Predicting the Voltage Distribution for Low Voltage Networks using Deep Learning
Predicting the Voltage Distribution for Low Voltage Networks using Deep Learning
复制标题
使用深度学习预测低压网络的电压分布
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Fiona Fulton
中科院分区：
文献类型：
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作者：
M. Mokhtar;V. Robu;D. Flynn;Ciaran Higgins;Jim Whyte;Caroline Loughran;Fiona Fulton
低压（LV）电网的能源格局开始发生变化；变化是由于可再生能源渗透率的增加和/或预计在家充电的电动汽车的增加。以往被动的LV网络管理“先适应后忘记”的方式将无法保证其有效运行。需要一种更具适应性的方法，包括对风险和电路容量的预测。许多提出的方法都要求电网的完全可观察性，这促使许多国家安装智能电表和先进的计量基础设施。然而，对“完美数据”的期望在实际操作中是不现实的。智能电表（SM）的推出有其自身的问题，这可能导致所有低压网络完全覆盖SM的可能性很低。这一点，再加上限制高粒度需求电力数据可用性的隐私要求，导致许多方法的采用率很低。为了解决这个问题，提出了深度学习神经网络来预测部分SM覆盖的电压分布。结果表明，关键位置的SM测量足以有效预测电压分布。
The energy landscape for the Low-Voltage (LV) networks are beginning to change; changes resulted from the increase penetration of renewables and/or the predicted increase of electric vehicles charging at home. The previously passive ‘fit-and-forget’ approach to LV network management will be inefficient to ensure its effective operations. A more adaptive approach is required that includes the prediction of risk and capacity of the circuits. Many of the proposed methods require full observability of the networks, motivating the installations of smart meters and advance metering infrastructure in many countries. However, the expectation of ‘perfect data’ is unrealistic in operational reality. Smart meter (SM) roll-out can have its issues, which may resulted in low-likelihood of full SM coverage for all LV networks. This, together with privacy requirements that limit the availability of high granularity demand power data have resulted in the low uptake of many of the presented methods. To address this issue, Deep Learning Neural Network is proposed to predict the voltage distribution with partial SM coverage. The results show that SM measurements from key locations are sufficient for effective prediction of voltage distribution.