This paper proposes a solution to the power grid system's reactive power optimization scheduling problem (RPSP) based on a novel Manta ray forging algorithm (MRFO) evolutionary algorithm. By applying the penalty function for the reactive power optimization model, the management of the constraints of the RPSP optimization formula is counting on for calculation. | A Manta-Ray Forging Algorithm Solution for Practical Reactive Power Optimization Problem Hong-Jiang Wang1 Thi-Kien Dao1 Van-Dinh Vu2 Truong-Giang Ngo3 Thi-Xuan-Huong Nguyen4 Trong The Nguyen4 1Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China 2Information Technology Faculty Electric Power University Hanoi Vietnam 3Faculty of Computer Science and Engineering Thuyloi University Hanoi Vietnam 4Haiphong University of Management and Technology Haiphong Vietnam 704328074@ jvnkien@ dinhvv@ giangnt@ huong_ntxh@ vnthe@ Abstract. This paper proposes a solution to the power grid system s reactive power optimization scheduling problem RPSP based on a novel Manta ray forg- ing algorithm MRFO evolutionary algorithm. By applying the penalty function for the reactive power optimization model the management of the constraints of the RPSP optimization formula is counting on for calculation. The experimental results of the proposed MRFO scheme are contrasted with other approaches for the IEEE 30 bus system such as Particle swarm optimization PSO Grey wolf optimizer GWO Moth-flame optimization algorithm MFO and Whale opti- mization algorithm WOA . Comparative results show that the MRFO algorithm can generate stable strong convergence high reliability effectively and a feasi- ble figuration needed space in solving optimization problems with reactive power optimization. Keywords Power system Manta ray foraging algorithm Reactive power opti- mization 1 Introduction In the age of exponential growth of science and technology the conventional meth- ods of optimization are increasingly suffering the computational complicated time whenever facing large scaling problems 1 such as the combinatorial problem of the reactive power optimization scheduling problem RPSP 2 . The traditional methods of optimization would be replaced by modern approaches 3 . The rise of the .
