Application of Sine–Cosine Optimization Algorithm for Minimization of Transmission Loss

Abstract

On recent trends, the reactive power planning problem has received a considerable amount of attention for the allocation of reactive power resources, both static and dynamic such as switchable capacitors and/or reactors, as well as Var compensators, respectively. The reactive power has a significant impact on voltage profile, stability and transmission loss in power systems. In recent years, voltage instability is recognized as a major threat to system operation due to a shortage of reactive power in an interconnected transmission line. These voltage conditions could be analyzed and improved by proper coordination of reactive sources/sink. In this article, the minimization of transmission loss and operating cost is performed by using the sine cosine algorithm (SCA) and quasi-oppositional based sine cosine algorithm (QOSCA) technique for standard IEEE 14 and IEEE 30 and IEEE 57 bus test system of the connected power network. The proposed QOSCA reduces the transmission losses by 6.38% in IEEE 14, 4.56% in IEEE 30 and 12.77% in IEEE 57 bus system. The results obtained by QOSCA and SCA are compared with other popular techniques recently reported in recent state-of-literature. It is observed that QOSCA yields better results in terms of reducing transmission loss, operating costs, and improving the voltage profile of each bus.

Optimal allocation of distributed generation for power flow enhancement in distribution network

“…Saurav Raj and Biplab Bhattacharyya 31 have implemented a Whale optimization algorithm for optimal placement of TCSC and SVC for reactive power planning for minimizing active power loss and improving voltage profile in the system. The multi‐constrained optimization problems for attaining the objective function can be obtained by employing metaheuristic algorithm namely Ameliorated Harris Hawk Optimizer (AHHO) is proposed by Sheila Mahapatra et al 32 A proper allocation of the both static and dynamic switchable capacitors and reactors are employed for reactive power planning problem with a considerable amount of reactive power resources is provided by Rohit Babu et al 33 Chandan Kumar Shiva et al 34 have proposed a new oppositional crow search optimization algorithm for optimal placement of DG along with Fuzzy‐based Shunt operated reactive power requirement. Rohit Babu et al 35 have suggested an Optimal reactive power planning solution using oppositional gray wolf optimization by considering bus vulnerability analysis for reducing the power system instability and improved voltage profile in the system.…”

OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm

“…Among many optimization algorithms, the meta-heuristic optimization algorithm is widely used because of its low cost, high accuracy, and fast speed. So far, various optimization algorithms have been introduced to deal with nonlinear and practical applications, such as genetic algorithm (GA) [13], particle swarm optimization (PSO) [14], whale optimization algorithm (WOA) [15], butterfly optimization algorithm (BOA) [9], sine cosine optimization algorithm (SCA) [16], crow optimization algorithm (CSA) [17], gray wolf optimization algorithm (GWO) [18], and bee colony optimization algorithm (ABC) [19]. In recent years, Raj et al proposed the Whale Optimization Algorithm (WOA) to optimize TCSC and SVC reactive power planning for the problems of transmission loss and high operating costs.…”

“…Shiva and Gudadappanavar proposed an opposite crow search algorithm for transmission loss, which has better performance in reducing active power and system operation cost [16]. In order to solve the problems of insufficient reactive power and unstable voltage of transmission lines, Babu and Kumar et al proposed an improved sine cosine optimization algorithm, which uses the techniques of the sine cosine algorithm (SCA) and quasi inverse sine cosine algorithm (QOSCA) to minimize transmission losses and operating costs [17]. Xu et al proposed an improved hunger game search algorithm (IHGS) for solar photovoltaic system parameter identification, which solved the problem of stability of the algorithm when solving the global optimal solution.…”

Statistical analysis based reactive power optimization using improved differential evolutionary algorithm

“…This paper considers several unrestricted practical constraints in the OPF problem, the most prominent of which are the valve‐point effect, multi‐fuel option, and, above all, prohibited operating zone. Several meta‐heuristic approaches have been taken in to consideration over the years to address optimal strategy of reactive power like teaching and learning based optimization (TLBO) (Bhattacharyya & Babu, 2016), gravitational search algorithm (GSA) (Bhattacharyya & Kumar, 2016; Duman et al, 2012), sine cosine algorithm (Babu, Kumar, et al, 2022), artificial bee colony (ABC) algorithm (Ettappan et al, 2020), brainstorm optimization algorithm (BSOA) (Jordehi, 2015), ant lion optimization (ALO) (Mouassa et al, 2017) algorithm, and so forth. The Whale optimization algorithm (WOA) was proposed in (Raj & Bhattacharyya, 2018a) to reduce the system real power loss as well as the operational cost of network.…”