A Novel Hybrid Maximum Power Point Tracking Technique for PV System under Complex Partial Shading Conditions in Campus Microgrid

Abstract

Solar generation has become increasingly important in grid applications. In order to improve the energy efficiency of the photovoltaic array (PV), factors such as temperature, nonlinear characteristics, and partial shadow conditions (PSCs) of the PV must be fully considered. An excellent maximum power point tracking (MPPT) control strategy can effectively improve the energy utilization efficiency of photovoltaic cells and provide strong support for the construction of smart campuses in terms of environmental protection and energy saving. A traditional method such as Perturb & Observe (P&O) and incremental conductance (INC) will fall into the local maximum power point (LMPP). In the past decade, researchers have proposed many MPPT methods to solve the difficulties of the PV system. However, they have failed to fully consider dynamic changes in irradiance conditions. Changes in the irradiance of photovoltaic arrays can lead to an extension of the convergence time and an increase in the oscillation amplitude. Many current MPPT methods have shortcomings such as requiring a long convergence time, large oscillation amplitude, and being prone to falling into LMPP. In order to reduce the oscillation amplitude and improve the convergence speed, a novel Multi-strategy Improved Tuna Swarm Optimization hybrid INC (ITSO-INC) method is introduced in this article. This strategy involves improving the Tuna Swarm Optimization (TSO) through Levy Flight and a linear weight coefficient. In addition, the INC method is added in the later stage to improve the accuracy of MPPT tracking. The proposed algorithm can extract the global maximum power point under different partial shading. In order to verify the effectiveness of the proposed method, the proposed method was compared with other metaheuristic algorithms such as Cuckoo Search (CS) and TSO. The proposed ITSO-INC technique was tested over four different patterns of partial shading conditions. Modulation was performed by tracking the sudden change in the shadow pattern of the MPP. These simulation results confirm that the proposed method has fast convergence, high accuracy, zero steady state oscillation, and a rapid response to dynamic change.

A Comprehensive Review of Existing and Pending University Campus Microgrids

“…Numerous different optimization techniques have been proposed to analyze the campus microgrid performance systems. Among these techniques, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tuna Swarm Optimization (TSO), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), and Gradient-based Grey Wolf Optimizer (GGWO), the Hybrid Optimization of Multiple Electric Renewables (HOMER), Firefly Algorithm (FA), LabVIEW Simulation Model (LSM), Mixed Integer Linear Programming (MILP) [101], nonlinear programming [90], High-Reliability Distribution System (HRDS), YALMIP toolbox of MATLAB, Mixed Integer Conic Programming (MICP), and Quantum Teaching Learning-Based Optimization (QTLBO), NSGA-II, and EDNSGA-II [102][103][104][105][106][107]. Sardou et al [108] proposed a robust algorithm that integrates the PSO algorithm with the primal-dual interior point (PDIP) method for the efficient management of microgrid energy.…”

“…Mellouk et al [113] developed the Parallel Genetic-Particle Swarm Optimization Algorithm (PGPSO) to address the challenges of solving energy management optimization problems and determining the appropriate size of renewable energy components. Li et al [106] introduced a novel approach that combines incremental conductance (INC) and Improved Tuna Swarm Optimization Hybrid INC (ITSO-INC) to accurately track the maximum power point. Moreover, Rajagopalan et al [107] enhanced the Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) algorithm to clarify the microgrids' optimal operation.…”

“…Hertzog, P. E et al [118] concluded that the LabVIEW simulation model (LSM) was more confident than HOMER. Furthermore, Li et al [106] proved that the ITSO-INC algorithm outperformed both the CS and TSO algorithms in high accuracy, fast response to dynamic changes, rapid convergence, and the lack of steady-state oscillation. Rajagopalan et al [93] found that the OGGWO algorithm surpassed the PSO, CS, GWO, GGWO, NSGA-II, and EDNSGA-II to reduce costs and mitigate pollution.…”