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Optimal AGC scheme design using hybrid particle swarm optimisation and gravitational search algorithm

机译:使用混合粒子群优化和引力搜索算法的最佳AGC方案设计

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In this paper, a novel hybrid particle swarm optimisation and gravitational search algorithm (HPSO-GSA) is proposed to design an optimal automatic generation control (AGC) scheme in interconnected power system. The proposed algorithm combines the advantages of both particle swarm optimisation (PSO) and gravitational search algorithm (GSA). This new meta-heuristic HPSO-GSA is applied to achieve the optimal proportional-integral-derivative (PID) controller parameters. Hence, the optimal PID controller is used to reduce the system fluctuations with the best dynamic performances. The AGC issue is formulated as an optimal load frequency control problem, where the frequency fluctuations and the tie-line power flow deviations are to be minimised in the same time. In order to test the performance of the proposed HPSO-GSA strategy, the integral time multiplied by absolute error (ITAE) is used as an objective function. To evaluate the efficiency of the proposed approach over disturbances, the standard two-area power system is used for the simulation. The obtained simulation results are compared to those yielded using classical and heuristic optimisation techniques surfaced in the recent state-of-the-art literature. The comparative study demonstrates the potential of the proposed strategy and shows its robustness to solve the optimal AGC problem.
机译:本文提出了一种新颖的混合粒子群优化和重力搜索算法(HPSO-GSA)来设计互连电力系统中的最佳自动生成控制(AGC)方案。所提出的算法结合了粒子群优化(PSO)和重力搜索算法(GSA)的优点。此新的元启发式HPSO-GSA应用于实现最佳比例积分衍生物(PID)控制器参数。因此,最佳PID控制器用于减少具有最佳动态性能的系统波动。 AGC问题被制定为最佳载荷频率控制问题,其中频率波动和系带电流偏差在同一时间最小化。为了测试所提出的HPSO-GSA策略的性能,用绝对误差(ITAE)乘以的积分时间用作目标函数。为了评估所提出的方法过度干扰的效率,标准的两区域电力系统用于模拟。将获得的模拟结果与最近的最新文献中浮出水面的经典和启发式优化技术产生的结果进行了比较。比较研究表明拟议策略的潜力,并展示了解决最佳AGC问题的鲁棒性。

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