Journal of Intelligent & Fuzzy Systems - Volume 20, issue 3

Authors: Beaubouef, Theresa | Petry, Frederick E.

Article Type: Research Article

Abstract: This paper introduces the intuitionistic rough set and intuitionistic rough relational and rough object oriented database models. Rough set, fuzzy set, and intuitionistic set uncertainty management are discussed and compared, and the model based on intuitionistic and rough sets developed here is applied to databases. The intuitionistic rough set database models draw benefits from both the rough set and intuitionistic techniques, providing greater management of uncertainty for databases applications in a less …than certain world. Show more

DOI: 10.3233/IFS-2009-0422

Citation: Journal of Intelligent & Fuzzy Systems, vol. 20, no. 3, pp. 105-117, 2009

Authors: Niknam, Taher

Article Type: Research Article

Abstract: Distributed energy resource (DER) systems are small-scale power generation technologies used to provide an alternative to or an enhancement of the traditional electric power system. These generators are usually connected to distribution networks. This paper presents a new price based methodology to daily Volt/Var control in distribution systems including DERs. DERs have much impact on the daily Volt/Var control according to the small X/R ratio and radial configuration of distribution systems. Due to private ownership of …DERs, a price-based compensation methodology is proposed as a proper signal to encourage owners of DERs in active and reactive power generation. Since daily Volt/Var control is a mixed integer nonlinear programming problem, an evolutionary algorithm based on Ant Colony Optimization (ACO) is used to determine the active and reactive power values of DERs, reactive power values of capacitors and tap positions of transformers for the next day. The results indicate that the proposed encouraging factor has improved the performance of distribution networks on a large scale. Show more

Keywords: Ant Colony Optimization (ACO), distributed energy resource, voltage and reactive power control

DOI: 10.3233/IFS-2009-0420

Citation: Journal of Intelligent & Fuzzy Systems, vol. 20, no. 3, pp. 119-132, 2009

Authors: Mohammadi, M. | Gharehpetian, G.B.

Article Type: Research Article

Abstract: This paper presents a multi-class Support Vector Machine (SVM) based algorithm for on-line static security assessment of the power systems. The proposed SVM based security assessment algorithm has a very small training time and space in comparison with the traditional machine learning methods such as Artificial Neural Networks (ANN) based algorithms. In addition, the proposed algorithm is faster than existing algorithms. One of the main points, to apply a machine learning method is feature selection. In …this paper, a new Decision Tree (DT) based feature selection algorithm has been presented. The proposed SVM algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line static security assessment. The effectiveness of the proposed feature selection algorithm has been investigated, too. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm. Show more

Keywords: Machine learning, multi-class Support Vector Machines (SVM), feature selection, power system security

DOI: 10.3233/IFS-2009-0421

Citation: Journal of Intelligent & Fuzzy Systems, vol. 20, no. 3, pp. 133-146, 2009