Grid Sensing Internship Engineer

Context:
For over 120 years, Nexans has been at the forefront of energy solutions, providing advanced cabling systems, innovative services, and customer-driven solutions. The Group’s strategy focuses on continuous innovation across four main business areas: Building & Territories, High Voltage & Projects, Telecom & Data, and Industry & Solutions. Nexans is committed to enhancing the safety and resilience of power networks, adopting environmentally friendly processes and focusing on electricity security.

Grid Sensing is a Nexans initiative aimed at developing innovative hardware and software solutions for real-time monitoring of medium voltage (MV) networks. The system focuses on online detection, classification, and localization of PDs in underground meshed networks, enabling predictive maintenance and minimizing supply interruptions. However, several challenges remain, such as synchronization, noise interference, and sensor accuracy, which affect the performance of these systems.

Objective:
As part of the Grid Sensing project, the objective of this internship is to support the development and application of advanced fault analysis algorithms, combining fault detection, classification, and location in meshed Medium Voltage (MV) networks. This mission aligns with Grid Sensing’s goal of creating an innovative, real-time monitoring solution for underground cables, utilizing partial discharge (PD) data to improve network resilience.

The intern will engage in the following tasks for 6 months:

Algorithm Development: Develop and refine algorithms for fault detection, classification, and localization based on PD signals.
Data Analysis and Synchronization: The intern will explore and implement solutions for time-synchronization in data acquisition systems, focusing on GPS-based time-stamping and sensor placement optimization. These techniques are essential to ensure the precise capture and analysis of PD events.
Experimental Testing and Validation: The intern will participate in field tests and experimental setups to validate the performance of the developed algorithms.
Integration of AI Methods: The internship will include work on incorporating AI-driven models for the classification of PDs and fault detection. The intern will work on adapting machine learning techniques to further enhance the accuracy and reliability of PD event classification.

By contributing to this project, the intern will gain hands-on experience with cutting-edge technology in power system monitoring and AI-driven data analysis, while playing a pivotal role in shaping a solution designed to revolutionize fault management in MV networks.

Required Skills:

Strong knowledge of distribution grids and fault analysis in electrical networks.
Proficiency in Python for algorithm development, with knowledge of Matlab being an asset.
Experience with signal processing techniques and machine learning for fault detection and classification.
Familiarity with power system modeling tools like Pandapower or PowerFactory is preferred.
Strong understanding of data acquisition systems, particularly GPS-based synchronization.
Analytical mindset with the ability to conduct field tests and analyze real-world data to optimize fault location methodologies.

Ready to join the adventure?