Alfredo Alcayde

Minimization of voltage deviation and power losses in power networks using Pareto optimization methods

Voltage regulation is an important task in electrical engineering for controlling node voltages in a power network. A widely used solution for the problem of voltage regulation is based on adjusting the taps in under load tap changers (ULTCs) power transformers and, in some cases, turning on Flexible Alternating Current Transmission Systems (FACTS), synchronous machines or capacitor banks in the substations. Most papers found in the literature dealing with this problem aim to avoid voltage drops in radial networks, but few of them consider power losses or meshed networks. The aim of this paper is to present and evaluate the performance of several multi-objective algorithms, including hybrid approaches, in order to minimize both voltage deviation and power losses by operating ULTCs located in high voltage substations. In particular, a well-known multi-objective algorithm, PAES, is used for this purpose. PAES finds a set of solutions according to Pareto-optimization concepts. Furthermore, this algorithm is hybridized with simulated annealing and tabu search to improve the quality of the solutions. The implemented algorithms are evaluated using two test networks, and the numerical results are analyzed with two metrics often used in the multi-objective field. The results obtained demonstrate the good performance of these algorithms.

Adaptive community detection in complex networks using genetic algorithms

Abstract Community detection is a challenging optimisation problem that consists in searching for communities that belong to a network or graph under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. A large number of methods have been proposed to address this problem in many research fields, such as power systems, biology, sociology or physics. Many of those optimisation methods use modularity to identify the optimal network subdivision. This paper presents a new generational genetic algorithm (GGA+) that includes efficient initialisation methods and search operators under the guidance of modularity. Further, this approach enables a flexible and adaptive analysis of the characteristics of a network from different levels of detail according to an analyst’s needs. Results obtained in networks of different sizes and characteristics show the good performance of GGA+ in comparison with other five genetic algorithms, including efficient algorithms published in recent years.

A fast method for identifying worldwide scientific collaborations using the Scopus database

Abstract Science is essential for human prosperity because social and technological advances often depend on scientific advances. Science is living a golden era characterized by a rapidly growing number of researchers worldwide exploring different disciplines and research fields. Keeping in mind that funding is limited, many researchers are encouraged to establish new collaborations with individuals or groups of researchers. Furthermore, the funding bodies use increasingly complex criteria to determine the researchers and projects to be supported. In this regard, the analysis of scientific collaboration networks can help to determine the main areas of specialization of universities and research centres, as well as the type of internal and external collaborations of their researchers. This paper presents an advanced method for analysing scientific collaboration networks at universities and research institutions. This method is based on automatically obtaining bibliographic data from scientific publications through the use of the Scopus Database API Interface, which are then analysed using graph visualization software and statistical tools. This model has been validated through the analysis of a real university, and the results show that it is possible to determine in a fast way and with high reliability the main research lines of an institution as well as the structure of the collaboration network. The method opens new perspectives for the study of scientific collaboration networks because it can be applied at different levels of detail, from small research groups to large academic and research centres, and over different time frames.

Comparative analysis of power variables in high performance embedded and x86 architectures using GNU/Linux

In this work a comparative analysis of typical power variables is made using several hardware architectures and GNU/Linux software. Voltage and current data are simulated for an industrial device, comparing the performance of x86 and ARM in order to demonstrate the technical feasibility of using embedded hardware to manage high volumes of interesting data in the study of power quality systems. Voltage, current, active power, reactive power and harmonic distortion (both voltage and current) were obtained with simulated data provided by MATLAB and using Discrete Fourier Transform (DFT) implementations like Fast Fourier Transform (FFT) and libraries like FFTW and KISS FFT. All the software used in our work was open source, running Linux behind them. Results show the feasibility of using high performance embedded systems to develop advanced tasks in analyzing power signals.

Annealing-tabu PAES: a multi-objective hybrid meta-heuristic

Most real-life optimization problems require taking into account not one, but multiple objectives simultaneously. In most cases these objectives are in conflict, i.e. the improvement of some objectives implies the deterioration of others. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined, but rather a set of solutions. In the last decade most papers dealing with multi-objective optimization use the concept of Pareto-optimality. The goal of Pareto-based multi-objective strategies is to generate a front (set) of non-dominated solutions as an approximation to the true Pareto-optimal front. However, this front is unknown for problems with large and highly complex search spaces, which is why meta-heuristic methods have become important tools for solving this kind of problem. Hybridization in the multi-objective context is nowadays an open research area. This article presents a novel extension of the well-known Pareto archived evolution strategy (PAES) which combines simulated annealing and tabu search. Experiments on several mathematical problems show that this hybridization allows an improvement in the quality of the non-dominated solutions in comparison with PAES, and also with its extension M-PAES.

A New Memetic Algorithm for the Two-Dimensional Bin-Packing Problem with Rotations

The two-dimensional bin-packing problem (2D-BPP) with rotations is an important optimization problem which has a large number of practical applications. It consists of the non-overlapping placement of a set of rectangular pieces in the lowest number of bins of a homogenous size, with the edges of these pieces always parallel to the sides of bins, and with free 90 degrees rotation. A large number of methods have been proposed to solve this problem, including heuristic and meta-heuristic approaches. This paper presents a new memetic algorithm to solve the 2D-BPP that incorporates some operators specially designed for this problem. The performance of this memetic algorithm is compared with two other heuristics previously proposed by other authors in ten classes of frequently used benchmark problems. It is observed that, in some cases, the method here proposed is able to equal or even outperform to the results of the other two heuristics in most test problems.

Cryptanalysis of Hash Functions Using Advanced Multiprocessing

Every time it is more often to audit the communications in companies to verify their right operation and to check that there is no illegal activity. The main problem is that the tools of audit are inefficient when communications are encrypted.

Community detection in national-scale high voltage transmission networks using genetic algorithms

Abstract The large-scale interconnection of electricity networks has been one of the most important investments made by electric companies, and this trend is expected to continue in the future. One of the research topics in this field is the application of graph-based analysis to identify the characteristics of power grids. In particular, the application of community detection techniques allows for the identification of network elements that share valuable properties by partitioning a network into some loosely coupled sub-networks (communities) of similar scale, such that nodes within a community are densely linked, while connections between different communities are sparser. This paper proposes the use of competitive genetic algorithms to rapidly detect any number of community structures in complex grid networks. Results obtained in several national- scale high voltage transmission networks, including Italy, Germany, France, the Iberian peninsula (Spain and Portugal), Texas (US), and the IEEE 118 bus test case that represents a portion of the American Electric Power System (in the Midwestern US), show the good performance of genetic algorithms to detect communities in power grids. In addition to the topological analysis of power grids, the implications of these results from an engineering point of view are discussed, as well as how they could be used to analyze the vulnerability risk of power grids to avoid large-scale cascade failures.

Community detection in power grids by an evolutionary method

Community detection is a complex optimization problem that consists on searching homogeneous communities that belong to a given graph. This graph, which represent a network, has properties that enable the detection of characteristics or functional relationships in the network. A large number of approaches have been proposed to solve this problem in different disciplines. Nevertheless, only a few research papers have applied community detection to power grids. This paper presents a new evolutionary algorithm for community detection that is applied in power grids. This evolutionary approach employs an efficient initialization strategy that only considers feasible solutions and uses two different search operators that allow the algorithm to obtain a good convergence and diversity of solutions. The preliminary results show that the proposed algorithm obtain quality results in real power grids.