Implementation of quassi-oppositional TLBO technique on economic load dispatch problem considering various generator constraints

D. Maity, Sumit Banerjee, C. K. Chanda, Sabyasachi Samanta
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引用次数: 5

Abstract

This paper presents a novel modified teaching learning based optimization method(TLBO) i.e quasi-oppositional TLBO (QOTLBO) algorithms to solve economic load dispatch problem of the thermal plant without considering transmission losses. The offered methodology can manage ELD problems using nonlinearities such as valve point loading, ramp rate limit and prohibited zone. The objective of ELD problem is to evaluate the optimal power generation allocation of units to satisfy the load demand, and also the overall cost of generation should be minimized, and different operational constraints should be fulfilled. General TLBO, a newly appeared evolutionary algorithm depend on two basic ideas of education namely teaching phase and learning phase. The efficiency of the offered algorithms has been checked on test system with equality and inequality constraints. The result is compared with the other existing techniques which prove the efficiency of algorithms.
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考虑各种发电机约束的经济负荷调度问题的准对抗性TLBO实现
本文提出了一种改进的基于教学的优化方法——准对抗性优化算法,用于解决不考虑输电损耗的火电厂经济负荷调度问题。所提供的方法可以使用非线性来管理ELD问题,例如阀点加载、斜坡速率限制和禁区。ELD问题的目标是在满足负荷需求的前提下,评估机组的最优发电分配,同时使发电总成本最小,并满足不同的运行约束条件。一般TLBO是一种新出现的进化算法,它依赖于教育的两个基本思想,即教学阶段和学习阶段。在具有等式和不等式约束的测试系统上验证了所提算法的有效性。结果与其他现有技术进行了比较,证明了算法的有效性。
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