Two practical performance indexes for monitoring the Rhine–Meuse Delta water network via wavelet-based probability density function

• The Rhine–Meuse Delta water network is a large scale system.
• This paper presents control performance assessment for the Rhine–Meuse Delta water network.
• Two new data driven performance indexes are considered for monitoring the system.
• The performance indexes are designed based on wavelet-based probability density function.
• Simulation results show the ability of the suggested methods in detecting the floods.

Large scale systems (LSS) have a large size with several control loops. It is always demanding that all the local controllers of the LSS work in an optimal situation to raise the efficiency of the system. Therefore the performance monitoring of the distributed systems is common to detect any roots of deficiency in the subsystems. This paper presents control performance assessment (CPA) for the Rhine–Meuse Delta water network. The Delta water system consists of many rivers and sea outlets with barriers and sluices. The water network is in the low-lying area surrounded by rivers and the North Sea, and because of its location, the system is at high risk of floods. For the monitoring of the system, two new data driven performance indexes based on wavelet-based probability density function (PDF) are designed for the system to assess the performance of the subsystems and also the whole system. Finally, a comparison with Model predictive control benchmark is made to show the capability of the new benchmark. Simulation results show the effectiveness of the suggested methods in detecting the floods.