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In this paper, modified dragonfly algorithm (MDA) with ant lion optimization (ALO) based Load Flow Analysis (LFA) with hybrid power flow controller in comparison with unified power flow controller is established. The proposed technique is exemplify at enhanced search ability, random reduction and decreased complexity. Here, search behavior of dragonflies is updated to ALO algorithm. Hence, the combination of these two optimization algorithm is named as MDA2LO technique. In this paper, load flow studies are suggested with obtain voltage, real and reactive power and load angle in each bus of power system to divide their steady state circumstances utilizing MDA2LO approach. The proposed system is actualized on MATLAB/Simulink working platform. The proposed study is experienced at IEEE 30 and 14 bus system. The performance of proposed system is compared to previous method like salp swarm algorithm-firefly (SSA-FF), combination of modified salp swarm optimization algorithm and moth-flame optimization called CMSSMFO and dragonfly algorithm (DA).The result accuracy (RA) of proposed and existing method is also analyzed. The RA of the proposed hybrid system is 52.7699% and SSA-FF is 9.665%, CMSSMFO is 11.995%, DA is 10.721%. Overall, the comparison outcomes decide the benefit of proposed system and confirm their potential for resolving these issues.

Development of various maximum power point tracking (MPPT) control techniques for proposed systems such as solar photo-voltaic (PV), wind turbine (WT), fuel cell (FC) and hybrid renewable energy system (HRES). HRES is the combination of PV, WT and FC which is connected parallelly by DC link. It is implemented in real-time using OPAL-RT system. In this research article, the MPPT algorithms viz. Perturb and Observe (P&O), Fuzzy Logic (FL), Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been analyzed and compared. Results have been carried out to record tracking performance of MPPT controllers by introducing changes in the radiation, wind speed hydrogen fuel rate. It has been observed that the proposed HRES using ANFIS-based MPPT controller provides better response as compared to other specified MPPT controllers.

High-resolution information is needed for precision agriculture to achieve precise management of inputs. High spatial and temporal resolution is requisite to get the actionable information for the timely response. The objective of the present study is to estimate the leaf chlorophyll Concentration using high-resolution (2 cm) images captured from UAV-mounted multispectral sensors for crop health monitoring. In this study, a hexacopter was flown at an elevation of 25 m to capture the images in green, red, red edge and NIR bands of turmeric plots grown at ICAR Research Complex, Northeast Hilly Region, India. A handheld SVC spectroradiometer having spectral range from 350 to 2500 nm was also used to collect the spectra of sample plants to support the UAV study. We evaluated an advanced machine learning algorithm kernel ridge regression combined with spectral information and ground-truth chlorophyll data to model the chlorophyll estimation. The multivariate analysis was also applied on spectroradiometer and UAV data, which recommended red band for chlorophyll prediction with R2 value greater than 0.6. We also found that kernel ridge regression is a robust method for developing chlorophyll estimation model with lesser training time. The results indicate that kernel ridge regression with a radial basis kernel function with four multispectral input bands can be utilized to evaluate the leaf chlorophyll concentration with an root mean squared error RMSE = 0.10 mg/g and regression coefficient R2 = 0.7452. However, this study is site specific and needs to be practiced in different crop sites in order to generalize this method for precision agriculture.

There is a sudden fluctuations in wind speed and change of direction that affects the generator speed thus power generation of wind turbine system, so a controller like maximum power point tracking (MPPT) algorithm is essential in tracking the maximum power out of the available wind speed. MPPT algorithm can be classified into two categories such as with and without sensor as well as according to the algorithms used to find the maximum peak. A comparative analysis of different types of MPPT control methods on the basis of different speed responses and ability to obtain maximum power has been conducted. The literature based on the simulation results points out that the optimal torque control is a better MPPT control method for achieving the maximum power from wind turbine energy system against the most frequently used perturb and observe (P&O) method. The P&O method on the other hand is flexible and easy to implement but provide fluctuations about the maximum power point and is less efficient. The different types of MPPT techniques have been studied for wind turbine renewable energy system. The study points out that MPPT may also include of different dc–dc power converter and various control methods, and hybrid renewable energy system can be developed with multi-input energy systems.

The non-conventional energy sources possess a major problem of power reliability due to their dependencies on the environment. Advance research and development in solar, wind and other renewable energy sources are needed to solve the problem of power demand and reliability. The proposed hybrid system incorporates three renewable energy systems consisting of photo-voltaic (PV), wind turbine (WT) and fuel cell (FC) systems working together to provide maximum power at DC-link. A vast deal of novel research is done for improving the efficiency of an overall system using maximum power point tracking (MPPT) method. In this paper, the developed fuzzy logic controller (FLC)-based MPPT method has been used to analyze PV, WT and FC with DC–DC converters. The FLC-based MPPT control method optimizes the output of the proposed hybrid system with variable inputs to extract maximum power. In order to fabricate a reliable hybrid system, there is a massive scope of research to develop multi-input renewable energy systems.

In modern years due to rising environmental issues such as energy cost and greenhouse gas emission have motivated new research into alternative methods of generation of electrical power. A vast deal of new research and enlargement for the renewable energy photovoltaic (PV) system. The PV module is conducted to search out non-polluting and renewable sources. New inventions are in development and exploring the perfection of solar cells to increase the efficiency and reduce the cost of power in per peak watt. The analysis of different kinds of control methods in PV system according to reviewed previous studies, shows that the most useful method is a hybrid technique as compared to other maximum power point tracking (MPPT) control methods. MPPT control method used to optimize the output of solar PV system with variable inputs such as solar radiations and temperature. The MPPT may include the use of a different DC–DC converter and also some different MPPT algorithms such as current based MPPT. Multi-input energy systems for the hybrid wind/solar energy systems need to be developed.