Vol 5 No 1 (2026)

Near the rated wind speed, due to the random variations of wind speed, wind direction, and sea conditions, large-scale floating wind turbines encounter coupled interference between torque control and pitch control. It may result in substantial drops or fluctuations in electrical power. We propose a multi-stage adaptive decoupling control strategy to address the issue of electrical power drops near the full-load operation. It dynamically adjusts the closed-loop input error of PI controllers by correlating the state of the wind turbine with its torque/pitch outputs. The simulation results demonstrate that this strategy can enhance operational stability, significantly increase electrical power generation, and reduce fatigue/extreme loads of key components in large-scale floating wind turbines.

This paper presents the application of probabilistic multi-objective optimization method (PMOO) in enterprise production management, which involves the simultaneous optimization of "high long-term profit target" and "small investment amount". PMOO method is an effective approach to deal with multi-objective optimization problems from the viewpoint of system theory and method of probability theory, in which the new concept of "preferable probability" is introduced to formulate the methodology of PMOO. In PMOO, the evaluated attributes (objectives) of candidates are preliminarily divided into two basic types: beneficial attributes and unbeneficial attributes, and the corresponding quantitative evaluation method of partial preferable probability of each type of attribute is established. Furthermore, the total preferable probability of each candidate alternative is the product of partial preferable probabilities of all possible attributes, and the maximum value of the total preferable probability presents the overall optimization of the system. In the enterprise production management problem of three kinds of products, the objective function is to maximize the long-term profit target and minimize the investment amount, the discretization of Hua's "good lattice point" and uniform mixture design are applied to simplify the optimization process and data processing. Finally, a rational result is obtained.

The technique used here emphasizes pivotal quantities and ancillary statistics relevant for obtaining statistical predictive or confidence decisions for anticipated outcomes of applied stochastic models under parametric uncertainty and is applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. It does not require the construction of any tables and is applicable whether the experimental data are complete or Type II censored. The proposed technique is based on a probability transformation and pivotal quantity averaging to solve real-life problems in all areas including engineering, science, industry, automation & robotics, business & finance, medicine and biomedicine. It is conceptually simple and easy to use.