Multiobjective Optimal Control of Wind Turbines: A Survey on Methods and Recommendations for the Implementation

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.

Active Edgewise Blade Damping Control of Large Wind Turbines by Using the Pitch Controller and an Interval Observer

“…For the current design, the CPC is chosen as one player, while the second player is a team constituted by all active damping controllers. The game is solved by using parametric multi-objective Pareto optimisation in order to tune all controllers together and a decision maker (see [47] for a survey) to choose one solution from the Pareto front. As payoffs for the game, the performance indices…”

“…However, for the variable parameters, finite intervals of variation can be set, and therefore it is an interval model leading to an interval observer. The model development process follows the abstraction process described in Figure 4 (see [47,73]).…”

“…However, for the variable parameters, finite intervals of variation can be set, and therefore it is an interval model leading to an interval observer. The model developmen process follows the abstraction process described in Figure 4 (see [47,73]). Interval parameters Jb_root, Db, Kb, Bxl and Bxh depend on inputs and steady-state values which also depend on the wind speed within a determined range.…”

Multi-Objective Cooperative Control for a Ship-Towing System in Congested Water Traffic Environments

“…Pareto-based methods [33]. The first methods usually consider an overarching objective function to express all the objective components.…”

Characterization of a low power wind turbine prototype for interconnection to the grid

“…For large wind turbines it is increasingly complex, currently high-level optimization techniques are imposed to satisfy the design requirements. Within these techniques, multi-object optimal control, in particular, is a widely used methodology to achieve a control system that reconciles multiple design objectives that may normally be incompatible according to [8].…”