Dissolved Gas-in-oil analysis (DGA) is used to monitor the condition of
bushings on large power transformers. There are different techniques used in
determining the conditions from the data collected, but in this work the
Artificial Intelligence techniques are investigated. This work investigates
which gases in DGA are related to each other and which ones are important for
making decisions. When the related and crucial gases are determined, the other
gases are discarded thereby reducing the number of attributes in DGA. Hence a
further investigation is done to see how these new datasets influence the
performance of the classifiers used to classify the DGA of full attributes. The
classifiers used in these experiments were Backpropagation Neural Networks
(BPNN) and Support Vector Machines (SVM) whereas the Principal Component
Analysis (PCA), Rough Set (RS), Incremental Granular Ranking (GR++) and
Decision Trees (DT) were used to reduce the attributes of the dataset. The
parameters used when training the BPNN and SVM classifiers are kept fixed to
create a controlled test environment when investigating the effects of reducing
the number of gases. This work further introduced a new classifier that can
handle high dimension dataset and noisy dataset, Rough Neural Network (RNN).

此研究使用人工智能技术来确定与变压器绕组状况相关的气体成分，采用 PCA，RS，GR++ 和 DT 等技术来减少气体成分属性以提高分类器性能，还介绍了一种能够处理高维和嘈杂数据的 RNN 分类器。

利用计算智能技术进行变压器套管状态监测

Condition Monitoring of Transformer Bushings Using Computational Intelligence

Semi-supervised anomaly detection is an approach to identify anomalies by
learning the distribution of normal data. Backpropagation neural networks
(i.e., BP-NNs) based approaches have recently drawn attention because of their
good generalization capability. In a typical situation, BP-NN-based models are
iteratively optimized in server machines with input data gathered from edge
devices. However, (1) the iterative optimization often requires significant
efforts to follow changes in the distribution of normal data (i.e., concept
drift), and (2) data transfers between edge and server impose additional
latency and energy consumption. To address these issues, we propose ONLAD and
its IP core, named ONLAD Core. ONLAD is highly optimized to perform fast
sequential learning to follow concept drift in less than one millisecond. ONLAD
Core realizes on-device learning for edge devices at low power consumption,
which realizes standalone execution where data transfers between edge and
server are not required. Experiments show that ONLAD has favorable anomaly
detection capability in an environment that simulates concept drift.
Evaluations of ONLAD Core confirm that the training latency is 1.95x~6.58x
faster than the other software implementations. Also, the runtime power
consumption of ONLAD Core implemented on PYNQ-Z1 board, a small FPGA/CPU SoC
platform, is 5.0x~25.4x lower than them.

提出了 ONLAD 与其 IP 核 ONLAD Core，并且将其优化到能够在一毫秒以内快速序列学习以跟随概念漂移，在边缘设备上进行低功耗的设备学习，从而实现了不需要边缘和服务器之间执行数据传输的独立执行。实验结果表明，ONLAD 具有优秀的异常检测能力，并且 ONLAD Core 的评估证实其训练延迟比其他软件实现快 1.95x 到 6.58x，PYNQ-Z1 板上实现的运行时功耗比它们低 5.0x 到 25.4x。