This paper presents two new intelligent systems for power quality assessment applications. One is for automated power quality disturbance detection and classification, and the other is for power system model validation. Seeking improved performance over existing approaches for power quality disturbance detection and classification, a novel fuzzy-expert system utilizing fuzzy logic and expert sy...

This paper introduces a new concept of advanced power quality assessment. The concept is implemented using the software for event detection, classification and characterization. The role of the modeling and simulation in the power quality assessment is also discussed. The use of the field recorded data and simulated data in the equipment performance analysis is outlined. All the mentioned appro...

This paper presents the combination of advanced signal processing techniques and the machine intelligence approach to classify the power quality events. The Wavelet Transform (WT) and the S – Transform (ST) are utilized to extract the important useful features of the disturbance signal. The features extracted by using the above approaches are used to train a PNN classifier for automatic classif...

• A deep learning framework for intelligent classification of PQD signals is proposed. The improved Bagging algorithm has better accuracy and generalization performance. active strategy selects the most representative samples from a dataset. uses less labeled data thus saving manual labeling cost. automatic power quality disturbances (PQD) great significance solving problems. In this study, we ...

Abstract Power quality is one of the most important research eras for energy sector. Suddenly dropped voltages or suddenly rising and harmonics in should be identified. All these distortions are called power disturbances (PQDs). Deep learning based convolutional artificial neural networks with an attention model approach has been carried out. The main idea to develop a new network (CNN) which c...