Gold Enhanced Graphene-Based Photodetector on Optical Fiber with Ultrasensitivity over Near-Infrared Bands

Graphene has been widely used in photodetectors; however its photoresponsivity is limited due to the intrinsic low absorption of graphene. To enhance the graphene absorption, a waveguide structure with an extended interaction length and plasmonic resonance with light field enhancement are often employed. However, the operation bandwidth is narrowed when this happens. Here, a novel graphene-based all-fiber photodetector (AFPD) was demonstrated with ultrahigh responsivity over a full near-infrared band. The AFPD benefits from the gold-enhanced absorption when an interdigitated Au electrode is fabricated onto a Graphene-PMMA film covered over a side-polished fiber (SFP). Interestingly, the AFPD shows a photoresponsivity of >1 × 104 A/W and an external quantum efficiency of >4.6 × 106% over a broadband region of 980–1620 nm. The proposed device provides a simple, low-cost, efficient, and robust way to detect optical fiber signals with intriguing capabilities in terms of distributed photodetection and on-line power monitoring, which is highly desirable for a fiber-optic communication system.

Energy Consumption Delay Evaluation of NB-IoT Terminal Based on Markov Model

The energy consumption of terminal of Internet of Things has attracted much attention in the study of smart Internet of Things. How to simulate the energy consumption process of the terminal from the theoretical level, so as to analyze the energy consumption and delay of the terminal are important issues. In this paper, taking the power monitoring terminal as an example, a Markov model is established for the Narrow-Band Internet of Things (NB-IoT) terminal with periodic automatic reporting. The working state of the terminal includes PSM (Power Saving Mode), random access (RACH), data transport and receive (Tx/Rx), short eDRX (Extended Discontinuous Reception), long eDRX and terminal disconnection (ERROR). According to the proposed model, the effects of network quality, maximum possible number of RACH request times (Rmax ) and data retransmission times (N1, N2) on terminal energy consumption and delay are analyzed. The numerical results show that network quality, maximum number of random access and maximum number of data retransmission directly affect the energy consumption and service quality of the terminal. Reasonable configuration of the above indicators can effectively improve the service life of the terminal and meet the customer’s requirements for the terminal service quality under the condition of maximum power saving. The model provides a reference for energy consumption and delay optimization of NB-IoT terminal.

An IOT-Enabled Generator for Power Monitoring and Load Management with Power Factor Improvement

The quality of power supply and reliability play a vital role in the smooth operation and maintenance of commercial use. These requirements have significant applications when dealing with residential areas, hospitals, industries, educational sectors, banks and airports, etc. In this regard, backup diesel generators are considered the most important source for an uninterrupted supply of electricity. However, there is an emergent need to avoid sudden shutdown of generators in the events of overload, shortage of fuel flow, service interval and lagging of power factor. These common problems can be addressed through monitoring of power generator parameters, for instance, real time remote monitoring to measure the health of the generator, the problem of load management due to high demand of power during peak hours and power factor improvement due to exceeding inductive load. In this paper, our proposed architecture—based on an IOT solution—consists of different sensors, namely a current transformer for measuring load, fuel gauge for fuel level monitoring, and temperature measurement with the energy module to determine the power factor of the system. Our proposed system is operated and tested on a real trolley-mounted 25 KVA generator.

Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.

Energy Harvesting Under Harsh Conditions for the Oil & Gas Upstream Industry

Environmental and safety sensing is becoming of high importance in the oil and gas upstream industry. However, present solutions to feed theses sensors are expensive and dangerous and there is so far no technology able to generate electrical energy in the operational conditions of oil and gas extraction wells. In this paper it is presented, for the first time in a relevant environment, a pioneering energy harvesting technology based on nanomaterials that takes advantage of fluid movement in oil extraction wells. A device was tested to power monitoring systems with locally harvested energy in harsh conditions environment (pressures up to 50 bar and temperatures of 50ºC). Even though this technology is in an early development stage this work opens a wide range of possible applications in deep underwater environments and in Oil and Gas extraction wells where continuous flow conditions are present.

Switchable Instantaneous Frequency Measurement by Optical Power Monitoring Based on DP-QPSK Modulator

We propose and analyze an instantaneous frequency measurement system using optical power monitoring technique with improved resolution. The primary component employed in the proposal is a DP-QPSK modulator which is used to modulate the microwave signal with a designed time delay and phase shifting. Then the generated optical signal is sent to polarization beam splitter (PBS) via polarization controller (PC). Thanks to the complementary transmission nature of polarization interference introduced by PBS, the frequency information is converted to the optical power and the relationship between the amplitude comparison function (ACF) and microwave frequency to be measured is established. Thus, the frequency of the microwave signal could be easily measured through monitoring the optical powers of the two output ports of the PBS. Furthermore, by adjusting the DC biases of the DP-QPSK modulator instead of changing the electrical delay, the measurement range and resolution can be switched. In this paper, the basic principle of the instantaneous frequency measurement system is derived in detail, and simulation has been performed to investigate the resolution, the measurement range and the impact of imperfection devices. The proposed scheme is wavelength independent and measurement range switchable, which can avoid the laser wavelength drifting problem and greatly increase the system flexibility.