基于改进生物地理学优化算法的SVC次同步阻尼控制器设计

针对常用的次同步振荡控制器不能较好地适应电力系统时变非线性的特点,提出了一种引入余弦迁移模型、早熟判断机制、变尺度混沌变异策略及排重操作的改进生物地理学优化算法。基于该算法结合静止无功补偿器(SVC)抑制次同步振荡的机理,对次同步阻尼控制器进行优化设计,并采用特征值分析和时域仿真验证了控制系统的有效性。锦界电厂算例分析表明:经改进生物地理学算法优化的SVC次同步阻尼控制器能较好地提高机组扭振的模态阻尼,可有效抑制次同步振荡,进而保证机组和电网的安全稳定运行;与传统的生物地理学优化算法、粒子群算法及遗传算法相比,改进生物地理学优化算法在搜索最优控制参数时具有较快的搜索速度和较高的搜索精度。

Design of SVC Subsynchronous Damping Controller Based on Improved Biogeography Based Optimization Algorithm

In view of the common subsynchronous oscillation (SSO) controller incapability of suiting the time-varying and nonlinear characteristics of power system, the cosine migration model, the premature judging mechanism, the mutative scale of chaos mutation strategy, and re-scheduling operations are introduced into the improved biogeography-based optimization (IBBO) algorithm to design subsynchronous damping controller (SSDC) optimally based on the mechanism of suppressing SSO by static var compensator (SVC). Finally, eigenvalue analysis and electromagnetic simulation are conducted to verify the effectiveness of the controller developed. The simulation analysis of Jinjie Plant indicates that SVC-SSDC optimized by the IBBO algorithm can greatly improve the damping of the three torsional modes and thus effectively depress the multimodal SSO, ensuring stability of the system and safety of the generator shafts. Moreover, The IBBO algorithm has a faster search speed and higher search accuracy in searching for the optimal control parameters compared with the traditional biogeography-based optimization (BBO) algorithm, the particle swarm optimization (PSO) algorithm, as well as the genetic algorithm (GA).