• Energy Research

Irrigation is a crucial component of the agriculture industry. The gross domestic output of India is about 15 % derived from its farmers. Crop failure is common among farmers, primarily because of inadequate irrigation techniques and irregular power and water supplies. To begin with, a survey of farmers from various parts of India was undertaken in this regard, and the results indicated that most of them lacked an effective irrigation system. An inventive solar-powered irrigation system devised and deployed to address this problem includes an automated Internet of Things (IoT) system. This IoT system, which functions based on the moisture content of the soil, plays a crucial role in ensuring that the crops receive the right amount of water at the right time. Furthermore, the PV pumping system addresses irrigation issues in various Indian climate conditions, including composite, warm and humid, cold, moderate, hot, and dry. The locations chosen for these climate conditions are Jaisalmer in Rajasthan, Bangalore in Karnataka, Itanagar in Arunachal Pradesh, Patna in Bihar, and Amaravati in Andhra Pradesh. Performance ratios range from 0.514 to 0.739, system and pump efficiencies from 61 % to 88.80 %, and 57.10 %–58.60 %, respectively.

Predicting PV system electricity output is necessary for daily operational management and annual power system planning when integrating solar collector-based photovoltaic (PV) stations into micro grids. Tilting the panels at the ideal angle to maximize solar energy capture is necessary to maximize PV station production. This optimal tilt angle (OTA) must be predicted as it is a nonlinear function of the total solar radiation, diffuse solar radiation, and direct solar radiation. This research explores the use of feature selection-based artificial neural networks (ANN) with various machine learning algorithms to predict the OTA for PV systems at specific locations, aiming to maximize PV output in micro grids. The study identifies global solar radiation, diffuse solar radiation, clarity index, and global solar radiation on inclined surfaces as the most critical inputs for predicting OTA, while extraterrestrial radiation is deemed the least significant. Implementing the appropriate input variables significantly enhanced prediction accuracy from 38.59 % to 90.72 %. Among the neural networks evaluated, the Elman neural network demonstrated the greatest improvement.

Abstract In this study new technique Rapid Miner is used for relevant input variable selection for prediction of solar radiation using different ANN techniques. The prediction accuracy of ANN models developed with artificial neural network fitting tool (nftool), Radial Basis Function Neural Network (RBFNN) and Generalized Regression Neural Network (GRNN) are compared. The Rapid Miner shows that clearness index, extraterrestrial radiation, latitude and longitude are least relevant input parameters and maximum temperature, minimum temperature, altitude, sunshine hour are found to be the most relevant input parameters for solar radiation prediction. The ANN models developed with artificial neural network fitting tool (nftool) give better results than RBFNN and GRNN for solar radiation prediction. The mean absolute percentage error (MAPE) for ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5 are found to be 16.91, 16.89, 16.38, 6.89 and 9.04. The ANN-5 model utilized most accessible input variables so it can be used to predict solar radiation for 41 locations of Gujarat and 35 locations of Rajasthan in Northwestern India. The yearly average solar radiation varies from 4.92 to 5.62 kWh/m2/day for Gujarat and it is varies from 4.66 to 5.54 kWh/m2/day for Rajasthan.

There are many renewable energy sources available, especially wind energy, but it is not being fully utilized. In the wind energy industry, Wind Power Potential (WPP) is essential since it is critical to the development, operation, and optimization of wind power plants. WPP plays a significant role in the wind energy project life cycle, impacting site selection, project viability, technology choices, and ultimate success. This means that specific WPP for certain places need to be determined for development in wind industry.The goal of this study is to conduct a statistical comparison and analysis of the efficacy of various numerical methods, including the method of moments (MoM), the energy pattern factor method (EPFM), the maximum likelihood method (MLM), the energy density method (EDM), the energy pattern factor method of Sathyajith (EPFMS), Rayleigh's distribution (Rayl), and the novel energy pattern factor method (NEPFM). These methods are compared for different sites of Andhra Pradesh India. The NEPFM is considered the most effective approach for assessing the wind energy density in the regions of Visakhapatnam, Amaravati, and Tirupati. Conversely, the MLM (Modified Logarithmic Model) technique has demonstrated superior performance in evaluating the wind energy potential specifically for the Rajamahendravaram site. The Rayleigh distribution, also known as Rayl., was utilized as the primary approach for calculating the probability density of the geographical sites of Visakhapatnam, Rajamahendravaram, and Amaravati. Additionally, the energy pattern factor method was employed to analyze the site of Tirupati. The Rayleigh distribution is found to be the most suitable statistical model for estimating the cumulative density of site locations in Visakhapatnam and Amaravati. Similarly, the innovative energy pattern factor technique is recommended for analyzing the cumulative density of site locations in Rajamahendravaram and Tirupati.

Optimum Photovoltaic (PV) system integration in power grid depend upon the total of power accessible from the PV. To figure the PV systems highest power yield, PV panels must be positioned at an optimal tilt angle (OPTA) to absorb maximum solar radiation (SR). This OPTA is a function of the latitude, clearness index, diffuse SR, global SR, direct SR and optimum PV size. Therefore OPTA has an impact on maximum power generation and optimal PV system sizing. The PV is not installed at OPTA for most of the sites in India which is important for maximum power generation and optimum sizing of standalone PV systems. This results in variation of OPTA from site to site and its effect on PV sizing needs to be investigated. The innovative aspect of this work is the calculation of OPTA, which are employed as sensitive factors in Hybrid Optimization of Multiple Energy Resources (HOMER), to determine their impact on maximum optimum sizing and power generation for 26 cities in India’s various climate zones. This methodology can be applied all over the world to determine the impact of OPTA on maximum power generation and size. It is found that OPTA varies from 63° to 0° throughout year in India and it is maximum for December in India. The results indicates that Net Present Cost varies from $\$ $ 1105 to $\$ $ 1280 and Cost of Energy (COE) variation is 0.041 to 0.048 $\$ $ /kWh throughout India cities and low temperature sites are good for photovoltaic (PV) power generation. Two axis tracking system produces more power in comparison to other tracking systems. This research is beneficial for researcher and industry to install PV system in different climatic zones of India to generate maximum power at minimum cost of energy.

Abstract Solar potential of western Himalayan Indian state of Himachal Pradesh is assessed using Artificial Neural Network (ANN) based global solar radiation (GSR) prediction model. J48 algorithm in Waikato Environment for Knowledge Analysis (WEKA)is used for the selection of input parameters for ANN model for predicting GSR. Most relevant input parameters are found to be temperature, altitude and sunshine hours whereas latitude, longitude, clearness index and extraterrestrial radiation are found to be least influencing variables. The usefulness of J48 algorithm in variable selection is checked by developing five ANN models: ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5. The maximum mean absolute percentage error (MAPE) for ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5 are found to be 16.91%, 16.89%, 16.38%, 6.89% and 9.04% respectively. ANN-5 model is used to develop the solar maps of Himachal Pradesh. The estimated GSR varies from 3.59 to 5.38 kWh/m 2 /day indicating good solar potential for solar energy applications. A correlation is developed between NASA satellite data and ground measured GSR data to find values close to ground measured GSR for different locations. The correlation coefficient is found to be 0.97. Models developed can be used to assess solar potential of any location worldwide.

Wind power generated by wind has non-schedule nature due to stochastic nature of meteorological variable. Hence energy business and control of wind power generation requires prediction of wind speed (WS) from few seconds to different time steps in advance. To deal with prediction shortcomings, various WS prediction methods have been used. Predictive data mining offers variety of methods for WS predictions where artificial neural network (ANN) is one of the reliable and accurate methods. It is observed from the result of this study that ANN gives better accuracy in comparison conventional model. The accuracy of WS prediction models is found to be dependent on input parameters and architecture type algorithms utilized. So the selection of most relevant input parameters is important research area in WS predicton field. The objective of the paper is twofold: first extensive review of ANN for wind power and WS prediction is carried out. Discussion and analysis of feature selection using Relief Algorithm (RA) in WS prediction are considered for different Indian sites. RA identify atmospheric pressure, solar radiation and relative humidity are relevant input variables. Based on relevant input variables Cascade ANN model is developed and prediction accuracy is evaluated. It is found that root mean square error (RMSE) for comparison between predicted and measured WS for training and testing wind speed are found to be 1.44 m/s and 1.49 m/s respectively. The developed cascade ANN model can be used to predict wind speed for sites where there are not WS measuring instruments are installed in India. Comment: Malik, H., Yadav, A. K., M\'arquez, F. P. G., & Pinar-P\'erez, J. M. (2022). Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India. Energy Strategy Reviews, 41, 100864

Abstract In this paper, an attempt has been made to examine the effectiveness of Neuro-Fuzzy Scheme (NFS), to identify the deterioration of the winding insulation paper (WIP) in a oil-immerged power transformer, and to compare its performance over conventional methods (IEEE/IEC). The comparison of convergence characteristics of IEEE and IEC approach reveal that the NFS approach is quite faster in investigations leading to reduction in computational burden and give rise to minimal computer resource utilization. Simultaneous identification of deterioration of the WIP and operating conditions in oil-immersed power transformer has never been attempted in the past using NFS. The technique proposed in this paper provides not only best dynamic response for the deterioration of the WIP diagnosis and condition assessment of power transformer but also present its appropriate maintenance scenario as well. This approach will address a proactive assertion to the power utilities for effective realization of electrical health of oil-immersed power transformer under consideration. In this paper, testing analysis of 25 transformer samples has been carried out to demonstrates the robustness of the investigated four status conditions (Normal Operation – NO; Modest Concern – MCI; Major Concern – MCMI and Imminent Risk Failure – IRF) for wide changes in operating condition and loading condition perturbation.