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Polyamide 66 (PA66) is one of the commonly used polymer gear materials. This paper focuses on the tribological properties of glass fiber reinforced PA66 composites in self-mated contact using a pin-on-disc tribometer. The effects of glass fiber content, PV (the product of the contact pressure and sliding speed), and lubrication on the tribological properties of the specimens are also investigated. The results show that the glass fiber reinforced PA66 exhibit higher coefficients of friction and specific wear rates than PA66 under dry sliding conditions. This is probably due to the peeled glass fibers during the sliding process acting as abrasive particles which have an aggressive effect on the surface. Under grease lubricated conditions, PA66 + 33% GF has the lowest coefficient of friction and specific wear rate due to its higher strength. Under dry sliding conditions, all specimens show the highest friction coefficient and specific wear rate at 30 MPa·m/s with the change of PV value. Under grease lubricated conditions, all specimens show the highest friction coefficient and specific wear rate at 4 MPa·m/s with the change of PV value. The addition of grease improves friction and wear of PA66 composites under most of the experimental conditions. However, the specific wear rates of PA66 and PA66 + 13% GF under grease lubrication are higher than those under dry sliding conditions at low PV values. This may be due to the fact that greases can reduce the surface mechanical strength of PA66 and PA66 + 13% GF. [ABSTRACT FROM AUTHOR]

Previous studies on changes in the distribution of GABAergic interneurons and excitation/inhibition (E/I) balance in Alzheimer's disease (AD) and aging were mainly conducted in the neocortex and hippocampus. However, the limbic system is the primary and crucial location for AD progression. Therefore, in this study, we utilized AD and aging mouse models to investigate the E/I balance and the distribution of parvalbumin (PV)- and somatostatin (SST)-expressing cells in S1BF (barrel field of primary somatosensory cortex, barrel cortex), CA1 hippocampal area and brain regions beyond the neocortex and hippocampus, including retrosplenial cortex (RSC, which is composed of RSG and RSA), piriform cortex (Pir), amygdala (BMA), and hypothalamus (DM). We discovered that amyloidosis may disrupt the alignment of excitatory pre- and postsynaptic quantities. Amyloidosis reduces the quantity of synapses and SST cells, but does not impact the counts of PV cells. By contrast, aging is linked to a decline in synapses, I/E ratios, SST and PV cells. Amyloidosis affects the S1BF and BMA, while aging may harm all studied regions, including the S1BF, RSC, hippocampus, Pir, BMA, and DM. Aging mostly affects synapses and I/E ratios in Pir, BMA, and DM, and PV and SST interneurons in the hippocampus. [ABSTRACT FROM AUTHOR]

In this work, a low voltage ride through (LVRT) scheme for a single-stage grid-connected photo voltaic (PV) system has been proposed to support the drooping point of common coupling (PCC) voltage profile during grid fault by supplying adequate reactive power in the form of quadrature axis current (Iq) to the grid, as per grid code. Since the current generated by the PV inverter increases during fault is dependent on the severity of the fault, the proposed model is designed to adjust the direct axis current (Id) accordingly to restrict the inverter current within appreciable limits in order to ensure safe operation of the inverter system. The PV system is designed and run on various platforms with the proposed control logic to meet LVRT capability. Besides, the proposed algorithm works well to restrict inverter current within 1.5 per unit (pu) without necessitating any additional resources or causing the existing model's operation vulnerability. The proposed scheme also possesses good efficiency as it avoids any deliberate energy loss through other mechanisms, as it tunes Maximum Power Point Tracking (MPPT) operation according to the requirements, making the present scheme well suited for real-life applications. [ABSTRACT FROM AUTHOR]

The development of a solar trigeneration system for households is a complex and difficult task. The development of a rational system requires a multifaceted and balanced solution. Energy technology should be capable of independently and fully satisfying consumer demand and compatible with today's widely used engineering systems. To ensure broad application and use of the system, it must be sufficiently compact, cost-effective, and easy to install and repair. [ABSTRACT FROM AUTHOR]

Overview: For Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and diagnostics are performed in a timely and accurate manner. In general, wind power is one of the three major renewable energy sources, along with solar power and hydropower. Wind power is well distributed around the world, making it suitable to be exploited in human activities for the general welfare of society. Objectives: A prototype security situational awareness system applicable to the power data communication network service and traffic model should be developed. This will help to successfully enhance the security and service quality of the power data communication network, effectively cope with network security threats in the new environment, and ensure the security of the power plant network access. The traffic of the main network of the existing data communication network will be combined and analyzed, and NQA traffic management algorithms will be studied and proposed. These actions will improve the SLA hierarchical service capability, the service quality of the core services carried by the backbone network, and strengthen the security capability of the new energy power plant communication network access system. Methodology: For the purpose of this investigation, an attention-based long short-term memory (Att-LSTM) model was used for the categorization of time series actual data. The approach that has been developed is able to identify defects in photovoltaic arrays and inverters, which offers a dependable option for improving the efficiency and dependability of solar energy systems. For the purpose of evaluating the proposed method, a real-world solar energy dataset is used. Results: The findings acquired from this evaluation are compared to the results received from existing detection approaches such as Cryptography, Intrusion Detection System (IDS) methods, and Network Defense Schemes. The results obtained demonstrate that the suggested method surpasses current fault detection techniques, providing greater accuracy and better performance. [ABSTRACT FROM AUTHOR]

In modern agriculture today, the cultivation of agricultural products cannot be imagined without greenhouses. This paper presents an energy optimization of a solar greenhouse with a photovoltaic system (PV) and a ground-source heat pump (GSHP). The PV system generates electricity, while the GSHP is used for heating and cooling. A greenhouse is designed with an Open Studio plug-in in the Google SketchUp environment, the EnergyPlus software (8.7.1 version) was used for energy simulation, and the GenOpt software (2.0.0 version) was used for optimization of the azimuth angle and PV cell efficiency. Results for different solar greenhouse orientations and different photovoltaic module efficiency are presented in the paper. The obtained optimal azimuth angle of the solar greenhouse was −8°. With the installation of a PV array with higher module efficiency (20–24%), it is possible to achieve annual energy savings of 6.87–101.77%. Also, with the PV module efficiency of 23.94%, a concept of zero-net-energy solar greenhouses (ZNEG) is achieved at optimal azimuth and slope angle. Through the environmental analysis of different greenhouses, CO2 emissions of PV and GSHP are calculated and compared with electricity usage. Saved CO2 emission for a zero-net-energy greenhouse is 6626 kg CO2/year. An economic analysis of installed renewable energy systems was carried out: with the total investment of 19,326 € for ZNEG, the payback period is 8.63 years. [ABSTRACT FROM AUTHOR]

The sustainable integration of distributed energy resources (DER) into distribution networks requires accurate forecasting of hosting capacity. The network and DER variables alone do not capture the full range of external influences on DER integration. Traditional models often overlook the dynamic impacts of these exogenous factors, leading to suboptimal predictions. This study introduces a Sensitivity-Enhanced Recurrent Neural Network (SERNN) model, featuring a sensitivity gate within the neural network's memory cell architecture to enhance responsiveness to time-varying variables. The sensitivity gate dynamically adjusts the model's response based on external conditions, allowing for improved capture of input variability and temporal characteristics of the distribution network and DER. Additionally, a feedback mechanism within the model provides inputs from previous cell states into the forget gate, allowing for refined control over input selection and enhancing forecasting precision. Through case studies, the model demonstrates superior accuracy in hosting capacity predictions compared to baseline models like LSTM, ConvLSTM, Bidirectional LSTM, Stacked LSTM, and GRU. Study shows that the SERNN achieves a mean absolute error (MAE) of 0.2030, a root mean square error (RMSE) of 0.3884 and an R-squared value of 0.9854, outperforming the best baseline model by 48 per cent in MAE and 71 per cent in RMSE. Additionally, Feature engineering enhances the model's performance, improving the R-squared value from 0.9145 to 0.9854. The sensitivity gate also impacts the model's performance, lowering MAE to 0.2030 compared to 0.2283 without the sensitivity gate, and increasing the R-squared value from 0.9152 to 0.9854. Incorporating exogenous factors such as the time of day as a sensitivity gate input, further improves responsiveness, making the model more adaptable to real-world conditions. This advanced SERNN model offers a reliable framework for distribution network operators, supporting intelligent planning and proactive DER management. Ultimately, it provides a significant step forward in hosting capacity analysis, enabling more efficient and sustainable DER integration within next-generation distribution networks. [ABSTRACT FROM AUTHOR]

Stimulated by climate change, sustainability concerns have increased the literature on socioeconomic impact assessments of solar energy and Photovoltaic (PV) technologies. PV systems have gained prominence as a key renewable energy source since the 2000s. This paper conducts a bibliometric analysis of this literature and the main objective is to review the existing studies on socioeconomic impacts systematically. For the research design, we adopted a non-traditional review method and used scientometric analysis tools using HistCite and VosViewer programs. According to the findings, there has been a significant increase in publications on the socioeconomic impact assessment of Solar Energy and PV technologies from the 2000s to 2020. The top ten research countries are developed, except for China and India. The findings from the narrative analysis of the most frequently cited articles suggest that public policies and patent structures play a prominent role in improving the social acceptance of solar technologies. The paper highlights the most influential works and authors in the field emphasize their essential findings on solar energy's socioeconomic impact and provides recommendations for future research. The literature of 964 articles reviewed for this paper indicates an emerging bridge between the photovoltaic/solar energy technical research and social sciences. [ABSTRACT FROM AUTHOR]

The energy demand is increasing unprecedentedly day by day due to the rapid development of technology. Clean and sustainable energy sources are used more intensively to meet environmental demands. This paper aims to design a grid-connected hybrid system consisting of a 12.45 kW Photovoltaic (PV) system and a 10 kWh capacity battery energy storage system (BESS). In the design procedure, Perturb and Observe (P&O), Incremental Conductance (INC), Artificial Neural Network (ANN), and Hybrid Maximum Power Point Tracking (MPPT) algorithms are implemented and tested. The most efficient method is selected to yield the maximum power output from the PV module under a variety of environmental conditions. Since the capacity of PV and BESS are different, it is determined that Total Harmonic Distortion (THD) of injected current is high during night mode. To solve this issue and enhance power quality, a double-mode LCL filter is developed. When a single-mode LCL filter is used, the THD value is obtained as 4.8%. In the two-mode LCL filter application, this value has been reduced to 3.2%. The overall efficiency is measured as 97.5% under low irradiation conditions for the system with ANN-MPPT. [ABSTRACT FROM AUTHOR]

Fixed solar panels face significant energy loss as they cannot consistently capture optimal sunlight. Because of that, the overall efficiency of the PV panel will be reduced, and the installation requires larger land space to generate appropriate power; this stems from the use of a dual-axis solar tracking system, which can significantly increase overall energy production. The system is based on the combination of two approaches to precisely track the sunlight: first, using multiple LDRs (light-dependent resistors) as photo sensors to track the position of the sun by balancing the resistivity using a proportional integral deprival (PID) controller, and the second approach using the time-based control for cloudy days when sunlight is diffused, getting the time GPS coordinates and time to calculate the accurate position of the sun by determining the azimuth and altitude angle. This dual system significantly improves energy production by 33.23% compared to fixed systems and eliminates errors during shaded conditions while reducing unnecessary energy use from continuous GPS activation. The prototype uses two linear actuators for both angles and a 100-watt solar panel mounted on the dual-axis platform. [ABSTRACT FROM AUTHOR]