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Hybrid neuro-fuzzy system for power generation control with environmental constraints

机译：具有环境约束的发电控制混合神经模糊系统

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摘要

The real time controls at the central energy management centre in a power system, continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is least. However due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained combined economic dispatch formulation is to estimate the optimal generation allocation to generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques are cumbersome for such complex optimization tasks and are not suitable for on-line use due to increased computational burden. This paper proposes a neuro-fuzzy power dispatch method where the uncertainty involved with power demand is modeled as a fuzzy variable. Then Levenberg-Marquardt neural network (LMNN) is used to evaluate the optimal generation schedules. This model trains almost hundred times faster that the popular BP neural network. The proposed method has been tested on two test systems and found to be suitable for on-line combined environmental economic dispatch.
机译：电力系统中中央能源管理中心的实时控制，连续跟踪负载变化，并以使运行成本最小的方式努力使总电力需求与总发电量相匹配。但是，由于政府对环境保护的严格规定，以最低成本运行不再是分配电力的唯一标准。环境约束的组合经济调度公式背后的思想是，以给定负载需求同时最小化燃料成本和有害排放水平的方式，估算对发电机组的最佳发电分配。常规的优化技术对于这种复杂的优化任务是繁琐的，并且由于增加的计算负担而不适用于在线使用。本文提出了一种神经模糊功率分配方法，其中将与功率需求有关的不确定性建模为模糊变量。然后使用Levenberg-Marquardt神经网络（LMNN）评估最佳发电计划。该模型的训练速度比流行的BP神经网络快了近百倍。所提出的方法已经在两个测试系统上进行了测试，发现适合于在线组合环境经济调度。

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