Furthermore, the feature selection process has extensively been used in PQD classification research, aiming to improve the performance of the classification system [39; 40; 41]. The main goal of this process is to optimally select the appropriate data of feature extracting data, so that the processing time and classification accuracy can be improved by eliminating unnecessary data [40; 41]. Many research works in the literature have implemented feature selection techniques for PQD classification, demonstrating that this tool can significantly improve classification accuracy, as following detail. In 2009, [PERSON] et al. proposed the optimal feature selection algorithm for PQD classification using the adaptive particle swarm optimization (PSO) method, while the ST was used as feature extraction and the fuzzy C-means was performed as a classifier. It was found that this system could provide a classification accuracy of 96.33% [42]. In 2013, the PQD classification system using the k-means with an Apriori algorithm as optimal feature selection and WT as feature extraction was introduced, while the least squares support vector machine (LS-SVM) was performed as a classifier to classify five PQD signals [43]. It showed that 98.88% classification accuracy can be obtained. Next, [PERSON] et al. presented a combination of PSO algorithm and DWT in PQD classification based on the extreme learning machine (ELM) classifier, it was demonstrated this system could achieve 97.60% classification accuracy [44]. In 2016, [PERSON] et al. proposed the PQD classification system that uses the sequential forward selection algorithm for optimal features selection, ST with variational mode decomposition (VMD) for feature extraction, and SVM as a classifier. It was shown that this system could reach a classification accuracy of up to 99.66% for nine PQD signal types [21]. In 2017, [PERSON] et al. showed that the PQD classification with the Fischer linear discriminant analysis algorithm for optimal feature selection, VMD as feature extraction, and reduced kernel extreme learning machine (RKELM) as a classifier could provide a classification accuracy of 98.82% [45]. Recently, [PERSON] et al. performed the permutation entropy and the Fisher score algorithm for finding the optimal features of PQD classification using VMD for feature extraction, and one-versus-rest support vector machine (OVR-SVM) for a classifier [46]. Classification accuracy of 97.6% was demonstrated. From the literature surveys, it shows that numerous recent works focus mainly on developing the ability of feature selection to improve the performance of PQD classification system. We also noticed that this process strongly influences the signal's features before entering the classifier. Rather than state of the art in PQD classification, our hypothesis was the performance of the existing PQD classification system can be improved increasingly, especially system accuracy and convergence rate. Therefore, the contribution of this work is to introduce the PQD classification system based on a novel algorithm, namely, the adaptive salp swarm algorithm (SSA) as the feature selection algorithm. This new algorithm is supposed to reach a higher classification performance than the previous studies. The proposed approach was inspired by the fact that the SSA can efficiently find the solution of optimization problems within a short time due to its great exploration, which are typically the required properties in PQD classification. Another research gap was that, despite the high capability of SSA in solving many optimization problems in power systems, the use of this algorithm in power quality classification had not yet been reported. The proposed classification system performed DWT as feature extraction, while the probabilistic neural network (PNN) was used as the classifier. The verification of our proposed system was made based on the real dataset of distribution system. The performance of proposed classification system was compared to that of the previous PQD classification systems.