Concentrated Solar Power (CSP) plants store energy by heating a storage
medium with an array of mirrors that focus sunlight onto solar receivers atop a
central tower. Operating at high temperatures these receivers face risks such
as freezing, deformation, and corrosion, leading to operational failures,
downtime, or costly equipment damage. We study the problem of anomaly detection
(AD) in sequences of thermal images collected over a year from an operational
CSP plant. These images are captured at irregular intervals ranging from one to
five minutes throughout the day by infrared cameras mounted on solar receivers.
Our goal is to develop a method to extract useful representations from
high-dimensional thermal images for AD. It should be able to handle temporal
features of the data, which include irregularity, temporal dependency between
images and non-stationarity due to a strong daily seasonal pattern. The
co-occurrence of low-temperature anomalies that resemble normal images from the
start and the end of the operational cycle with high-temperature anomalies
poses an additional challenge. We first evaluate state-of-the-art deep
image-based AD methods, which have been shown to be effective in deriving
meaningful image representations for the detection of anomalies. Then, we
introduce a forecasting-based AD method that predicts future thermal images
from past sequences and timestamps via a deep sequence model. This method
effectively captures specific temporal data features and distinguishes between
difficult-to-detect temperature-based anomalies. Our experiments demonstrate
the effectiveness of our approach compared to multiple SOTA baselines across
multiple evaluation metrics. We have also successfully deployed our solution on
five months of unseen data, providing critical insights for the maintenance of
the CSP plant. Our code is available at: this https URL

集中式太阳能发电厂的异常检测方法研究，使用深度学习模型对高维热像数据进行特征提取，处理不规则数据、时间依赖和强烈季节性模式等问题，并采用预测模型有效地区分温度异常，实验证明了方法的有效性以及对发电厂维护提供的关键见解。

从热图的不规则序列中检测集中式太阳能发电厂的异常操作

Detecting Abnormal Operations in Concentrated Solar Power Plants from  Irregular Sequences of Thermal Images

Concentrated solar power (CSP) is one of the growing technologies that is
leading the process of changing from fossil fuels to renewable energies. The
sophistication and size of the systems require an increase in maintenance tasks
to ensure reliability, availability, maintainability and safety. Currently,
automatic fault detection in CSP plants using Parabolic Trough Collector
systems evidences two main drawbacks: 1) the devices in use needs to be
manually placed near the receiver tube, 2) the Machine Learning-based solutions
are not tested in real plants. We address both gaps by combining the data
extracted with the use of an Unmaned Aerial Vehicle, and the data provided by
sensors placed within 7 real plants. The resulting dataset is the first one of
this type and can help to standardize research activities for the problem of
fault detection in this type of plants. Our work proposes supervised
machine-learning algorithms for detecting broken envelopes of the absorber
tubes in CSP plants. The proposed solution takes the class imbalance problem
into account, boosting the accuracy of the algorithms for the minority class
without harming the overall performance of the models. For a Deep Residual
Network, we solve an imbalance and a balance problem at the same time, which
increases by 5% the Recall of the minority class with no harm to the F1-score.
Additionally, the Random Under Sampling technique boost the performance of
traditional Machine Learning models, being the Histogram Gradient Boost
Classifier the algorithm with the highest increase (3%) in the F1-Score. To the
best of our knowledge, this paper is the first providing an automated solution
to this problem using data from operating plants.

本文介绍了一种使用机器学习算法自动检测集中式太阳能发电厂的损坏和故障问题的自动化解决方案。该方案使用无人机提供数据，以及来自 7 个真实工厂传感器提供数据，同时考虑了分类不平衡问题，有效提高了检测准确率。同时，该研究提供了一个此类数据集，为后续研究提出了标准化的解决思路。