基于2阶段家族保护遗传算法的电压稳定霍普夫分岔控制

Hopf Bifurcation Control of Voltage Stability Based on Two-Stage Kindred-Protected Genetic Algorithm

系统的电压稳定裕度由亚临界霍普夫分岔值表征,如何延迟甚至消除霍普夫分岔,提高稳定裕度,对于防止系统电压失稳具有重要意义。根据一种新的霍普夫分岔指标建立了电压稳定霍普夫分岔控制的最优化模型。该模型考虑了更为切合实际的约束条件,包括系统阻尼限制、PV节点无功出力限制、励磁电压限制以及各节点电压限制等。为有效求解该优化模型,提出了一种2阶段家族保护遗传算法。算法利用混沌变量的遍历性生成初始种群,第1阶段完成家族内部的选择,使得每个家族成员都是优良个体;第2阶段实现家族间的选择,这是一种"优–优"选择,使算法能以更快的速度收敛到全局最优解。通过对测试函数和WSCC-9节点系统的仿真表明该模型和算法的有效性和可行性。

The voltage stability margin of power system can be characterized by subcritical Hopf bifurcation value, thus it is of great significance to prevent system voltage instability by delaying or even eliminating the Hopf bifurcation to improve voltage stability margin. Based on a new kind of Hopf bifurcation indicator, an optimal model to control the Hopf bifurcation of voltage stability, in which the down-to-earth constraints including the restrictions of system damping, reactive power output at PV node, excitation voltage and voltage at buses are taken into account, is proposed. To solve the proposed optimal model effectively, a two-stage kindred-protected genetic algorithm （KPGA） is put forward. Utilizing the ergodicity of chaotic variables, an initial population is generated and in the first-stage the selection inside the kindred is completed to make each member in the kindred becoming excellent individual; in the second-stage the choice among kindreds, which is a choice from excellent to more excellent to make the algorithm can be converged to global optimal solution more rapidly, is implemented. Simulation results from both test function and WSCC 9-bus system show that the proposed model and algorithm are effective and feasible.