<p>A grid supervisory control and data acquisition system is developed in this study to address the problem that the existing power grid information system often fails to achieve the high-precision requirements of modern power grid situational awareness. By introducing a nonlinear convergence factor and a microecological evolution mechanism, the grey wolf optimization algorithm is improved to enhance population diversity and local search performance. The enhanced optimization algorithm is then employed to fine-tune the key hyperparameters of a long short-term memory neural network, leading to a more accurate and stable situational prediction approach. Experimental results demonstrate that the mean square error of the proposed method was reduced by 78.33%, 65.79%, and 53.57%, respectively, compared with other benchmark models. The root mean square error was reduced by an average of 44.62%, and the coefficient of determination reached 0.85. The false positive rate and false alarm rate of the system designed by the study in situational assessment were reduced by 52.33% and 53.33% respectively as compared to the traditional methods of situational assessment. The results demonstrate that improving the grey wolf optimizer algorithm by using opposition-based learning and small habitat evolution mechanism can improve the algorithm’s search accuracy for localization. By utilizing the enhanced grey wolf optimizer method to identify and enhance the long short-term memory’s hyperparameters, it is possible to anticipate possible network intrusions and enhance the security of the grid information system.</p>