基于伴随灵敏度的暂态稳定预防控制快速算法

针对已有算法难以适应现代电力系统在线预防控制的实时性要求的问题,研究了一种基于伴随灵敏度分析的暂态稳定预防控制快速算法,采用了约束转换技术来缓解大规模电力系统暂态稳定预防控制中的"维数灾"问题,提出了一种优化层-仿真层的双层结构以实现算法:优化层基于预测-校正内点法,利用近似海森矩阵的扩展生成方法以提高算法的准确性和收敛性;仿真层中采用伴随灵敏度方法实现了灵敏度快速计算,并使用非诚实牛顿法和雅克比矩阵重用技术以进一步提高计算速度。研究结果表明,该算法能够显著提高大规模电力系统的暂态稳定预防控制求解效率,且相较其他已有算法体现出极大的速度优势,完全能够达到现代电力系统在线预防控制对控制算法的实时性要求。

Efficient method based on adjoint sensitivity analysis for transient stability constrained optimal power flow

Aiming at the real-time requirement of online preventive control in modern power system, an efficient method based on adjoint sensitivity analysis for transient stability constrained optimal power flow was proposed. Constraints conversion technology was adopted to re-lieve the curse of dimensionality. A two-layer-structure was proposed to implemented the algorithm:extended approximate Hessian matrix was adopted to improve the accuracy and convergence in optimization layer which was based on predictor-corrector interior point method;in simu-lation layer, adjoint sensitivity analysis was adopted to achieve fast calculation of dynamic sensitivity, and very dishonest Newton method and Jacobian matrix reusing approach were applied to speed up calculation. The results indicate that the proposed approach can significantly im-prove computing efficiency of solving transient stability constrained optimal power flow. Comparison with existing method also shows the great speed advantage of the proposed method, which can meet the requirement of online preventive control in modern power system.