基于GAN的光伏逆变器数据异常检测技术

随着实际环境中实时传感器数据的增加,定位异常情况变得越来越困难.同时,在基于图像的异常检测领域,生成对抗网络因其能够对复杂的高维图像分布进行建模而得到发展.为了能够精准快速地定位光伏发电系统中光伏逆变器的异常,提出了一种新的基于GAN的异常检测和定位框架.并将多变量时间序列利用角场转换为一系列二维图像,以此利用编码器和...

Abnormal detection technology of photovoltaic inverter data based on GAN

With the increase of real-time sensor data in the actual environment, it becomes more and more difficult to locate an abnormal situation. At the same time, in the field of image-based anomaly detection, generative adversarial networks (GAN) have been developed because of their ability to model complex high-dimensional image distribution. In order to accurately and quickly locate an abnormality of a photovoltaic inverter in a photovoltaic power generation system, a new abnormal detection and location framework based on GAN is proposed. The multi-variable time series is transformed into a series of two-dimensional image using the angle field, and a structure of encoder and decoder is used. Particularly for a series of images, a convolutional long-term and short-term memory network encoder is used to ensure the extraction of the time information of each time series data and the relevant information between variables. Finally, the anomaly can be detected and located by executing the anomaly score function, and the relevant experiments prove the effectiveness of this method in the actual photovoltaic inverter data anomaly detection task. This work is supported by the Ningxia Natural Science Foundation (No. 2020AAC03034).