Journal of Intelligent Industrial Systems

G. Rigatos, P. Siano
2016 Intelligent Industrial Systems  
The articles included in vol.2, no. 4 of the Journal of Intelligent Industrial Systems can be classified in three main categories: (i) approaches to improved management of electric power systems, (ii) control methods for electric power systems (iii) control methods for complex electromechanical systems. In the first class of articles, that is improved management of electric power systems, one can distinguish a computational approach for solving the multi-objective optimization problem of the
more » ... figuration of the power distribution grid. Moreover, in this thematic area belongs an article on the development of an improved home energy management system, showing that electric home appliances can be operated in an energy efficient manner despite the processing of ambiguous sensor measurements. In the second class of articles, that is control of electric power systems, one can distinguish first a vector control method for voltage inverters addressed to active power filters. The method contributes to the compensation of distortions in the grid's voltage and thus it allows for improvement of the quality of the provided electric power. In the same class of articles a manuscript on differential flatness theory-based control of DC-DC converters is included. The method allows for more efficient conversion and exploitation of the power produced by photovoltaic units. In the third class of articles, that is control B G. Rigatos of complex electromechanical systems, one can distinguish first a method for differential flatness theory-based control of chaotic dynamical systems. As a case study, control and stabilization of the Lorenz chaotic oscillator is presented. In the same class of articles one can assign a manuscript presenting results on adaptive neurofuzzy (model-free) control of the turbocharged diesel engine. The presented method is based again on differential flatness theory and achieves control of diesel engines without need for prior knowledge about the its dynamic model. In article Multi-objective distribution network reconfiguration based on Pareto-front making by A. Mazza, G. Chicco, A. Russo and E. Virjoghe the problem of multi-objective optimization of the power distribution grid through the application of evolutionary programming is treated. Taking into account the power distribution system's losses together with other objectives, among which reliability indicators, one arrives at the formulation of the associated multi-objective optimization problem. Pareto front analysis enables the grid's operator to handle conflicting and even non-commensurable objectives without needing the use of additional hypotheses. The paper presents new results on the computation of the Pareto front as part of the solution of the multi-objective optimization problem for distribution network reconfiguration. Starting from previous results in which genetic algorithms were effectively adopted to find the best-known Pareto front, a version of the multi-objective binary particle swarm optimization, customized for distribution network reconfiguration, has been developed by exploiting the internal ranking of the solutions and the network topology. Furthermore, the Pareto front mismatch metric has been generalized so as to be used in large systems for which only the best-known Pareto front is found. Applications to a test network and to a real urban distribution network are discussed, showing the consistent superiority of the customized multi-objective binary 123
doi:10.1007/s40903-016-0066-5 fatcat:hbz6p3vohrembgksh3qk22izwq