Search results

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 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.

Entrepreneurial intention is one of the main predictors of venture creation. However, the approaches used to analyze the entrepreneurial intention and venture creation are mostly linear approaches, leaving aside the fact that new ventures arise in a context characterized by fluctuations and instability, especially in emerging economies where economic and social factors are highly variables. Nevertheless, a dynamic approach could best represent its behavior. This study aims to propose an alternative approach and a starting point for more complex dynamic models in the entrepreneurship process that surpass the limitation of the current linear methodologies and allow gathering isolated studies' contributions.

This study proposes a method to shed light on the processes related to the venture creation process and entrepreneurial intention by designing a system dynamics simulation model.

The results reveal that the delayed effect of expectations produces a growing tendency in project creation, venture establishment and venture creation. Likewise, the entrepreneurial intention is not a static variable; it changes by the system’s dynamics and disturbs the venture creation process, which produces an increase in oscillations in the model and, therefore, reduces the project’s growth and venture creation.

This model is a generic approach for the study of venture creation and entrepreneurial intention. The model can analyze entrepreneurial intention and venture creation in different contexts, adjusting the different model parameters. The authors run a sensitivity analysis to encompass deviation from the parameter established and the uncertainty about them. However, the empirical data used for the model’s testing, in this case, correspond to an approximation to the behavior of venture creation in Colombia, which is considered an emerging economy. The model proposed does not pretend to incorporate all the variables and phenomena about entrepreneurship.

The approach suggested in this work aims to conceptualize venture creation as a complex process that emerges from the occurrence and combination of simpler states, instead of activities that represent building blocks. In addition, the term “entrepreneurial process” is defined as a composite of different perspectives that use a series of multidisciplinary theories to address the topic.