Optimal energy extraction under partial shading conditions from a photovoltaic (PV) array is particularly challenging. Conventional techniques fail to achieve the global maximum power point (GMPP) under such conditions, while soft computing techniques have provided better results. The main contribution of this paper is to devise an algorithm to track the GMPP accurately and efficiently. For this purpose, a ten check (TC) algorithm was proposed. The effectiveness of this algorithm was tested with different shading patterns. Results were compared with the top conventional algorithm perturb and observe (P&O) and the best soft computing technique flower pollination algorithm (FPA). It was found that the proposed algorithm outperformed them. Analysis demonstrated that the devised algorithm achieved the GMPP efficiently and accurately as compared to the P&O and the FPA algorithms. Simulations were performed in MATLAB/Simulink.

Renewable energy resources are being used to overcome energy shortage. From the point of view of energy management the interconnectivity of electric utility with renewable energy resources is difficult. Renewable energy resources need to be managed with electric utility. A Smart Energy Management System (SEMS) is designed and developed for monitoring an efficient load management of electric utility and photovoltaic power system is presented in this research. The design consists of an Energy Management Center (EMC) and Field Programmable Gate Array (FPGA). Energy Management Center shows the runtime data and also keeps the data log and offers control of the load shifting between utility source and photovoltaic power system. Analog to digital converter is used to interface the current and voltage sensors with FPGA. ZigBee is connected for wireless radio data transmission between the FPGA and energy management & monitoring center. The SEMS increases the efficiency of energy up to 10.5 percent in comparison with normal systems.

In competitive industry, economical and environmentally friendly production techniques are essential. In this sense, cleaner and more sustainable machining techniques are the industry’s focus. In addition to green methods, effective parametric control is necessary for hard-to-cut materials, particularly titanium Ti-6Al-4V, which is extensively used in a diversity of industries, including aerospace, medical, and military applications. Therefore, the current study aims to improve the machining performance of Ti-6Al-4V alloy using sustainable lubrication conditions. The effect of Al₂O₃ nanoparticles based on the minimum quantity lubrication (N-MQL) condition on surface quality and productivity are compared with minimum quantity lubrication (MQL). The performance measures, including surface roughness (Ra), material removal rate (MRR), and temperature, are evaluated at three machining variables, i.e., cutting speed (V_c), feed rate (f), and depth of cut (a_p). These performance measures are further assessed by tool wear and surface morphology analysis. a_p, f, and V_c are the most influencing parameters for Ra, MRR, and temperature, regardless of lubrication mode. The optimized values of RA of 0.728443 µm, MRR of 2443.77 m³/min, and temperature of 337 °C are achieved at N-MQL. For the N-MQL state, the optimized values of Ra of 0.55 µm, MRR of 2579.5 m³/min, and temperature of 323.554 °C are attained through a multi-response optimization desirability approach. Surface morphology analysis reveals a smooth machined surface with no obvious surface flaws, such as feed marks and adhesion, under N-MQL conditions, which significantly enhances the surface finish of the parts. The machining performance under the N-MQL condition has been enhanced considerably in terms of an improvements in surface finish of 32.96% and MRR of 11.56%, along with a decrease in temperature (17.22%) and higher tool life (326 s) than MQL. Furthermore, Al₂O₃ is advised over MQL because it uses less energy and has reduced tool wear and improved surface quality, and it is a cost-effective and sustainable fluid.

Introduction. The huge demand of green energy over past few decades have drawn the interest of scientists and researchers. Solar energy is the most abundant and easily available source but there have been so many problems with its optimum extraction of output. The factors affecting the maximum power point tracking of PV systems are input irradiance, temperature, load etc. The variations in irradiance level lead to partial shading that causes reduction in performance by not letting system to operate at maximum power point. Many methods have been proposed in literature to optimize the performance of PV systems but each method has shortcomings that have failed all of them. The actual problem occurs when partial shading is very strong; this is where most of the methods totally fail. So proposed work addresses this issue and solves it to the fullest. The novelty in the proposed work is that it introduces a new nature-based algorithm that works on the principle of plant propagation. It is a natural optimization technique that plants follow to survive and propagate in different environmental conditions. The proposed method efficiently tracks the global peak under all shading conditions and is simple to implement with high accuracy and tracking speed. Purpose. Building an algorithm that can track global peak of photovoltaic systems under all shading conditions and extracts the maximum possible power from the system, and is simple and easy to implement. Methods. The method is implemented in MATLAB / Simulink on an electrical model that uses a PV array model. Different shadings are applied to check for the results. Results. The results have shown that for different photovoltaic configurations the algorithm performs very good under uniform and partial shadings conditions. Its accuracy, tracking efficiency and tracking time has increased reasonably. Practical value. The project can be very beneficial to people as it enhances the performances of PV systems that can make them self-sufficient in electrical energy, focuses on sustainable development and reduces pollution. This way it can have huge impact on human life.

Power Quality (PQ) improvement in grid-integrated photovoltaic (PV) and wind energy hybrid systems for effective power transfer is presented in this paper. Due to interlinked hybrid renewable energy resources and nonlinear loads, various issues arise which affect the power quality, i.e., voltage sag, harmonic distortion increases, and also reactive power demand. In order to mitigate these issues, flexible alternating current transmission system (FACTS) devices are utilized. In this paper, hysteresis band current controller (HBCC)-based static synchronous compensator (STATCOM) is modeled to reduce PQ problems. HBCC is a robust and simple technique to improve voltage profile, reduce total harmonic distortion (THD) and fulfill the reactive power demand. Two case scenarios of the hybrid system, i.e., (I) grid integrated hybrid system without HBCC (II) grid integrated hybrid system with HBCC, are tested. Results demonstrate that under scenario II, load bus voltage is regulated at 1.0 p.u., THD of system voltage and current is reduced 0.25% and 0.35%, respectively, and reactive power demand of 30 kVAR is fulfilled. The HBCC was designed for reducing THD of the system with the limits specified by standards IEEE 519-1992 STATCOM using hysteresis band current controller to improve power quality in the distribution system which is simulated using MATLAB/SIMULINK. After that, the performance of the system is better in terms of power quality.

The soiling phenomenon occurs when photovoltaic panels are covered with layers of dust resulting in the reduction of radiation reaching the panels, thus reducing PV performance. This research focuses on the PV performance degradation due to soiling and the chemical composition of dust particles. Four types of dust samples having different physical, chemical, and optical properties were collected for experimental analysis. Three of them were coal, cement, and iron oxide, which are extensively used in cement plants, collected to investigate the impact of darker color dust on albedo and hence on the transmission of sunlight, and the fourth sample was named as panel dust, collected from solar plates installed at a cement plant. The experimental results show that the transmission of sunlight strongly depends on particle size distribution. Fine particles increase the transmission loss and degrade the performance of the solar cell more than larger ones, as these particles leave no empty spaces between grains and hence do not allow sunlight to reach the solar cell, while randomly accumulated larger particles leave voids between grains, allowing light to penetrate. Moreover, albedo and a higher quantity of iron oxide in dust boost the absorption of sunlight and play a significant role in the scattering and attenuation of solar irradiance.