ArticleScientific reports2026
Real-time implementation of a new MPPT technique based on Shifted Hyperbolic Tangent Function under various weather conditions.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In this study, a new Maximum Power Point Tracking (MPPT) algorithm is proposed for solar energy systems using the Particle Swarm Optimization (TF-IPSO) algorithm based on hyperbolic tangent function parameters. The disadvantages of the PSO algorithm are addressed by using hyperbolic tangent function parameters to update the acceleration coefficients and inertia weights of the proposed TF-IPSO algorithm. The proposed TF-IPSO algorithm is investigated for both partial shading conditions (PSC) conditions and variable radiation conditions with sudden changes in solar radiation. The TF-IPSO algorithm is compared with the most widely used metaheuristic optimization algorithms that demonstrate superior performance, such as Particle Swarm Optimization (PSO), Deterministic PSO (DPSO), Flower Pollination Algorithm (FPA), Improved Social spider optimization algorithm (ISSO), Hyperbolic Slime Mould Algorithm (HSMA) and EPSO algorithm based on shifted sigmoid function parameters Algorithm (ST-PSO). Simulation studies have shown that the proposed TF-IPSO algorithm outperforms similar methods by providing a high MPPT efficiency of 99.97%, fast tracking time, and low power emissions. Furthermore, the success of the proposed TF-IPSO algorithm has also been tested in real time implementation. For this purpose, the Chroma 62,020 H-150HS Solar Array Simulator was used. The performance of the TF-IPSO algorithm is evaluated under partial shading conditions using a PV system with a 4Sx2P connection. From the real-time results, it is observed that the proposed TF-IPSO algorithm has 1.28% higher MPPT efficiencies than the DPSO algorithm, 7.05% higher than the FPA, 0.42% higher than the HSMA, 2.4% higher than the ISSO, 2.31% higher than the PSO algorithm, and 1.18% higher than the ST-PSO algorithm. Simulation studies and results from real-time experiments show that the proposed MPPT method outperforms other metaheuristic algorithms and provides high efficiency by converging to the Maximum Power Point (MPP) under sudden changes in PSC and solar radiation.
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