To obtain reliable high-quality power and improve the accuracy of Power Quality Disturbance (PQD) type identification, a new PQD type based on a Two-Dimensional Discrete Cosine S-Transform (2D-DCST) is proposed. First, based on mathematical models, 17 kinds of power quality events including 7 kinds of complex disturbances are generated. Then, one-dimensional PQD signals are upgraded into two-dimensional signals with equal rows and columns. An amplitude matrix is obtained from the two-dimensional signals using the 2D-DCST method. The statistics, energy and image features of the amplitude matrix are first extracted, and then reduced into a small number of useful feature groups using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, a Support Vector Machine (SVM) classifier is used to construct a type identification model with high identification accuracy and few features. Experimental results show that the method can identify 17 kinds of power quality events accurately and efficiently and also exhibits a good noiseproof feature. At the same time, the method also has a high identification accuracy for complex disturbances. This provides a new method for power quality disturbance type identification. This work is supported by the National Natural Science Foundation of China (No. 61672032) and Anhui Science and Technology Major Project (No. 18030901018).