EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams -- With Application to Power Quality Disturbance Detection and Classification
This addresses power-quality disturbance detection for industrial applications, but it is incremental as it builds on existing fuzzy and data-stream methods.
The authors tackled real-time detection and classification of power-quality disturbances, proposing an evolving Gaussian fuzzy classifier that learns autonomously from semi-supervised data streams, with results described as encouraging.
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.