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This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits). Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.

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We extend the Twitter interface to stimulate exploratory browsing of social media and develop a creative cognition method to establish its efficacy. Exploratory browsing is a creative process in which users seek and traverse diverse and novel information as they investigate a conceptual space. The TweetBubble browser extension extends Twitter to enable expansion of social media associations-@usernames and #hashtags-in-context, without overwriting initial content. We build on a prior metadata type system, developing new presentation semantics, which enable an integrated look and feel consistent with Twitter.We show how exploratory browsing constitutes a mini-c creative process. We use prior ideation metrics as a basis for new ideation metrics of exploratory browsing. We conducted a mixed methods crowdsourced study, with data from 54 participants, amidst the 2014 Academy Awards. Quantitative and qualitative findings validate the technique of in-context exploratory browsing interfaces for social media. Their consistency supports the validity of ideation metrics of exploratory browsing as an evaluation methodology for interactive systems designed to promote creative engagement.

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