Performance, cost, and aesthetics are all difficult to beat in today’s expanding distributed rooftop solar sector, and flat-plate PV is no exception. Photovoltaics will be able to take advantage of some of their most significant advantages as a result of this marketplace, including the elimination of transmission losses and the generation of power at the point of sale. Concentrated photovoltaic (CPV) technology, on the other hand, represents a viable alternative in the quest for ever-lower normalised energy costs and ever-shorter energy payback times. Material, components, and manufacturing techniques from allied sectors, particularly the power electronics industry, have been adapted to lower system costs and time-to-market for the system under development. The LFR is less than 30 mm wide to maximise thermal efficiency, and a densely packed cell array has been used to maximise electrical output. The Matlab simulations show that the proposed machine learning-based LFR technique has a greater concentration rate than the present LFR method, as demonstrated by the results.

Photovoltaic (PV) research is rapidly growing, and the need for controlled environments to validate new MPPT controllers is becoming increasingly important. Currently, researchers face several challenges in testing MPPT algorithms due to the unpredictable nature of solar PV power generation. In this paper, we propose a new photovoltaic emulator (PVE) that could replace solar panels and ensure a highly controllable environment suitable for testing photovoltaic (PV) systems. In this PVE, the complex nonlinear equations of the PV cell/module are fast computed and resolved by a new linearization technique which involves the systematic breakdown of the current-voltage (-) curve of the PV into twelve linear segments. Based on input environmental conditions, an artificial neural network (ANN) was constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. Using simple linear equations, with the segment boundary coordinates, a reference voltage was generated for the PVE. A nonlinear backstepping controller was designed to exploit the reference voltage and stabilize the power conversion stage (PCS). The PVE was optimized using particle swarm optimization (PSO). Several tests have shown that the proposed nonlinear controller provides better dynamic and robust performance than the PI controller, the most reputable and recurrent control method in the area of PVE. The PVE was coupled with a recently proposed integral backstepping MPPT controller and analyzed under several dynamic conditions, including the MPPT test specified by EN 50530. It was found that the accuracy of the proposed PVE measured by its relative error is less than 0.5%, with an MPPT efficiency of greater than 99.5%. The attractive results achieved by this PVE make it especially suitable for simulating and validating MPPT controllers.

This paper presents an enhanced perturb and observe (P&O) method for reconciling the trade-off problem between the dynamic response and steady-state oscillations in maximum power point tracking (MPPT). The constraint of having to sacrifice either the dynamic response or the steady-state oscillations has been solved. The method uses the relationship between the open-circuit voltage and maximum power voltage from the fractional open-circuit voltage (FOCV) MPPT method to establish a valid, reduced, and confined search space within which an enhanced P&O via dynamic adaptive step size terminates the search for the maximum power point. The feasibility of the proposed method has been validated by comparing its performance with the conventional P&O algorithm. It was noted that the proposed method increased the operational efficiency of the PV module to 99.89%, reduced the tracking time to 1.8 ms, and preserved the good steady-state response with a power attenuation of less than 0.10 W or relative 0.16% under MATLAB environment. An experimental setup was used to collect real irradiance and temperature data which was used in real-time simulations. The enhanced P&O method was able to resist abrupt changes in irradiance and temperature as it effectively and efficiently followed the maximum power point (MPP). Finally, to appreciate the supremacy of the proposed method, it was compared to nineteen different MPPT methods from literature. The comparison showed that the enhanced P&O MPPT method is highly efficient and effective for MPPT in photovoltaic (PV) generation systems.

In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.

The temperature in solar cells is one of the main factors affecting their efficiency. Increasing the temperature in solar cells reduces efficiency. According to previously published and recently published studies by our team, with increasing temperature in 5-layer FTO/i-SnO2/CdS/CdTe/Cu2O solar cells, the efficiency has decreased by 8.86% per 100 K. In this research, phase change materials have been used to control the temperature in 5-layer solar cells. Our overall goal in this study is to control the temperature in FTO/i-SnO2/CdS/CdTe/Cu2O solar cells to increase their efficiency. The results obtained using simulations and numerical analysis and comparative analysis show that if one layer is used as a cooling arrangement in 5-layer FTO/i-SnO2/CdS/CdTe/Cu2O solar cells, it reduces the surface temperature of solar cells and increases efficiency.

This research work focuses on augmenting the power conversion efficiency of the polycrystalline silicon solar cell with the aid of antireflection coating (ARC) of synthesized molybdenum disulphide (MoS2). The sol-gel technique and electrospraying method were preferred for synthesizing and depositing MoS2 as transparent thin films on the surface of the solar cells. The optical, electrical, structural, and thermal properties of the coated solar cells were analyzed for understanding the influence of the MoS2 coating. Five different samples (A-II, A-III, A-IV, A-V, and A-VI) were coated with varying coating time. Among them, 120 min coated sample experienced a maximum power conversion efficiency (PCE) of 17.96% and 18.82% under direct sunlight and neodymium light with resistivity as low as . The investigation of optical properties of the coated solar cells revealed a maximum transmittance of 93.6% and minimum reflectance of 6.3%, achieved for A-IV sample in the visible UV spectrum. Sample A-IV showed prominent results in the temperature analysis with temperatures as low as 38.9°C in uncontrolled and 43.2°C in controlled source environments. The results from various analyses proved that MoS2 was an appropriate material for an antireflection coating to enhance the performance of polycrystalline solar cell.

A smart grid environment is prone to data explosion while controlling a microgrid system. Islanded Microgrid’s stability analysis involves a large number of system state variables thus consuming more computational memory due to parallel connected inverter dynamics. Parallel inverters generate reference voltage and frequency using droop controllers, unlike grid-connected inverters where the primary grid provides the reference voltage and frequency. This paper develops feature-reduced stability analysis of the parallel inverters thus reducing the computational memory of its stability analysis. Principal component analysis being a feature extraction technique is applied to reduce the number of variables determining stability. MATLAB is used to develop the average model of a parallel inverter with an LCL filter and a three-phase AC load. Evaluation of the stability analysis using the state variable analysis with the virtual resistance method is simulated. Simulation validates stability analysis of the model with reduced state variables. An average model developed using MATLAB and PCA carried out using Python clearly indicated the validation of the dimensionality reduction in the stability analysis. The reduced number of variables is validated for a stable range of the parallel inverter droop controller. Both cases validated the dimensionality reduction in the stability analysis of parallel inverters.

In this work, an innovative approach based on the estimation of the photovoltaic generator (GPV) parameters from the Bald Eagle Search (BES) optimization algorithm, associated with a support vector machine (SVM) classification algorithm, allowed to highlight a new tool for the classification of the signatures of shading and moisture PV defects. It recognizes signatures generated by the GPV in healthy and erroneous operation using the optimized parametric vector and classifies defects using the same optimized vector. The technique emphasizes the resilience of parameter estimate in terms of error on all parameters. The classification accuracy is 93%. The residuals between the estimated curve in healthy operation with a minimum error of the order of 10-4 and the one at fault are used as an indicator of faults.

In this paper, the effects of particle size difference of aluminum (Al) powder on physical properties of Al powder and sintering properties of Al paste were investigated. For this purpose, respectively, Al powders with four mean particle sizes were used, which were obtained by two classifications using the same nitrogen atomization process. Thermal analysis results showed that the smaller the Al particle size, the lower the oxidation temperature and the higher the reaction enthalpy, indicating higher reactivity of the Al powder. Four Al powders were prepared into Al paste by the same process, and the resistances of the paste increased with the decrease of the particle size of the aluminum powder. Electrochemical capacitance-voltage profiler (ECV) and scanning electron microscope (SEM) analysis showed that the smaller the Al particle size, the thicker the back surface field (BSF) and the higher the doping concentration of BSF. It was found that the smaller size of Al powder limited the migration of silicon, which resulted in higher concentration of silicon (Si) in the Al-Si liquid phase. This leads to reaching a balance at a higher temperature between the recrystallization of silicon from the alloy liquid and the dissolution of silicon in the liquid alloy and a higher doping concentration and a higher effective BSF doping thickness. Results of the study will provide reference for further study and application of Al paste.

A series of tests were conducted to investigate the performance of a solar tunnel dryer for drying ginger. To supply hot air to the dryer, two axial flow fans with a power rating of 28 W, a supply voltage of 220 V, and a 50 W photovoltaics (PV) module were employed. By dividing the 8.5-meter-long solar tunnel dryer into four equal portions every thirty minutes, solar radiation, dry air temperature, ambient temperature, relative humidity, and air velocity were measured at five solar tunnel dryer stations. The hot air temperature at the collector output grew from 34°C to 65.5°C for an 8-hour operation in the no-load condition when the solar radiation was changed between 540 and 820 W/m2. At 9:00 a.m., the average maximum temperature was 30°C. During the loading operation, the temperature was 77°C at 1:00 p.m. The moisture content of sliced ginger was reduced from 90.4 to 11.8% on a wet basis using the solar tunnel dryer. With a solar collector area of 6 m2, open sun drying takes 40 hours to achieve the same wet basis condition. A total of eight experiments were carried out, both with and without loads. The dry air temperature at the collector outlet ranged from 34.0 to 65.5 °C. As the drying efficiency, collector area, and time savings improve, the drying time decreases. The ginger is kept in a controlled area, resulting in high-quality dried ginger. The solar tunnel dryer showed a net saving in drying time of 40% over open sun drying.

In the early days, greenhouse energy did not pay much attention to coating inspections and new applications, spending more attention on repair solar energy projects instead. However, these attitudes have recently changed. Energy producers realize that preventing corrosion and deterioration is less expensive than solving the greenhouse problems when they occur. The proposed model also provides coating, paint control, and error analysis services within the scope of solar machinery and equipment-related services while the greenhouse equipment reached a low energy level. The greenhouse monitoring services ensure that a solar plant is economical, reliable, and of high quality, meets legal requirements, conforms to standards published by domestic and foreign organizations, and determines conditions that cause short circuits or power outages. In this context, with the help of cloud computing-based Internet of things (IOT), the industrial power stations, high-voltage substations, low-voltage networks, power stations that comply with legal regulations on safety from electricity, electrical installations for machinery, alarm systems, fire alarm systems, cathodic corrosion protection mechanisms in oil tanks and pipelines, emergency power supply installations, electrical installations in buildings, and gas alarm systems are inspected and documented.

Renewable energy is the best option for the challenge of dwindling natural resources and energy scarcity. The utilization of solar photovoltaic (PV) systems is the best option for eliminating the energy deficit in Tanzania due to the available great potential of solar energy. Animal manure is a significant source of waste in rural locations which can be transformed into biogas fuel by an anaerobic process. Livestock and agriculture greatly support economically the majority of the sub-Saharan African (SSA) region’s rural population including Tanzania, and excreta from cattle are beneficial for biogas fuel production. Unfortunately, the high potential of animal waste for generating electricity is underutilized. Integrating solar energy sources and biogas fuel derived from animal manure is useful for mitigating energy shortage, power instability, and environmental issues. Off-grid solar PV biogas-based hybrid microgrid systems for rural electrification applications in the Tanzanian environment are limited, and also, most of the studies are extensively carried out using soft computing tools especially hybrid optimization of multiple energy resources (HOMER) software with limited applications of artificial intelligence (AI) optimization techniques. This paper presents technoeconomic viability analysis for a hybrid renewable energy supply system (HRESS) for the Simboya village in Mbeya region, Tanzania. Off-grid HRESS is designed and optimized to meet the load of the chosen location executed using HOMER software and the grey wolf optimization (GWO) method. The microgrid is anticipated to supply daily maximum demand of 63.41 kW. The residential load profile equals 30 kW representing 50% of the daily demand. Optimization results by the HOMER platform indicate that the system has a total net present cost (NPC) and levelized cost of energy (LCOE) of $106,383.50 and $0.1109/kWh, respectively. Furthermore, this paper presents the optimization and sensitivity analysis results acquired by the GWO method under varied values of Loss of Electrical Power Probability (LEPP). Total NPC and LCOE based on LEPP values of 0, 0.04, and 0.06 are $85,106.8, $79,545.99, and $71,747.36 and $0.0887/kWh, $0.0316/kWh, and $0.0102/kWh, respectively. HRESS is economically and environmentally beneficial for supplying electricity to the selected area and worldwide in similar situations.

This paper is concerned with the fast state observer for a class of continuous-time linear systems with unknown bounded parameters and sufficiently slowly time varying which satisfy the usual assumptions of conventional state observer for time-invariant plants. A less conservative approach based on hyperstability analysis is proposed to deal with the tracking error involved in Popov’s inequality. Sufficient conditions that ensure the asymptotic stability of the closed-loop system are established and formulated in term of a nonlinear part which is designed with appropriate proportional and derivative gains. This observer included the derivative of the estimation error. The results obtained are satisfactory and less conservative than the Lyapunov stability analysis for the estimation error dynamic system. Also, it is showed that with a good choice of Proportional-Derivative (PD) gains, it is possible to reduce in this case to zero, the estimation error on the one hand, and on the other hand to reduce it to small residues in an asymptotic way. Finally, a numerical example of a lateral motion of CESSNA 182 aircraft system is presented to reconstruct the sideslip angle and the roll angle, respectively, and to highlight the efficiency of the approach that has been developed.

In a grid-connected photovoltaic system, forecasting is a necessary and critical step. Solar Power is very nonlinear; this article develops and analyses building integrated photovoltaic (BIPV) forecasting algorithms for different timeframes, such as an hour, a day, and a week ahead, to manage grid operation effectively. However, a model built for a certain time scale may improve performance at that time scale but cannot be utilized to make predictions at other time scales. Here, we demonstrate how to use the multitask learning algorithm to create a multitime scale model for solar BIPV forecasting. Effective resource distribution across several tasks is shown. The suggested multitask learning approach is implemented using LSTM neural networks and evaluated over a range of horizons. We employed a modified version of the Chicken Swarm Optimizer (CSO) that takes the best features of the CSO and the GWO algorithms and merges them into one efficient approach to estimate the hyperparameters of the proposed LSTM model. The proposed approach consistently outperformed state-of-the-art single-timescale forecasting algorithms across all time scales.

Perovskite solar cells (PSCs) were fabricated using a two-step spin-coating method with MACl added to the inorganic layer. The properties of the perovskite films were characterized by SEM, XRD, PL, UV-vis spectra, etc. The morphology of the PbI2 film was significantly changed, and the formation of MACl-related intermediate phase was observed at the grain boundaries. The grain size and crystallinity of the perovskite film increased, and the morphology at grain boundaries was optimized, while the composition of perovskite remained unchanged. The introduction of MACl improved the open circuit voltage () and fill factor () of PSCs, and the optimal device efficiency reached 21.59%. This paper presents a new approach using additives in inorganic layers to optimize the crystallization process for efficient PSCs.

To address the problems of wind and solar generation volatility and lose of wind and photovoltaic resources, on the basis of the complementary property of wind-solar-water, the topology structure of the wind-solar-water synergy power generation system is constructed. Taking the minimum grid fluctuation index, the minimum wind-photovoltaic-hydro discard rate and the greatest economic effectiveness of the power station as the goal functions and considering various constraints of the wind, photovoltaic, and hydrostation units, a triobjective optimization running model of the wind-photovoltaic-water synergy system is established. Meanwhile, this essay suggests an IMOSSA on the basis of tent chaotic sequence and random wandering strategy to settle the described triobjective optimization issue. Taking Hubei Pankou as an example for simulation analysis, after choosing the best scheme, IMOSSA compared with MOSSA, MOGWO, and NSGA-II, the volatility of sunny days is reduced by 12.39%, 19.5%, and 36.71%, respectively; the wind-photovoltaic abandonment rate is reduced by 11.17%, 22.5%, and 38.03%, respectively, while in the rainy days the volatility is reduced by 8.09%, 18.34%, and 47.03%, respectively; the wind-photovoltaic abandonment rate is reduced by 14.84%, 16.86%, and 40%, respectively. Therefore, it is possible to demonstrate the validity of the proposed three-objective model and the efficiency of the IMOSSA in solving the issue. The efficiency of the optimization operation approach suggested in this research is confirmed by the case study, providing a new idea for the large-scale consumption of new energy in high-proportion hydropower grids.

In machining processes, cutting tools play a dominant role in producing quality products. The quality of finished goods is directly related to the cutting tool condition. Several types of research have been carried out in cutting tool condition monitoring. On the other hand, the manufacturing industries should be aware of the cutting tool selection, operating conditions, and performance of cutting tools. This article emphasizes the performance of coated cutting tools and tool materials for various machining operations. Nowadays, the nanocoating of CNC tool inserts increases the wear resistance, vibration emissions, metal removal rate, etc. These coating techniques influence the manufacturing industry to increase the productivity and quality of the finished goods and reduce the machining cost. The performance of thin film multilayered coatings such as TiN, TiAlN, AlTiN, Ti, and TiCN on plain silicon carbide tool inserts is revealed by the researchers to guide the manufacturing industry for proper tool selection and standard machining inputs for metal removal operation. The influence of coating material such as TiBN, TiN, TiAlN, and CrAlSiN in cutting tools leads to increase the life time of the cutting tools, which decreases the material sticking and cutting forces. Titanium carbo nitride is wear-resistant and corrosion-resistant. Compared to TiCN, TiAlN is harder due to the higher hardness of 32 GPa. This article concludes the material selection based on the work piece material which yields good metal removal with less cutting forces. The article concludes the cutting material selection based on the work piece for machining operations.

Sustainable generation is impacted by the adoption of renewable energy, the growth of energy markets, and economic strategies. This paper offers a sustainable strategy and a technoeconomic analysis of off-grid hybrid energy systems (HES) in remote islands of Iran, including Lavan, Larak, and Failaka, utilizing PV module, wind turbine, and hydrokinetic turbines. Hourly wind speed, solar irradiation, and hydrovelocity have been implemented under load following (LF) and cycle charging (CC) dispatch strategies in order to ascertain the most appropriate systems. Lavan Island achieves the winning HES with a CC dispatch strategy, which consists of 3 hydroelectric turbines, 1 wind turbine, 349 kW of solar power, 150 kW of generator power, 316 kWh of batteries, and 287 kW of the converter. This ideal HES, which generates a consistent generation profile and reasonable net present cost (NPC) and cost of energy (COE) of M0.160$ and $0.013 kWh, respectively, can be practically attained in these areas. LF-controlled optimal solutions use less fuel than CC-based ones, leading to a higher share of renewable energy. Compared to Larak and Lavan, the CC- and LF-controlled options on Failaka Island generate cleaner electricity with emissions that are 57% and 44% lower. Regarding the ability to recoup the project’s initial investment costs, long-term energy production would be more financially viable than short-term. Short-term projects with higher financial uncertainty due to the salvage cost should use the CC method.

Presently, photovoltaic systems are an essential part of the development of renewable energy. Due to the inherent dependence of solar energy production on climate variations, forecasting power production using weather data has a number of financial advantages, including dependable proactive power trading and operation planning. Megacity electricity generation is regarded as a current research problem in the modern features of urban administration, particularly in developing nations such as Iran. Machine learning could be used to identify renewable resources like transformational participation (TP) and photovoltaic (PV) technology; based on resident motivational strategies, the smart city concept offers a revolutionary suggestion for supplying power in a metropolitan region. The sustainable development agenda is introduced at the same time as this approach. Therefore, the article’s goals are to estimate Mashhad, Iran’s electrical power needs using machine learning technologies and to make innovative suggestions for motivating people to generate renewable energy based on the expertise of experts. The potential of solar power over the course of a year is then assessed in our research study in Mashhad, Iran, using the solar photovoltaic modelling tool. The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. The amount of power that will be produced by solar panels can be estimated using the mean sun irradiance at a particular spot. A precise prediction can also be used to determine the complexity of the system, return on investment (ROI), and system load metrics. Several regression techniques and solar irradiance-related metrics have been combined to forecast the mean sun irradiation in terms of kilowatt hours per square metre. Azimuth and zenith factors considerably enhance the performance of the model, as demonstrated by the proposed method. The results of this study demonstrate 99.9% reliability rate for ANN model prediction of the electrical power usage during the summer and winter seasons. Thus, the maximum of power requirement during the hottest and coolest periods can be managed by using the photovoltaic system’s renewable power projections.

This paper proposes a mobile sun-tracking (MST) system to track the sun on the moving vehicle. We have developed a novel MST algorithm using general sun-tracking formulas associated with simple-moving average linear regression (SMALR) to smoothen sun-tracking activity. Furthermore, two different timing control schemes to activate sun tracking are investigated: time lapse (TL) mode and azimuth actuation (AA) mode. For experimental validation, a prototype of MST has been constructed on a small truck moving at constant speed of 30 km/h for field measurements. For the result, AA mode has average pointing error of 110 mrad for the open-loop tracking design (13% better than that of TL mode), which is still far below the half acceptance angle of compound parabolic concentrator at 659 mrad. Since there is no feedback sensor to be implemented in this prototype, optical encoders and CCD camera can be employed in the future work to further reduce the pointing error of MST system.