Achieving enhanced grid management through digital asset feature identification and automation techniques to improve operational efficiency

Abstract

Reliable grid management is critical for the efficiency and stability of electrical transmission and distribution systems. As grid systems become more sophisticated and complex, there is an increased need for digital asset management and automation to make operations more efficient. A lot of standard grid management techniques include manual physical inspection and repair of assets. These methods tend to take too long and raise operational costs. The proposed project will improve the grid management methods by employing both digital asset feature identification and automation. The intention is to promote greater operational efficiency through automation identified through real-time monitoring of decision-making processes. The proposed process will include identifying digital maintenance features on grid assets, including transformers, electricity lines, and meters, using IoT sensors. To standardize the input values, the data is pre-processed with procedures such as z-score normalization. Features including voltage, temperature, and current are extracted using the Discrete Wavelet Transform (DWT). Attention-based Bidirectional Gated Recurrent Units with Grid Search Optimization (AttenBi-GRU-GSO) method are used to analyze the processed data to predict the faults and optimize performance. The AttenBi-GRU-GSO combines the attention mechanism to focus on critical features, Bi-GRU to capture sequential dependencies from both past and future, and GSO to fine-tune the model’s hyperparameters for optimal performance, ensuring efficient and accurate fault detection and prediction in grid management. Experimental results demonstrate that the AttenBi-GRU-GSO method achieves superior performance, with an accuracy of 95.31%, an MAE loss of 0.11, a total loss of 0.07, and an RMSE loss of 0.03. Automated systems provide control of detected problems by responding with remedial actions, including rerouting power and organizing maintenance. The automation increases the accuracy and speed of fault detection, as well as reduces downtime and maintenance costs. Digital asset feature identification and automation enhances grid management, lowers operating costs, and increases total electrical network reliability. The proposed technology provides a scalable platform for current grid operations.