This paper proposes a structure-inspired parameter estimation method for the composite load model with distributed generation (CMPLDWG) developed by Western Electricity Coordinating Council (WECC). The high dimensionality and strong nonlinearity of this model, due to the aggregated distributed energy resource (DER_A) component, greatly complicate the reliable parameter estimation. We put forth a parameter interdependency analysis to partition the entire parameter space into smaller subsets, thereby decomposing the original high-dimensional estimation problem into multiple tractable subproblems. After applying the interdependency-based parameter grouping, the estimation for each subset is performed using both the Levenberg-Marquardt (LM) algorithm and the enhanced snake optimizer (ESO), demonstrating the solver-agnostic improvements in the convergence stability and estimation accuracy. An initialization strategy is developed to improve the robustness of subsequent optimization. Case studies in the New England 68-bus system confirm that the interdependency-based parameter grouping significantly improves the convergence speed, numerical stability, and estimation accuracy across different disturbance scenarios. The ability of the proposed estimation method is further validated through real-world measurements, and it can be broadly applicable to other modeling problems.