MATLAB or Python,
Experience with DIgSilent Power Factory is a bonus.
Strong analytical, problem-solving, and debugging skills.
Ability to understand technical literature and produce clear technical reports.

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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Incipient Fault Detection Using Waveform Analysis

Senior Scientist II, Data Science (CompBio for Translation and Biomarkers in Cardio-Renal-Metabolics)

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