Industrial power quality enhancement using fuzzy logic based photovoltaic integrated with three phase shunt hybrid active filter and adaptive controller

Das, Soumya Ranjan; Hota, Ambika Prasad; Pandey, Hari Mohan; Sahoo, Biswa Mohan

doi:10.1016/j.asoc.2022.108762

Cited by 13 publications

(2 citation statements)

References 25 publications

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“…One of the major things working against fossil fuels is the pollution that is linked with the combustion and burning of fossil fuels, which is one of the main reasons for this to happen. In addition to this, the supplies of fossil fuels throughout the globe are rapidly depleting, which is another one of the forces acting against the use of fossil fuels (Nandagopal and Jasmine, 2018;Das et al, 2022). On the other hand, it is a common knowledge that conventional energy sources are detrimental to the ecosystem in which they are located.…”

Section: Introductionmentioning

confidence: 99%

Power quality improvement of PV interfaced distribution system

Sharma,

Bala,

Singh

et al. 2023

Int. J. Eng. Sci. Tech

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In renewable energy, there is solar energy which is on top of the chart when it comes to the generation of electricity as it is abundantly available in India due to the geographical and climate conditions. These power quality problems and degradation of energy can be avoided by using a control mechanism. It is vital to include a reliable control mechanism in the solar energy conversion system that is connected to the system which will reduce the problem in power quality. This work includes the use of adaptive back propagation which provides an improvement in the degree of precision and accuracy over a longer period of time that the technology is capable of achieving as a consequence. With the help of adaptive back propagation which is being used as the foundation for this procedure, we can achieve accurate results which are possible. This work deals with adaptive back propagation-based control for optimal operation by providing the active power to loads and rest of the remaining power will be provided to the grid. It will help in the mitigation of harmonics in the system, balancing the loads, and improving the power factor of the system. To extract the highest amount of energy possible from the solar photovoltaic array, a process that is known as Incremental-conductance (INC) using the maximum power point tracking (MPPT) is used. The outcomes of computer simulations are used to evaluate how well the suggested system functions, which contributes to the assurance that the system is operating in the most suitable way possible.

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Section: Introductionmentioning

confidence: 99%

Power quality improvement of PV interfaced distribution system

Sharma,

Bala,

Singh

et al. 2023

Int. J. Eng. Sci. Tech

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“…It was observed that the LMS algorithm exhibits superior performance with a weight adaptation step size of 0.01. Das et al [6] introduced a solar PV system integrated with a shunt hybrid active filter to mitigate current harmonics and utilize the active power generated by the PV system. The findings indicated that the proposed system effectively maximizes the power output of the PV unit and compensates for current harmonics in nonlinear loads.…”

Section: Introductionmentioning

confidence: 99%

Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models

Li,

Zhu,

Tao

et al. 2023

Processes

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A photovoltaic (PV)-powered electric motor is used for hose-drawn traveler driving instead of a water turbine to achieve high transmission efficiency. PV power generation (PVPG) is affected by different meteorological conditions, resulting in different power generation of PV panels for a hose-drawn traveler. In the above situation, the hose-drawn traveler may experience deficit power generation. The reasonable determination of the PV panel capacity is crucial. Predicting the PVPG is a prerequisite for the reasonable determination of the PV panel capacity. Therefore, it is essential to develop a method for accurately predicting PVPG. Extreme gradient boosting (XGBoost) is currently an outstanding machine learning model for prediction performance, but its hyperparameters are difficult to set. Thus, the XGBoost model based on particle swarm optimization (PSO-XGBoost) is applied for PV power prediction in this study. The PSO algorithm is introduced to optimize hyperparameters in XGBoost model. The meteorological data are segmented into four seasons to develop tailored prediction models, ensuring accurate prediction of PVPG in four seasons for hose-drawn travelers. The input variables of the models include solar irradiance, time, and ambient temperature. The prediction accuracy and stability of the model is then assessed statistically. The predictive accuracy and stability of PV power prediction by the PSO-XGBoost model are higher compared to the XGBoost model. Finally, application of the PSO-XGBoost model is implemented based on meteorological data.

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