Application of big data technology in optimizing emergency response to power grid disasters

In the era of rapidly advancing technology, the increasing prevalence and complexity of power grid disasters pose substantial challenges to the stability and efficiency of electricity delivery systems worldwide. Big Data technology, characterized by its ability to process and analyze vast and diverse data sets at an unprecedented scale, emerges as a pivotal tool in addressing these challenges. Neural networks offer a promising pathway for the intricate analysis and interpretation of big data relevant to power grid operations. This paper introduces an innovative application of big data analytics through an enhanced Backpropagation (BP) algorithm, designed to assess the severity of power grid disasters accurately. By leveraging the comprehensive processing capabilities of neural networks, the proposed method aims to optimize emergency response strategies, thereby minimizing the detrimental impacts of such disasters. Integrating artificial neural networks with power grid catastrophe level evaluation, this paper provides an IACO-BP technique. The primary goal of this study is to develop a better ant colony optimization algorithm (IACO) that incorporates local route optimization and differential initialization pheromone. In order to optimize parameters and increase the likelihood of achieving the ideal parameters of the prediction model, it is used to substitute the BP network's own gradient descent approach. Secondly, in order to enhance the accuracy of the evaluation, this study creates an IACO-BP model for power grid disaster level assessment using the enhanced ACO optimized BP network.