Machine Learning-Based Restoration Forecast with Predictive Power Outage for Diverse Power Outage Scenarios
Machine Learning-Based Restoration Forecast with Predictive Power Outage for Diverse Power Outage Scenarios
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DOI：
10.1109/etfg55873.2023.10407711
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Susan Oluropo Adedokun;Mohammad Farhan Khan;Patrick Luk;Zhenhua Luo
中科院分区：
文献类型：
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作者：
Susan Oluropo Adedokun;Mohammad Farhan Khan;Patrick Luk;Zhenhua Luo
有效恢复停电（PO）对于确保不间断的电力分配至关重要，因为它深刻影响经济发展，基础设施，企业和人民的福祉。准确预测停电后的恢复时间表对于有效的运营规划和电力系统弹性至关重要。这对发展中国家尤其重要，因为在这些国家，由于多个地点的各种设备的技术故障而造成的频繁和影响深远的停电往往持续很长时间。虽然以前的研究分别集中在预测停电（PPO）或恢复预测，但考虑这两个方面对于制定弹性措施至关重要。包括人工智能（AI）在内的机器学习（ML）应用程序在PO恢复预测方面出现了显着增长。然而，很少有研究研究将ML恢复预测和PPO结合起来进行弹性规划。这项研究提出了一个综合框架，将ML恢复预测与PPO相结合，利用太阳能自给自足的可持续能源资源（SSER）。一个全面的ML工作流程预处理数据，并采用神经网络（NN），利用外部输入的非线性自回归（NARX）来准确预测时空太阳特征，促进有效的PO恢复。该框架的有效性证明了使用一年的历史太阳能天气和PO中断数据从33千伏业务中心（BH）由伊巴丹配电公司在尼日利亚。结果表明，在预测PO的时空太阳特征方面表现出色，在训练、验证和测试期间，NARX回归R值分别为99.51%、99.43%和99.48%。微小的均方误差（MSE）是非常低的，达到低至7.8360e-06的值，证实了所提出的ML方法的高精度和可靠性。
Efficiently restoring power outages (PO) is vital for ensuring uninterrupted electricity distribution, as it profoundly impacts economic development, infrastructure, businesses, and people’s well-being. Accurately predicting restoration timelines after a blackout is crucial for effective operational planning and power system resilience. This is especially important for developing countries where frequent and impactful blackouts, caused by technical faults in diverse equipment across multiple locations, often persist for extended durations. While previous studies have focused either on predictive power outage (PPO) or restoration forecasting separately, considering both aspects is essential for formulating resilience measures. Machine learning (ML) applications, including artificial intelligence (AI), have seen a notable rise in PO restoration forecasting. However, few studies have examined the combination of ML restoration forecasting and PPO for resilience planning. This study presents an integrated framework that combines ML restoration forecasting with PPO, leveraging solar self-sufficient sustainable energy resources (SSER). A comprehensive ML workflow preprocesses data and employs a neural network (NN) utilizing nonlinear autoregression with external inputs (NARX) to accurately forecast spatiotemporal solar features, facilitating efficient PO restoration. The framework’s validity is demonstrated using one year of historical solar weather and PO interruption data from a 33kV business hub (BH) operated by the Ibadan Electricity Distribution Company in Nigeria. Results indicate outstanding performance in forecasting spatiotemporal solar features for PO, with NARX regression R-values of 99.51%, 99.43%, and 99.48% during training, validation, and testing, respectively. The minute mean square errors (MSE) are remarkably low, reaching values as low as 7.8360e-06, confirming the high accuracy and reliability of the proposed ML approach.