Scientists from India's GMW Institute of Technology, the M. Kumarasamy College of Engineering, and the National Institute of Technology have used a new bio-inspired optimization algorithm, known as the salp swarm algorithm (SSA), to create a new hybrid algorithm technique for maximum power point tracking (MPPT) optimization in PV systems under partial shading.
The SSA, which has demonstrated its efficiency in a range of applications since it was created in 2017, is based on the swarming behavior of salps in the ocean. It can be applied to both single and multi-objective optimization problems.
The researchers noted the need to create a hybrid algorithm, as the SSA alone presents two main shortcomings – a lack of variety in solutions, and tenacious early convergence.
“Due to these limitations, SSA calls for additional improvement, modifying or hybridizing with other searching techniques, to avoid the early convergence to enhance the performance,” they said.
Their HSSA hybrid technique was simulated through MATLAB-based Simulink software. The HSSA operates first through the SSA, which finds the initial global peak (GP) operating point. This is then followed by a perturb and observation (P&O) algorithm, which is most commonly used for PV applications, in the last stage to achieve a faster convergence rate.
“Thus, the computational burden met by the conventional methods such as standalone P&O, hybrid gray-wolf-optimization (HGWO), and hybrid whale-optimization algorithm (HWOA) algorithm reported in the literature is overcome by the proposed hybrid SSA algorithm called HSSA,” the academics explained. “The HSSA also results in a high tracking efficiency with a quicker convergence.”
They presented the hybrid algorithm in “A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems,” which was recently published in Automatika Journal for Control, Measurement, Electronics, Computing and Communications.
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