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The interconnection of the renewable-resources-based distributed generation (DG) system to the existing power system could lead to power quality (PQ) problems, degradation in system reliability, and other associated issues. This paper presents the classification of PQ disturbances caused not only by change in load but also by environmental characteristics such as change in solar insolation and wind speed. Various forms of sag and swell occurrences caused by change in load, variation in wind speed, and solar insolation are considered in the study. Ten different statistical features extracted through S-transform are used in the classification step. The PQ disturbances in terms of statistical features are classified distinctly by use of modular probabilistic neural network (MPNN), support vector machines (SVMs), and least square support vector machines (LS-SVMs) techniques. The classification study is further supported by experimental signals obtained on a prototype setup of wind energy system and PV system. The accuracy and reliability of classification techniques is also assessed on signals corrupted with noise.