Abstract
Advanced control system design for large wind turbines is becoming increasingly complex, and high-level optimization techniques are receiving particular attention as an instrument to fulfil this significant degree of design requirements. Multiobjective optimal (MOO) control, in particular, is today a popular methodology for achieving a control system that conciliates multiple design objectives that may typically be incompatible. Multiobjective optimization was a matter of theoretical study for a long time, particularly in the areas of game theory and operations research. Nevertheless, the discipline experienced remarkable progress and multiple advances over the last two decades. Thus, many high-complexity optimization algorithms are currently accessible to address current control problems in systems engineering. On the other hand, utilizing such methods is not straightforward and requires a long period of trying and searching for, among other aspects, start parameters, adequate objective functions, and the best optimization algorithm for the problem. Hence, the primary intention of this work is to investigate old and new MOO methods from the application perspective for the purpose of control system design, offering practical experience, some open topics, and design hints. A very challenging problem in the system engineering application of power systems is to dominate the dynamic behavior of very large wind turbines. For this reason, it is used as a numeric case study to complete the presentation of the paper.
Highlights
Control engineering is obligated to evolve in line with world progress, where the mastery of engineering systems is becoming more and more difficult
Experience shows that earlier information about the control problem, the tuning parameters, the numerical behavior of the optimization approach, as well as the objective functions supplemented by much working time is essential before a Multiobjective optimal (MOO) optimizer yields satisfactory outcomes
The population is renewed by the action of several genetic operators, which are known as recombination, mutation and selection
Summary
Control engineering is obligated to evolve in line with world progress, where the mastery of engineering systems is becoming more and more difficult. Experience shows that earlier information about the control problem, the tuning parameters, the numerical behavior of the optimization approach, as well as the objective functions supplemented by much working time is essential before a MOO optimizer yields satisfactory outcomes This point can be especially frustrating if the sought-after Pareto front is unknown a priori. While the research in the field of MOO is focused on the obtention of new algorithms, whose aim is to find more complex Pareto frontiers or more accurate solutions by utilizing specially constructed test objective functions, MOO users, for example, control engineers, work with realistic cost functions In such circumstances, the forms of Pareto fronts are unknown in advance, and adjusting and tuning of optimization methods is not straightforward.
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