| Popis: |
Anomaly detection in power systems is crucial for operational reliability and safety, often addressed through binary classification in existing research. However, a research gap exists in multiclass classification for enhanced reliability. To bridge this gap, this study employs four machine learning (ML) classifiers: Random Forest (RF), Decision Tree, Naive Bayes (NB), and Support Vector Machine (SVM) using comprehensive testing on a dataset comprising sixteen indices and their pair combinations (totaling 136 pairs). These classifiers, trained on a dataset derived from simulating a test system with hybrid DGs, exhibit superior anomaly detection, especially with the $\frac {dv}{dq}\& \frac {dv}{dp}$ pair. Among them, RF and DT classifier achieves precision, recall, and F score of unity and outperforming NB and SVM. The performance of the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair is compared with existing research papers in terms of accuracy and data division. The comparison shows that the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair achieve 100% accuracy even with 50% data division, whereas other techniques fail to achieve it even at 20% for testing and 80% for training. The study underscores the critical role of pair selection and classifier combinations in effective anomaly detection, facilitating the implementation of robust mitigating strategies for power system stability. |
