In this paper we propose a classifier for generalized sequences that is conceived in the granular computing framework. The classification system processes the input sequences of objects by means of a suited interplay among dissimilarity and clustering based techniques. The core data mining engine retrieves information granules that are used to represent the input sequences as feature vectors. Such a representation allows to deal with the original sequence classification problem through standard pattern recognition tools. We have evaluated the generalization capability of the system in an interesting case study concerning the protein folding problem. In the considered dataset, the entire E. Coli proteome was screened as for the prediction of protein relative solubility on a pure amino acids sequence basis. We report the analysis of the dataset considering different settings, showing interesting test set classification accuracy results. The developed system consents also to extract knowledge from the considered training set, by allowing the analysis of the retrieved information granules. © 2013 IEEE

In this paper the problem of the minimization of active power losses in a real Smart Grid located in the area of Rome is faced by defining and solving a suited multi-objective optimization problem. It is considered a portion of the ACEA Distribuzione S.p.A. network which presents backflow of active power for 20% of the annual operative time. The network taken into consideration includes about 100 nodes, 25 km of MV lines, three feeders and three distributed energy sources (two biogas generators and one photovoltaic plant). The grid has been accurately modeled and simulated in the phasor domain by Matlab/Simulink, relying on the SimPowerSystems ToolBox, following a Multi-Level Hierarchical and Modular approach. It is faced the problem of finding the optimal network parameters that minimize the total active power losses in the network, without violating operative constraints on voltages and currents. To this aim it is adopted a genetic algorithm, defining a suited fitness function. Tests have been performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. First results are encouraging and show that the proposed optimization technique can be adopted as the core of a hierarchical Smart Grid control system. © Springer-Verlag Berlin Heidelberg 2013

Power losses reduction is one of the main targets for any electrical energy distribution company. In this paper, we face the problem of joint optimization of both network topology and distributed generator parameters in a real smart grid. We consider a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. located in Rome, Italy. We perform both the power factor correction (PFC) for tuning the generators and the distributed feeder reconfiguration (DFR) to set the optimal state of the breakers. This joint optimization problem is faced considering a suitable objective function and by adopting genetic algorithms as global optimization strategy. We analyze admissible network configurations, showing that some of these violate constraints on current and voltage at branches and nodes. Such violations depend only on topological properties of the network configurations. We perform experiments by feeding the simulation environment with real data concerning samples of dissipated and generated active and reactive power values of the ACEA smart grid. Results show that removing the configurations violating the electrical constraints from the solution space leads to important improvements in terms of power losses reduction. Moreover, we provide also an electrical interpretation of the phenomenon using graph-based pattern analysis techniques

In this paper we face the problem of the joint optimization of both topology and network parameters in order to minimize the total active power losses in a real Smart Grid. It is considered a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. located in Rome which presents back-flows of active power for 20% of the annual operative time. It includes about 1200 user loads, 70 km of MV lines, 6 feeders, a thyristor voltage regulator (TVR) and 6 distributed energy sources (5 generator sets and 1 photovoltaic plant). Network topology can be changed by 106 breakers. The grid has been accurately modelled and simulated in the phasor domain by Matlab/Simulink, relying on the SimPowerSystems ToolBox, following a Multi-Level Hierarchical and Modular approach. Network optimization is faced by defining and solving a suited multi-objective optimization problem, considering suited constraints on nominal operative ranges on voltages and currents, as well as on generator's capability functions, in order to take into account safety and quality of service issues. To this aim it is adopted a genetic algorithm, defining a suited fitness function. Tests have been performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. First results are very interesting, showing that relying on evolutionary computation it is possible to yield a satisfactory power factor correction, confirming that the proposed optimization technique can be adopted as the core of a hierarchical Smart Grid control system. © 2013 IEEE

In this paper the problem of the minimization of active power losses in a real Smart Grid located in the area of Rome is faced by defining and solving a suited multi-objective optimization problem. It is considered a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. which presents backflow of active power for 20% of the annual operative time. The network taken into consideration includes about 1200 user loads, 70 km of MV lines, 6 feeders, a thyristor voltage regulator (TVR) and 6 distributed energy sources (5 generator sets and 1 photovoltaic plant). The grid has been accurately modeled and simulated in the phasor domain by Matlab/Simulink, relying on the SimPowerSystems ToolBox, following a Multi-Level Hierarchical and Modular approach. It is faced the problem of finding the optimal network parameters that minimize the total active power losses in the network, without violating operative constraints on voltages and currents. To this aim, after defining a suitable fitness function, two evolutionary computation paradigms are compared: genetic algorithms and particle swarm optimization. Tests have been performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. Results show that both optimization techniques can be adopted as the core of a hierarchical Smart Grid control system. © 2013 IEEE

The power loss reduction is one of the main targets for any electrical energy distribution company. In this paper the problem of the joint optimization of both topology and network parameters in a real Smart Grid is faced. A portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. located in Rome is considered. It includes about 1200 user loads, 70 km of Medium Voltage (MV) lines, 6 feeders, a Thyristor Voltage Regulator (TVR) and 6 distributed energy sources (5 generator sets and 1 photovoltaic plant). The power factor correction (PFC) is performed tuning the 5 generator sets and setting the state of the breakers in order to perform the distributed feeder reconfiguration (DFR). The joint PFC and DFR problem is faced by considering a suited objective function and by adopting a genetic algorithm. In this paper we present a heuristic method to compare the graphs of two admissible topologies, such that similar graphs are characterized by close active power loss values. This criterion is used to define a suited ordering of the list of admissible configurations, aiming to improve the continuity of the fitness function to the variation of the configurations parameter. Tests are performed by feeding the simulation environment with real data concerning dissipated and generated active and reactive power values. Preliminary results are very interesting, showing that, for the considered real network, the proposed ordering criteria for admissible network configurations can facilitate the optimization process.

No abstract

Text categorization is an interesting application of machine learning covering a wide range of possible applications, from document management systems to web mining. In designing such a system it is mandatory to correctly define both a suited preprocessing procedure and an effective document representation as closely related as possible to the semantic nature of document categories. To this aim, relying on a Granular Computing approach and considering a document as an ordered sequence of words, we propose a system able to automatically mine frequent terms, considering as a term not only a single word, but also a subsequence of (a few) consecutive words. The whole classification system is tailored to process sequences of atomic elements (i.e., encoded words) by means of an embedding procedure based on clustering methods. However, when dealing with unbalanced data sets, i.e. when classes are not evenly represented in the data set, the frequent substructures search procedure must be carefully designed. We prove the effectiveness of the system over a well-known benchmarking data set, achieving competitive test set classification accuracy results, with a remarkable low structural complexity of the synthesized classification models. © 2013 IEEE