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Due to the significant increase of line-to-ground capacitive current, for urban distribution networks mixed by overhead lines and a larger number of cables, the higher electromagnetic transient overvoltage presents not only always on fault phase and neutral point, but also on non-fault phases when some intermittent arc grounding faults occur. Some arc-suppression coil which has a relatively smaller overcompensation tuning-off degree is mainly used to compensate the line-to-ground capacitive current, reduce the arc residual current and reignition times, and then increase the self-restoration probability after a single phase intermittent arc grounding fault occurs. At the same time, according to the characteristics and performance requirements of distribution systems, a relatively smaller shunt resistance is mainly used to quickly release the charge of line-to-ground capacitance, improve the decay rate of overvoltage of fault phase, non-fault phases and neutral point respectively, reduce the rising speed of neutral point recovery voltage and its amplitude after the intermittent arc dies out, so that the transient overvoltage can be restrained effectively. Results of transient numerical analysis and ATP simulation have demonstrated the mode of neutral grounding via arc-suppression coil with shunt resistance is an optimum choice which is applicable to 35 kV urban distribution networks mixed by overhead lines and cables.

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The game industry has been on exponential growth, has different businesses of varying size, ethos, scope and beyond. Success of these video-games comes from a lot of labor-intensive work by developers. Every little nuance of each character, the objects within a character’s environment must be hand-coded. Repetitive work takes up a significant part of development time, which leads to an increase in glitches and logical flaws. Artificial intelligence has been used to simulate human players in software games, provides an opportunity for game developers to create unique experiences and different outcomes for each player. Computer chess players are well-known examples, wherein modern chess programs are trained to defeat best human players. AI based algorithms that can be implemented for games, but a need for optimal solutions is on a rise. We require a comparative analysis of multiple algorithms for understanding the most efficient and ideal one. In our work, through use of a game Tic-Tac-Toe various algorithms will be carried out with its prototype compared in terms of effective rate and optimality.