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发明公开
CN109784692A 一种基于深度学习的快速安全约束经济调度方法 有权

一种基于深度学习的快速安全约束经济调度方法
摘要：
本发明公开了一种基于深度学习的快速安全约束经济调度方法，主要步骤为：1)确定适用于安全约束经济调度模型的深度神经网络。2)对堆栈降噪自动编码器SDAE进行训练。3)建立基于深度学习的安全约束经济调度模型。4)令k＝1，将电力系统运行条件输入到深度神经网络中，得到安全约束经济调度模型的起作用约束集J(1)。5)将约束集J(1)。输入到安全约束经济调度模型中，得到安全约束经济调度方案。6)对安全约束经济调度方案进行N-1检验，若有新约束J(new)，则令k＝k+1,约束集更新为J(k)＝J(k)∪J(new)，并返回步骤5。若无新约束，则输出安全约束经济调度方案。本发明可广泛应用于电力系统各个行业的安全约束经济调度分析。
摘要（英）：

The invention discloses a fast security constraint economic scheduling method based on deep learning. The method mainly comprises the following steps: 1) determining a deep neural network suitable fora security constraint economic scheduling model; 2) training the stack noise reduction automatic encoder SDAE; 3) establishing a security constraint economic dispatching model based on deep learning;4) making k = 1, and inputting the operation condition of the power system into the deep neural network to obtain an action constraint set J (1) of the security constraint economic dispatching model;5) setting the constraint set J (1); and inputting the data into the security constraint economic dispatching model to obtain a security constraint economic dispatching scheme; 6), carrying out N-1 inspection on the constraint economic dispatching scheme, carrying out, if a new constraint J (new) exists, enabling k to be equal to k + 1, updating the constraint set to J (k) = J (k) and J (new), and returning to the step 5; and if no new constraint exists, outputting a security constraint economic scheduling scheme. The method can be widely applied to safety constraint economic dispatching analysis of each industry of the power system.

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