Application of hybrid TLBO-PSO algorithm for allocation of distributed generation and STATCOM

Abstract

This paper proposes a hybrid teaching learning-based optimization- particle swarm optimization (TLBO-PSO) Algorithm for optimal distributed generation (DG) and STATCOM placement. A multi-objective formulation is developed that optimizes the DG and STATCOM placement in IEEE 33 and real-time 52 bus distribution system. The objective function formulated involves maximizing cost-benefit and voltage stability factors while minimizing network security index and power losses. Simulation is carried out in MATLAB/Simulink for different systems and different cases. The results for single and multiple STATCOM and DG are compared with TLBO and PSO algorithm and the results exhibit better convergence performance with the hybrid optimization algorithm.

Optimization and analysis of distributed generation units in distributed system for minimizing losses

“…These VSI calculations are used to find the sensitivity node of the system. The VSI of the bus "t" is defined [12] as per the given in (1). Here, the Vt represents the voltage of the kth node where 'n' shows the number of nodes.…”

“…Ansari and Byalihal [12] introduced a hybrid algorithm combining teaching learning-based optimization with particle swarm optimization for optimally placing DG and STATCOM. Their approach, applied to both IEEE 33 and a real-time 52 bus distribution system, aimed to balance cost-benefit and voltage stability while reducing power losses and network security risks.…”

Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms

“…Different algorithms have been employed to investigate the suitable capacity and placement of Distributed Generation (DG) and DSTATCOM units are mentioned as follows: the Bacterial Foraging Optimization Algorithm (BFOA) [4], Multi-Verse Optimization Algorithm (MVOA) [5], Differential Evolution Optimization Algorithm (DEOA) [6], Slime Mould Algorithm [7], Multi-Objective Grasshopper Optimization Algorithm [8], Teaching Learning Based Optimization-Particle Swarm Optimization [9], Genetic Salp Swarm Algorithm [10], Northern Goshawk Optimization algorithm [11], Dwarf Mongoose Optimization Algorithm [12] The goal of the paper is to identify the optimum placement and size of photovoltaic distributed generation and distribution static synchronous compensators on a radial distribution network according to the best-obtained result from the different particle swarm optimization applied algorithms and compare it to the other algorithms existing in the literature. The study was conducted using a standard IEEE-33 bus as the testing system by lessening active power dissipation and voltage profile enhancement.…”