Optimal detection and classification of grid connected system using MSVM-FSO technique

• Published in: Environmental science and pollution research international Vol. 31; no. 21; pp. 31064 - 31080
• Main Authors: Stallon, Samuel Raj Daison, Anand, Ramanpillai, Kannan, Ramasamy, Rajasekaran, Seenakesavan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Classification; computer software; Design; Earth and Environmental Science; Ecotoxicology; Engineering; Environment; Environmental Chemistry; Environmental Health; Environmental science; Fault diagnosis; Hybrid systems; Methods; Neural networks; Photovoltaic cells; Photovoltaics; Research Article; Support Vector Machine; Support vector machines; Turbines; Waste Water Technology; Water Management; Water Pollution Control; Wind power; Wind turbines; Photovoltaic; Accuracy; Feeders; Fault detection; Buses; Grid; Wind turbine; Power Quality
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-024-32921-x

This paper, a hybrid method, is proposed for protecting the hybrid photovoltaic (PV) and wind turbine (WT) system. The proposed protecting method is the hybrid wrapper of both the multiple support vector machine (MSVM) and firebug swarm optimization (FSO), commonly named as MSVM-FSO method. The proposed technique is diagnosing the appropriate fault occurring in the hybrid system. The main purpose of the proposed system is to assure the system with lower complexity for the fault diagnosis and detection (FDD) for improving the power quality (PQ) of hybrid method. Here, the MSVM approach is used to detect the fault conditions of grid-tied system. To evaluate the events of voltages, fault and the currents of hybrid systems are analyzed at the feeder of buses. The FSO categorizes the types of fault, which is occurred in grid-connected system. By then, the proposed method’s performance is done in the MATLAB software and it is contrasted with different existing methods. From this, the proposed method provides accuracy as 99.7% and efficiency as 98%, which is high compared to existing methods.