论文标题
EGFC：从无休止的半监督数据流中不断发展的高斯模糊分类器 - 应用于电源质量干扰检测和分类
EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams -- With Application to Power Quality Disturbance Detection and Classification
论文作者
论文摘要
功率质量干扰导致了几个缺点，例如生产能力的限制，线路和设备电流的增加以及随之而来的欧姆损失；较高的工作温度，过早故障，机器预期寿命的降低，设备故障以及计划外停机。实时检测和干扰分类被认为对行业标准必不可少。我们提出了一个不断发展的高斯模糊分类（EGFC）框架，用于半监督干扰检测和分类，并结合了在电压波形的Landmark窗口上应用的混合Hodrick Prescott和离散式转移属性属性 - 攻击方法。考虑了尖峰，切口，谐波和振荡瞬变等干扰。与其他监视系统不同，这些监视系统需要基于有限的数据和事件来离线培训模型，而拟议的基于数据流的EGFC方法能够通过适应飞行中模糊规则基础的参数和结构来从永无止境的数据流中自动学习干扰模式。此外，获得的模糊模型在语言上是可解释的，可以提高模型的可接受性。我们显示出令人鼓舞的分类结果。
Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher operating temperatures, premature faults, reduction of life expectancy of machines, malfunction of equipment, and unplanned outages. Real-time detection and classification of disturbances are deemed essential to industry standards. We propose an Evolving Gaussian Fuzzy Classification (EGFC) framework for semi-supervised disturbance detection and classification combined with a hybrid Hodrick-Prescott and Discrete-Fourier-Transform attribute-extraction method applied over a landmark window of voltage waveforms. Disturbances such as spikes, notching, harmonics, and oscillatory transient are considered. Different from other monitoring systems, which require offline training of models based on a limited amount of data and occurrences, the proposed online data-stream-based EGFC method is able to learn disturbance patterns autonomously from never-ending data streams by adapting the parameters and structure of a fuzzy rule base on the fly. Moreover, the fuzzy model obtained is linguistically interpretable, which improves model acceptability. We show encouraging classification results.
