An innovative neural network-based technique for identifying power quality issues

Power quality (PQ) is defined as a combination of voltage quality and current quality. PQ is fast and difficult to predict. The primary issues that customers in the industry are consider being related to transitory interruptions, drawback include voltage sags, surges, harmonics and interruptions. To overcome this problem, we proposed an Adaptive Feedforward Bidirectional Gated Recurrent Neural Network (AF-BiGRNN) method to improve power quality issues. A PQ measurement shows a much more valuable asset. In the study, we gather the Reference Energy Disaggregation (REDD) dataset. The collected data is preprocessed using min max normalization to clean the data. Wavelet Packet Transform (WPT) is employed to extract the data features. The selected appropriate data is used to test the process using AF-BiGRNN method. The simulation findings of the result use a Python tool. As a result, our proposed method achieves significant outcomes with Accuracy (96%), Energy consumption (67.8%), Computational time (2.0%) and Voltage stability (97%). The Conclusion that identifies power quality issues is crucial for ensuring the reliability and efficiency of electrical systems.