Synchrophasor Data Event Detection using Unsupervised Wavelet Convolutional Autoencoders
Synchrophasor Data Event Detection using Unsupervised Wavelet Convolutional Autoencoders
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DOI：
10.1109/smartcomp58114.2023.00080
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Jacob Buckelew;S. Basumallik;Vasavi Sivaramakrishnan;Ayan Mukhopadhyay;Amal Srivastava;Abhishek Dubey
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文献类型：
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作者：
Jacob Buckelew;S. Basumallik;Vasavi Sivaramakrishnan;Ayan Mukhopadhyay;Amal Srivastava;Abhishek Dubey
及时准确地检测影响输电系统稳定性和可靠性的事件对于电网安全运行至关重要。本文提出了一种有效的无监督机器学习算法的事件检测使用的离散小波变换（DWT）和卷积自编码器（CAE）与同步相量相量测量的组合。这些测量值是从配备数字实时模拟器的硬件在环测试台设置中收集的。利用离散小波变换，得到了测量的细节系数。接下来，分解后的数据被送入CAE，CAE捕获转换后数据的底层结构。当在输入样本和它们的重构输出之间检测到显著误差时，识别异常。我们证明了我们的方法，IEEE-14总线系统考虑不同的事件，如发电机故障，线到线故障，线到地故障，甩负荷，和线路停电模拟的实时数字仿真器（RTDS）。该方法的分类准确率为97.7%，精确率为98.0%，召回率为99.5%，F1得分为98.7%，与基线方法相比，在时间和空间要求方面都是有效的。
Timely and accurate detection of events affecting the stability and reliability of power transmission systems is crucial for safe grid operation. This paper presents an efficient unsupervised machine-learning algorithm for event detection using a combination of discrete wavelet transform (DWT) and convolutional autoencoders (CAE) with synchrophasor phasor measurements. These measurements are collected from a hardware-in-the-loop testbed setup equipped with a digital real-time simulator. Using DWT, the detail coefficients of measurements are obtained. Next, the decomposed data is then fed into the CAE that captures the underlying structure of the transformed data. Anomalies are identified when significant errors are detected between input samples and their reconstructed outputs. We demonstrate our approach on the IEEE-14 bus system considering different events such as generator faults, line-to-line faults, line-to-ground faults, load shedding, and line outages simulated on a real-time digital simulator (RTDS). The proposed implementation achieves a classification accuracy of 97.7%, precision of 98.0%, recall of 99.5%, F1 Score of 98.7%, and proves to be efficient in both time and space requirements compared to baseline approaches.