article · IEEE Access
Integrating photovoltaic systems into existing electrical grids poses power quality challenges due to solar intermittency and converter interactions. To maximise energy yield and maintain grid stability, control schemes must accurately track the maximum power point and filter out electrical distortion. An evaluation of two tracking methods, artificial neural networks and cuckoo search, demonstrated that the neural network approach tracks peak power more effectively. Concurrently, an active LCL power filter was designed and optimised to minimise component size and suppress total harmonic distortion. Comparing optimisation methods showed that the generalised reduced gradient algorithm achieved smaller filter dimensions than a genetic algorithm. Operating the system with this optimised filter decreased total harmonic distortion by 99.78 percent relative to unfiltered operation. These performance findings were confirmed using both computer simulations and a practical experimental configuration.
Solar power generation often injects electrical noise and fluctuations into the power grid, risking damage to sensitive equipment and destabilising energy networks. Demonstrating that artificial neural networks and compact, algorithm-optimised filters dramatically cut harmonic distortion ensures cleaner, more reliable renewable electricity can flow into national grids without degrading overall power quality.
This research targets grid-connected solar power installations and inverter manufacturers seeking to boost energy harvesting while complying with strict grid power-quality standards. Because the approach combines algorithmic maximum power point tracking with an optimised, compact LCL filter, equipment designers could implement it to reduce component footprint and filter costs. Having been tested in a practical configuration alongside simulations, the technology demonstrates applied experimental validation, placing it at an intermediate stage of development towards commercial deployment.
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The expanding use of photovoltaics (PV) as a green energy resource has been rising in these years, mostly due to the possibility of being incorporated with traditional power systems, to meet the world’s energy needs and reduce carbon emissions. However, providing green electricity from this renewable generator is frequently vulnerable to power quality (PQ) disruptions resulting from the PV’s intermittent nature and other factors associated with the electric grid, power converters, and linked loads. These disruptions need to be reduced to keep the investigated system’s PQ from deteriorating. The investigated system includes PV, DC-DC, and DC-AC converters, filter, power grid, and control schemes. If the DC-DC converter is not managed, a deviation from the maximum power point (MPP) extrapolated from the PV system will take place. In order to maximize the energy harvested from the PV system by managing the DC-DC converter, this research developed two MPP tracking (MPPT) algorithms: artificial neural networks (ANN) and cuckoo search (CS). Additionally, a design and implementation for a shunt active power filter (LCL) using genetic algorithm and GRG is provided to lower the injected total harmonic distortion (THD) and thereby enhance the PQ. To achieve the smallest size of the LCL components, the generalized reduced gradient (GRG) was the best compared to genetic algorithms GA. The results of the simulation showed that ANN performed better at tracking maximum power than CS. With the designed LCL, the THD is reduced by 99.78% compared to without a filter. To verify the simulation’s findings, a practical configuration is implemented.
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DOI: 10.1109/access.2023.3317980
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