Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter
Power management in advanced grid systems requires the seamless integration of diverse renewable energy sources. This study investigates the optimization of a grid-connected system comprising a photovoltaic (PV) solar panel, energy storage system, fuel cell (FC), and diesel generator (DG) using the...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/5661381 |
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| author | Nivedita Singh M. A. Ansari Manoj Tripathy Pratiksha Gupta Ikbal Ali Adel Saleh Rawea |
| author_facet | Nivedita Singh M. A. Ansari Manoj Tripathy Pratiksha Gupta Ikbal Ali Adel Saleh Rawea |
| author_sort | Nivedita Singh |
| collection | DOAJ |
| description | Power management in advanced grid systems requires the seamless integration of diverse renewable energy sources. This study investigates the optimization of a grid-connected system comprising a photovoltaic (PV) solar panel, energy storage system, fuel cell (FC), and diesel generator (DG) using the bioinspired metaheuristic technique called jellyfish optimization (JF). The objective is to maximize power generation from the PV system under normal and partial shading conditions. The performance of JF is compared against particle swarm optimization (PSO) using various parameters. As India heavily relies on solar PV, the results highlight JF’s exceptional effectiveness in extracting maximum power during partial shading scenarios. Inspired by the active and passive motions of jellyfish in the ocean, the JF algorithm is utilized. To further optimize the power output, the system is integrated with an efficient battery management system, PEM fuel cell stacking, and diesel generators. The system’s performance is analyzed using fast Fourier transform (FFT) to evaluate harmonic distortions, which consistently meet the limits specified in IEEE STD 1547-2018. Furthermore, unscented Kalman filter-based analysis is employed to assess total harmonic distortion (THD) and power rating for the grid system across various renewable energy scenarios. The contribution of the jellyfish optimization (JF) algorithm lies in its ability to efficiently and effectively maximize power generation from the PV system, regardless of normal or partial shading conditions. JF, a bioinspired metaheuristic optimization technique, successfully emulates the collective behavior of jellyfish in the ocean to identify optimal solutions. In this study, JF outperforms particle swarm optimization (PSO) in terms of power generation under partial shading conditions. Notably, JF exhibits remarkable capability in exploring the search space and discovering the global optimum, even when the system operates under challenging conditions. Overall, this study demonstrates the tremendous potential of JF in maximizing power generation in grid-connected systems with renewable energy sources while also highlighting the benefits of integrating additional components to further enhance the system performance. |
| format | Article |
| id | doaj-art-8a400635f7e74fe68ea88fe341b40b61 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-8a400635f7e74fe68ea88fe341b40b612025-02-03T06:48:32ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/5661381Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman FilterNivedita Singh0M. A. Ansari1Manoj Tripathy2Pratiksha Gupta3Ikbal Ali4Adel Saleh Rawea5Electrical Engineering DepartmentElectrical Engineering DepartmentElectrical Engineering DepartmentElectrical Engineering DepartmentElectrical Engineering DepartmentElectrical Engineering DepartmentPower management in advanced grid systems requires the seamless integration of diverse renewable energy sources. This study investigates the optimization of a grid-connected system comprising a photovoltaic (PV) solar panel, energy storage system, fuel cell (FC), and diesel generator (DG) using the bioinspired metaheuristic technique called jellyfish optimization (JF). The objective is to maximize power generation from the PV system under normal and partial shading conditions. The performance of JF is compared against particle swarm optimization (PSO) using various parameters. As India heavily relies on solar PV, the results highlight JF’s exceptional effectiveness in extracting maximum power during partial shading scenarios. Inspired by the active and passive motions of jellyfish in the ocean, the JF algorithm is utilized. To further optimize the power output, the system is integrated with an efficient battery management system, PEM fuel cell stacking, and diesel generators. The system’s performance is analyzed using fast Fourier transform (FFT) to evaluate harmonic distortions, which consistently meet the limits specified in IEEE STD 1547-2018. Furthermore, unscented Kalman filter-based analysis is employed to assess total harmonic distortion (THD) and power rating for the grid system across various renewable energy scenarios. The contribution of the jellyfish optimization (JF) algorithm lies in its ability to efficiently and effectively maximize power generation from the PV system, regardless of normal or partial shading conditions. JF, a bioinspired metaheuristic optimization technique, successfully emulates the collective behavior of jellyfish in the ocean to identify optimal solutions. In this study, JF outperforms particle swarm optimization (PSO) in terms of power generation under partial shading conditions. Notably, JF exhibits remarkable capability in exploring the search space and discovering the global optimum, even when the system operates under challenging conditions. Overall, this study demonstrates the tremendous potential of JF in maximizing power generation in grid-connected systems with renewable energy sources while also highlighting the benefits of integrating additional components to further enhance the system performance.http://dx.doi.org/10.1155/2023/5661381 |
| spellingShingle | Nivedita Singh M. A. Ansari Manoj Tripathy Pratiksha Gupta Ikbal Ali Adel Saleh Rawea Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter International Transactions on Electrical Energy Systems |
| title | Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter |
| title_full | Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter |
| title_fullStr | Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter |
| title_full_unstemmed | Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter |
| title_short | Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter |
| title_sort | enhancing the hybrid microgrid performance with jellyfish optimization for efficient mppt and thd estimation by the unscented kalman filter |
| url | http://dx.doi.org/10.1155/2023/5661381 |
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