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In this letter, three battery-powered, custom Internet of Things (IoT) sensor nodes for the agribusiness, are presented: first, a Sigfox-based temperature-humidity index (THI) sensor to monitor the impact of heat stress in livestock, then a LoRaWAN version of an estrus detection collar for dairy farms, and finally a NB-IoT low-power A-GPS geolocation device for animals. Detailed power consumption measurements are presented and compared to highlight the benefits of each low-power wide-area network technology for the industry. The measured energy to transmit a single 10Byte payload packet was 90, 20, and 90 mJ for Sigfox, LoRa, and NB-IoT, respectively. With an adequate power management strategy, the nodes could operate up to 10 years in the case of the THI and estrus detector, and >1 yr in the case of the GPS tracker, powered by a single 1900 mA $\cdot \mathrm {h}~\mathrm {LiSOCl}_{2}$ battery. [ABSTRACT FROM AUTHOR]

This letter presents an analytical study of the performance of the maximum-likelihood gamma constant false alarm rate (MLG-CFAR) detector operating over a homogenous gamma-distributed (GM) clutter. To accomplish this task, a novel closed-form expression for the probability of false alarm and a highly accurate approximation for the probability of detection are derived. Specifically, this proposed approximation is also given in a closed form. These expressions, which can be of great interest to the radar community, are given in terms of the Gauss hypergeometric function and the Tricomi function. Besides, the proposed expressions can be easily implemented and computed using most popular scientific software packages such as MATLAB, Maple, and Mathematica. Our propositions are then tested and validated using numerical computing and Monte Carlo simulations. The obtained results demonstrate a high accuracy and significant computational advantages of the proposed expressions compared to the alternative ones. Moreover, the detection performance of the MLG-CFAR detector is also conducted assuming a homogenous clutter with a known shape parameter. [ABSTRACT FROM AUTHOR]

In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be protected. The proposed method is an output perturbation mechanism that adds noise to the release of query response such that the analyst is unable to infer the underlying class-label. The proposed DP method is capable of not only protecting the privacy of class-based data but also meets quality metrics of accuracy and is computationally efficient and practical. We illustrate the efficacy of the proposed method empirically while outperforming the baseline additive Gaussian noise mechanism. We also examine a real-world application and apply the proposed DP method to the autoregression and moving average (ARMA) forecasting method, protecting the privacy of the underlying data source. Case studies on the real-world advanced metering infrastructure (AMI) measurements of household power consumption validate the excellent performance of the proposed DP method while also satisfying the accuracy of forecasted power consumption measurements. [ABSTRACT FROM AUTHOR]

In this work, Siwa, a micropower 32-bits RISC-V core aimed at implantable medical SoCs is presented. The core was fabricated in a 180-nm CMOS-HV technology to directly drive biological stimuli circuits within the same ASIC. A complete set of power consumption measurements is presented; the core properly operated up to 30 MHz with a current consumption of $52~\mu \text{A}$ /MHz at 1.8-V supply voltage, and < 20-nA leakages at room temperature. Since the existing benchmarks are not completely adequate to compare Siwa performance to other microcontrollers used on implantable medical devices (IMDs), a simple, specific benchmark inspired in a pacemaker operation was developed. The new benchmark considers both the CPU current consumption and performance, sleep and run states, and allows to compare broadly different CPUs and operating conditions for specific IMD applications. Siwa’s performance was compared using this benchmark with 8 and 16-bits MCUs. [ABSTRACT FROM AUTHOR]

Due to the close relevance to the reliability and efficiency of power systems, network parameters such as branch admittance have been the target of various cyberattacks. However, existing attack models are generally based on the impractical assumption that attackers can directly modify the data of network parameter stored in well-secured control centers. This article proposes a practical attack model and designs an optimal strategy to detect malicious modification of critical network parameters. Specifically, the vulnerability of network parameter error processing is discovered and exploited to indirectly modify the data of network parameter without accessing to the well-secured control center. A model of false-data-injection-enabled network parameter modification is proposed, which significantly reduces the requirements on attackers’ capability and system information. An optimal detection strategy is designed based on the analysis of the minimal protection set at a single branch, which can significantly reduce the number of protected measurements in detecting malicious modification of critical network parameters. Finally, numerical simulations are carried out on the PJM 5-bus and the IEEE 118-bus test systems to validate the theoretical results. [ABSTRACT FROM AUTHOR]

While the very high switching speed of the gallium nitride devices can easily enable power electronics converters to operate at multi megahertz, it also brings huge challenges to switching current measurement, which is very important for the evaluation of switching performance. The conventional switching current measurement methods either have low bandwidth or have influence on layout inductance. To address these issues, this article proposes a high-bandwidth and easy-to-integrate switching current measurement method, with almost no influence on power loop layout. The proposed method utilizes the power loop trace from the source terminal of bottom switch to circuit ground as the sensing trace. By measuring the sensing voltage and considering the frequency-dependent parasitic inductance and resistance of the sensing trace, the switching current is derived based on the Fourier series theory. The simulation verification is firstly performed by using LTspice, and shows consistent results. Then, to accurately measure the sensing voltage, the used voltage probe TPP1000 is carefully modeled for measurement compensation and correction. Finally, two double pulse test (DPT) boards are designed, one with coaxial current shunt and the other without coaxial current shunt. Through DPT experiment, the accuracy of the proposed method with almost no insertion inductance is verified. [ABSTRACT FROM AUTHOR]

The performance and applications of the optical inline-Sagnac current sensor/transducer in utility high voltage alternating current measurements (metering and protection) and high voltage direct current measurements (control, metering, and protection) are presented. New measurements of commercially available sensors are presented for a 216 kApeak transient current, scale factor linearity for alternating currents from 5 mArms to 32 kArms and for direct currents from 100 mA to 27 kA, and temperature performance of ±0.11% over the temperature range of −60 °C to +65 °C. New properties of polarizer temperature-wavelength effects on the scale factor of the sensor are presented. Power quality measurements are discussed over the frequency range of 16.66 Hz to 5 kHz. A discussion of the limitations of optical noise on low current measurements is given. The experimental and calculated sensor response functions are compared. The limitations for low current measurements due to the IEC 61869 standard digital output are presented. [ABSTRACT FROM AUTHOR]

The use of synchronized measurement technology leads to a more reliable and secure operation of the power system. Phasor calculation is needed for all buses where a phasor measurement unit is installed, and fast and precise estimation is necessary for accurate monitoring, protection and control. The Prony algorithm is one promising method due to its capability to estimate phasor adaptively with changing frequency. The algorithm projects the signal on $L$ exponentially damped sine functions, where $L$ is the order of the Prony. However, the order under different conditions should be adapted automatically to reduce computation time. To adaptively specify the order, the empirical mode decomposition (EMD) method is proposed in this paper to be combined with the Prony and named EMD-Prony. The EMD decomposes a signal into finite single oscillatory modes, representing the number of modes in the signal. EMD is also used as a pre-processing step to filter noise from an input signal of Prony. Therefore, EMD is proposed here as an assistant of Prony in a phasor estimation. Finally, the proposed method is tested on the benchmark signals proposed in the IEEE standard, signals obtained from a simulated power system, and measured data from a real-world power system. [ABSTRACT FROM AUTHOR]

In an effort to reduce switching loss, increase switching frequencies, and shrink passive component sizes, power device switching times are being pushed into nanosecond and subnanosecond intervals. However, these fast switching times require high rates of change of current making the power devices susceptible to overvoltage damage from parasitic inductance in the power loop. This presents challenges in measuring the device switching current accurately and, in turn, makes accurate measurements of device switching energy and stored charge difficult. Traditional current measurement methods either do not have the required frequency response to view the switching edge accurately or have a high parasitic inductance that can significantly alter switching performance and cause damage to the power device. In this article, an active current measurement system is developed. This circuit achieves up to 1.6-GHz bandwidth with zero overshoot. By utilizing mutual inductance cancellation, an insertion inductance down to 20 pH has been achieved. The research performed in this article thereby allows for an accurate measurement of device switching loss with minimal power circuit disturbance. The performance of this current measurement is experimentally verified in the frequency domain using a vector network analyzer, and time-domain switching measurements are compared with state-of-the-art current measurement. [ABSTRACT FROM AUTHOR]

This article elaborates on an accurate energy model (EM) for wireless sensor devices (WSDs) based on power consumption measurement. A deep understanding of WSD’s energy cost depends on the WSD’s functional components contributing to the wireless communication process. The model includes different stages of transmission (Tx) and reception (Rx) communication modes. The practical EM is developed with data packet lengths, data rate, operation status, predesigned stages versus appropriate communication bus, and power mode, including mutual data transfers between the microcontroller and the radio module. The validity of the EM is verified through an experimental setup having a TelosB device. As expected, the modeled and measured energy consumed by the WSD operating from 1-to-4-MHz CPU frequencies and the 2-to-100-byte payload is in very good agreement. It was found that, compared to the energy consumption in processing, Tx, listening, and Rx phases, the data transfer consumes up to 40% of total WSD energy, whereas this aspect is often neglected in usual models. Overall, the proposed model provides significant accuracy and might be helpful in improving the energy efficiency of future wireless sensor networks. [ABSTRACT FROM AUTHOR]

This brief presents a three-stage wideband power amplifier (PA) monolithic microwave integrated circuit (MMIC) using 100 nm GaN-on-Si process. A synthesized matching strategy is introduced to achieve high efficiency and improve gain flatness over a wide operation bandwidth. For demonstration, one monolithic microwave integrated circuit (MMIC) PA working from 24 to 29 GHz is designed, which covers the typical 5G millimeter-wave (mm-wave) bands. The measured output power reaches 30 dBm and the measured power added efficiency (PAE) exceeds 27.6% over the frequency range of 24 to 29 GHz. Meanwhile, the peak PAE of 40.6% and the peak power of 31.3 dBm are obtained at 27.5 GHz. Besides, the PA shows a measured gain of 18.7 to 21.2 dB from 25 to 30.5 GHz, with gain flatness of ±1.26dB. [ABSTRACT FROM AUTHOR]

In this letter, two compact, colocated, low-coupled, quad-polarized (QP) antennas are proposed based on two dual-mode radiation bodies to achieve compactness and high isolation. The first antenna obtains four polarizations by orthogonally placing two dual-mode loops; the second antenna includes a dual-mode loop and a miniaturized dual-polarized patch based on fractal structures inside the loop. To investigate the performance of QP multiple-input–multiple-output (MIMO) systems, channel parameters including correlation coefficients, degree of power balance, and channel capacities are analyzed based on the measured data. Results show that, high channel capacities can be achieved both in a realistic office room and a rich scattered reverberation chamber, which are very close to that of the 4 × 4 independently identically distributed (i.i.d.) Rayleigh channel. It is revealed that both correlation coefficient and degree of received power balance have strong influence on the performance of a MIMO system, which are related to the radiation properties of the antenna and the polarimetric characteristics of the propagation environment. [ABSTRACT FROM AUTHOR]

Moving target defense (MTD) is becoming promising in thwarting the false data injection attacks (FDIAs) on power system state estimation (SE). However, due to the nonlinear dynamics of AC power systems, the investigation of the general evaluation metrics of MTD, namely the effectiveness in terms of attack detection and the hiddenness, is still challenging. To this end, in this paper, we attempt to conduct an explicit analysis on the MTD performance in AC power systems. First, we derive explicit approximations of measurement residuals to quantify the two metrics. Then, based on the projection matrix, maximizing the effectiveness is transformed to maximizing the lower bound of the approximated residual, under which the matrix inverse issue is addressed. Moreover, the maximization of hiddenness is achieved by the minimization of the approximated power flow difference caused by reactance perturbation. To balance the trade-off between effectiveness and hiddenness, the design of explicit residual-based MTD (EXR-MTD) is accomplished by aggregating the two sub-problems with an appropriate weight. Finally, extensive simulations are conducted to validate the performance of EXR-MTD. Numerical results indicate that EXR-MTD performs better than existing MTD strategies in terms of hiddenness, while the effectiveness of EXR-MTD is comparable to those of existing MTD strategies. [ABSTRACT FROM AUTHOR]

Evaluation of conservation voltage reduction (CVR) factor is critical to peak load reduction, 556 energy conservation, and many CVR-related power system control/optimization strategies. This paper proposes a novel robust CVR factor evaluation method using soft constrained gradient analysis based on time-varying load modeling and real utility field measurements. Firstly, the time-varying ZIP parameter identification is formulated as an over-determinant problem composed of first-order gradient with respect to each coefficient and soft constraints representing temporal correlation of loads in each time step, in order to improve the robustness and smoothness of CVR factor evaluation. Then, problems in time series are coordinated with a sliding window approach. A necessary condition for selecting the smallest window size is proposed and strictly proved to guarantee the existence and uniqueness of the solution of time-varying load modeling problem. Finally, time-varying CVR factors are accurately and robustly calculated with field measurements and the identified load model. Case studies are performed using sufficient field measurements obtained from two real utilities. Unlike existing methods that require a large number of measurements to obtain precise estimation of CVR factor, the proposed method is sufficiently accurate even when the measurements are limited or of low time resolution. Further, the accuracy and robustness of the proposed approach are validated under different types of uncertainties and compared with other existing data processing and CVR factor evaluation methods. [ABSTRACT FROM AUTHOR]

As the penetration of photovoltaic (PV) solar generation has increased, a significant number of residential and commercial solar PV systems have been installed without meters. The majority of these systems, however, are also not monitored by power system operators. Therefore, the uncertainty of the net load owing to this “invisible” solar power generation has raised additional challenges for power system operation. To reduce the impact of this, we propose a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that could assist in the identification of the representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating aggregated power generation compared to other existing methods. [ABSTRACT FROM AUTHOR]

As one of the most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. Unfortunately, it always suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering masurements. Moreover, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode informaton of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis. [ABSTRACT FROM AUTHOR]

Amplifier design, both in traditional approaches and real-time circuit optimization, greatly benefits from fast and thorough extraction of information from measurement data. Using only a few performance samples at varying impedances, deep learning image completion techniques can be utilized to extrapolate an entire set of Smith chart load-pull contours. In addition to speeding nonlinear device characterizations, this extrapolation can be performed in an iterative fashion for use as a circuit optimization algorithm with a very low number of measurements. The techniques of this work have been tested in the measurement of a nonlinear, large-signal amplifier. The load impedance can be estimated with a typical error of < 0.1 linear units using as few as seven impedances and yields even better accuracy with larger sample sizes. [ABSTRACT FROM AUTHOR]

In this article, we propose an energy-efficient architecture, which is designed to receive both images and text inputs as a step toward designing reinforcement learning agents that can understand human language and act in real-world environments. We evaluate our proposed method on three different software environments and a low power drone named Crazyflie to navigate toward specified goals and avoid obstacles successfully. To find the most efficient language-guided reinforcement learning model, we implemented the model with various configurations of image input sizes and text instruction sizes on the Crazyflie drone GAP8, which consists of eight reduced instruction set computer-V cores. The task completion success rate and onboard power consumption, latency, and memory usage of GAP8 are measured and compared with Jetson TX2 ARM central processing unit and Raspberry Pi 4. The results show that by decreasing 20% of input image size we achieve up to 78% energy improvement while achieving an 82% task completion success rate. [ABSTRACT FROM AUTHOR]

Due to vulnerabilities exposed to cyberattacks in the cyber physical power system, increasing concerns have been paid to its cybersecurity, especially on the so-called false data injection attack. Timely recovering true values of measurements and states after encountering cyber-attacks is of paramount importance for ensuring the subsequent controls and operations of the cyber physical power system. This article, for the first time, discovers a measurement data inertia effect, and uses this effect to deduce coarse values of preattack measurements as a preliminary work for data recovery. Then, based on the deduced coarse values and suggested state bounds, an optimization model is proposed to recover the measurements and states contaminated by attacks in-time. Moreover, an error criterion named interval error is proposed to assess the entire performance of the proposed recovery scheme. Extensive and comprehensive experiments are implemented on the IEEE 30-bus test benchmark to verify the feasibility and effectiveness of the proposed recovery scheme. The numerical studies reveal that the proposed method can achieve high accuracy and efficient timeliness for data recovery. [ABSTRACT FROM AUTHOR]

We present measurements and analysis of self-interference in multi-panel millimeter wave (mmWave) full-duplex communication systems at 28 GHz. In an anechoic chamber, we measure the self-interference power between the input of a transmitting phased array and the output of a colocated receiving phased array, each of which is electronically steered across a number of directions in azimuth and elevation. These self-interference power measurements shed light on the potential for a full-duplex communication system to successfully receive a desired signal while transmitting in-band. Our nearly 6.5 million measurements illustrate that more self-interference tends to be coupled when the transmitting and receiving phased arrays steer their beams toward one another but that slight shifts in steering direction (on the order of one degree) can lead to significant fluctuations in self-interference power. We analyze these measurements to characterize the spatial variability of self-interference to better quantify and statistically model this sensitivity. Our analyses and statistical results can be useful references when developing and evaluating mmWave full-duplex systems and motivate a variety of future topics including beam selection, beamforming codebook design, and self-interference channel modeling. [ABSTRACT FROM AUTHOR]

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Load event matching is the key for event-based non-intrusive load monitoring (NILM). It aims to find the load event sequence corresponding to the appliance’s operation cycle from all detected load events. Firstly, a novel combinatorial optimization model for load event matching is established. To reduce the dimension of the estimation matrix in the proposed optimization model, the concept of balanced window (BW) is introduced to indicate a period of load operation within which events can be fully matched. Then an iterative load event optimal matching strategy based on nested BWs is proposed, where most inner BWs are processed firstly. For the event matching in one specific BW, a two-stage optimization algorithm is designed. In the first stage, a set of candidate load event sequences is generated by using the depth first search algorithm with pruning, which reduces the solution space of the combinatorial optimization problem. In the second stage, the optimal load event sequences that result in the optimal fit of the aggregated load power is selected. Comparative experiments on the publicly-accessible REDD datasets have shown that the proposed method achieves better performance against the existing unsupervised NILM methods, and is promising for practical implementation. [ABSTRACT FROM AUTHOR]

This letter proposes a multifrequency impedance matching broadband power amplifier (PA) design, which is different from the traditional PA impedance value changes with the change in the operating frequency. This method selects the impedance value of the transistor device through the load–pulling system. Then, matching networks composed of a cascade of microstrip lines are used to match the device impedance to 50 $\Omega $. The matching networks are optimized to provide the desired impedance transfer characteristic at multiple frequencies. This design approach effectively reduces the amplifiers’ output impedance variation over the covered frequency range. The overall structure of the PA is simple, and the bandwidth is expanded while ensuring performance. Based on this method, a broadband PA was designed and fabricated using the self-developed laterally-diffused metal-oxide semiconductor (LDMOS) transistor of the Institute of Microelectronics of the Chinese Academy of Sciences. The measurement results show that the saturated output power of the designed PA in the range of 0.5–2.3 GHz is greater than 41 dBm. The drain efficiency is 43.62%–55.38%. When the average output power is 34.3 dBm, the adjacent channel leakage ratio (ACLR) is also better than −31.58 dBc. [ABSTRACT FROM AUTHOR]

In this letter, an efficient and compact tri-band rectifier with large frequency ratio is proposed, which adopts a novel low-cost direct current (dc)-pass filter structure in the design. Three large frequency ratio rectifiers are combined together by using diode arrays with a frequency-selective topology without additional impedance matching networks. The dc-pass filter is composed of microstrip line structures cascaded low-cost lumped components, which can effectively suppress the high-order harmonics at high- and low-frequency points while reducing the circuit size and costs of large frequency ratio. For validation, the proposed rectifier working at 0.915, 2.45, and 5.8 GHz is fabricated with the dimensions of 29.5 mm $\times21.3$ mm. The measured results show that the RF-dc power conversion efficiency (PCE) reaches 72.6%, 71.8%, and 73.5% at 0.915, 2.45, and 5.8 GHz, respectively, when the input power is 19 dBm. Compared with traditional multiband rectifiers, the proposed topology exhibits merits of compact size, high efficiency, and large frequency ratio. [ABSTRACT FROM AUTHOR]

This letter presents a fully integrated frequency doubler in the TSMC 28-nm CMOS technology. MOS transistor capacitor neutralization is introduced in the differential driving amplifier, which shows better robustness than MOM capacitor neutralization under PVT variations. Besides, a stacked transformer-based balun is designed to achieve good balance performance. The effect of dummy metal on the performance of the proposed balun is also discussed. The measured results show that the 3-dB bandwidth is 25 GHz (208–233 GHz) and the peak power-added efficiency is 1.1% with a dc power consumption of 26.4 mW at 222 GHz. The peak conversion gain is −7.2 dB and the variation in conversion gain is less than 1 dB within −7- to 1-dBm input power range. Moreover, the chip size is $492\times 412\,\,\mu \text{m}^{2}$ , including GSG and DC pads. [ABSTRACT FROM AUTHOR]

This article presents the degradation effects of long-term use of a dual vibrating intrinsic reverberation chamber (dual VIRC) for shielding effectiveness (SE) measurements. At the beginning with a new fresh dual VIRC setup it is possible to achieve a very high dynamic range (DR) but the DR degrades due to long-term use of the dual VIRC. The effect of 10 years use of the dual VIRC on the DR is shown as function of usage time. Also, the effect on the DR of a new dual VIRC with better conductive cloth has been presented. [ABSTRACT FROM AUTHOR]

A high-power Y-junction circulator is proposed for radar applications in the Ku-band (12–18 GHz). The design consists of two identical circulators combined using power splitters at all ports. The overall circulator network achieves 20-dB isolation level and return loss over the Ku-band with a peak power level of 14 kW. An internally matched divider is used at one port of the overall system to eliminate the cavity resonances produced by the practical asymmetries between the two circulators. The asymmetry problem is analyzed in detail to understand the consequences of these practical variations fully. Experimental validation is done by a fabricated prototype and shows a good agreement between the simulated and the measured results with differences in the range of ±2 dB. [ABSTRACT FROM AUTHOR]

Multi-frequency (or harmonic) emissions and interactions are ongoing challenges for converter-dominated power systems. Consequently, multi-frequency models are introduced for power quality and harmonic stability studies. Rotor-Speed-Dependent (RSD) emissions from Type 3 Wind Turbines (WT) can lead to unexpected interactions. However, there are only a few specific studies on Type 3 WTs’ emissions. Besides, the provided analyses have been only based on field measurements. In this paper, the uncertainties of the field measurements are demonstrated experimentally. This paper provides an experimental analysis of emissions in different parts of a Type 3 WT, including stator, rotor, grid-side converter, and DC-link. In this way, theoretical emission patterns are formulated in sequence domain and verified by experimental measurements from a 2.2 MVA Type 3 WT tested by a 15 MVA grid emulator. The tests include emission measurements in different rotor speeds with identical output power set-point. To the best of our knowledge, the provided experiments are unique presentations from different parts of a Type 3 WT using MVA-scale torque and grid emulators. The test results provide a good impression of the existence, patterns, and amplitudes of the RSD emissions. Addressing such emissions in multi-frequency models can be necessary for the improvement of harmonic studies. [ABSTRACT FROM AUTHOR]

In this letter, an innovative technique is presented, which allows the experimental extraction of the junction-to-ambient thermal impedance (ZTH) of power devices operating in their application environment (in situ). The technique draws inspiration from the thermal characterization of RF transistors, and is based on simple measurements of electrical signals, while not requiring a thermochuck, the calibration of a thermometer, as well as temperature sensors or IR cameras. The validation of the technique is unambiguously performed by applying the “simulated experiments” strategy on an SiC-based multichip power module. [ABSTRACT FROM AUTHOR]

Multi virtual cathodes (MVCs) were created in an axial vircator utilizing two dielectric reflectors (DRs). The wall charge of drifting electrons on the surface of DRs led to the creation of VC2 and VC3 in the downstream area of drift tube, according to phase space simulations. The microwave generated by VC1 interacted with the charge of VC2 and VC3 to raise the power amplitude, resulting in an increase in output power from 284 MW to 1028 MW. The use of two dielectric reflectors was proven to have a remarkable impact in reducing electron loss, increasing output power, and improving efficiency. The suggested vircator’s efficiency with MVCs improved from 7.9 % to 28.6 %. The findings indicate important signs of progress in vircator research to decrease electron losses and boost beam–wave interaction, which might lead to the advancement of high-efficiency vircators. [ABSTRACT FROM AUTHOR]

In this letter, a frequency selective rasorber with a waveform selective passband (FSR-WSP) is proposed based on the combination of frequency selective absorber and waveform selective surface techniques to achieve the multiple functions, including absorbing stealth, energy protection, and waveform selection. The structure achieves more than 90% absorptance in the range of 8.4–18.5 GHz (relative bandwidth reaches 75.1%) and a waveform selective transmission band around 3.8 GHz. For waveform selective function, the transmittance reaches more than 80% when a continuous wave is incident, while the transmittance is less than 25% when a high-power short pulse wave is incident. The absorption characteristics and waveform selective transmission mechanism are analyzed and validated by full-wave simulation and cosimulation. Finally, the FSR-WSP is fabricated and measured to verify its functionality. [ABSTRACT FROM AUTHOR]

Electromechanical systems experience both gradual and sudden fault conditions. Power monitoring provides a valuable approach for detecting faults, essentially turning a machine into its own sensor for observing developing and abrupt failures. Machines can be monitored individually or nonintrusively (as a collection of loads) and signal processing can tease out relevant indicators of operational status and health. Load identification and diagnostics with aggregate electrical monitoring rely on the correct physical interpretation of measurements. Specifically, ties between the observed measurements and the actual physical task performed by a load ensure the relevance of a measurement, or a feature space derived from the measurement, for reliable identification and diagnostics. This article examines three-phase mathematical relationships for different load configurations, specifically with an eye toward selecting a feature space useful for automated diagnostics. The utility of these three-phase measurement representations is demonstrated with experimental data from several microgrid systems.

Human blockage and its dynamics are potential challenges for millimeter-wave (mm-wave) mobile communication. This article presents the results of wideband measurements at 27 GHz with one human blocker close by a dynamic mobile terminal (MT) as well as one or multiple dynamic human blockers further away from an MT. The measured human blockage loss is largest when the direct path (DP) in a line-of-sight (LOS) is blocked, but this loss is limited by other multipath components (MPCs). For nonline-of-sight (NLOS) channels, it is shown that human blockage loss is typically negligible. The presented measurement results show that human blockage loss in multipath channels is much smaller than that reported in diffraction- and measurement-based models, which neglect or minimize the contribution of all MPCs other than the DP. This suggests that the multipath nature of the indoor wireless channel highly limits the impact of human blockage. [ABSTRACT FROM AUTHOR]

This paper addressesthe problem of real-time state estimation in European-type four-wire low-voltage distribution networks with advanced metering infrastructure based on power line communication (PLC). The main drawback of this type of infrastructure is its high latency and a sequential data sampling process. Both factors make it difficult to obtain instantaneous values from the smart meters in a timely and adequate manner to estimate the real-time state of the system. This problem may be solved in the future with the deployment of metering systems based on other technologies such as 5G. However, PLC technology prevails in many countries, and is not likely to be replaced in the medium term. In the meantime, a solution is needed to provide acceptable real-time state estimates in low voltage networks, not to hinder the active management of these assets in the context of smart grids. In this work, a solution is proposed that allows estimating the state of the low voltage network with a reasonable accuracy considering the low quality of the measurements provided by the deployed infrastructure. The system is validated using a portion of a real pilot network located in the north of Spain fed by a transformer station with seven feeders. [ABSTRACT FROM AUTHOR]

The Largest Normalized Residual Test (LNRT) has been widely utilized in commercial Power System State Estimation (PSSE) software for bad data identification. The LNRT has proved effective in dealing with single bad data as well as multiple non-interacting and multiple interacting but non-conforming bad data. However, it is known for a long time that when two bad data are both interacting and conforming, i.e. their errors are in agreement, the LNRT may fail to identify either one. Moreover, it has been shown recently that even two interacting and non-conforming bad data can cause the failure of the LNRT. Drawing on sensitivity analysis in linear regression, we develop normalized deleted residuals for suspected measurements so that the agreement in measurement errors are broken. Therefore, the LNRT for normalized deleted residuals will be able to identify the actual bad data point. Furthermore, in the case of AC PSSE, the method does not require calculation of a new hat matrix when a measurement is deleted from the data set. This makes the method computationally cost-effective. Simulation results for identifying different conforming and non-conforming interacting bad data proves that the proposed method can enhance the effectiveness of the LNRT. [ABSTRACT FROM AUTHOR]

With the power system operating close to its stability margin, the increased penetration of renewable resources and loads with diverse characteristics, appropriate stability studies, and control decisions have become crucial. Due to the importance of accurate representation of aggregate loads in such studies, research interests for load modeling have increased. This paper puts forward a novel measurement-based load modeling approach that adapts itself based on the complex dynamics exhibited by the installed aggregate loads. The proposed approach primarily utilizes the collected measurements to gain insight regarding the dynamic trend in the measurements and subsequently determines the optimal order of an equivalent system or load model capable of effectively representing the aggregate load dynamics. An advanced signal pre-processing technique is employed, which effectively suppresses the noise and also preserves the transients present in the measurements. Afterward, an adaptive dynamic load model (ADLM) accurately represents the installed aggregate load. Lastly, the Refined Instrumental Variable (RIV) approach estimates the equivalent dynamic load model parameters. The results on a 2 bus and the IEEE 118 bus networks in DIgSILENT Powerfactory and a 7 bus network in Real-Time Digital Simulator (RTDS) reveal the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The inertia information of both synchronous generators (SGs)/equivalent SGs and converter interfaced generators (CIGs) are crucial to maintain the frequency stability of modern power grids. This paper proposes a novel data-driven method using ambient measurements, which seems to be the first method that can estimate the inertia constants of SGs/equivalent SGs and the virtual inertia constants of CIGs simultaneously without disturbing the system’s normal operating condition. Numerical studies in the IEEE 68-bus system demonstrate that the developed estimation method can estimate the inertia constants of different generation units accurately and is robust to measurement noises, missing measurements and the variation of operating conditions. [ABSTRACT FROM AUTHOR]

Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensors to quickly detect, in real-time, when any unusual events, whether natural faults or malicious, occur on the power grid. Existing bad-data detectors in the industry lack the sophistication to robustly detect broad types of anomalies, especially those due to emerging cyber-attacks, since they operate on a single measurement snapshot of the grid at a time. New ML methods are more widely applicable, but generally do not consider the impact of topology change on sensor measurements and thus cannot accommodate regular topology adjustments in historical data. Hence, we propose DynWatch, a domain knowledge based and topology-aware algorithm for anomaly detection using sensors placed on a dynamic grid. Our approach is accurate, outperforming existing approaches by 20 $\%$ or more (F-measure) in experiments; and fast, averaging less than 1.7 ms per time tick per sensor on a 60K+ branch case using a laptop computer, and scaling linearly with the size of the graph. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements. [ABSTRACT FROM AUTHOR]

The nonlinearity of the fifth-generation (5G) millimeter-wave (mmWave) phased array transmitter (TX) depends on the variations in the beamforming coefficients, e.g., due to unwanted amplitude errors of the phase shifters or purposely introduced amplitude variations to shape the beam, as well as other analog component variations. Therefore, also the digital predistortion (DPD) coefficients depend on the beamforming coefficients and require a continuous update for different beamsteering directions in a conventional DPD. We propose a robust DPD approach that uses the average array response for DPD training. The average array response is estimated using the known experimental histogram of power amplifier (PA) input power variation resulting from the amplitude variations due to beamforming. The DPD training based on the average array response makes the DPD coefficients insensitive to beamforming variations. The proposed DPD strategy requires a shared feedback path for training and only a single set of DPD coefficients to linearize the array response in all beamsteering directions. The performance of the proposed DPD strategy is validated by over-the-air (OTA) measurements of a 28-GHz phased array TX over different steering angles. Experimental results show that the proposed DPD method using one set of DPD coefficients provides similar linearization performance across the steering angles compared with the DPD trained to all steering angles through the OTA reference antenna. [ABSTRACT FROM AUTHOR]

Capacitance–voltage (C–V) gate characteristics of power metal-oxide-semiconductor field-effect transistors (mosfets) play an important role in the dynamic device performance. C–V characterization of the mosfet gate structure is a necessary step for evaluating the mosfet switching behavior and calibrating lumped equivalent capacitances of mosfet compact models. This article presents a comprehensive analysis on gate C–V measurements of silicon (Si) and silicon carbide (SiC) power mosfets leading to clear measurement guidelines. The requirements on the measurement setup, the selection of equivalent models used for the mosfet capacitance extraction, and the measurement frequency range are defined and supported by an accurate C–V characterization of several Si- and SiC power mosfets. The results show that the gate-source and gate-drain capacitances should be extracted at a frequency of some 10 kHz rather than at 1 MHz, as typically adopted in datasheets, to avoid parasitic effects introduced by the measurement setup and package. Furthermore, analytical expressions for $C_\text{dg}$ and $C_\text{sg}$ were derived based on a lumped equivalent circuit, which explain the influence of the measurement setup and the package parasitics on the C–V measurements. Nonideal measurement conditions are identified and correlated to the differences in C–V extraction with either parallel or series-equivalent model. A new method is proposed to estimate the ratio of the mosfet’s on-state resistance components $R_\text{ch}$ and $R_\text{drift}$ based on the presented C–V measurement guidelines, which are applicable to all three- and four-terminal power mosfets. [ABSTRACT FROM AUTHOR]

This letter a novel approach to event-based power modeling for embedded platforms that do not have a performance monitoring unit (PMU). The method involves complementing the target hardware platform, where the physical power data is measured, with another platform on which the CPU performance data, that is needed for model generation, can be collected. The methodology is used to generate accurate fine-grain power models for the Gaisler GR712RC dual-core LEON3 fault-tolerant SPARC processor with onboard power sensors and no PMU. A Kintex UltraScale field-programmable gate array (FPGA) is used as the support platform to obtain the required CPU performance data, by running a soft-core representation of the dual-core LEON3 as on the GR712RC but with a PMU implementation. Both platforms execute the same benchmark set and data collection is synchronized using per-sample timestamps so that the power sensor data from the GR712RC board can be matched to the PMU data from the FPGA. The synchronized samples are then processed by the Robust Energy and Power Predictor Selection (REPPS) software in order to generate power models. The models achieve less than 2% power estimation error when validated on an industrial use case and can follow program phases, which makes them suitable for runtime power profiling during development. [ABSTRACT FROM AUTHOR]

Wide-area measurement systems (WAMS) are efficient tools to estimate the fault location in the large-scale power systems. In this study, a fast and accurate deep learning based structure is proposed to detect the faulty lines and locate the fault based on the measured data of WAMS including phasor measurement units. To this end, a modulated gated recurrent unit deep learning based network named improved gated recurrent neural network has been proposed. The designed network, which has a recurrent neural network based structure, benefits from one update gate that can capture sudden changes in transient voltage and current signals and also enhance computational efficiency. Furthermore, to enhance robustness of the designed network under noisy conditions, a new informative-based loss function is formulated. The proposed loss function utilizes generalized form of mutual information to enhance robustness of designed network in the presence of Gaussian and non-Gaussian noises. The numerical results on IEEE 68-bus system verifies the effectiveness and superiority of the proposed method considering several operational conditions and comparison by two combined shallow-based networks and one deep learning based networks. [ABSTRACT FROM AUTHOR]

The accelerated digitalization of today’s electric power infrastructure has highlighted the gravity of cyber resilience in the electricity ecosystem. Due to the expanded attack surface inherited in the digitalized operational and information technologies, the electricity sector is facing a continually escalating cyberthreat landscape and must act to prepare for more frequent and sophisticated cyberattacks as the new normal. To aid in this effort, this paper presents a novel approach to identify critical branches to strengthen and by doing so, shield the smart-grid power system from the threat of load redistribution attacks (LRAs) under a wide range of operating scenarios. Compared with conventional critical branch identification approaches, we propose a new concept, namely, chain of defense, that empowers the system operator to incorporate existing cyber protections and develop branch strengthening strategy in a more dynamic, adaptive, and flexible way. We then propose a novel reinforcement learning framework to identify the most effective chain of defense. A cross-updating search strategy is developed to specifically ensure that the identified chain of defense can safeguard the network from the two-fold damaging effects of the LRAs on operation economics and security simultaneously. The effectiveness of the proposed approach is evaluated on the IEEE 14-bus system and the European 89-bus system. Simulation results indicate that the proposed approach provides more comprehensive and effective mitigation against the damaging effects of LRAs compared with the conventional approaches. [ABSTRACT FROM AUTHOR]

With the rapid development of distributed renewables, DC transmission, and active loads, increasingly more power electronics are and will be introduced into distribution grids. This has led to an increasing need for a new synchronized measurement system that covers different scenarios and applications to improve the stability and reliability of power-electronics-enabled power grids. In this paper, a full-view synchronized measurement system (SYMS) for the renewables, controls, loads, and waveforms of power-electronics-enabled power distribution grids is established, and is composed of different types of synchronized measurement devices (SMDs) and a data center. A general design method for the synchrophasor estimation of SMDs in different scenarios is proposed by deducing phasor measurement error models. This is further applied in combination with the design of hardware and a communication protocol to develop SMDs for the measurement of the renewables, controls, loads, and waveforms of power-electronics-enabled power distribution grids. Then, a framework of the data center of the SYMS, as well as several potential applications based on real-time measurements, are presented and discussed. The SYMS is a comprehensive, shared, and open power system data platform for use by future researchers. [ABSTRACT FROM AUTHOR]

In this article, an effective method using reverberation chambers (RCs) to measure the total radiated power (TRP) from mobile phones (MPs) is shown. The method exploits a novel measurement setup that links the MP under test inside the working volume of the RC to a real external wireless communication system. Therefore, no radio base station emulator (BSE) is necessary in the proposed method. For the aim of the method, we have found that the connection of the MP under test with the real wireless network system is held stable by the measurement setup in both line of sight and non-line-of-sight configurations. Hence, TRPs from MPs are measured in different conditions of connection even though the received and transmitted signals are not completely controllable. Measured TRPs are shown up to maximum values, which are useful for both electromagnetic interference evaluation and human exposure assessment in a world where wireless devices are spreading more and more. By maximum TRP values, which meaningfully allow result comparisons regardless of the use or not of a BSE, we also show a novel method to estimate the part of TRP absorbed into user body during a call. [ABSTRACT FROM AUTHOR]

This paper presents a fast model-agnosticmethod for recovering noisy Phasor Measurement Unit (PMU) data streams with missing entries. The measurements are first transformed into a Page matrix, and the original signals are reconstructed using low-rank matrix estimation based on optimal singular value thresholding. Two variations of the recovery algorithm are shown- a) an offline block-processing method for imputing past measurements, and b) an online method for predicting future measurements. Information within a PMU channel (temporal correlation) as well as from different PMU channels in a network (spatial correlation) are utilized to recover degraded data. The proposed method is fast and needs no explicit knowledge of the underlying system model or measurement noise distribution. The performance of the recovery algorithms is illustrated using simulated measurements from the IEEE 39-bus test system as well as real measurements from an anonymized U.S. electric utility. Extensive numeric tests show that the original signals can be accurately recovered in the presence of additive noise, consecutive data drop, as well as simultaneous data erasures across multiple PMU channels. [ABSTRACT FROM AUTHOR]

Parameter Identification plays an important role in electric power transmission systems. Existing approaches for parameter identification tasks typically have two limitations: (1) They generally ignored development trend of historical data, and did not mine characteristics of corresponding power grid branches. (2) They did not consider the constraints of power grid topology, and treated different branches independently. Therefore, they could not characterize correlations between the center node and its neighborhoods. To overcome these limitations, this work proposes a multi-task graph convolutional neural network (MT-GCN) which utilizes the graph convolutional network (GCN) and the fully convolutional network (FCN) as building blocks for parameter identification. Specially, GCN can extract the structure information to enhance local feature extraction. FCN is a decoding module following GCN module, and it is used to identify the parameters of each branch according to its characteristics. Compared with previous methods, the proposed method is significantly improved in accuracy. Besides, this method is robust to measurement noise and errors, and can cope with multiple conditions in real power transmission systems. [ABSTRACT FROM AUTHOR]

High penetration level of single-phase solar PVs in low voltage distribution systems has raised concern about the Voltage Unbalance (VU) level. Real-time phase reconfiguration using Dynamic Switching Devices (DSD) has been proposed as a cost-effective solution to mitigate the excessive level of Voltage Unbalance. DSDs are fast power electronics devices that could change the phase connection of a solar PV with minimum disturbances. Most of the proposed methodologies for controlling DSDs are based on a central controller and the assumption of availability of Advanced Metering Infrastructure (AMI) and a communication system. In this paper, we propose a distributed control methodology to control DSDs based on the measurement from the local node only and using the sensitivity of nodal voltage to power injection. Each DSD decides on phase swapping if it rectifies the unbalance index of the local node. We show that decreasing the Voltage Unbalance Factor (VUF) of individual nodes will improve the unbalance situation of the whole feeder. The proposed scheme does not require a communication system nor a central controller. The result of simulations confirms the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Detecting and classifying the occurrence of multiple class events is important in enhancing the wide-area situational awareness ability of power distribution systems. Multiple class event classification based on voltage magnitude measurement has not been largely deployed due to the inadequate elucidation between the occurrence of events and the corresponding eigenvalue perturbations in the measurement data. Herein, a detection and classification method for multiple class events in power distribution systems is proposed based on an eigenvalue fluctuation model. We first elucidated the interrelation between event occurrence and the eigenvalue fluctuations in the system voltage magnitude measurement data. Based on the discovery that different classes of events lead to their respective data eigenvalue behavior, we proposed detection criteria CSRL, CMLP1, and CMLP2 to quantitatively assess these behaviors. Since the different value changes in CSRL, CMLP1 and CMLP2 can indicate the occurrence of different classes of events, we trained and utilized a supervised classifier to achieve multiple event detection and classification. Simulation tests are performed in 7 different test feeders. Eight classes of events can be accurately detected and classified with only voltage magnitude measurement data. An 80% correct detection rate can be obtained with only a 20% measurement device penetration rate. [ABSTRACT FROM AUTHOR]