改进粒子群算法的厂内机组避振及负荷分配研究

Study Vibration Avoidance and Load Distribution of Plant Units with Improved Particle Swarm Optimization Algorithm

水风光多能互补发电系统中风光出力不稳定且不连续,使得系统内水电机组需要频繁地进行工况转换,这给水电站运行的经济性和安全稳定性带来了挑战。当水电站厂内负荷分配中考虑了机组避振策略,普通粒子群优化模型的决策变量会变成高维度、互相耦合、不连续的高阶矩阵,模型中粒子速度更新过程的适配性降低导致了其收敛结果大打折扣。创新性地提出了双向更新的多目标粒子群模型,在水电站日前小时级调度方式和机组避振策略基础上,优化了机组综合穿越次数和机组全天的最优工况差之和这两个目标。结果表明:该优化模型相较于普通粒子群算法计算速度提升了14.7%,收敛精度范围平均提升了4%到6%,可为水电站兼顾经济性和安全稳定性的厂内负荷分配提供新的参考依据。

In the multi-energy complementary power generation system,the wind-photovoltaic output is unstable and discontinuous,which makes the hydropower units in the system need to change the working condition frequently,which brings challenges to the economy,safety and stability of hydropower station operation.When the unit vibration avoidance strategy is considered in the load distribution of the hydro⁃power plant,the decision variables of the ordinary particle swarm optimization model will become a complex matrix with high dimensionali⁃ty,mutual coupling and discontinuity.The convergence result of the particle velocity updating process in the model is greatly reduced due to the reduced suitability of the process.In this paper,a bidirectional updating multi-objective particle swarm model is proposed innovatively.Based on the day-ahead hour-level scheduling mode and vibration avoidance strategy of the unit,two objectives of the unit combined cross⁃ing number and the sum of the optimal working conditions of the unit throughout the day are optimized.The results show that compared with the ordinary particle swarm optimization algorithm,the calculation speed of the optimization model is improved by 14.7%,and the conver⁃gence accuracy range is improved by 4%to 6%on average,which can provide a new theoretical support for the optimization operation of hy⁃dropower station with both economy and safety stability.