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This paper presents an investigation on the use of the Simulated Annealing (SA) algorithm to find the best tuning of a PI speed regulator for a speed control DC motor drive. Two control loops will be considered, an inner one for the armature current control and the outer one for the speed. The gains of the PI current regulator will be kept constant whereas the SA will be used to tune the speed regulator. The integral of the absolute of the speed error will be used as the evaluating function. Then, the faster the speed response reaches the speed reference for a load condition, the better the tuning. The range of the proportional and the integral gains will be limited such as the armature voltage and current does not exceed the rated value, keeping the system linear. Because of this, the best tuning of the speed controller can be easily predicted, and the SA algorithm will be put to the test. Two important SA parameters will be changed, what determines how fast the algorithm can converge towards the best solution. Simulation results will be presented, showing how accurate and fast SA can be to find the best tuning for the PI speed regulator.

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Finding optimum balances between conflicting interests in multipurpose reservoirs often represents an important challenge for decision makers. This study assesses the use of different computational tools to obtain optimal reservoir operations applied to the Hatillo dam in the Dominican Republic. A multiobjective optimization approach is used, in which non-dominated sorting genetic algorithm II (NSGAII) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) optimizers are applied to models that simulate reservoir operations. Three different Machine Learning (ML) models, namely, the multilayer perceptron (MLP), the radial basis network (RBN) and the linear function (LF), are employed to learn the current operation of the system. Subsequently, a general model is proposed to simulate daily reservoir operations (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019), integrating water balances, physical constraints of the dam components and the ML models, the latter defining daily controlled discharges. In the optimization process, the ML parameters are the decision variables, while the objectives evaluated are irrigation, hydropower generation and flood control. The results are compared with the actual operation of the reservoir. Three dimensional Pareto fronts are obtained, from which, the wide variety of operations can be evidenced. The flood control objective was found to have a wide room for improvement over the current operation of the reservoir, and several of the solutions found improve the current operation for the three proposed objectives. The MLP models tend to generate the best results for this case study and the NSGAII optimizer generates the best optimization results.

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