Open AccessA hybrid machine learning and meta‐heuristic algorithm based service restoration scheme for radial power distribution system
Author(s) -
Srivastava Ishan,
Bhat Sunil,
Thadikemalla Venkata Sainath Gupta,
Singh Arvind R.
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12894
Subject(s) - support vector machine , algorithm , particle swarm optimization , ant colony optimization algorithms , computer science , meta heuristic , estimator , sorting , fault (geology) , heuristic , genetic algorithm , mathematical optimization , artificial intelligence , machine learning , mathematics , statistics , seismology , geology
Summary In‐service Restoration (SR), the healthy section of the feeder can be re‐energized by finding the optimal path for power flow. Through conventional methods which are mainly deterministic in nature, the computational burden is very high. Therefore, researchers have proposed various meta‐heuristic based methods to solve the SR problem. But since, these methods are probability based; one single algorithm cannot guarantee optimal solution for all scenarios. Hence, the authors have proposed a Machine Learning (ML) based framework, which can predict the best SR scheme for a particular fault scenario among the SR solutions obtained through various meta‐heuristic algorithms. The supervised ML model is developed using the fault features as input values and the best performing meta‐heuristic algorithm as the target value. To check the validity of the ML framework, the authors have taken four different meta‐heuristic algorithms, which are, Enhanced Integer Coded Particle Swarm Optimization (EICPSO), Shuffled Frog Leaping Algorithm (SFLA), Non‐Dominated Sorting Genetic Algorithm‐II (NSGA‐II), and Ant Colony Optimization (ACO) algorithm. The ML model can be extended for any number of algorithms. Multi‐class Support Vector Machine (SVM) algorithm is used as the estimator in the current work to train and test the developed model. The results obtained through SVM are 95.95% accuracy, 94% precision, and 94% f1‐score. The performance of SVM is better when compared with other state‐of‐the‐art ML estimators namely K‐Nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LR). The experiment is conducted for IEEE 33 bus and IEEE 69 bus test systems. A total of 320 fault instances are created using Power System Computer‐Aided Design (PSCAD) software. The features of these fault points are used as input for the ML model which is extracted using a discrete wavelet transform. This study is conducted for the balanced radial test system.