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The traditional stator single-phase-to-ground fault protective schemes are hard to completely satisfy the requirement of selectivity for bus-connected Powerformers. In order to overcome the aforementioned problem, a new stator single-phase-to-ground fault protective scheme based on the hierarchical clustering algorithm is proposed in this paper. The direction and magnitude of zero-sequence current and leakage current are discussed and selected as feature vectors. Then, the data set are divided into two groups by hierarchical clustering algorithm, and cluster center of each group is calculated. Finally, the space relative distance among detected pattern and two cluster centers is calculated to discriminate the faulty Powerformer. The fundamental component and the third harmonic component of the data set are combined to realize 100% coverage of fault detection for the stator windings of Powerformer. Simulation results have shown that, under different fault conditions, the new scheme can distinguish internal faults from external faults reliably, and can detect in which machine a stator single-phase-to-ground fault is occurring even if the fault resistance is at 5 kΩ. [ABSTRACT FROM PUBLISHER]

In a multimarket environment, a GENCO produces electricity subject to a number of factors, including physical and environmental constraints, together with trading strategies in the electricity market (EM), fuel market (FM) and carbon market (CM). To assist a GENCO to maximize its profits from EM, FM and CM, this paper proposes a dynamic decision making model with two consecutive stages. Fuzzy differential evolution algorithm is used to solve this decision-making problem. Taking transactions in the three interactive markets into account, the proposed model has been tested for a GENCO consisting of seven thermal units and a wind farm. A rational tradeoff between the profit-making and emission reduction has been demonstrated by the GENCO using the proposed model, indicating a well alignment with the intended goal of the introducing emission trading scheme (ETS). [ABSTRACT FROM PUBLISHER]

The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, game theory has been used to design optimized strategies for defending an electric power system against deliberate attacks. In this paper, we extend the current static model to a more generalized framework which includes several interaction models between defenders and attackers. A new criterion of reliable strategies for defending power systems has been derived. In addition, two allocation algorithms have been developed to seek reliable strategies for two types of defense tasks. The new criterion and algorithms are complementary to current security criteria and can provide useful information for decision-makers to protect their power systems against possible targeted attacks. Numerical simulation examples using the proposed methods are given as well. [ABSTRACT FROM PUBLISHER]

The article reports that evolutionary computation includes stochastic search and optimization algorithms originated from the natural evolution principles. They are robust, adaptive and have found their application in a wide variety of theoretical and practical problems involving search and optimization tasks. Different from the traditional calculus based optimization techniques, evolutionary algorithms are based on a population of encoded tentative solutions which are processed with some evolutionary operators to find a good acceptable solution, if not the global optimum one.

• Integrated planning of Internet data centers and battery energy storage systems. • Coupled cyber-physical modelling of Internet data centers and a smart grid. • Multi-objective modelling of the planning task. Modern power grids have been becoming complex cyber-physical systems integrated with distributed energy sources and information and communication facilities. With prevalence of cloud computing, geo-distributed, networked data centers have become an integrated part of modern grids. The coupling impact between data centers and smart grids thus becomes an important consideration. This paper proposes an integrated planning scheme that optimally determines the locations and capacities of interconnected Internet data centers and battery energy storage systems in a smart grid. The model is formulated as a multi-objective optimization problem, in which both computational performance metrics of Internet data centers and operational criteria of the grid are coordinately considered as three inter-related but conflict objectives; the coupling impact between the cyber and energy resources are modelled. An advanced evolutionary algorithm – Multi-Objective Natural Aggregation Algorithm is used to solve the model. Extensive case studies are conducted to demonstrate the reasonability and effectiveness of the proposed integrated planning method. [ABSTRACT FROM AUTHOR]