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.

Self-Powered Implantable Skin-Like Glucometer for Real-Time Detection of Blood Glucose Level In Vivo

Implantable bioelectronics for analyzing physiological biomarkers has recently been recognized as a promising technique in medical treatment or diagnostics. In this study, we developed a self-powered implantable skinlike glucometer for real-time detection of blood glucose level in vivo. Based on the piezo-enzymatic-reaction coupling effect of GOx@ZnO nanowire, the device under an applied deformation can actively output piezoelectric signal containing the glucose-detecting information. No external electricity power source or battery is needed for this device, and the outputting piezoelectric voltage acts as both the biosensing signal and electricity power. A practical application of the skin-like glucometer implanted in mouse body for detecting blood glucose level has been simply demonstrated. These results provide a new technique path for diabetes prophylaxis and treatment.

TECHNIQUE USED INDETECTING EXTRANEOUS HARMONICS FOR VARIABLE SPEED CONSTANT FREQUENCY（VSCF）POWER SUPPLY

An extraneous harmonics detection scheme is introduced, based on the frequency shift technique. The main principle is described; practical operation scheme is il-lustrated; both related microcomputer system circuit and its software designs are alsobriefly described. Finally, the scheme is validated by the experiment results.

Blackout Prevention by Power Swing Detection and Out-of-Step Protection

Power swing evoked by sudden changes like faults or switching operations will become more and more important for protective relaying, due to the growing load flow in electrical power networks. Unwanted trips of the distance protection function must be avoided to prevent cascading effects and blackouts in the network. Selective out-of-step-tripping is required to stop unstable power swing and to prevent damage to affected generators. Therefore a reliable method for detection of power swing is presented, which requires no settings for operation. Power swing can be detected from 0.1 Hz up to 10 Hz swing frequency, also during open pole condition and during asymmetrical operation. A blocking logic prevents unselective trips by the distance protection. However, faults that occur during a power swing must be detected and cleared with a high degree of selectivity and dependability. For unstable power swing a flexible out of step tripping function will be proposed. The coordination of power swing detection, distance protection and out of step protection provides a reliable system protection.

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.

A high dynamic range linear RF power detector with a preceding LNA

A design of high dynamic range linear radio frequency power detector （PD）, aimed for transmitter carrier leakage suppression is presented in this paper. Based on the logarithmic amplifier principle, this detector utilizes the successive detection method to achieve a high dynamic range in the radio frequency band. In order to increase sensitivity, a low noise amplifier （LNA） is placed in the front of this detector. DC coupling is adopted in this architecture to reduce parasitics and save area, but this will unavoidably cause DC offsets in the circuit which are detrimental to the dynamic range. So a DC offset cancelling （DCOC） technique is proposed to solve the problem. Finally, this detector was fabricated in the SMIC 0.13μm CMOS process. The measured results show that it achieves a wide dynamic range of 50 dB/40 dB with log errors in 4-1 dB at 900 MHz/2 GHz, while draws 16 mA from a 1.5 V power supply. The active chip area is 0.27×0.67 mm2.

Study on Voltage Sag Detection of Wind Power System Based on HHT

For the output of wind power system has the characteristics of randomness, volatility and intermittence, the voltage of wind power system fluctuates frequently and voltage sag is one of the most common voltage fluctuations in wind power system. For the problem of voltage sag of wind power system, the limitations of the detection methods such as the square detection method, the half-wave RMS detection method and wavelet transform are summed up, and a new detecting method named Hilbert-huang Transform(HHT) is put forward in this paper, which can detect the voltage sag accurately and timely. In order to solve the problem of end effect in the process of empirical mode decompostion (EMD), a self-adaptive method named improved waveform matching is applied in dealing with the end issue. Voltage fluctuations are reflected by two parameters named voltage amplitude and frequency of each intrinsic mode function (IMF) in HHT. The practicality of the method is verified by Matlab simulation.

Low Cost,Low Power Magnetic IEDs Detection Method Using Biaxial Magnetometry Digital Sensors

The magnetic improvised explosive devices （IEDs）, also commonly known as a type of a sticky bomb, is simply constructed devices yet very lethal. This paper puts forward the idea of an electronic compass that is capable of sensing the change of a magnetic field generated by a magnet and translating it into interpretable data, which could act as the base for the further studies and assist in developing a greener automated system for detecting this device. The electronic compass is specifically chosen for reducing power consumption of systems in addition to the fact that it is available at a low cost.

Active Power Filter Based on Adaptive Detecting Approach of Harmonic Currents

The ip-iq detection method based on instantaneous inactive power theory has been applied widely in active power filter because of its good real-time. But it needs large computation, and three-phase currents are processed as integrity, thus calculation accuracy can't be ensured. Based on adaptive interference canceling theory, this paper presents a new ad- aptive detection method for harmonic current, it is a continuously regulated closed-loop system, and its operating characteristics are almost independent of the parameter variations of the elements, thus it performs better than that based on traditional theory. At last this paper provides the simulation of active power filter including the detecting cir-cuit which proved the design is feasible and correct.

Ag/Bi_(2)O_(3)纳米块自供能紫外探测器的制备及性能

为了实现在无外部供能下对紫外光的有效探测,基于Ag修饰的Bi_(2)O_(3)纳米块(Ag/Bi_(2)O_(3))纳米块制备了自供能紫外探测器。通过煅烧法制备Bi_(2)O_(3)纳米块,随后采用室温溶液法在其表面沉积Ag纳米粒子,进而成功制备了Ag/Bi_(2)O_(3)纳米块,且对所制备样品的晶体结构和微观形貌等进行了表征。结果表明,Ag/Bi_(2)O_(3)纳米块的平均尺寸约为1μm,且Ag纳米粒子随机分布在Bi_(2)O_(3)纳米块表面。将涂覆Ag/Bi_(2)O_(3)纳米块的FTO作为工作电极,并进一步构建了自供能紫外探测器。在365 nm的紫外光照射下,Ag/Bi_(2)O_(3)纳米块紫外探测器能在零偏压下实现对紫外光的快速检测,这证实其具有自供能特性。相比于Bi_(2)O_(3)纳米块紫外探测器,Ag/Bi_(2)O_(3)纳米块紫外探测器的光电流得到明显提升,上升和下降时间分别缩短至29.1 ms和40.2 ms,并具有良好的循环稳定性。

基于计算机视觉的电力作业人员行为分析研究现状与展望

电力作业人员的有效监管是保障电力安全生产的基础。该文对电力视频中作业人员的行为识别研究进行了归类总结,涵盖静态行为分析(穿戴分析、动作分析和组合分析)和动态行为分析(复杂动作、时序行为和行为预测等);详细综述了电力作业行为分析中的核心算法模块,包括目标检测、姿态估计和视频跟踪等;论述了电力作业行为识别在算法高效性、鲁棒性、灵活性等方面所面临的应用难点和挑战,并展望了电力作业行为智能监控领域的未来发展方向,特别强调了在软硬件结合、通用大模型、生成式人工智能方面进行技术创新和改进所蕴含的潜在机会。

A Novel Low-Complexity Low-Latency Power Efficient Collision Detection Algorithm for Wireless Sensor Networks

Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error detection mechanism, such as a CRC check. The obvious drawback of full detection of a received packet is the need to expend a significant amount of energy and processing complexity in order to fully decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a suite of novel, yet simple and power-efficient algorithms to detect a collision without the need for full-decoding of the received packet. Our novel algorithms aim at detecting collision through fast examination of the signal statistics of a short snippet of the received packet via a relatively small number of computations over a small number of received IQ samples. Hence, the proposed algorithms operate directly at the output of the receiver's analog-to-digital converter and eliminate the need to pass the signal through the entire. In addition, we present a complexity and power-saving comparison between our novel algorithms and conventional full-decoding (for select coding schemes) to demonstrate the significant power and complexity saving advantage of our algorithms.

AN INTERNATIONAL VIEW OF DIFFERENT APPROACHES FOR FAULTS DETECTION AND IDENTIFICATION IN NUCLEAR POWER PLANTS

An extensive survey of computer based systems that apply different approaches for faults diagnostics and identifications in nuclear power plants (NPPs) was presented. In the light of reviewed material, the classification criteria were developed. The classification of computational techniques (class of computing devices, class of programming languages, and simulation programs) was discussed. The classification of theoretical aspects applied (brief aspects, and detailed aspects) in computer based diagnostic systems were established. The classification of metholology applied (symbolic reasoning methodology, event based methodology, and function based methodology) in the diagnostic systems was also depicted. In the end, the personal comments on the reviewed material, and scope of the study were described.

锂离子电池储能电站的热失控状态检测与安全防控技术研究进展

锂离子电池具有能量密度大、使用寿命长、工作温度范围宽、自放电小等优点,是中国电化学储能电站的主流电池技术。然而,在加热、过充等恶劣工况下,锂离子电池易发生热失控引发火灾甚至爆炸,因此其安全问题已逐渐成为锂离子电池储能电站建设及大规模应用的首要问题。该文系统分析了锂离子储能电池热失控的诱因、电池内部反应过程及外部特征参量的变化规律,重点总结了当前主要的电池热失控状态检测技术、智能诊断算法及储能电站安全防控技术,最后对储能电站热失控状态检测及安全防控技术进行了总结和展望。

基于电阻变化率和有功功率的混合孤岛检测方法

为解决传统检测方法阈值设置困难、对电能质量影响大的问题,提出一种基于电阻变化率和有功功率扰动相结合的混合孤岛检测方法。电阻变化率作为被动方法,该特征量在孤岛后可快速响应,有功功率扰动模块仅在被动方法怀疑孤岛时开启,自适应注入能使公共耦合点电压越限的最小有功功率。仿真结果表明,该方法可快速正确地检测出孤岛,即便在非孤岛情况下也不会发生误判。

航拍电力杆塔图像中销钉唯一性识别研究

近年来,基于深度学习的缺陷销钉检测技术在电力杆塔巡检中得到广泛应用。但是该技术只能在图像空间对缺陷销钉进行定位,无法准确给出缺陷销钉在实际电力杆塔中对应销钉的唯一编号。针对该问题,该文提出一种融合深度学习与三维重投影的缺陷销钉唯一性识别方法。首先通过构建巡检电力杆塔系统的三维模型,进行电力杆塔系统中部件模型的编号获取,以及部件模型三维空间包围盒顶点和中心点坐标的计算;其次,利用YOLOv5模型在图像空间对销钉及上下文相关目标进行检测;再次,通过该文提出的融合结构约束的相机位姿估计算法对航拍图像的相机位姿进行估计;最后,将三维电力杆塔系统中的销钉包围盒按相机位姿进行重投影与销钉检测框进行匹配度计算,在图像空间中输出带有具体编号的销钉检测框。在YOLOv5模型输出的销钉检测框基础上进行销钉唯一性识别,利用仿真数据进行实验得到的唯一性识别正确率为100%,利用实拍数据进行实验得到的唯一性识别正确率为93.3%,验证了该文提出的销钉唯一性识别方法的有效性。通过对缺陷销钉进行唯一性识别,可以减少人工对故障图像的二次审核工作量,并准确地统计缺陷销钉的数量及具体位置信息,为后续故障维修及故障相关性分析等精细化管理提供支持。

基于功率谱流形的信息几何DP-TBD算法

针对复杂杂波背景下目标信杂比起伏而导致的漏检问题,本文结合信息几何检测器的性能优势与动态规划检测前跟踪(Dynamic Programming Track Before Detect,DP-TBD)的多帧信息积累能力,提出了基于功率谱流形的信息几何DP-TBD算法.该算法利用功率谱流形与矩阵流形对偶关系,设计了功率谱信息几何检测器,将信息几何检测器的计算复杂度降低了近两个数量级.通过实测数据实验验证,功率谱DP-TBD算法可实现与矩阵DP-TBD算法相近的检测性能,并将运算时间降低为矩阵DP-TBD算法的3%~8%.此外,相较于信息几何检测器,功率谱DP-TBD可将检测信杂比(Signal-to-Clutter Ratio,SCR)提高2~3 dB.

大模型时代:电力视觉技术新起点

随着无人机、巡检机器人和远程监控系统在输电、变电、配电、安监等电力场景中的广泛应用,利用电力视觉技术完成对海量巡检图像的自动处理,能够进一步提升电力系统智能化运维水平,对我国源网荷储一体化进程的快速推进具有至关重要的作用。随着通用视觉大模型的兴起,电力视觉技术正处于从传统深度学习时代向大模型时代跨越的重要节点。该文首先综述了电力视觉技术和通用视觉大模型的最新研究进展,结合视觉大模型在多种公共场景的应用先例,探讨视觉大模型在电力视觉领域将面临的3重能力边界问题。从初步探索通用视觉大模型的潜力,到逐步构建电力视觉大模型的过程,提出4种模型应用范式以突破视觉大模型能力边界。最后分析了视觉大模型对电力视觉研究者的影响,并对大模型浪潮下电力视觉技术的发展方向进行了展望。

基于MPPT控制的两级式直流孤岛检测方法研究

针对主动式孤岛检测方法存在扰动同步注入、影响电网供电质量等问题,提出了一种基于最大功率点跟踪(MPPT)控制的两级式直流孤岛检测方法。该方法通过检测并网变换器直流侧电流来判断疑似孤岛,然后利用疑似孤岛信号触发电压扰动的注入使得孤岛后的公共耦合点(PCC)电压出现特定频率的波动。由于扰动信号的注入受疑似孤岛信号控制,减少了正常状态下扰动的注入时长,有效降低对系统电能质量的影响。此外建立小信号模型分析了耦合点电压对扰动分量的频域特性,选取扰动信号的最优频率。最后通过仿真与实验验证了所提方法在单机与多机工况下的有效性。

电力巡检中改进YOLOv5s的缺陷检测算法研究

针对无人机进行电力巡检时关键零件的检测精度较低的问题,提出了一种基于YOLOv5s的改进型缺陷检测算法。在骨干网络中引入卷积神经网络注意力模块(CBAM),增强网络对特征图中重要信息的提取效率;将YOLOv5s中原有的PANet特征融合框架替换为双向特征金字塔网络(BiFPN),引入可学习的权重,映射不同的学习特征,增加对贡献较大特征的映射。在空间金字塔池化模块(spatial pyramid pooling,SPP)的基础上加入上下文卷积模块,提升特征的表达能力。通过构建航拍数据集进行实验验证,结果表明,改进后的算法mAP达到95.6%,准确率达到93.7%,召回率达到93.8%。为进一步验证算法在嵌入式系统的运行效果,通过缩小网络宽度进行轻量化,利用TensorRT推理引擎,优化了网络结构并加速了模型的推理。将模型加速后部署至Jetson Xavier NX平台进行测试,单帧图像平均运行时间为24.6 ms,检测准确率为90.8%,召回率为90.5%,能够在Jetson Xavier NX设备上对目标实现精准识别。改进后的模型提高了检测精度,体现了算法的有效性,满足电力巡检作业的实时检测需求。