Wild Goats Optimization Approach for Capacitor Placement Problem

This paper deals with Capacitor Placement (CP) issue. The topic is an optimization problem including two types of variables: capacitor location as an integer variable, capacitor size as a continuous one. To cope with this problem, a new approach entitled Wild Goats Algorithm (WGA) is used. WGA is a new heuristic approach which has been proved recently. In this paper, WGA is successfully implemented to the CP problem with the objective of total loss reduction. Power flow criteria as well as operation constraints are all together accommodated in the process of optimization. Two various scenarios at three load levels are also recognized to cover all possible conditions. The validity of the WGA approach in handling CP problem is assured by testifying it on IEEE 33-bus and 69-bus test systems

Impact of online professional social networks on organizational attractiveness : a social capital perspective

Ce travail de recherche aborde la question de l’impact des réseaux sociaux numériques professionnels sur l’attractivité organisationnelle. Malgré la complexité et la diversité des études théorique et pratique portant sur la manière avec laquelle les membres des réseaux sociaux professionnels interagissent, les études portant sur l’attractivité organisationnelle et l’identification des membres dans le contexte d’e-recrutement demeurent limitées. Dans ce travail, le champ théorique qui a été mobilisé est la théorie du capital social. Il s’agit de mieux comprendre l'impact de l'identité sociale, la confiance sociale, bouche-à-oreille virtuel, le site des réseaux sociaux et le rôle médiateur de qualité d’information sur l’attractivité organisationnelle en tant qu’un employeur. La thèse propose un cadre d'analyse et conceptuel. En plus de la revue de la littérature, une étude complémentaire a été réalisée sur la base de 8 entretiens semi-directifs auprès de directeurs des ressources humaines afin de connaitre leur avis sur les motivations des entreprises qui intègrent l'utilisation des réseaux dans leurs pratiques de recrutement. Une étude quantitative a été menée à partir de 288 individus résidant en France et au Canada qui sont présents sur les réseaux sociaux numériques professionnels. Le but est de mesurer l’impact des facteurs identifiés dans la première étude qui influencent l’attractivité organisationnelle. Les résultats obtenus ont permis de comprendre l’importance du réseautage social, l’attractivité de site des réseaux sociaux ainsi que l’effet médiateur de qualité d’information sur l’attractivité organisationnelle, le prestige et l’intention des individus pour suivre des offres d’emploi sur ces plateformes.This research work addresses the impact of professional social networks on the organizational attractiveness as an employer. Despite the complexity and diversity of studies on social networks both theoretical and practical regarding the way the members of professional social networks interact, the studies related to organizational attractiveness and membership identification in the context of e-recruitment is scarce remains limited. In this research, the social capital theory has been considered in order to better understand the impact of social identity, social trust, virtual word of mouth, the social networking platform and the mediating role of information quality on organizational attractiveness as an employer. In addition to the literature review, a first complementary study was carried out on the basis of 8 semi-directive interviews with human resources managers to obtain their opinions on the various stakeholders who integrate the use of these networks in their practices. A quantitative study was carried out on the basis of 288 individuals currently living in France and Canada who are present on professional online social networks in order to measure the impact of identified factors that influence the organizational attractiveness. The obtained results allowed us to understand the importance of social networking, the attractiveness of social networks sites and also the mediating effect of information quality on the organizational attractiveness as an employer, organizational prestige and intention of individuals to pursue job offers on these platforms

Une exploration sur les valeurs créés par les réseaux sociaux: La plate forme numérique de la gestion et l’acquisition des talents

International audienc

Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach

In these days, when complex, IT-controlled systems have found their way into many areas, models and the data on which they are based are playing an increasingly important role. Due to the constantly growing possibilities of collecting data through sensor technology, extensive data sets are created that need to be mastered. In concrete terms, this means extracting the information required for a specific problem from the data in a high quality. For example, in the field of condition monitoring, this includes relevant system states. Especially in the application field of machine learning, the quality of the data is of significant importance. Here, different methods already exist to reduce the size of data sets without reducing the information value. In this paper, the multidimensional binned reduction (MdBR) method is presented as an approach that has a much lower complexity in comparison on the one hand and deals with regression, instead of classification as most other approaches do, on the other. The approach merges discretization approaches with non-parametric numerosity reduction via histograms. MdBR has linear complexity and can be facilitated to reduce large multivariate data sets to smaller subsets, which could be used for model training. The evaluation, based on a dataset from the photovoltaic sector with approximately 92 million samples, aims to train a multilayer perceptron (MLP) model to estimate the output power of the system. The results show that using the approach, the number of samples for training could be reduced by more than 99%, while also increasing the model’s performance. It works best with large data sets of low-dimensional data. Although periodic data often include the most redundant samples and thus provide the best reduction capabilities, the presented approach can only handle time-invariant data and not sequences of samples, as often done in time series

Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults

Timely and accurate detection of transmission line faults is one of the most important issues in the reliability of the power systems. In this paper, in order to assess the effects of impedance and location of the fault in identifying and classifying it, the frequency response analysis (FRA) method is utilized. This method clearly shows the smallest effects of the faults on voltage and current signals in the frequency domain. Interpretation of the results associated with the FRA procedure is considered a weakness of this method. To overcome this issue and accurately categorize types and locations of various transmission lines faults such as asymmetric faults and symmetric faults, machine learning, and deep learning applications called support vector machine (SVM), decision tree (DT), k-Nearest Neighbors (k-NN), convolutional neural network (CNN), long short term memory (LSTM), and a hybrid model of convolutional LSTM (C-LSTM) are utilized. Introduced faults are applied with various impedances in 6 segments of an IEEE standard transmission line system. Then, the frequency response curves (FRCs) for them are computed and selected as input datasets for the suggested networks. After categorizing the types and locations of faults, the results for each network are analyzed via different statistical performance evaluation metrics. Finally, in order to early detection of faults, the new high impedance faults (7000 and 9000 O) are applied based on the previous routine in the transmission line. At this stage, evaluations demonstrate the capability of the C-LSTM followed by SVM, DT, k-NN, CNN, and LSTM in categorizing the type and location of transmission line faults.</p

Present status and future trends in enabling demand response programs

This paper addresses implementation of Demand Response (DR) programs in competitive electricity markets. An overview of present status of the application of DR programs in major electricity markets is provided. In this paper, An objective-wised classification of DR measures is proposed which is rooted in practical DR experiences. Market opportunities and associated barriers are investigated. Further, enabling technologies for implementing DR programs are discussed. Finally, the role of smart grid in enabling DR is highlighted