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The protection of sensitive loads connected to power distribution grids from the existing disturbances has become an important issue in recent years. This paper aims to…
The protection of sensitive loads connected to power distribution grids from the existing disturbances has become an important issue in recent years. This paper aims to evaluate the advantages of a new control strategy, known as the generalized proportional‐integral (GPI) control, to compensate voltage sags when using dynamic voltage restorers (DVR).
The DVR application and the principles of the GPI control method are first introduced. In addition, a procedure to adjust the controller for the DVR application is described. Finally, the performance of the controller is extensively tested using the PSCAD/EMTDC simulation software for a variety of conditions including: balanced and imbalanced voltage sags, frequency deviations and parameter variations.
The GPI controller provides an excellent tradeoff between accuracy, response time and robustness.
The GPI controller is presented here as a new approach to compensate balanced and imbalanced voltage sags using a DVR. The results obtained with the proposed control system and the described methodology to adjust the control parameters make it a very suitable solution for this application. It is important to note that fast tracking and high accuracy are achieved as illustrated in the control responses. Furthermore, the analysis of the robustness against parameter variations and frequency deviations demonstrates one of the most remarkable advantages of the new control method.
This paper aims to propose a reliable local search algorithm having steepest descent pivot rule for computationally expensive optimization problems. In particular, an…
This paper aims to propose a reliable local search algorithm having steepest descent pivot rule for computationally expensive optimization problems. In particular, an application to the design of Permanent Magnet Synchronous Motor (PMSM) drives is shown.
A surrogate assisted Hooke‐Jeeves algorithm (SAHJA) is proposed. The SAHJA is a local search algorithm with the structure of the Hooke‐Jeeves algorithm, which employs a local surrogate model dynamically constructed during the exploratory move at each step of the optimization process.
Several numerical experiments have been designed. These experiments are carried out both on the simulation model (off‐line) and at the actual plant (on‐line). Moreover, the off‐line experiments have been considered in non‐noisy and noisy cases. The numerical results show that use of the SAHJA leads to a saving in terms of computational cost without requiring any extra hardware components.
The surrogate approach in the design of electric drives is novel. In addition, implementation of the proposed surrogate model allows the algorithm not only to reduce computational cost but also to filter noise caused by the sensors and measurement devices.
This chapter is designed with the aim to determine the influence of sociodemographic variables on the capacity to generate social enterprises, such as sex, the student’s…
This chapter is designed with the aim to determine the influence of sociodemographic variables on the capacity to generate social enterprises, such as sex, the student’s country, if only they study or if they study and work, as well as if they participate or direct a social enterprise in university students of Latin American business schools. This research adopted an inductive quantitative approach using a questionnaire. The participants were university students of business schools from Colombia, Mexico and Peru. Second-generation structural equation method (SEM-PLS) was used to analyse the results, using the SmartPLS 3.2.7 software applied to data on 3,739 university students. The results suggest that the entrepreneur role, labour situation, country and sex have a moderating effect in the relation between entrepreneurial orientation and entrepreneurial intention. Also, by using resampling technique Bootstrapping (5,000 times,p < 0.01), significance of the trajectory coefficients (beta) and effect size of the coefficients (beta) were measured to demonstrate significance. Finally, with this research the authors ascertain that entrepreneurial orientation positively influences entrepreneurial intention. thus explaining 42.4% of its variance. This chapter is the first attempt on investigating in university students of Latin American business schools about factors of entrepreneurship orientation and entrepreneurship intention, and has strong potential to contribute to development of policies and strategies to promote the growth of entrepreneurship activities in the universities.