An improved corona-virus herd immunity optimizer algorithm for network reconfiguration based on fuzzy multi-criteria approach. (January 2022)

• Record Type: Journal Article
• Authors: Naderipour, Amirreza; Abdullah, Aldrin; Marzbali, Massoomeh Hedayati; Arabi Nowdeh, Saber
• Is Part Of: Expert systems with applications. Volume 187(2022)
• Publication Date: 2022-01
• Subjects: Network reconfiguration -- Fuzzy multi-criteria approach -- Power quality -- Reliability -- Improved corona-virus herd immunity optimizer algorithm; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115914
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Distribution network reconfiguration considering losses, reliability and power quality. Network reconfiguration based on new fuzzy multi-criteria approach. Using of a new Improved Corona-virus Herd Immunity Optimizer Algorithm (ICHIOA) Investigation of load variations impact on unbalanced network reconfiguration. Superiority of ICHIOA compared to CHIO, PSO, GWO, MFO, BA and ALO. Abstract: Reconfiguration of the distribution network to determine its optimal configuration is a technical and low-cost method that can improve different characteristics of the network based on multi-criteria optimization. In this paper reconfiguration of unbalanced distribution networks is presented with the objective of power loss minimization, voltage unbalance minimization, voltage sag improvement, and minimizing energy not supplied by the customers based on fuzzy multi-criteria approach (FMCA) using new improved corona-virus herd immunity optimizer algorithm (ICHIOA). The voltage unbalances and voltage sag is power quality criteria and also the ENS refers to the reliability index. Conventional CHIOA is inspired based on herd immunity against COVID-19 disease via social distancing and is improved using nonlinearly decreasing inertia weight strategy for global and local exploration improvement. The methodology is implemented as single and multi-objective optimization on 33 and 69 bus IEEE standard networks. Moreover, the performance of the ICHIOA in problem-solving is compared with some well-known algorithms such as particle swarm optimization (PSO), grey wolf optimizer (GWO), moth flame optimizer (MFO), ant lion optimizer (ALO), bat algorithm (BA) and also conventional CHIOA. The simulation results based on the FMCA showed that all criteria are improved with reconfiguration due to compromising between them while in single-objective optimization, some criteria may be weakened. Also, the obtained results confirmed the superiority of the ICHIOA in comparison with the other algorithms in achieving better criteria with lower convergence tolerance and more convergence accuracy. Moreover, the results cleared that the ICHIOA based on FMCA is capable to determine the best network configuration optimally to improve the power loss, voltage sag, voltage unbalance, and ENS in different loading conditions.