Ensuring the reliability of power electronic converters is a matter of great
importance, and data-driven condition monitoring techniques are cementing
themselves as an important tool for this purpose. However, translating methods
that work well in controlled lab environments to field applications presents
significant challenges, notably because of the limited diversity and accuracy
of the lab training data. By enabling the use of field data, online machine
learning can be a powerful tool to overcome this problem, but it introduces
additional challenges in ensuring the stability and predictability of the
training processes. This work presents an edge computing method that mitigates
these shortcomings with minimal additional memory usage, by employing an
autonomous algorithm that prioritizes the storage of training samples with
larger prediction errors. The method is demonstrated on the use case of a
self-commissioning condition monitoring system, in the form of a thermal
anomaly detection scheme for a variable frequency motor drive, where the
algorithm self-learned to distinguish normal and anomalous operation with
minimal prior knowledge. The obtained results, based on experimental data, show
a significant improvement in prediction accuracy and training speed, when
compared to equivalent models trained online without the proposed data
selection process.

通过利用边缘计算方法，使用自主算法来优化存储具有更大预测误差的训练样本，解决电力电子变换器可靠性的问题，并在自动感知条件监测系统中进行了实证研究，结果显示与在线训练等等效模型相比，预测准确性和训练速度都有显著改善。

一种用于数据驱动的电力电子系统异常检测的自校准边缘计算方法

A Self-Commissioning Edge Computing Method for Data-Driven Anomaly  Detection in Power Electronic Systems

Physics-Informed Neural Networks (PINNs) have recently been proposed to solve
scientific and engineering problems, where physical laws are introduced into
neural networks as prior knowledge. With the embedded physical laws, PINNs
enable the estimation of critical parameters, which are unobservable via
physical tools, through observable variables. For example, Power Electronic
Converters (PECs) are essential building blocks for the green energy
transition. PINNs have been applied to estimate the capacitance, which is
unobservable during PEC operations, using current and voltage, which can be
observed easily during operations. The estimated capacitance facilitates
self-diagnostics of PECs. Existing PINNs are often manually designed, which is
time-consuming and may lead to suboptimal performance due to a large number of
design choices for neural network architectures and hyperparameters. In
addition, PINNs are often deployed on different physical devices, e.g., PECs,
with limited and varying resources. Therefore, it requires designing different
PINN models under different resource constraints, making it an even more
challenging task for manual design. To contend with the challenges, we propose
Automated Physics-Informed Neural Networks (AutoPINN), a framework that enables
the automated design of PINNs by combining AutoML and PINNs. Specifically, we
first tailor a search space that allows finding high-accuracy PINNs for PEC
internal parameter estimation. We then propose a resource-aware search strategy
to explore the search space to find the best PINN model under different
resource constraints. We experimentally demonstrate that AutoPINN is able to
find more accurate PINN models than human-designed, state-of-the-art PINN
models using fewer resources.

本文介绍了基于物理知识的神经网络（Physics-Informed Neural Networks）可以用于预测无法通过物理工具观测到的关键参数，并提出了一种称为 AutoPINN 的框架，以自动化设计基于物理知识的神经网络模型，该模型结合了自动机器学习技术和针对特定资源限制的搜索策略，可以提高模型准确度，减少设计时间。