A Swarm Intelligence Approach to the Power Dispatch Problem

This paper examines how two techniques of the Particle Swarm Optimization method (PSO) can be used to solve the Economic Power Dispatch (EPD) problem. The mathematical model of the EPD is a nonlinear one, PSO algorithms being considered efficient in solving this kind of models. Also, PSO has been successfully applied in many complex optimization problems in power systems. The PSO techniques presented here are applied to three case studies, which analyze power systems having four, six, respectively twenty generating units

Expressing Sentiments in Game Reviews

Opinion mining and sentiment analysis are important research areas of Natural Language Processing (NLP) tools and have become viable alternatives for automatically extracting the affective information found in texts. Our aim is to build an NLP model to analyze gamers’ sentiments and opinions expressed in a corpus of 9750 game reviews. A Principal Component Analysis using sentiment analysis features explained 51.2 % of the variance of the reviews and provides an integrated view of the major sentiment and topic related dimensions expressed in game reviews. A Discriminant Function Analysis based on the emerging components classified game reviews into positive, neutral and negative ratings with a 55 % accuracy.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains

A Hybrid Particle Swarm Optimization Algorithm for the Economic Dispatch Problem

This article proposes a hybrid global-local algorithm - Hybrid Particle Swarm Optimization (HPSO) - applied to solve the Economic Dispatch (ED) problem. The HPSO algorithm combines the classical Particle Swarm Optimization (PSO) with the Conjugate Gradient (CG) non-linear optimizing method, included in the optimizing tool within MathCAD commercial software product. The global optimizer is the PSO algorithm, and the local one is the CG method. Two variants including the CG within the PSO, which are analyzed, called HPSO-RC (randomly controlled) and HPSO-RU (randomly uncontrolled). Both PSO and CG methods are easy to implement and together help reaching the best solution. The HPSO algorithmâs ability to avoid premature convergence and provide a stabile solution is tested on three systems consisting of 6, 13 and 38 thermal generating units. The HPSO algorithmâs efficiency in solving the ED problem is shown through a comparison with several other recently published algorithms

Modified Social Group Optimization to Solve the Problem of Economic Emission Dispatch with the Incorporation of Wind Power

Economic dispatch, emission dispatch, or their combination (EcD, EmD, EED) are essential issues in power systems optimization that focus on optimizing the efficient and sustainable use of energy resources to meet power demand. A new algorithm is proposed in this article to solve the dispatch problems with/without considering wind units. It is based on the Social Group Optimization (SGO) algorithm, but some features related to the selection and update of heuristics used to generate new solutions are changed. By applying the highly disruptive polynomial operator (HDP) and by generating sequences of random and chaotic numbers, the perturbation of the vectors composing the heuristics is achieved in our Modified Social Group Optimization (MSGO). Its effectiveness was investigated in 10-unit and 40-unit power systems, considering valve-point effects, transmission line losses, and inclusion of wind-based sources, implemented in four case studies. The results obtained for the 10-unit system indicate a very good MSGO performance, in terms of cost and emissions. The average cost reduction of MSGO compared to SGO is 368.1 $/h, 416.7$/h, and 525.0 $/h for the 40-unit systems. The inclusion of wind units leads to 10% reduction in cost and 45% in emissions. Our modifications to MSGO lead to better convergence and higher-quality solutions than SGO or other competing algorithms

PROBABILISTIC APPROACH OF STABILIZED ELECTROMAGNETIC FIELD EFFECTS

The effects of the omnipresence of the electromagnetic field are certain and recognized. Assessing as accurately as possible these effects, which characterize random phenomena require the use of statistical-probabilistic calculation. This paper aims at assessing the probability of exceeding the admissible values of the characteristic sizes of the electromagnetic field - magnetic induction and electric field strength. The first part justifies the need for concern and specifies how to approach it. The mathematical model of approach and treatment is presented in the second part of the paper and the results obtained with reference to 14 power stations are synthesized in the third part. In the last part, are formulated the conclusions of the evaluations

A Swarm Intelligence Approach to the Power Dispatch Problem

This paper examines how two techniques of the Particle Swarm Optimization method (PSO) can be used to solve the Economic Power Dispatch (EPD) problem. The mathematical model of the EPD is a nonlinear one, PSO algorithms being considered efficient in solving this kind of models. Also, PSO has been successfully applied in many complex optimization problems in power systems. The PSO techniques presented here are applied to three case studies, which analyze power systems having four, six, respectively twenty generating units.</jats:p