EMT Simulation results of Synchronized Lissajous D, Q, and synchronized Lissajous dv vs di (Classification Purposes)

<div> <h2>Abstract</h2> <p>This dataset supports the research paper "Synchronized Waveform Monitoring Unit Application: Lissajous DQ Curve to Improve Situational Awareness in Power Distribution Networks". It contains electromagnetic transient (EMT) simulation results from the IEEE 33-bus distribution system, including:</p>   <ul> <li>36,000 training samples (6,000 per class across 6 event categories)</li> <li>18,000 validation samples (3,000 per class)</li> <li>Pre-trained LSTM models for event classification</li> </ul>   <p>The dataset enables reproduction of the novel <i>synchronized Lissajous DQ curve</i> method, which compresses three-phase power quality events into single plots using Park Transform. Data is provided in both CSV (individual samples) and NPY (compiled arrays) formats, with dimensions (samples, timesteps, features) for direct machine learning applications. The accompanying pre-trained models demonstrate 100% classification accuracy while reducing computational requirements by 43-44% compared to conventional methods.</p>   <p>This comprehensive dataset supports research in:</p> <ul> <li>Power quality event detection</li> <li>Waveform data compression</li> <li>Time-domain analysis of synchronized measurements</li> <li>Machine learning applications in power systems</li> </ul> </div>