Measurement: Energy最新文献

Global warming concerns, along with international agreements and regulations, reflect a broader effort to change the public's high energy demand in recent years. Smart public lighting systems are gaining popularity due to their energy-saving capabilities, reduction in carbon dioxide emissions, and improved public comfort. However, transitioning to smart public lighting requires careful planning and multiple stages. This is not only to accommodate public behavior, revise scenarios, and test citizen acceptance but also due to the necessary infrastructure investments. Smart public lighting incorporates new technologies, often with a breakeven point that takes several years to reach. To promote the widespread adoption of smart public lighting, it is essential to produce relatively expensive components in large quantities and explore cost-effective solutions. This research focuses on investigating a cost-effective photoelectric sensor for smart public purposes. The primary originality of this study lies in identifying a cost-effective photoelectric sensor that can replace technically equivalent but more expensive sensor solutions for indoor and outdoor lighting control purposes.

The state characterization inside the lithium-ion battery during charge/discharge cycling is extremely crucial for understanding the electrochemical reaction mechanism. However, current methods exhibit a challenge to overcome the specific battery environment obstacles, including strong redox properties, strong electromagnetic interference, and fast reaction processes. Hence, more efforts are still needed to monitor the actual state inside the battery accurately and reliably. To address this issue, we designed and developed a compact two-cavity cascade fiber-optic Fabry-Perot interferometer (FPI) sensor that can be safely implanted in batteries to measure internal temperature and pressure simultaneously. With its high pressure and temperature sensitivity of 26.6 ​nm/kPa and 107 ​nm/°C, this sensor exhibits an ultra-low cross-sensitivity of −40 ​Pa/°C. During charge/discharge cycling tests, regular cyclic pressure and temperature signals are obtained at various rates cycling in real-time and in situ, revealing details about the actual state characterization inside the battery. From the experiment results, the pressure inside the battery is divided into reversible changes caused by respiration effects and irreversible changes caused by trace gas production. Furthermore, the FPI sensor provides a more precise temperature than thermocouples that measure the surface temperature of the battery, reflecting the internal/external temperature difference to a maximum of 3.5 ​°C at 1 ​C rate cycling. This operando FPI sensor provides a valuable technological tool for battery performance testing and safety monitoring.

Most metal industries use reheating furnaces (RHF) for their finishing operations. This RHF is highly energy-consuming equipment that heats the material inside the chamber for rolling or shaping using the by-product gases, natural gas, or oil as fuel. It is necessary to minimize or optimize the fuel consumption to the extent possible. By analyzing the plant operating data, plant measurements, and energy balance calculation, this work aims to determine the potential for decreasing the fuel consumption of a billet reheating furnace. Predictions are made by modeling operating data to reveal the hidden problems and uncover underlying issues. The study results in increasing productivity by 11 ​% while oil consumption was reduced by 14 ​%. These actions significantly decreased carbon emissions considerably and generated significant cost savings.

Naval shipboard power systems (SPS) are rapidly embracing electrification, resulting in loads that generate pulsation currents and encounter substantial transients. However, conventional time-based features alone are inadequate for effectively monitoring and safeguarding these loads against faults. This highlights the critical requirement for advanced machine learning based methods to discern and differentiate between the various transient stages within the load profile. In this paper, we propose a Wavelet Graph Neural Network (WGNN) model for non-intrusive fault detection in SPS. The fault detection system leverages the dynamic model of the SPS to train and test performance with varying fault scenarios. The underlying structure and the interdependence among component states in the SPS network are effectively captured using the WGNN model, resulting in accuracies over 99% for intrusive fault detection and 97% for non-intrusive fault detection. The developed WGNN model has also shown to be robust in the presence of pulse loads and noise, achieving an accuracy of over 95%. At the end, a real-time simulation of the proposed method is validated on a hardware-in-the-loop system, guaranteeing the high fidelity and low latency of the proposed approach. These findings validate the effectiveness of the proposed WGNN model for fault detection and real-world applications in SPS.

Multiport power electronics converters enable interfacing of multiple sources and loads within a renewable-rich dc microgrid. The system evaluation of these microgrids such as dynamic performance of components, stability analysis are often evaluated using hardware-in-loop (HIL) approach for different real time conditions. The digital twin of device under test (DUT) is realized within real time simulator using voltage and current sensor measurements in the HIL-based testing approach. Current sensing systems for multiport power converter systems require transducers to be connected in path of current with restrictions on sensor bandwidth, auxiliary circuit overhead requirements for biasing and signal conditioning. This paper addresses the development of high frequency current sensing method using shunt-type measurements with reduced auxiliary circuit overheads. The proposed method provides a digital estimate of inductor current which can be implemented in an embedded processor.

In order to maximize the solar radiations falling on a Photo-voltaic (PV) panel and hence, to maximize the solar power generation, an optimum tilt angle of the PV panels for a specific geographic location plays a critical role. This paper exploits the tilt angle and establishes an empirical relation among optimum tilt angle, module temperature and ambient temperature. Moreover, estimating accurate solar photovoltaic power output depends on the correct modelling of the PV module. Temperature and irradiance dependent modelling need statistical support for their behaviour and pattern. This work also examines and institutes the relationship between Ambient temperature and Module temperature throughout the year. Furthermore, in order to determine the impact of irradiance, ambient temperature and module temperature on the solar power generation of a grid-connected solar power plant, this paper evaluates Karl Pearson correlation coefficients for each of the following three pairs (1) generation and irradiance (2) generation and ambient temperature and (3) generation and module temperature, for all the 12 months of a year. The results obtained shall help to better understand, manage, plan, forecast and stabilize the solar power output. Earlier researchers usually used weather data for their study, which are not location-specific therefore, accuracy is questionable. Hence, the data used in this research is recorded from a 3.3 MWp grid-tied ground-installed solar power plant and a 119 ​KW grid-tied rooftop-installed solar power plant, both located at Aligarh Muslim University, Aligarh, India. This paper presents an exhaustive analysis of the two grid-tied solar power plants as there is very little work with actual data of generation, irradiance, temperature and tilt angle, all measured on the spot with high accuracy; results obtained are realistic with a novel approach.

Proof of concept is established for the thermal management of PV modules for the simultaneous benefit of electrical and thermal efficiency. It was achieved by designing the controlled open-loop water-based hybrid PV-T system and demonstrated for thermal management of 150W photovoltaic panel at natural solar conditions. In this integrated front glass-covered PV-T hybrid (IFG-PV-T) system, the PV panel is clipped between the front glass and rear aluminium collector without causing any permanent changes in the existing panel. The flow of water from the source tank is controlled by automated solenoid valve assembly coupled with thermocouples. The solenoid operational temperature is fixed at 40°C to controlled the PV surface temperature at an optimum range to mitigate the adverse effect on voltage and current output. The water layer thickness in the front glass box is optimized to filter the maximum infrared radiations and the collector's toughened glass feature allows the maximum light transmittance with super safety. The performance of the IFG-PV-T system has been evaluated in terms of variation in open circuit voltage, short circuit current, maximum power output, electric and thermal efficiency under the natural solar insolation for a week. Experimental investigations revealed that the open-circuit voltage is increased by ∼16.1 % with IFG-PV-T as compared to conventional PV panel. The increment can be attributed to the synergy of the front glass collector and solenoid valve operation at 40^oC that regulates PV surface temperature and boost the current output. These factors have ameliorated the electrical efficiency of IFG-PV-T compared to conventional PV panel. The average thermal efficiency was 39.4 ​% wherein the IFG-PV-T system provides ∼100 ​L of hot water (38–41 ​°C) per day. The present controlled loop operation system and collector configuration have proven their significance for electrical power increment and concomitantly able to deliver moderate hot water which can be useful for any household or commercial application.

The use of perovskite solar cells (PSCs) holds immense promise in electricity generation due to their high efficiency and potential for cost-effective production. However, their practical application faces limitations due to issues like sensitivity to moisture, ion migration, and interface defects, affecting their stability and lifespan. This work delves into the critical role of interface materials in enhancing the stability and effectiveness of perovskite solar cells. Techniques such as passivation and encapsulation designed to mitigate these challenges are comprehensively explored. The study investigates the root causes of perovskite deterioration and how engineering interfaces can bolster the durability of these devices. Various methods for passivation, including surface modification, self-assembled monolayers, and utilizing materials with wide band gaps, are scrutinized for their ability to reduce defects and control degradation problems. Furthermore, strategies involving barrier films, polymers, and hybrid inorganic-organic materials are evaluated for their potential to shield perovskite layers from moisture and environmental influences, thereby prolonging the devices' lifetime. The interconnected nature of passivation layers, encapsulation techniques, and their suitability for large-scale manufacturing processes are presented. The analysis outlines the challenges and opportunities in developing interface materials for perovskite solar cells, considering the trade-offs between device performance, stability, and affordability. Accordingly, potential future pathways and emerging trends in interface engineering for the next generation of perovskite solar cells are suggested, aimed at propelling these devices towards commercial success by achieving high efficiency and long-term stability.

The forceful energy efficiency to manage the demand is essential to meet development goals. Palestine has suffered from an electricity deficit, whereas the city of Tulkarm suffers from a chronic one. The dataset was collected from Tulkarm city in Palestine; this city is considered one of the cities that suffers the most from frequent power outages. It's difficult to determine the most powerful Artificial intelligence (AI) approaches that can accurately forecast electricity consumption. This paper presents a hybrid model that combines Recurrence Neural Networks (RNNs) and Genetic Algorithms (GAs) [RNN-GAs] to forecast electricity consumption and optimize demand. In the proposed model the K-means clustering technique produces specific initial population seeding and optimization crossover operators to enhance the efficiency and find the optimal solution. The results showed that the proposed Nonlinear Autoregressive with External (Exogenous) (NARX) (NARX-GAs) with the K-means clustering technique outperforms the hybrid model NARX-GAs. The NARX-GAs-K Mean Clustering recorded an RMSE value of 0.08759, which performs a good balance with the lowest RMSE, especially in long-term forecasting, and also outperforms the other hybrid forecasting models that depend on RNN-GAs. Finally, the forecasting results of the hybrid NARX-GAs-K Mean Clustering can predict accurately the energy consumption in a city, which leads to the use of the model in similar cities to forecast and manage the demand for electricity consumption.

Electronic devices are equipped with blower fans as a means of removing the heat that accumulates in them. This type of fan operates smartly by increasing the speed of the impeller as the electronic devices become overloaded. When the speed of the motor increases, it creates unwanted noise that may be harmful to the ears of the user. Therefore, it is imperative to reduce this noise while maintaining the same dimensions of the fans. The purpose of this work is to demonstrate how critical measurements can be used to improve the design of blower fan housings. By making a change in the housing of the fan, this study proposes an innovative solution to the noise problem associated with heat radiation fans. A punch has been added to the new housing of notebook system, which may be located on either the upper or lower sides. A punch should be located at the air inlet on the fan's air outlet side, between 0 and 90°. Moreover, a punch should have a height ranging from 0.3 to 1 ​mm and a circle size ranging from one eighth to one fourth. Additionally, the details of noise measurement are presented. The results of the study showed that the noise reduction was enhanced by more than 2 ​dB(A) which can either result in a performance enhancement by increasing the flow rate to reach the same flow rate as the original fan or in a decrease in human discomfort by lowering the noise level. The work has been patented under patent numbers TWM624190U, and CN216554487U.