Research team
System-level fault detection and diagnosis from substation measurements in district heating systems.
Abstract
District heating systems are critical infrastructures for thermal decarbonization, yet their performance depends on coordinated thermo-hydraulic behavior across primary networks, substations, and secondary building systems. In practice, monitoring relies on measurements including supply temperature, return temperature, and mass flow. From these measurements, operators must detect abnormal behavior and identify its root cause across a coupled thermo-hydraulic system. Fault propagation is regime- and control-dependent. Variations in load, hydraulic conditions, and closed-loop control reshape how faults propagate and appear at the substation interface. Under certain operating conditions, faults may remain undetectable, while distinct mechanisms may generate indistinguishable measurement signatures. Fault detection and diagnosis is therefore constrained by detectability and distinguishability limits rather than by algorithmic performance alone. This PhD proposal will address these limits through a system-level, physics-consistent investigation. A validated thermo-hydraulic simulation environment will be used to emulate parametric faults under controlled variation of operating regimes and control configurations, while restricting observation to standard interface measurements. Detectability and diagnostic ambiguity will be quantified and embedded into a regime-aware fault detection and diagnosis framework.Researcher(s)
- Promoter: Verhaert Ivan
- Co-promoter: Jacobs Stef
- Fellow: Kamali Hoda
Research team(s)
Funding
- FWO
- FWO
Project type(s)
- Research Project
Optimal prosumer-based district heating and cooling using reinforcement learning agents.
Abstract
District Heating and Cooling (DHC) is a promising technology to shift to a sustainable energy supply, offering flexibility to the electric grid. Thermal storage can provide the necessary flexibility to balance production an demand for both electrical and thermal renewable energy sources (RES). Especially, the integration of decentralised thermal prosumers (e.g. boosters, thermal solar panel) in DHC have great potentials to improve the overall efficiency. Therefore, future DHC will need advanced control strategies facilitating the operation of prosumers-based DHC and providing flexibility to RES-dominated electric grids. Hereby, two main questions arise: (i) how should the temperature be controlled to improve the energetic, ecologic and economic performance of a DHC? And (ii) how to take into account the requirements of every direct stakeholder in the DHC? By simulating the DHC's behaviour, considering hydronics and prosumer behaviour, I will research the potential of a data-based control strategy, including multi-agent reinforcement learning (MARL). Every agent (per consumer, heat storage, etc.) pursues the local as well as the global objectives. The RL-agents are capable of self-learning a control strategy based on feedback (rewards). Besides valorisation throughout implementing such controls, the feedback and/or reward itself can be subject of follow-up research with respect to policy support.Researcher(s)
- Promoter: Verhaert Ivan
- Co-promoter: Hellinckx Peter
- Fellow: Jacobs Stef
Research team(s)
Funding
- FWO
- FWO
Project type(s)
- Research Project