Hesitant fuzzy network topsis methods with the incorporation of z-numbers and social network analysis for small and large scale group decision making

It is critical to arrive at an acceptable level of consensus in a group for an agreeable and implementable decision. The evaluation of alternatives in conventional TOPSIS decision making does not take into account the inherent vagueness of information as it requires a systematic decision-making process in which relies on numerous conditions and unpredictable situations. It is common in deciding the best choice with the highest satisfaction degree that are evaluated based on attributes. However, two or more alternatives with the same or nearest satisfaction degree would lead to hesitancy in the decision. Thus, fuzzy network TOPSIS is incorporated with the hesitant fuzzy set is developed namely hesitant fuzzy network TOPSIS. Nevertheless, the reliability of decisions by experts and the complexity in large scale are less highlighted in hesitant fuzzy network TOPSIS. Therefore, this study aims in formulating hesitant fuzzy network TOPSIS with the incorporation of Z-numbers. The formulation also implies social network analysis for large-scale group decision making. In this study, four new fuzzy network TOPSIS are developed in small scale and large-scale group decision making. The proposed methods enhance the transparency and reliability by incorporating fuzzy network and Z numbers respectively. In addition, social network analysis is suitable for dealing with the complexity involved in large scale group decision making. For the practicality and the effectiveness of the proposed methods in a realistic scenario, a case study of stock selection and the analysis of results comparing proposed methods to the established methods has been considered. The ranking of the proposed methods are validated comparatively using performance indicators namely Spearman rho correlation, Root Means Squared Error, and Absolute Distance by assuming ranking based on Return on Investment as a benchmarking. Based on the case study, the proposed methods outperform the established methods in terms of average rank position. In conclusion, the proposed methods contribute significantly toward the implementation of small scale and large scale group decision making using hesitant fuzzy set, fuzzy network and Z numbers

Real-time hardware in the loop simulation methodology for power converters using labview FPGA

Nowadays, the use of the hardware in the loop (HIL) simulation has gained popularity among researchers all over the world. One of its main applications is the simulation of power electronics converters. However, the equipment designed for this purpose is difficult to acquire for some universities or research centers, so ad-hoc solutions for the implementation of HIL simulation in low-cost hardware for power electronics converters is a novel research topic. However, the information regarding implementation is written at a high technical level and in a specific language that is not easy for non-expert users to understand. In this paper, a systematic methodology using LabVIEW software (LabVIEW 2018) for HIL simulation is shown. A fast and easy implementation of power converter topologies is obtained by means of the differential equations that define each state of the power converter. Five simple steps are considered: designing the converter, modeling the converter, solving the model using a numerical method, programming an off-line simulation of the model using fixed-point representation, and implementing the solution of the model in a Field-Programmable Gate Array (FPGA). This methodology is intended for people with no experience in the use of languages as Very High-Speed Integrated Circuit Hardware Description Language (VHDL) for Real-Time Simulation (RTS) and HIL simulation. In order to prove the methodology’s effectiveness and easiness, two converters were simulated—a buck converter and a three-phase Voltage Source Inverter (VSI)—and compared with the simulation of commercial software (PSIM® v9.0) and a real power converter.This research was partially funded by the PROMINT-CM: S2018/EMT-4366 program fromthe Comunidad de Madrid, Spain, and also partially funded by CONACyT, Mexic

Advancements in Real-Time Simulation of Power and Energy Systems

Modern power and energy systems are characterized by the wide integration of distributed generation, storage and electric vehicles, adoption of ICT solutions, and interconnection of different energy carriers and consumer engagement, posing new challenges and creating new opportunities. Advanced testing and validation methods are needed to efficiently validate power equipment and controls in the contemporary complex environment and support the transition to a cleaner and sustainable energy system. Real-time hardware-in-the-loop (HIL) simulation has proven to be an effective method for validating and de-risking power system equipment in highly realistic, flexible, and repeatable conditions. Controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) are the two main HIL simulation methods used in industry and academia that contribute to system-level testing enhancement by exploiting the flexibility of digital simulations in testing actual controllers and power equipment. This book addresses recent advances in real-time HIL simulation in several domains (also in new and promising areas), including technique improvements to promote its wider use. It is composed of 14 papers dealing with advances in HIL testing of power electronic converters, power system protection, modeling for real-time digital simulation, co-simulation, geographically distributed HIL, and multiphysics HIL, among other topics