Condition monitoring and fault diagnosis for wind turbines can effectively reduce the impact of failures. However, many wind turbines cannot establish fault diagnosis models due to insufficient data. The operational data of similar wind turbines usually contain some universal information about failure properties. In order to make full use of these useful information, a fault diagnosis method based on parameter-based transfer learning and convolutional autoencoder (CAE) for wind turbines with small-scale data is proposed in this paper. The proposed method can transfer knowledge from similar wind turbines to the target wind turbine. The performance of the proposed method is analyzed and compared to other transfer/non-transfer methods. The comparison results show that the proposed method has advantages in diagnosing faults for wind turbines with small-scale data.