Over time, power network equipment can face defects and has to be maintained to ensure transmission network reliability. Once an equipment is planned to be withdrawn from the network, it becomes unavailable and can lead to power outages when other adjacent equipment fails. This problem is commonly referred to as transmission maintenance scheduling problem (TMS) and remains a challenge for power utilities. Numerous combinatorial constraints must be satisfied to ensure the stability and the reliability of the transmission network. While most of these constraints can be naturally formalized in constraint programming (CP), there are, however, some complex constraints like transit-power limits that are challenging to model because of their continuous and non-linear nature. This paper proposes a methodology based on active constraint acquisition to automatically formalize these constraints. The acquisition is carried out using a simulator developed by Hydro-Québec (HQ) power utility to compute the power-flow of their transmission network. The acquired constraints are then integrated into a CP model to solve the HQ network's TMS problem. Our experimental results show the relevance of the methodology to learn transit-power constraints in an automated way. It allows HQ to automatically schedule a maintenance plan for an instance that remained intractable until now. To our knowledge, it is the first time that active constraint acquisition is successfully used for the TMS problem.