Levy Flight based Chameleon Optimization Algorithm with Modified Gated Recurrent Unit for the Detection of Load Imbalance in Electrical Systems

The electricity is the basic need in the current generation where the whole world is dependent on the electrical appliances. As the usage of electricity is increasing the demand of electricity is also rising in the world. balancing the load to maintain the electrical demands has become a difficult task for the management of electricity. The existing algorithms tried to handle the issues of imbalanced load in the electrical system but did not attain the expected outcome for balancing the load demands. To overcome the problem of load imbalance a Levy Flight based Chameleon Optimization Algorithm with Modified Gated Recurrent Unit (LFCOA-MGRU) is proposed in the system for the detection of load imbalance. The State Grid Corporation of China (SGCC) dataset is used as an input data for the model. The data is cleaned using min-max normalization and then the Levy Flight based Chameleon Optimization Algorithm (LFCOA) selected the important features from the data that increases the analysis of load balance details. The Modified Gated Recurrent Unit (MGRU) was the classifier used for the detection of load balance in the electrical system. The developed LFCOA-MGRU has shown accuracy of 0.981 and precision of 0.979 compared to the existing STL-FractalNet-LightGBM model.