Power quality disturbance (PQD) defines the presence of inconsistencies that occur in the usual wave shapes of voltage and current signals. Power quality is considered the main challenge for power industry with the increase in dynamic load and highly subtle electronic devices. Besides, the islanding events, particularly unintended islanding, grasp significant challenges and it needs to be identified at the early stage. Islanding is an anomalousstate in the power system, where the distributed generators (DGs) are placed on supplying electrical energy to the local load even after the shortage of the major grid. Therefore, it is essential to identify and differentiate the PQ events and islanding events in ensuring pollution-free power, equipment, and labor safety. With this motivation, this paper presents an automated optimal deep learning based islanding detection (AODL-ID) technique. The proposed AODL-ID technique involves three major stages namely decomposition, classification, and hyperparameter tuning. Firstly, an empirical mode decomposition (EMD) approach is utilized to decompose the basic signals from the polluted signals. In addition, bidirectional gated recurrent neural network (BiGRNN) technique is employed for the classification of islanding and non-islanding PQ events in the wind energy penetrated DG systems by means of features (Voltage and current (RMS, half-cycle, peak and fundamental) Frequency. Power Factor / Cos Phi. Power and energy (active, reactive, harmonic, apparent)). Since the hyperparameters play a significant role in overall classification performance, the hyperparameter tuning of the BiGRNN model takes place using chaotic crow search algorithm (CCSA). To examine the enhanced classification outcome of the AODL-ID technique, a set of experimental analyses is carried out and the outcomes are investigated interms of various evaluation metrics. The simulation outcomes highlighted the supremacy of the AODL-ID technique over the compared techniques.