Assessing Flood Risk Using Reservoir Flood Control Rules

The application of conventional flood operation regulation is restricted due to insufficient description of flood control rules for the Pubugou Reservoir in southern China. Based on the requirements of different flood control objects, this paper proposes to optimize flood control rules with punishment mechanism by defining different parameters of flood control rules in response to flood inflow forecast and reservoir water level. A genetic algorithm is adopted for solving parameter optimization problem. The failure risk and overflow volume of the downstream insufficient flood control capacity are assessed through the reservoir operation policies. The results show that an optimised regulation can provide better performance than the current flood control rules.

Adaptive PID Control for Hydraulic Turbine Regulation Systems Based on INGWO and BPNN

To ensure system stability,the fixed-PID(F-PID)controller with small parameters is usually adopted in hydropower stations.This involves a slow setting speed and it is difficult to realize optimal control for full working conditions.To address the problem,this paper designs a variable-PID(V-PID)controller for a hydraulic turbine regulation system(HTRS)based on the improved grey wolf optimizer(INGWO)and back propagation neural networks(BPNN).These can achieve excellent regulation under full working conditions.First,the nonlinear HTRS model containing the nonlinear hydroturbine model is constructed and the stable domain is obtained using Hopf bifurcation theory to determine the available range of PID parameters.The optimal PID parameters in typical working conditions are then calculated by the INGWO,and the optimal PID parameters are generalized through training the V-PID neural networks which take the optimal PID parameters as sample data.The V-PID neural networks with different structures are compared to determine the optimal structure of the variable-PID controller model.The V-PID controller-based nonlinear HTRS model shows that the PID parameters can be automatically adjusted online according to the working condition changes,realizing optimal control of hydropower units in full working conditions.

Optimal Nonlinear Robust Sliding Mode Control of an Excitation System Based on Mixed Linear H_(2)/H_(∞) Matrix Inequalities

In this paper,an optimal nonlinear robust sliding mode control(ONRSMC)based on mixed H_(2)/H_(∞)linear matrix inequalities(LMIs)is designed for the excitation system in a“one machine-infinite bus system”(OMIBS)to enhance system stability.Initially,the direct feedback linearization method is used to establish a mathematical model of the OMIBS incorporating uncertainties.ONRSMC is then designed for this model,employing the mixed H_(2)/H_(∞)LMIs.The chaos mapping-based adaptive salp swarm algorithm(CASSA)is introduced to fully optimize the parameters of the sliding mode control,ensuring optimal performance under a specified condition.CASSA demonstrates rapid convergence and reduced like-lihood of falling into local optima during optimization.Finally,ONRSMC is obtained through inverse transformation,exhibiting the advantages of simple structure,high reliability,and independence from the accuracy of system models.Four simulation scenarios are employed to validate the effectiveness and robustness of ONRSMC,including mechanical power variation,generator three-phase short circuit,transmission line short circuit,and generator parameter uncertainty.The results indicate that ONRSMC achieves optimal dynamic performance in various operating conditions,facilitating the stable operation of power systems following faults.