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An analysis and design of a new slotted power divider/combiner (PDC) that utilises an oversized substrate‐integrated waveguide (OS‐SIW) is presented to enhance power handling capability (PHC). It is interesting to note that, the power capability tolerance can be increased by increasing the width of structure and thickness of the substrate, although it may introduce higher‐order modes. The paper is focused on improving PHC of PDC that propagates only the TE10 mode while preventing the propagation of higher‐order modes. Additionally, the PHC of the proposed structure is studied in detail. The air breakdown or the corona effect is a physical phenomenon that limits the Peak Power Handling Capability (PPHC) of a device. In slotted microwave components, the corona effect plays a crucial role in determining the PPHC, and it is closely related to environmental conditions such as pressure. Another limiting factor is self‐heating, which affects the device's Average Power Handling Capability (APHC). The slotted OS‐SIW PDC is designed on Rogers RO4003 laminate with 32mil thickness, offering a low profile with an overall OS‐SIW PDC area of 170 × 55 mm2. The measured results of the fabricated PDC showcase a range of desirable features. Moreover, the proposed structure in power divider mode exhibits significant improvements in APHC compared to those of the conventional Substrate‐Integrated Waveguides structures, with respective enhancements of approximately 8.26% at 10 GHz (with a Fractional Band Width of 58.3%). These advantages are highly beneficial and hold great potential. [ABSTRACT FROM AUTHOR]

It is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low‐voltage monitoring and pre‐warning inspection. This study acquired a series of arc‐fault signals according to IEC 62606. The main time‐frequency features were strengthened with high efficiency by applying the generalized S‐transform to them with a bi‐Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high‐frequency harmonic energy reflections, thus increasing the rate of arc‐fault diagnosis and making it suitable for arc‐fault monitoring of non‐linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow‐up arc‐fault monitoring and inspection research. [ABSTRACT FROM AUTHOR]

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error. [ABSTRACT FROM AUTHOR]

The power output in cycling is one of the most important factors for athletes and coaches. The cycling community has several commercial gears that can be used. One of the most used is the TACX Neo 2T (TN2T) smart trainer. The objective of this study was to investigate the metrological proprieties of the TN2T (accuracy and reliability), as well as its agreement with the Garmin Vector 3 (GV3) pedals at different power stages. The sample consisted of ten regional-level cyclists with a mean age of 45.6 ± 6.4 years, who regularly participated in regional and national competitions. Residual relative differences were found between the two devices. Both devices showed good reliability with coefficients of variation and intraclass correlation coefficients ranging from 0.03% to 0.15% and from 0.731 to 0.968, respectively. Independent samples t-test comparison between devices showed no significant differences in all power stages (p > 0.05). Bland–Altman plots showed that more than 80% of the plots were within the 95% confidence intervals in all power stages. The present data showed that there were non-significant differences between the two devices at power stages between 100 W and 270 W, with a strong agreement. Therefore, they can be used simultaneously. [ABSTRACT FROM AUTHOR]

Abstract An analysis and design of a new slotted power divider/combiner (PDC) that utilises an oversized substrate‐integrated waveguide (OS‐SIW) is presented to enhance power handling capability (PHC). It is interesting to note that, the power capability tolerance can be increased by increasing the width of structure and thickness of the substrate, although it may introduce higher‐order modes. The paper is focused on improving PHC of PDC that propagates only the TE10 mode while preventing the propagation of higher‐order modes. Additionally, the PHC of the proposed structure is studied in detail. The air breakdown or the corona effect is a physical phenomenon that limits the Peak Power Handling Capability (PPHC) of a device. In slotted microwave components, the corona effect plays a crucial role in determining the PPHC, and it is closely related to environmental conditions such as pressure. Another limiting factor is self‐heating, which affects the device's Average Power Handling Capability (APHC). The slotted OS‐SIW PDC is designed on Rogers RO4003 laminate with 32mil thickness, offering a low profile with an overall OS‐SIW PDC area of 170 × 55 mm2. The measured results of the fabricated PDC showcase a range of desirable features. Moreover, the proposed structure in power divider mode exhibits significant improvements in APHC compared to those of the conventional Substrate‐Integrated Waveguides structures, with respective enhancements of approximately 8.26% at 10 GHz (with a Fractional Band Width of 58.3%). These advantages are highly beneficial and hold great potential.

Abstract It is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low‐voltage monitoring and pre‐warning inspection. This study acquired a series of arc‐fault signals according to IEC 62606. The main time‐frequency features were strengthened with high efficiency by applying the generalized S‐transform to them with a bi‐Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high‐frequency harmonic energy reflections, thus increasing the rate of arc‐fault diagnosis and making it suitable for arc‐fault monitoring of non‐linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow‐up arc‐fault monitoring and inspection research.

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.

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

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]

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.

The power output in cycling is one of the most important factors for athletes and coaches. The cycling community has several commercial gears that can be used. One of the most used is the TACX Neo 2T (TN2T) smart trainer. The objective of this study was to investigate the metrological proprieties of the TN2T (accuracy and reliability), as well as its agreement with the Garmin Vector 3 (GV3) pedals at different power stages. The sample consisted of ten regional-level cyclists with a mean age of 45.6 ± 6.4 years, who regularly participated in regional and national competitions. Residual relative differences were found between the two devices. Both devices showed good reliability with coefficients of variation and intraclass correlation coefficients ranging from 0.03% to 0.15% and from 0.731 to 0.968, respectively. Independent samples t-test comparison between devices showed no significant differences in all power stages (p > 0.05). Bland–Altman plots showed that more than 80% of the plots were within the 95% confidence intervals in all power stages. The present data showed that there were non-significant differences between the two devices at power stages between 100 W and 270 W, with a strong agreement. Therefore, they can be used simultaneously.

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.

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]

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]

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]

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 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]

The paper studies the power measurement methods of the operating power grids with various types of harmonics and inter-harmonics and gives a relatively complete solution， which sums up and integrates a variety of power and energy measurement methods， forming a universal power calculation theory. The simulation results show that this solution can not only achieve accurate measurement of active power and reactive power based on frequency-adaptive technology， but also obtain the power distribution of the fundamental wave and each harmonic， helping harmonic source identification， reactive power compensation， control of system parameters and improvement of power quality.

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.

In this paper, we take a deep dive into writing a plugin to export power, energy, and other system environmental data from a Cray® XC™ system. With the release of the System Management Workstation 8.0 software, Cray has enabled customers to create site-specific plugins to export all of the data that can flow into the Cray Power Management Database into site-specific infrastructure. In this paper, we give practical information on what data are available using the plugin, and how to write, test, and deploy a plugin. We also share and explain example plugin code, detail design considerations when architecting a plugin, and look at some practical use cases supported by exporting telemetry data off a Cray® XC™ system. This paper is targeted at plugin developers, system administrators, data scientists, and site planners. The plugin feature was developed by Cray in response to discussions with and requirements from select members of the CUG XTreme SIG. This paper demonstrates lessons learned developing prototype plugins that export data off Cray® XC™ systems using Kafka, Redis Pub/Sub, and RabbitMQ. This plugin capability is in-use internally at Cray, used in production at NERSC, and is under consideration for deployment on systems at LANL and Sandia.

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]

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]

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]