Grey Wolf Optimizer-Based Array Reconfiguration to Enhance Power Production from Solar Photovoltaic Plants under Different Scenarios

The extraction of maximum power is a big challenge in solar photovoltaic-based power plants due to varying atmospheric and meteorological parameters. The concept of array reconfiguration is applied for the maximum power extraction in solar PV plants. Using this approach, the occurrence of multiple peaks in P-V and I-V characteristics during partial shade can be smoothened and reduced significantly. Partial shading due to the movement of the cloud is considered in the research. The cloud movement mainly because of velocity and wind direction is used for creating various shading conditions. The main focus is to reduce the power losses during partial shading using a nature-inspired optimization approach to reconfigure the array for different types of shading conditions. A grey wolf optimizer-based bridge-linked total cross-tied (GWO-BLTCT) configuration is proposed in this paper. The performance of the proposed topology is compared with standard and hybrid topologies, namely, series-parallel, total cross-tied, BLTCT, and SuDoKu-BLTCT, based on performance indicators such as fill factor, performance ratio, power enhancement, and power loss. The proposed GWO-BLTCT outperforms the remaining topologies due to the least power loss and high fill factor. It also has the highest average power enhancement and performance ratio with 23.75% and 70.02% respectively

Grey Wolf Optimizer-Based Array Reconfiguration to Enhance Power Production from Solar Photovoltaic Plants under Different Scenarios

The extraction of maximum power is a big challenge in solar photovoltaic-based power plants due to varying atmospheric and meteorological parameters. The concept of array reconfiguration is applied for the maximum power extraction in solar PV plants. Using this approach, the occurrence of multiple peaks in P-V and I-V characteristics during partial shade can be smoothened and reduced significantly. Partial shading due to the movement of the cloud is considered in the research. The cloud movement mainly because of velocity and wind direction is used for creating various shading conditions. The main focus is to reduce the power losses during partial shading using a nature-inspired optimization approach to reconfigure the array for different types of shading conditions. A grey wolf optimizer-based bridge-linked total cross-tied (GWO-BLTCT) configuration is proposed in this paper. The performance of the proposed topology is compared with standard and hybrid topologies, namely, series-parallel, total cross-tied, BLTCT, and SuDoKu-BLTCT, based on performance indicators such as fill factor, performance ratio, power enhancement, and power loss. The proposed GWO-BLTCT outperforms the remaining topologies due to the least power loss and high fill factor. It also has the highest average power enhancement and performance ratio with 23.75% and 70.02% respectively

Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia

Solar photovoltaic (SPV) power penetration in dispersed generation systems is constantly rising. Due to the elevated SPV penetration causing a lot of problems to power system stability, sustainability, reliable electricity production, and power quality, it is critical to forecast SPV power using climatic parameters. The suggested model is built with meteorological conditions as input parameters, and the effects of such variables on predicted SPV power have been studied. The primary goal of this study is to examine the effectiveness of optimization-based SPV power forecasting models based on meteorological conditions using the novel salp swarm algorithm due to its excellent ability for exploration and exploitation. To forecast SPV power, a recently designed approach that is based on the salp swarm algorithm (SSA) is used. The performance of the suggested optimization model is estimated in terms of statistical parameters which include Root Mean Square Error (RMSE), Mean Square Error (MSE), and Training Time (TT). To test the reliability and validity, the proposed algorithm is compared to grey wolf optimization (GWO) and the Levenberg–Marquardt-based artificial neural network algorithm. The values of RMSE and MSE obtained using the proposed SSA algorithm come out as 1.45% and 2.12% which are lesser when compared with other algorithms. Likewise, the TT for SSA is 12.46 s which is less than that of GWO by 8.15 s. The proposed model outperforms other intelligent techniques in terms of performance and robustness. The suggested method is applicable for load management operations in a microgrid environment. Moreover, the proposed study may serve as a road map for the Saudi government’s Vision 2030

An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

The stochastic and erratic behavior of solar photovoltaic (SPV) is a challenge, especially due to changing meteorological conditions. During a partially irradiated SPV system, the performance of traditional maximum power point tracking (MPPT) controllers is unsatisfactory because of multiple peaks in the Power-Voltage curve. This work is an attempt to understand the performance uncertainties of the SPV system under different shading conditions and its mitigation. Here, a novel hybrid metaheuristic algorithm is proposed for the effective and efficient tracking of power. The algorithm is inspired by the movement of grey wolves and the swarming action of birds, and is thus known as the hybrid grey wolf optimizer (HGWO). The study focuses on the transient and steady-state performance of the proposed controller during different conditions. A comparative analysis of the proposed technique with incremental conductance and a particle swarm optimizer for different configurations is presented. Thus, the results are presented based on power extracted, shading loss, convergence factor and efficiency. The proposed HGWO–MPPT is found to be better as it has a maximum efficiency of 94.30% and a minimum convergence factor of 0.20 when compared with other techniques under varying conditions for different topologies. Furthermore, a practical assessment of the proposed controller on a 6.3 kWp rooftop SPV system is also presented in the paper. Energy production is increased by 8.55% using the proposed approach to the practical system

Energy Production Forecasting From Solar Photovoltaic Plants Based on Meteorological Parameters for Qassim Region, Saudi Arabia

Due to the increasing cost of crude oil because of pandemic COVID-19 and global environmental threats, the exploitation of fossil fuels for power generation is discouraged. Further, the demand for electrical power is increasing drastically, and therefore, the exploitation of renewable energy resources, particularly solar photovoltaic-based technology for power generation is invigorated. However, the large-scale penetration of solar photovoltaic is becoming a major challenge in terms of stability, reliability of power when integrated with the grid. Thus, it becomes important to develop a novel approach or strategy which is useful to improve power quality, reliability, and grid stability. Solar photovoltaic power forecasting is a key tool for this new era and becoming the main component for a smart grid environment. Here, in this paper, the ensemble trees approach-based machine learning approach is utilized to forecast the solar photovoltaic power with the help of various meteorological parameters. The high-quality measured data for meteorological parameters for Qassim, Saudi Arabia is used in this research. The performance of the proposed model is evaluated with the help of statistical indices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Training Time (TT) and found within the desired limits. To validate the obtained results a comparative analysis with other machine learning models is carried out. Moreover, the proposed research may provide the roadmap in achieving the vision 2030 of the government of Saudi Arabia