Electrical and Electronic Engineering - Theses
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ItemSystem identification and control of rivers( 2016)Water resource management has immense importance in the modern world. A large amount of water is wasted due to inefficient management of rivers, lakes and other water bodies associated with them. In order to achieve improved management of water resources, control and decision support systems can be employed. To design control systems for a river, a river model is required. Traditionally, the Saint Venant equations have been used for modelling purposes. The equations describe river flows accurately, however, they are complex, non-linear and require many unknown parameters. It is therefore difficult to use them for control design purposes. On the other hand, data-based models have proven to be very useful in control design for rivers. In this thesis, different data-based modelling methods are explored, and they are applied to the data from the upper part of Murray River in Australia. For each method, the thesis analyses the ease with which available prior knowledge can be incorporated in the modelling procedure and the ability of the obtained models to describe the river well. For efficient river control, forecasts of future water demands and flows in the unregulated tributaries are required to be taken into account. A Stochastic Model Predictive Control (S-MPC) or a randomised version of it can not only accommodate such forecasts, but it can also handle physical and environmental constraints well. However, due to uncertainties in the forecasts, the feasibility of optimisation problems cannot always be guaranteed in the presence of constraints. This thesis proposes an S-MPC based river control schemes, that not only incorporate the forecasts, but also ensure feasibility of the optimisation problems. The schemes are successfully applied in simulations to the past data from the upper part of Murray River. Another important aspect of river management is to mitigate flood risks. An ideal strategy is to reduce the risk of severe floods, and at the same time not being overly cautious while performing normal river operations. This thesis uses Value-at-Risk (VaR) as a risk measure and incorporates it into the river control problem, forming a Multiple Chance-Constrained optimisation Problem (M-CCP), to be solved in an S-MPC setup. A computationally tractable Optimisation and Testing algorithm is developed to find solutions to M-CCPs, with probabilistic guarantees on the solution. The algorithm is successfully applied to the historical data from the upper part of Murray River. The simulation results show better regulation and flood avoidance.