• Energy Research

One of the most important conditions for the efficient operation of solar power plants with a large installed capacity is to ensure the systematic monitoring of the surface condition of the photovoltaic modules. This procedure is aimed at the timely detection of external damage to the modules, as well as their partial shading. The implementation of these measures solely through visual inspection by the maintenance personnel of the power plant requires significant labor intensity due to the large areas of the generation fields and the operating conditions. Authors propose an approach aimed at increasing the energy efficiency of high-power solar power plants by automating the inspection procedures of the surfaces of photovoltaic modules. The solution is based on the use of an unmanned aerial vehicle with a payload capable of video and geospatial data recording. To perform the procedures for detecting problem modules, it is proposed to use “object-detection” technology, which uses neural network classification methods characterized by high adaptability to various image parameters. The results of testing the technology showed that the use of a neural network based on the R-CNN architecture with the learning algorithm—Inception v2 (COCO)—allows detecting problematic photovoltaic modules with an accuracy of more than 95% on a clear day.

Classical model-free predictive current control (MFPCC) is a robust control technique for a two-level inverter-fed induction-motor drive, with advantages that consist of a simple concept, rapid response, simple implementation, and excellent performance. However, the classic finite-control-set MFPCC still exhibits a significant current ripple. This article presents a method to enhance performance using a combination of model-free predictive current control (MFPCC) and discrete-space vector modulation (DSVM). The MFPCC employs an ultralocal model with an extended-state observer (ESO) that does not consider motor parameters, therefore improving the control system’s reliability by eliminating the parameter dependency. The proposed method integrates DSVM, which divides a single sample period into N equal intervals and generates virtual vectors to reduce stator current ripple. It achieves the minimum cost-function value across the entire operating range of the induction-motor (IM) drive by selecting the optimal vector from a limited set of permissible voltage vectors. Using DSVM effectively reduces the total harmonic distortion (THD) without any detrimental effects during transients or steady states. Experimental studies validate the effectiveness and superiority of the suggested technique over the Finite-Control-Set (FCS) MFPCC, which only considers real voltage vectors in its computations.

Abstract Two-stage anaerobic digestion (AD) is a promising method of converting organic waste into clean and sustainable energy. This work aimed to study the efficiency of a two-stage AD system with the production of hydrogen at the first acidogenic stage and methane in an electromethanogenic reactor. Two types of biocathodes based on carbon cloth were used in the electromethanogenic reactor: with and without retention of biomass on the electrode surface. A high applied voltage continuously supplied to the pair of electrodes was automatically maintained at 2.5 V. Hydrogen yield (0.11 NL/g volatile solids (VS)init or 2 mol H2/mol hexose) and hydrogen content in biogas (52%) were relatively high, even despite the extremely low pH (∼4.0). Improved retention of biomass on the surface of the carbon cloth-based biocathode led to greater stability of the AD process (no water electrolysis was observed) and a significant increase in the efficiency of electromethanogenesis, including the methane yield (by 40.5% up to 0.39 NL/g VSinit), volumetric methane production rate (by 38.8% up to 1.16 NL/L/d) and current density (by 233% up to 0.56 A/m2).

Workload prediction is essential in cloud data centers (CDCs) for establishing scalability and resource elasticity. However, the workload prediction accuracy in the cloud data center could be better due to noise, redundancy, and low performance for workload prediction. This paper designs a hierarchical tree-based deep convolutional neural network (T-CNN) model with sheep flock optimization (SFO) to enhance CDCs’ power efficiency and workload prediction. The kernel method is used to preprocess historical information from the CDCs. Additionally, T-CNN model weight parameters are optimized using SFO. The suggested TCNN-SFO technology has successfully reduced excessive power consumption while correctly forecasting the incoming demand. Further, the proposed model is assessed using two benchmark datasets: Saskatchewan HTTP traces and NASA. The developed model is executed in a Java tool. Therefore, associated with existing methods, the developed technique has achieved higher accuracy of 20.75%, 19.06%, 29.09%, 23.8%, and 20.5%, as well as lower energy consumption of 20.84%, 18.03%, 28.64%, 30.72%, and 33.74% when validating the Saskatchewan HTTP traces dataset. It has also achieved higher accuracy of 32.95%, 12.05%, 32.65%, and 26.54%.

Electrotechnical services spend a significant amount of time on cable tracing and conductor identification operations. Although the operation of conductor identification seems an easy job and a deeply researched topic, it may turn out to be time-consuming due to the large quantity of unidentified conductors (cables) and the certain stipulation that conductors should be de-energized and have uninsulated parts. This paper proposes a method based on the use of an acoustic signal as a carrier, making it possible to facilitate the identification process and significantly speed up the identification process. For the proposed method, mathematical modeling of the sound propagation in conductors and insulation is detailed. The sound pressure level subject to identified conductor length, conductor cross-section and insulation type was calculated on the basis of the model. The performed calculations revealed remarkable capabilities of the acoustic method for identifying conductors in comparison with other methods. So, the difference in sound pressure level is only 3.65% for distances from the identification point to the signal source of 5 m and 100 m, 0.42% between the minimum and maximum cross sections of conductors (1 mm2 and 25 mm2, respectively) and 0.22% between conductors with polyethylene and PVC insulations. The prototype of conductor identification device was assembled in a way that practically confirmed the theoretical research with the average percentage of discrepancy of about 1.44%. In addition, the paper analyzes the conductor identification operation constituents to justify the application of the acoustic method by comparison with other identification methods.

High-quality lighting of premises in the cultivation of farm animals is an important condition for the successful development of the agro-industrial complex. Sufficient lighting with the simulation of sunrise and sunset which increases daylight hours to 15–16 h can raise the level of production by 8–15%. The most innovative, economical and efficient light source for livestock, poultry and greenhouse buildings are LED lamps allowing for a short payback period. The difference between the models developed by the authors and the traditional point method is taking into account lamp length o, lamp suspension height, location of the illuminated area under the lamp at displacement and observation angles, and luminous flux of light source, and the most important thing is body angle of the light source. This paper presents the method for calculating illumination under the LED lamp due to the large error in the values obtained by theoretical methods due to the lack of a correction factor for changing the illumination of the lamp (0.8). Prediction of crystal heating was carried out by verification calculation in the ANSYS/CFX software package. Forecasting the illumination of the fodder table was carried out by calculation in the DIALux evo software package. The main parameters of an energy-saving LED lamp (power consumption, luminous flux, color temperature and color) were selected. The consumption of electrical energy decreased by 85%; the average level of illumination increased and amounted to 123.1 lux. The developed method for calculating the illumination under the LED lamp can be considered satisfactory, the Student and Fisher criteria do not exceed tabular values and the correlation coefficient showed a close relationship. To comply with electrical safety standards in the premises, a resonant power supply system for LED lamps is offered.

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a R2 value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet.

The combustion of sunflower husk pellets was investigated by kinetic analysis supplemented by the Kriging method. The nonisothermal thermogravimetric experiments in air were carried out at the temperatures from 20 to 700 °C and heating rates of 5, 10, and 20 °C/min. Kinetic analysis was carried out using the model-free OFW (Ozawa–Flynn–Wall) method and Coats–Redfern (CR) method. The activation energy values, calculated by the OFW method, ranged from 116.44 to 249.94 kJ/mol. These data were used to determine the combustion mechanism by the CR method. The kinetic triplet (Eα, A, g(α)) was determined in the conversion interval 0.2 to 0.8. The model of the chemical reaction F8 was recommended to describe the mechanism of the thermochemical conversion process. The relationship between the kinetic parameters was analyzed using the Kriging method. The patterns between the kinetic parameters were represented as three-dimensional surfaces and two-dimensional projections. The distribution’s surfaces were uniform; there were local extremes as well as linear regions. A new approach to the data analysis will allow predicting parameters of a thermochemical conversion of the various raw materials and contributes to a deeper understanding of the characteristics and mechanism of biomass combustion.