Caio C. O. Ramos

A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in order to propose a new fast and accurate approach for feature selection. Comparisons to some other pattern recognition and feature selection techniques showed that the proposed hybrid algorithm for feature selection outperformed them. The experiments were carried out in the context of identifying non-technical losses in power distribution systems.

BCS: A Binary Cuckoo Search algorithm for feature selection

Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.

Competitive electricity markets: simulation to improve decision making

Electricity markets are experiencing a deep restructuring process leading, all over the world, to an open and competitive market to which electricity companies were not used. The importance of the electricity industry in society imposes this process to be taken without a decrease in power system reliability. In this paper, the authors present the most important aspects of the electricity markets restructuring process and relates their work concerning the development of an electricity market simulation environment. This environment is based on a multi-agent architecture, which is very suitable for this situation. The use of this tool can be of great help for all the entities acting in the electricity markets, such as producers, distributors and regulators. These entities can use this tool to evaluate the consequences of their decisions before they have to be taken in the real market.

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Fast Non-Technical Losses Identification Through Optimum-Path Forest

Fraud detection in energy systems by illegal con- sumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented.

Social-Spider Optimization-based Support Vector Machines applied for energy theft detection

Social-Spider Optimization for model selection in Support Vector Machines.Three distinct scenarios were evaluated.Proposed approach validated in the context of of theft detection in power distribution systems. Display Omitted The problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been paramount in the last years, mainly because of the high computational burden for SVM training step. In this paper, we address this problem by introducing a recently developed evolutionary-based algorithm called Social-Spider Optimization (SSO), as well as we introduce SSO for feature selection purposes. The model selection task has been handled in three distinct scenarios: (i) feature selection, (ii) tuning parameters and (iii) feature selection+tuning parameters. Such extensive set of experiments against with some state-of-the-art evolutionary optimization techniques (i.e., Particle Swarm Optimization and Novel Global-best Harmony Search) demonstrated SSO is a suitable approach for SVM model selection, since it obtained the top results in 8 out 10 datasets employed in this work (considering all three scenarios). Notice the best scenario seemed to be the combination of both feature selection and SVM tuning parameters. In addition, we validated the proposed approach in the context of theft detection in power distribution systems.

Towards more intelligent and adaptive user interfaces for control center applications

User interfaces are a key factor for the success of any computer application and assume an enormous importance in the case of applications used in critical and emergency conditions, as real time applications in control centers. The introduction of intelligent applications in these centers, makes more obvious the need of better user interfaces, presenting more intelligent and adaptive behaviour. These new generation of user interfaces makes use of full-graphics displays but also of artificial intelligence concepts. Knowledge bases are used in order to provide the user interface with the required knowledge about the computer application and its users. This paper deals with the evolution of the user interface of SPARSE, an expert system for alarm processing and operator assistance in service restoration developed for the Portuguese transmission network. The architecture of this interface is presented and the results obtained are discussed.

What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles.

Cooperative Training of Power Systems~ Restoration Techniques

Adequate training programs for power systems restoration tasks must take into account that this is a cooperative activity involving several entities. The proposed architecture of the intelligent tutoring system presented in this paper is based on a multi-agent system offering a simulated training environment