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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]

Three-wheeled omnidirectional mobile robots (TOMRs) are widely used to accomplish precise transportation tasks in narrow environments owing to their stability, flexible operation, and heavy loads. However, these robots are susceptible to slippage. For wheeled robots, almost all faults and slippage will directly affect the power consumption. Thus, using the energy consumption model data and encoder data in the healthy condition as a reference to diagnose robot slippage and other system faults is the main issue considered in this paper. We constructed an energy model for the TOMR and analyzed the factors that affect the power consumption in detail, such as the position of the gravity center. The study primarily focuses on the characteristic relationship between power consumption and speed when the robot experiences slippage or common faults, including control system faults. Finally, we present the use of a table-based artificial neural network (ANN) to indicate the type of fault by comparing the modeled data with the measured data. The experiments proved that the method is accurate and effective for diagnosing faults in TOMRs. [ABSTRACT FROM AUTHOR]

With abundant bandwidth resources, terahertz communications are considered one of the key technologies to meet the requirement for high data-rate transmission in the future. In order to compensate for the severe propagation loss of terahertz communications, directional antennas with high gain and narrow beams are expected to be adopted, making beam tracking significant for robust communications. In this paper, a tracking method based on power measurement is proposed, consisting of beam status monitoring, recognition of the deviation direction, and movement toward the optimal angle. By observing the change in the received signal power, beam misalignment is first checked, and whether the misalignment is out of tracking range is also determined. Then, the deviation direction is recognized by comparing the received power variations in the candidate directions, and the beam angle is adjusted accordingly until it reaches the optimal angle. With a small scanning range, the deviation direction is recognized in a short duration, allowing for rapid beam tracking. Numerical results indicate that the alignment error is competitively low and stable in the proposed beam tracking method, and its technical superiority is particularly dominant in situations involving variable motion at high speeds. [ABSTRACT FROM AUTHOR]

In this research, the system is designed to monitor parameters of residential solar power sources such as voltage, current, power capacity, power factor and energy consumption. These parameters are measured by a highly accurate PZEM circuit and the specifications are suitable for residential solar power sources. Measurement results will be sent to the main control circuit (ARM controller) via the UART communication standard to analyze and filter the necessary data. The data of that parameter will then be displayed at the system installation site using the TFT screen. After an installation period, the control circuit will send measurement parameters to the dedicated WiFi module (ESP32), the WiFi module will continue to send parameters to the cloud server for storage and monitoring. The system also builds applications that run on many operating systems, can access data, and display data in real time using IoT technology. It should be noted that the system can still operate in the event of loss or absence of WiFi signal by using Bluetooth signal. In addition, the designed system also has the function of remotely controlling on/off the solar power supply to the loads when an incident occurs, ensuring safety for the electrical system. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of utilizing the line loss power factor to assess the reactive, unbalanced, and harmonic line losses in low-voltage distribution networks and explores the method of calculating decoupled line loss values based on this factor. To achieve this objective, we establish preliminary definitions of single-phase and three-phase reactive, unbalanced, and harmonic line loss power factors, drawing upon the principles of electrical theory outlined in IEEE Standard 1459. These power factors serve as crucial indicators for evaluating the severity of line losses caused by reactive power, unbalance, and harmonic problems. Subsequently, the values of line loss attributed to reactive, unbalanced, and harmonic components are decoupled and quantified using the line loss power factor as a fundamental parameter. The effectiveness and accuracy of the proposed method were verified in Matlab simulation and physical experiments. [ABSTRACT FROM AUTHOR]

The article is concerned with the operation of electric power quality meters and analyzers when incorrectly installed on a three-phase electrical network. An analytical study of the meter's errors under the conditions of faulty installation was carried out. All possible types of installation errors are considered according to the proposed classification, as well as possible combinations of a pair of installation faults simultaneously. Classification of installation faults is also proposed. Matrix expressions are derived that allow modeling of any type of installation faults to quantify power and symmetry measurement errors. The proposed method allows to obtain quantitative signs that can identify a specific installation fault. The results of this study can help the development of an algorithm for the automatic detection of installation faults and their correction. The analytical results were confirmed by simulation studies on a three-phase network model in the MATLAB/SIMULINK environment. Experimental results, obtained from a commercial power quality analyzer operated under controlled laboratory conditions, are also reported. [ABSTRACT FROM AUTHOR]

The subject of this study is the algorithms for measuring the angular positions of radio emission sources. The goal of this study is to develop an improved algorithm for signal processing in amplitude direction finders that will provide several unambiguous measurement angles at high steepness of the discrimination characteristic by combining measurements of systems with different widths of antenna patterns. The task: to develop an optimal signal processing algorithm for a four-antenna amplitude direction finding system, two antennas of which have a wide radiation pattern and the other two have a narrow one; to test the overall performance of the resulting algorithm by simulating the direction finder; to develop and conduct a study of an experimental model of a four-antenna direction finder, which includes two antennas with wide radiation patterns, two antennas with narrow radiation patterns, radio frequency paths, and a signal processing unit that implements the developed algorithm; to analyze the effectiveness and features of the application of the developed algorithm, and to compare the results of simulation modeling and experimental research. The methods used are statistical methods and optimal solutions for solving problems of statistical synthesis of signal processing algorithms in passive radio systems, computer simulation modeling methods, and experimental research methods. The following results were obtained. The algorithm for signal processing in a four-antenna direction finding system was synthesized using the maximum likelihood method. By simulation modeling, the overall effectiveness of measurement integration in multi-antenna amplitude direction finders was confirmed, and the peculiarities of the synthesized algorithm application were revealed, namely, the need to introduce additional proportionality coefficients into the measurement channels. During the experimental studies of the developed model of the direction finder, the results of the simulation modeling were confirmed. Conclusions. To determine the direction of radiation sources by amplitude direction finders, it is advisable to simultaneously use systems with wide and narrow antenna patterns, the measurements of which are combined by the proposed algorithm. This makes it possible to simultaneously expand the range of unambiguous measurement angles and increase the accuracy of measurements within the equal-signal zone. The disadvantage of using the algorithm is the nonlinear form of the obtained discrimination characteristics and the need to determine additional proportionality coefficients heuristically.

The increasing number of zero-emission vehicles on the roads demands novel vehicle charging solutions that ensure convenience, safety, increased charging infrastructure availability, and aesthetics. Wireless charging technology is seen as the one that could assure these desirable properties and could be applied not just in conventional implementations but also in off-grid solutions together with roadway energy harvesting systems. Both approaches require proper transfer of energy metering methods. In this paper, a method for measuring the power transferred to the load in a wireless charging system is presented, and its systematic error is assessed in the relevant range of influencing factors. The novelty of the method is that it does not require any metrologically certified measurement instrumentation on the receiver side of the wireless charging system. The error analysis is performed using a numerical simulation. Considered error-influencing factors included secondary side electrical load, coils' coupling coefficient and quality factor, current and voltage quantization resolution, and compensation topology type (serial-serial (SS) and serial-parallel (SP)). It was determined that the systematic error of the power assessment does not exceed 0.7% for SS and 1.1% for SP topologies when the coupling coefficient is in the range of 0.05 to 0.4 and the quality factor of the resonant system is in the range of 100 to 800. [ABSTRACT FROM AUTHOR]

The subject of this study is the algorithms for measuring the angular positions of radio emission sources. The goal of this study is to develop an improved algorithm for signal processing in amplitude direction finders that will provide several unambiguous measurement angles at high steepness of the discrimination characteristic by combining measurements of systems with different widths of antenna patterns. The task: to develop an optimal signal processing algorithm for a four-antenna amplitude direction finding system, two antennas of which have a wide radiation pattern and the other two have a narrow one; to test the overall performance of the resulting algorithm by simulating the direction finder; to develop and conduct a study of an experimental model of a fourantenna direction finder, which includes two antennas with wide radiation patterns, two antennas with narrow radiation patterns, radio frequency paths, and a signal processing unit that implements the developed algorithm; to analyze the effectiveness and features of the application of the developed algorithm, and to compare the results of simulation modeling and experimental research. The methods used are statistical methods and optimal solutions for solving problems of statistical synthesis of signal processing algorithms in passive radio systems, computer simulation modeling methods, and experimental research methods. The following results were obtained. The algorithm for signal processing in a four-antenna direction finding system was synthesized using the maximum likelihood method. By simulation modeling, the overall effectiveness of measurement integration in multiantenna amplitude direction finders was confirmed, and the peculiarities of the synthesized algorithm application were revealed, namely, the need to introduce additional proportionality coefficients into the measurement channels. During the experimental studies of the developed model of the direction finder, the results of the simulation modeling were confirmed. Conclusions. To determine the direction of radiation sources by amplitude direction finders, it is advisable to simultaneously use systems with wide and narrow antenna patterns, the measurements of which are combined by the proposed algorithm. This makes it possible to simultaneously expand the range of unambiguous measurement angles and increase the accuracy of measurements within the equal-signal zone. The disadvantage of using the algorithm is the nonlinear form of the obtained discrimination characteristics and the need to determine additional proportionality coefficients heuristically. [ABSTRACT FROM AUTHOR]

This paper examines how we can combine two big trends in solar energy: the spread of solar panels and wind turbines to renew the power grid, and cloud and edge computing technology to improve the way the grid works. Our study introduces a new strategy that is based on a means to exploit the power of cloud computing’s big data handling ability, together with the capacity of edge computing to provide real-time data processing and decision making. The method is designed to address major challenges in renewables systems making the system bigger and more reliable, and cutting the time delays in deciding how the system should respond. These are the kinds of changes that will be necessary so that we can blend solar and wind power into our current power grid, whether we are ready to say goodbye to coal or natural gas power. Our paper presents a way in which we believe that renewables systems can work more smoothly and effectively. This includes making it easier to measure how much power is being generated, to control these systems so that they function much like traditional power plants, and hence, to allow renewable energy to be part of a reliable and efficient part of our electricity supply. These are all crucial steps in using technology to make more of the green power from the sun – which we must do for our energy usage to be more earth friendly.

Accurate power consumption assessment is of critical importance in the fast-evolving world of cloud and edge computing. These technologies enable rapid data processing and storage but they also require huge amounts of energy. This energy requirement directly impacts operational costs, as well as environmental responsibility. We are conducting research to develop a specialized cloud-edge power measurement and security model. This model delivers reliable power usage data from these systems while maintaining security for the data they process and store. A combination of simulation-based analysis and real-world experimentation helped us to deliver these results. Monte Carlo based simulations produced power usage predictions under various conditions and Load Testing validated their real-world performance. A Threat Modeling-based security study identified potential vulnerabilities and suggested protection protocols. A collaborative approach enhances power measurements accuracy and encourages secure operation of the combined cloud-edge systems. By fusing these metrics, a more efficient and secure operation of computing resources becomes possible. This research underscores the critical importance of developing advanced techniques for power metering and security in cloud-edge computing systems. Future research may focus on both expanding the model’s use to an array of larger, more complex networks, as well as the inclusion of AI driven predictive analytics to amplify accuracy of power management.

Three-wheeled omnidirectional mobile robots (TOMRs) are widely used to accomplish precise transportation tasks in narrow environments owing to their stability, flexible operation, and heavy loads. However, these robots are susceptible to slippage. For wheeled robots, almost all faults and slippage will directly affect the power consumption. Thus, using the energy consumption model data and encoder data in the healthy condition as a reference to diagnose robot slippage and other system faults is the main issue considered in this paper. We constructed an energy model for the TOMR and analyzed the factors that affect the power consumption in detail, such as the position of the gravity center. The study primarily focuses on the characteristic relationship between power consumption and speed when the robot experiences slippage or common faults, including control system faults. Finally, we present the use of a table-based artificial neural network (ANN) to indicate the type of fault by comparing the modeled data with the measured data. The experiments proved that the method is accurate and effective for diagnosing faults in TOMRs.

Historically, for the measurement of electrical power, the wattmeter electrodynamometer was used, but when dealing with frequencies higher than those of power line, it was necessary to develop special equipment. Digital power meters are currently used. This work studies the use of the electrodynamometer wattmeter in a very frequent application such as power measurement at the output of a frequency converter. Measurements are made by comparing the readings of this instrument with those of digital instruments in different scenarios, depending on the frequency, cos φ, load, etc., to determine under which conditions it is useful.

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]

The extensive use of renewable energy systems with grid-connected inverters (GCIs) causes harmonic injection. Similarly, the imbalance in energy demand and supply causes frequency fluctuations. As a result of the increased harmonics and frequency fluctuations, the accuracy of power measurement using conventional methods continues to decline. Precision in power measurement is an essential factor for the billing and management of power supply and demand. Moreover, it is challenging to build a supply plan for the power demand and to manage the billing for the power consumption. To solve these problems, this paper proposes a novel method based on Lock-in Amplifier (LIA) and Lock-in Amplifier Frequency-Locked Loop (LIA-FLL) to measure the power with high precision and accuracy. The proposed method first tracks the variations in the input signal frequency using LIA-FLL and generates the updated reference signals for LIA. After that, the LIA is used to extract the accurate amplitude of each frequency component. The proposed method results in accurate and precise measurement, even with harmonics and frequency fluctuations. The validity of the proposed method is verified by comparing the power measurement results with the classical method, FFT, and ZERA COM3003 (a commercially available power measurement reference instrument). [ABSTRACT FROM AUTHOR]

As cloud computing technologies and applications develop rapidly in recent years, the quantity and size of cloud datacenters have been ever-increasing, making the overconsumption of energy in datacenters become a widespread concern. To reduce the energy cost by servers, we must first build an accurate power model to achieve flexible, device-free power consumption measuring. However, most of the previous work related to server power modeling solely apply to the server and virtual machine levels, and the existing power models fail to take into account the heterogeneity in workload. Therefore, we first propose separate power consumption models based on the distinction of workload types including CPU-intensive, I/O-intensive, memory-intensive, and mixed workload. Then, we present an adaptive workload-aware power consumption measuring method (WSPM) for cloud servers. Our method proactively selects an appropriate power model for the upcoming workload through workload clustering, forecasting and classification, which are implemented using K-means, ARIMA, and threshold-based methods, respectively. We conducted several experiments to evaluate the performance of the key components of our method. The result shows: (1) the accuracy of our future workload forecasting on real traces of requests to our servers, (2) the accuracy of the power consumption measured by WSPM, and (3) the effectiveness of our workload-aware method in reducing real-time power estimation lag. Overall, the proposed method simplifies power modeling under diverse workloads without losing accuracy, making it a general and highly available solution for cloud data centers. [ABSTRACT FROM AUTHOR]

Detecting anomalous behavior on smartphones is challenging since malware evolution. Other methodologies detect malicious behavior by analyzing static features of the application code or dynamic data samples obtained from hardware or software. Static analysis is prone to code's obfuscation while dynamic needs that malicious activities to cease to be dormant in the shortest possible time while data samples are collected. Triggering and capturing malicious behavior in data samples in dynamic analysis is challenging since we need to generate an efficient combination of user's inputs to trigger these malicious activities. We propose a general model which uses a data collector and analyzer to unveil malicious behavior by analyzing the device's power consumption since this summarizes the changes in software. The data collector uses an automated tool to generate user inputs. The data analyzer uses changepoint analysis to extract features from power consumption and machine learning techniques to train these features. The data analyzer stage contains two methodologies that extract features using parametric and non-parametric changepoint. Our methodologies are efficient in data collection time than a manual method and the data analyzer provides higher accuracy compared to other techniques, reaching over 94% F1-measure for emulated and real malware. [ABSTRACT FROM AUTHOR]

The electric power calculator (EPC) software, for the simulation and analysis of electrical networks at the industrial frequency, is described in this article, including its source code and operational diagram. Compared to well-known commercial software, EPC directly provides the values of the voltages, currents, and powers in each subsystem. The voltages and currents are calculated, in phasor notation, applying Kirchhoff's laws, while the powers are obtained through the components of the apparent power vector. In this paper, the EPC software could be applied to three-phase networks made up of a distribution transformer and two three-phase wye-configured loads. The correctness of the results of the EPC software was verified by comparison with those obtained using the Multisim commercial software and with the measurements recorded by the Fluke 435 analyzer in a real distribution network of residential buildings. The values of the voltages and currents provided by the EPC were the same as those measured by the Fluke, and the differences observed using Multisim were less than 0.6%. Consequently, the EPC software is an excellent complement for power analyzers, because it is capable of providing measurements in all the network subsystems at the same time, using a single analyzer. [ABSTRACT FROM AUTHOR]

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Amdahl's Law painted a bleak picture for large-scale computing. The implication was that parallelism was limited and therefore so was potential speedup. While Amdahl's contribution was seminal and important, it drove others vested in parallel processing to define more clearly why large-scale systems are critical to our future and how they fundamentally provide opportunities for speedup beyond Amdahl's predictions. In the early 2000s, much like Amdahl, we predicted dire consequences for large-scale systems due to power limits. While our early work was often dismissed, the implications were clear to some: power would ultimately limit performance. In this retrospective, we discuss how power-performance measurement and modeling at scale led to contributions that have driven server and supercomputer design for more than a decade. While the influence of these techniques is now indisputable, we discuss their connections, limits and additional research directions necessary to continue the performance gains our industry is accustomed to. [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 increasing number of zero-emission vehicles on the roads demands novel vehicle charging solutions that ensure convenience, safety, increased charging infrastructure availability, and aesthetics. Wireless charging technology is seen as the one that could assure these desirable properties and could be applied not just in conventional implementations but also in off-grid solutions together with roadway energy harvesting systems. Both approaches require proper transfer of energy metering methods. In this paper, a method for measuring the power transferred to the load in a wireless charging system is presented, and its systematic error is assessed in the relevant range of influencing factors. The novelty of the method is that it does not require any metrologically certified measurement instrumentation on the receiver side of the wireless charging system. The error analysis is performed using a numerical simulation. Considered error-influencing factors included secondary side electrical load, coils’ coupling coefficient and quality factor, current and voltage quantization resolution, and compensation topology type (serial-serial (SS) and serial-parallel (SP)). It was determined that the systematic error of the power assessment does not exceed 0.7% for SS and 1.1% for SP topologies when the coupling coefficient is in the range of 0.05 to 0.4 and the quality factor of the resonant system is in the range of 100 to 800.

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]

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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]