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首页> 外文期刊>Electric power systems research >Optimal multi-reservoir network control by two-phase neural network
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Optimal multi-reservoir network control by two-phase neural network

机译：两相神经网络的最优多水库网络控制

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摘要

An approach based on two-phase neural network (TPNN) is proposed for the optimal operation of multi-reservoir network control problems. The advantage of the proposed technique is that it takes into account the concurrent interaction among all the water release variables of the problem. Here, the main objective of this work is to figure out the optimal amounts of water releases from each hydro-plant during each interval in the interconnected system and to minimize and distribute uniformly the energy deficit if any. This TPNN approach is basically a two-stage solution method. In stage 1, the neural network is developed to bring the solution trajectory close to the boundary of the feasible region. In stage 2, the directional vector of the constraints is slowly shifted to the corresponding Lagrange multipliers and this moves the solution trajectory to the feasible region which satisfies all practical constraints. Application of this technique to a 10-reservoir network demonstrates efficacy of the proposed algorithm. It is concluded from the results that the proposed method with proper selection of network control parameters is very effective in providing a good optimal solution.
机译：针对多水库网络控制问题的最优运行，提出了一种基于两阶段神经网络的方法。所提出的技术的优点在于，它考虑了问题的所有释水变量之间的并发交互作用。在这里，这项工作的主要目的是找出互连系统中每个间隔期间每个水力发电厂的最佳水释放量，并最小化和均匀分布能量短缺（如果有）。 TPNN方法基本上是一个两阶段的解决方法。在第1阶段，开发了神经网络以使求解轨迹接近可行区域的边界。在阶段2中，约束的方向矢量缓慢移至相应的拉格朗日乘数，这将求解轨迹移动到满足所有实际约束的可行区域。该技术在10个水库网络中的应用证明了该算法的有效性。从结果可以得出结论，所提出的方法具有适当的网络控制参数选择，对于提供良好的最佳解决方案非常有效。

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