The integrity and reliability of wind turbine electrical data (such as active power, voltage, current, etc.) are crucial for operational monitoring, fault diagnosis, and predictive analysis in wind energy systems. However, due to various reasons such as hardware failures, network communication issues, environmental interference, and human errors, data gaps still exist in the Supervisory Control and Data Acquisition (SCADA) systems. Existing multivariate wind power time series imputation methods face two main limitations: (1) inadequate handling of continuous missing patterns (band missing and feature missing) and (2) insufficient utilization of spatiotemporal and feature correlations among wind turbines. To address these shortcomings, this study proposes an imputation framework that includes two types of SCADA data missing scenarios in wind turbines. For band missing, the framework leverages similar wind turbine data matching to explore spatiotemporal correlations in wind power data. For feature missing, the framework focuses on feature correlations in wind power data using Pearson coefficients and normalized mutual information. Additionally, we designed a novel Dual-Type Deep Convolutional Generative Adversarial Imputation Network (DT-DCGAIN) model within this framework to impute different types of missing data. Finally, by evaluating the proposed method on real-world wind farm SCADA datasets, it achieved a 13.91% to 28.32% improvement in Root Mean Square Error (RMSE). Ablation experiments on the model further validated the contributions of each correlation extraction module.