Robust mapping rule estimation for power flow analysis in distribution grids
Robust mapping rule estimation for power flow analysis in distribution grids
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DOI：
10.1109/naps.2017.8107397
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发表时间：
2017-09
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通讯作者：
Jiafan Yu;Yang Weng;Ram Rajagopal
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作者：
Jiafan Yu;Yang Weng;Ram Rajagopal
分布式能源（DERs）的日益整合需要新的监测和运行规划工具来确保配电网的稳定性和可持续性。一个想法是在输电网和一些主配电网中使用现有的监测工具。然而，它们通常依赖于系统模型的知识，例如拓扑结构和线路参数，这些在一次和二次配电网中可能不可用。此外，公用事业通常具有有限的主动控制器建模能力，因为它们可能属于第三方，如住宅客户。针对传统潮流分析中的建模问题，提出了一种支持向量回归（SVR）方法来揭示不同变量之间的映射规律，并基于历史数据恢复有用的变量。说明了用SVR模型求解配电网线路参数优于传统回归方法的优点。具体来说，当回归方法失败时，SVR模型具有足够的鲁棒性，可以恢复映射规则。当1)存在测量异常值，2)存在主动控制器，或3)测量仅在配电网的某些部分可用时，就会发生这种情况。我们通过在不同规模的配电网和IEEE测试总线上进行广泛的数值验证，证明了我们的方法的优越性能。结果表明，该方法具有较好的鲁棒性。
The increasing integration of distributed energy resources (DERs) calls for new monitoring and operational planning tools to ensure stability and sustainability in distribution grids. One idea is to use existing monitoring tools in transmission grids and some primary distribution grids. However, they usually depend on the knowledge of the system model, e.g., the topology and line parameters, which may be unavailable in primary and secondary distribution grids. Furthermore, a utility usually has limited modeling ability of active controllers as they may belong to a third party like residential customers. To solve the modeling problem in traditional power flow analysis, we propose a support vector regression (SVR) approach to reveal the mapping rules between different variables and recover useful variables based on historical data. We illustrate the advantages of using the SVR model over traditional regression method that finds line parameters in distribution grids. Specifically, the SVR model is robust enough to recover the mapping rules when the regression method fails. This happens when 1) there are measurement outliers, 2) there are active controllers, or 3) measurements are only available at some part of a distribution grid. We demonstrate the superior performance of our method through extensive numerical validation on different scales of distribution grids and IEEE test buses. Robustness of our method is observed.