There are certain unelectrified villages across the Indian subcontinent where providing supply through the grid is difficult due to forest cover or mountainous terrain. The most feasible option is to provide off-grid electrification through renewable energy resources such as solar or wind energy. These intermittent sources do not promise a 24 × 7 supply system. Thus, along with solar or wind energy systems, it becomes important to use a renewable resource, such as biomass, which is available in abundance in rural areas. The need for battery energy storage becomes mandatory in order to store the surplus energy produced by renewable resources and supply it at a time of insufficiency. Currently, many battery technologies are evolving with better characteristics than conventional battery systems in terms of efficiency, response time, deep cycle discharge, lifecycle, etc. The aim of this study is, firstly, to design and model a hybrid renewable energy system (HRES), using photovoltaic (PV)-Biogas (BG) system with HOMER software. Secondly, we aim to test this model using three different battery types: advanced lead acid (LA) batteries, lithium ion (LI) batteries, and zinc-bromine (Zn-Br) flow batteries (FB), used individually. Using these three battery technologies, the HRESs are then compared in terms of system sizing, economy, technical performance, and environmental stability. A case study for the unelectrified village of Madhya Pradesh (MP) is discussed to suggest the practical aspect of the comparative analysis.The results demonstrate that the HRES using LI batteries is the most favorable choice. Using this configuration, the economic parameters, including total net present cost (NPC) and levelized cost of energy (LCOE), are found to be lowest.The technical parameters, including battery state of charge (SOC), capacity shortage, and environmental parameters (CO 2 emissions) are found to be optimum.

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Generation and Transmission of power is a complex process, which requires the working of many components of the power system so as to deliver the maximum power output. We need to manage the flow of Active and Reactive power in an efficient and economic way so as to improvise the performance of power systems. Thus, a Static synchronous compensator (STATCOM) integrated with Photovoltaic (PV) module can be used to optimize the reactive power flow by varying the level of voltage for the Voltage Source Converter with respect to source bus voltage. By connecting PV modules directly to STATCOM, the requirement of DC-DC Converter can be completely overcome as STATCOM regulates DC voltage at optimal value. In the present integrated study system using PV and STATCOM simulated in Simulink software, the effect of varying solar irradiance and changing the number of solar cell modules on the reactive power have been studied.

The compressive strength is most reliable parameter to evaluate the ability of concrete in resisting compression. The paper presents a study on prediction of the compressive strength of roller compacted concrete using multiple regression analysis (MRA) and artificial neural networks (ANN). The compressive strength of roller compacted concrete was obtained experimentally at 3, 7 and 28 days of curing. The samples were prepared by varying the percentage of cement and superplasticizer. The data were organized in three different groups randomly using R statistical software. The models were executed with cement content, coarse and fine aggregate, superplasticizer content, water content and days of aging as input parameters that were used to predict compressive strength which is the output parameter. The analysis was performed using multiple regression and artificial neural networks methodology. Statistical measures like root-mean-square error (RMSE), mean absolute error (MAE) and coefficient of determination are used to assess the performance of models. The determination coefficient from multiple regression analysis is found to be 0.975 and 0.886 for testing and validating the data correspondingly, whereas the determination coefficient from artificial neural network analysis is found to be 0.9 for both testing and validating the data. The results obtained from ANN are highly accurate because of its own topology.

In modern day Psychiatric analysis, Epileptic Seizures are considered as one of the most dreadful disorders of the human brain that drastically affects the neurological activity of the brain for a short duration of time. Thus, seizure detection before its actual occurrence is quintessential to ensure that the right kind of preventive treatment is given to the patient. The predictive analysis is carried out in the preictal state of the Epileptic Seizure that corresponds to the state that commences a couple of minutes before the onset of the seizure. In this paper, the average value of prediction time is restricted to 23.4 minutes for a total of 23 subjects. This paper intends to compare the accuracy of three different predictive models, namely – Logistic Regression, Decision Trees and XGBoost Classifier based on the study of Electroencephalogram (EEG) signals and determine which model has the highest rate of detection of Epileptic Seizure.

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Water is essential for the survival of mankind on the surface of the Earth. The surface water bodies which act as a source of drinking water are prone to pollution in the current days. As a result people rely on groundwater sources for drinking, irrigation etc., It has become a necessity to evaluate the quality of groundwater as it is being polluted to a large extent because of rapid urbanization and industrialization. The present study aims to assess the quality of groundwater in Kurnool district. The samples are collected at various well locations and are studied for physico-chemical parameters H + ion concentration, bi-carbonate, carbonates, sulphates, and chloride concentration. Thematic maps for each physico-chemical parameter are prepared by Inverse distance weightage interpolation method in Geographical Information system. The groundwater quality is assessed using a water quality index, which expresses overall quality at a location. Based on the results potential zones are identified by query builder in ArcGIS. Thus it can be concluded that the potential zones act as sources of drinking water.