PhD on AI-driven state estimation and prediction
1 week geleden
Job Description
The project focuses on synthetical data generation, AI-driven state estimation, stochastic modelling and reliability assessment, and grid-edge optimal solutions. These models will be combined with the AI-driven state estimations to enhance network observability and grid monitoring. Additionally, integration with the stochastic modelling and reliability assessment process will provide valuable insights into the impact of uncertainties on grid reliability. Finally, in conjunction with the developed edge intelligence, these advancements will enable optimal solutions for the electricity grids in the Netherlands and e.g. the rest of Europe, while maintaining user privacy.
The research results will be immediately utilized by Alliander for congestion estimation and flexibility procurement. To achieve this, it is part of this project that all the developed (AI-driven) models and algorithms are also implemented in production-ready open-source packages.
On of the four main research tracks (RTs) of AISO is as follows:
RT2: AI-driven state estimation and prediction
This research aims to combine physics-based models, i.e. state estimation based on WLS, with physics-aware neural network structures to improve network observability and grid monitoring capability. Besides a normal estimation of system state, it should be also possible to determine anomalous events using AI-driven techniques. More specifically, the research will include:
Development of semi real-time measurement solutions Uncertainty modeling of (synthetic) LV load/generation profiles Increasing network observability with physics-aware neural network algorithms Anomaly detection and mitigation solutions against anomalous attacks Integrate and validate solutions in the virtual grid environment.See for the other 3 reseach tracks below:
PhD1 / RT1: Synthetical data generation using multivariate models.
PhD3 / RT3: Stochastic modelling and reliability assessment.
PhD4 / RT4: Grid-edge optimal solutions.
Job requirements
A MSc degree related to modeling and analysis of power systems and distributed energy resources. Having a good understanding of distribution grid planning and operation. Experience in data-driven modeling, probabilities, stochastic optimization solutions is an advantage. Skills in scientific programming and/or numerical computing in languages like Python, Julia, or MATLAB are advantages. Excellent modeling skills in simulation tools such as PowerFactory or other equivalent open-source power system packages. Having experience in connecting such simulation tools with the programming environment of MATLAB and/or Python is an advantage. Enthusiasm in open-source and motivated to learn basic skills of scientific software engineering. Ability to work in an interdisciplinary team and interested in collaborating with industrial partners. Motivated to develop your teaching skills and coach MSc and BSc students. Fluent in spoken and written English (C1 level). Dutch language skill is an advantage.Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks. Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539). A year-end bonus of 8.3% and annual vacation pay of 8%. High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process. An excellent technical infrastructure, on-campus children's day care and sports facilities. An allowance for commuting, working from home and internet costs. A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.-
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