Ensemble Learning Based Convex Approximation of Three-Phase Power Flow
Ensemble Learning Based Convex Approximation of Three-Phase Power Flow
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DOI：
10.1109/tpwrs.2021.3055481
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发表时间：
2021-09-01
影响因子：
6.6
通讯作者：
Qiu, Feng
中科院分区：
文献类型：
--
作者：
Hu, Ren;Li, Qifeng;Qiu, Feng
虽然凸优化方法在电力系统中得到了广泛的应用，但对某些问题仍不能保证得到严格（精确）的解。为了解决这个问题，本文提出了一种基于集成学习的交流（AC）潮流方程的凸近似，不同于现有的凸松弛。该方法基于直角坐标系下的三相二次潮流方程。为了开发这种数据驱动的潮流凸近似，首先部署多项式回归（PR）作为基本学习器来拟合自变量和因变量之间的凸关系。然后，集成学习算法，如梯度提升（GB）和装袋被引入到联合收割机学习器，以提高模型的性能。基于潮流的学习凸逼近，将最优潮流问题转化为凸二次规划问题。对IEEE标准平衡和不平衡系统的仿真结果表明，在求解最优潮流问题时，所提出的数据驱动凸逼近算法在精度和计算效率上均优于传统的半定规划松弛算法，尤其是在传统半定规划松弛算法失效的情况下.
Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex approximation for alternating current (AC) power flow equations that differs from the existing convex relaxations. The proposed approach is based on three-phase quadratic power flow equations in rectangular coordinates. To develop this data-driven convex approximation of power flows, the polynomial regression (PR) is first deployed as a basic learner to fit convex relationships between the independent and dependent variables. Then, ensemble learning algorithms such as gradient boosting (GB) and bagging are introduced to combine learners to boost model performance. Based on the learned convex approximation of power flow, optimal power flow (OPF) is formulated as a convex quadratic programming problem. The simulation results on IEEE standard cases of both balanced and unbalanced systems show that, in the context of solving OPF, the proposed data-driven convex approximation outperforms the conventional semi-definite programming (SDP) relaxation in both accuracy and computational efficiency, especially in the cases that the conventional SDP relaxation fails.