Miniaturized power detection module operating in millimeter wave band

The miniaturized broadband detection module can be embedded into the microwave application system such as the front end of the transmitter to detect the power or other parameters in real time.It is highly prospective in military and scientific research.In this paper,a broadband power detection module operating at 26.5 GHz-40.0 GHz is designed by using low-barrier Schottky diode as the detector and a comparator for threshold output.This module can dynamically detect the power range between-10 dBm and 10 dBm with the detection accuracy of 0.1 dB.Further,the temperature compensation circuit is also applied to improve the measurement error.As a result,the resulted error low to±1 dB in the temperature range of -55℃ to +85℃ is achieved.The designed module is encapsulated by a Kovar alloy with a small volume of 9 mm×6 mm×3 mm.This endows the designed module the advantages of small size,easy integration,and low cost,and even it is applicable to high-reliability environments such as satellites.

Multi-Equipment Detection Method for Distribution Lines Based on Improved YOLOx-s

The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.

Broken and fine powder detection method of fuses

Since powder and cotton wool may cause mechanical test fail,and the accuracy of ray detection method is not very ideal,this paper introduces the tiny capacitance detection method for broken gunpowder,in which the medium type capacitive sensor’s capacitance changes when the gunpowder core diameter changes.The detection is based on the relationship between non-electric quantity and electric quantity.Simulation results show that the designed system works well with easy installation and low power consumption.Therefore,it is suitable for broken gunpowder detection.

An effective PN blind estimation reconstruction strategy to solve data reversions interference

In a direct spectrum （DS） system, the PN code can be estimated by analyzing the singular vectors of the received data matrix in order to blind despread in a non-cooperative context. But as there are informa-tion data reversions in the analyzed data matrix, some parts of the estimated PN code may be invertible to the original PN code, which may bring about problems in the following despreading process. In order to solve this problem, a method to well reconstruct the PN code is proposed. This method is based on power detection. The combination scheme which has the maximum power is the best combination scheme that is most suitable to the original PN code. Simulation results show that the method can reconstruct the PN code very well,even if the signal-to-noise ratio is low.

Road surface condition sensor based on scanning detection of backward power

A method of detecting dry, icy and wet road surface conditions based on scanniag detection of single wavelength backward power is proposed in this letter. The detector is used to receive the backward scattered power which changes with the incidence angle. The relationship between backward power and incidence angle is used to find out the effective angle range and distinguish method. Experiment and simulation show that it is feasible to classifv these three conditions within incidence angle of 5.3 degree.

An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

Modeling of a Multiple Digital Automatic Gain Control System

Automatic gain control （AGC） has been used in many applications. The key features of AGC, including a steady state output and static/dynamic timing response, depend mainly on key parameters such as the reference and the filter coefficients. A simple model developed to describe AGC systems based on several simple assumptions shows that AGC always converges to the reference and that the timing constant depends on the filter coefficients. Measures are given to prevent oscillations and limit cycle effects. The simple AGC system is adapted to a multiple AGC system for a TV tuner in a much more efficient model. Simulations using the C language are 16 times faster than those with MATLAB, and 10 times faster than those with a mixed register transfer level （RTL）-simulation program with integrated circuit emphasis （SPICE） model.

Single molecule electro-catalysis of non-fluorescent molecule

Single molecule catalysis is very powerful in revealing catalytic mechanism at the single molecule level.But fluorescent molecule is always necessary to take part into the catalysis directly in previous research.In order to study the single molecule electro-catalysis of non-fluorescent molecule（SMECNFM） on nanocatalyst, we couple the SMECNFM with a single molecule fluorescence reaction. A certain number of fluorescent molecules will be generated and detected when the SMECNFM happens. Through this method, we can detect the electro-oxidation reaction of one HCOONa molecule. The stability of Pt nanocatalyst supported on active carbon is studied at the single molecule level by this method. This paper also provides a general way to make ultra-sensitive sensor, and to study the SMECNFM for the molecules,such as formic acid, hydrogen, oxygen, etc., on single nanoparticle.

Phasor Measurement Unit Incorporated Adaptive Out-of-step Protection of Synchronous Generator

The existing out-of-step(OOS)protection schemes have proven to be deficient in the prevention of significant outages.OOS protection schemes must not operate in stable power swing,and rapidly isolate an asynchronous generator or group of generators from the rest of the power system in case of unstable power swing.The paper proposes a novel phasor measurement unit(PMU)incorporating a polygon-shaped graphical algorithm for OOS protection of the synchronous generator.The unique PMU-based logic works further to classify the type of swing once the graphical scheme detects it,which can identify the complex power swing produced in the modern power system.The proposed algorithm can take the correct relaying decision in the event of power swing due to renewable energy integration,load encroachment,and transient faults.In this paper,the original and modified Kundur two-area system with a power system stabilizer(PSS)is used to test the proposed algorithm.In the end,it provides assessment results of the proposed relay on the Indian power system during the blackout in July 2012.The results demonstrate that the proposed algorithm is fast,accurate,and adaptive in the modern power system and shows better performance than the existing OOS protection schemes.