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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]

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]

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]

Increasing renewable penetration in distribution grids calls for improved monitoring and control, where power flow (PF) model is the basis for many advanced functionalities. However, unobservability makes the traditional way infeasible to construct PF analysis via admittance matrix for many distribution grids. While data-driven approaches can approximate PF mapping, direct machine learning (ML) applications may suffer from several drawbacks. First, complex ML models like deep neural networks lack the degradability and explainability to the true system model, leading to overfitting. There are also asynchronization issues among different meters without GPS chips. Last but not least, bad data is quite common in the distribution grids. To resolve these problems all at once, we propose a variational support matrix regression (SMR). It provides structural learning to (1) embed kernels to regularize physical form in observable area while achieving good approximation at unobservable area, (2) integrate temporal information into matrix regression for asynchronized data imputation, and (3) define support matrix for margins to be robust against bad data. We test the performance for mapping rule learning via IEEE test systems and a utility distribution grid. Simulation results show high accuracy, degradability from data-driven model to physical model, and robustness to data quality issues. [ABSTRACT FROM AUTHOR]

Rapid Voltage Changes (RVC) produce sudden illuminance changes that can generate flicker. The IEC 61 000-4-30 standard defines the procedure for the measurement of RVCs, but it does not include a metric to assess the effect of RVCs on the perception of flicker. The IEC 61 000-4-15 standard establishes the specification of a flickermeter, which assesses the effect of voltage fluctuations on flicker by calculating the flicker severity, $P_\text{st}$ , during a 10 min interval. However, $P_\text{st}$ is ineffective to adequately assess voltage variations as fast and short as RVCs. This work analyzes the main characteristics of the RVCs affecting flicker. The relationship between the amplitude and rate of change of the RVC and the instantaneous flicker perception, $P_\text{inst}$ , during the event is quantified. A method to estimate the amplitude of RVCs, and consequently to detect them, is proposed. The method is based on the maximum variation and rising slope of $P_\text{inst}$. The model is tested on 576 hours of recordings from five different low voltage locations. It provides high precision, with a mean deviation below 0.2% in the RVC estimated amplitude. This method could be used by the IEC flickermeter to define a more precise and complete strategy to address the flicker assessment. [ABSTRACT FROM AUTHOR]

The status of switches in distribution systems might be changed for protection purposes or to achieve the operation objectives. However, the system topology should be known for the efficacious management and control of these systems. In most practical systems, due to the lack of enough measurements, the topology cannot be identified accurately and speedily. The forecast output can be transformed into the joint probability density function (pdf) of uncertain parameters, e.g., power demands. The measured data and this pdf are first conflated to update the joint pdf to best comply with the measurements. The topology identification problem, i.e., finding the probability of each topology given a set of measured values, is converted to a simpler form, i.e., finding the probability of observing these measured values in this topology. These probabilities are then calculated by tracing how the measurements affect the shape of the new consolidated pdf in each topology compared to the original pdf. The proposed technique employs as much data as available, is able to find the accurate probabilities, and is yet quite fast. The mathematical treatment of extracting such probabilities is presented and the proposed technique is validated in numerical studies in terms of accuracy, speed, and versatility. [ABSTRACT FROM AUTHOR]

Linear power flow models are widely used in power system analysis. To control the maximum influence of linearization error on decision making, it is important to evaluate the worst-case linearization error (i.e., the error bound). This letter theoretically derives the error bound of linear power flow models based on the truncation error analysis of Taylor series expansion. A cold-start approach is proposed to restrain the error bound. Case studies show that 1) the proposed error bound is tight and effective and 2) the maximum linearization error can be successfully reduced using the proposed method. [ABSTRACT FROM AUTHOR]

At present, superconducting power technology has been applied more and more in the ac field, such as ac superconducting current limiter, ac superconducting cable, and so on. Critical current is the key parameter in the design, manufacture, and operation process of superconducting devices, but few studies were carried out on the ac critical current of superconductors now. In this article, a method for measuring the ac critic al current of the HTS coil based on the thermal stability was proposed, the ac critical current measurement experiment of the superconducting coil is carried out, and the electromagnetic–thermal coupling model is built to analyze the quench principle of the superconducting coil. This article explains the principle of ac critical current from the balance of heating and cooling and gives the rule that the value of ac critical current changes with frequency. [ABSTRACT FROM AUTHOR]

In this article, a six-port reflectometer is proposed, which utilizes nonmatched power detectors. In the presented solution, the signals reflected from power detectors are efficiently used to obtain a signal flow required for correct measurements. As a consequence, impedance matching networks for power detectors are no longer needed. Simultaneously, due to its balanced topology, the developed six-port exhibits advantageous measurement conditions and theoretically ideal matching at both its input and measurement ports. To experimentally validate the proposed concept, a six-port reflectometer operating at 2.4 GHz is designed, manufactured, and utilized in reflection coefficient measurements. The obtained results are consistent with the values measured using a commercial vector network analyzer. For additional verification, the impact of power detectors’ asymmetry is investigated theoretically and experimentally. [ABSTRACT FROM AUTHOR]

Modal decomposition of measurement equations has already been shown to simplify the formulation and resulting computational complexity of three-phase state estimation of systems where all the transmission lines are three-phase and fully transposed. When there are non-transposed and/or mixed-phase lines, modal decomposition can no longer fully decouple the three-phase measurement equations. This paper addresses the above shortcoming by proposing a simple yet practical solution based on the commonly used numerical compensation techniques. Thus, it enables application of the powerful decoupling approach to any type of three-phase networks which may contain non-transposed or mixed-phase lines and are fully observable by PMUs. The proposed procedure modifies the measurement set by deriving additive terms that compensate for the neglected unsymmetrical effects. It will be shown that unbalanced systems including non-transposed and mixed-phase elements, can still be transformed into three decoupled subsystems and solved in parallel by the proposed approach. Performance of the proposed algorithm is validated against several IEEE test cases. [ABSTRACT FROM AUTHOR]

In this paper, we explore various methods of measuring noise power in RF systems. We discuss some of the systems commonly used to make such measurements, then analyze each system's architecture to obtain the measurement bias and uncertainty for six different combinations of noise models (real and complex noise) and methods of averaging (linear power, magnitude, and logarithmic). Equipped with this knowledge, users can correct for post-measurement biases and determine the number of averages required to yield a desired uncertainty level. [ABSTRACT FROM AUTHOR]

This paper is concerned with the forecasting aided state estimation (FASE) problem for power distribution networks (PDNs) subject to nonlinear measurements and limited communication resources. For the sake of using bandwidth more effectively, a special closed-loop event triggering condition is first designed with the hope to reduce unnecessary data transmission from the measurement side to the remote estimator. And an event-based robust filtering algorithm is proposed to improve the estimation performance of estimator with the nonlinear intermittent observations. In this algorithm, the measurement output function with high-order nonlinearity is reasonably reduced to a quadratic alternative form, in order to avoid the computation burden caused by the use of the second-order extended Kalman filter (SOEKF), the quadratic nonlinear function is further linearized by Taylor series with quadratic norm bound. Furthermore, an upper bound of the filtering error covariance containing some uncertainties (includes quadratic term and non-triggering errors) is derived and subsequently minimized at each time step. The desired filter gain is obtained by recursively solving a set of Riccati-like matrix equations, which is easy to implement via online computation. Finally, the effectiveness of the proposed algorithm is demonstrated using the standard IEEE 13-bus and 123-bus distribution test feeders with both phasor measurement unit (PMU) and conventional measurements. [ABSTRACT FROM AUTHOR]

Measurement error is a crucial factor that determines the accuracy of state estimation (SE). Conventional estimators have fixed models, and can yield optimal performance only when the measurement error statistics exactly meet the assumptions. In reality, however, the error distribution is usually unknown and time-varying, resulting in suboptimal state estimates in most cases. This paper develops the concept of adaptive SE for power systems measured by phasor measurement units (PMUs). First, the Gaussian-Laplacian Mixture (GLM) model is developed to fit the body and tail of unknown measurement error distributions. Then, an adaptive estimation framework is proposed based on the Expectation-Maximization (EM) algorithm. It is capable of tracking the actual error statistics online, and adjusting the parameters of SE to maintain near-optimality of state estimates under complex measurement error conditions. Simulation results demonstrate that the proposed adaptive estimator yields more accurate state estimates than the well-known Weighted Least Squares (WLS) and Weighted Least Absolute Value (WLAV) estimators by adapting itself to the variations of measurement error statistics. [ABSTRACT FROM AUTHOR]

Smart meters (SMs) are being widely deployed by distribution utilities across the U.S. Despite their benefits in real-time monitoring. SMs suffer from certain data quality issues; specifically, unlike phasor measurement units (PMUs) that use GPS for data synchronization, SMs are not perfectly synchronized. The asynchrony error can degrade the monitoring accuracy in distribution networks. To address this challenge, we propose a principal component pursuit (PCP)-based data recovery strategy. Since asynchrony results in a loss of temporal correlation among SMs, the key idea in our solution is to leverage a PCP-based low rank matrix recovery technique to maximize the temporal correlation between multiple data streams obtained from SMs. Further, our approach has a novel multi-objective structure, which allows utilities to precisely refine and recover all SM-measured variables, including voltage and power measurements, while incorporating their inherent dependencies through power flow equations. We have performed numerical experiments using real SM data to demonstrate the effectiveness of the proposed strategy in mitigating the impact of SM asynchrony on distribution grid monitoring. [ABSTRACT FROM AUTHOR]

The paper describes the recent development of a system for calibration of electrical instruments with harmonics measurement capability, such as revenue meters and power analyzers, under nonsinusoidal test conditions at National Research Council (NRC) Canada. The improvements implemented with respect to an earlier NRC calibration system are aimed at increasing the calibration accuracy in a wider frequency range. [ABSTRACT FROM AUTHOR]

This article describes a new traceable measurement system, designed for the characterization of inductive charging stations for electric vehicles. This article particularly focuses on static charging (stationary vehicle), while dynamic charging (charging while driving) requires a significantly different system. The measurement system is able to measure on-site performance and efficiency of the charging station and converters. The relative uncertainty is ≤10−3. The measurement system aims at accurately measure the power at the batteries and the power transferred from the ground to the onboard resonant circuit and makes possible a benchmarking between station measuring systems. Finally, this measurement system allows the characterization of the magnetic emissions by correlating them with the electric current in the coils. This system improves the ease and ability to characterize wireless charging stations both in field and laboratory environments. This article describes in detail the hardware, the software, the GPS synchronization, and, in summary, the system architecture. [ABSTRACT FROM AUTHOR]

Power system state estimation (SE) plays a crucial role in the success of advanced tools employed for power system analysis and control in Energy Management Systems. SE is responsible for providing a consistent real-time dataset, free from errors that may compromise decision-making and put the system at risk. The presence of criticalities involving elements such as network branches, measuring units or measurements may impose difficulties for the detection/identification of gross errors in the acquired data. The simultaneous unavailability of the elements that form a given criticality makes the grid unobservable. This paper presents a flexible and effective network-based methodology for the integrated identification of critical groups of branches, measurements and measuring units in SE. The proposed algorithms are constructed, having the properties of such criticalities as their central pillar. Tests performed with IEEE 14-, 30-, and 118-bus benchmark systems are presented, and the obtained results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Finger-Braille is a tactile communication method used by people who are Deafblind. Individuals communicate Finger-Braille messages with combinations of taps on three fingers of each of the hands of the person receiving the communication. Devices have been developed to produce Finger-Braille symbols using different tactile stimulation methods. Before engaging in communication studies based on technologically-mediated Finger-Braille, we evaluated the relative efficacy of these methods by comparing two devices similarly constructed; the first based on widely employed eccentric rotating-mass vibrating motors and the other using specifically designed tapping actuators. We asked volunteers to identify the numerosity of presented items and for each device we measured (1) error-rate, (2) reaction time, (3) confidence ratings, and (4) a comparison of confidence ratings to actual performance. The four measures obtained for each device showed a net advantage of the tapping stimulation method over the method of vibrations. In this article, we conclude that the tapping stimulation method is recommended for use in the design of tactile communication devices based on Finger-Braille and fingerspelling methods reliant on finger tapping actions. The results did not demonstrate clear evidence for tactile subitising with passively experienced stimulation on the fingers. [ABSTRACT FROM AUTHOR]

Timing skew between measurement channels can introduce an unacceptable error in power loss measurement and is difficult to quantify, rendering precise in-situ measurement of magnetic loss essentially unattainable. This article rams the issue head-on by presenting the mathematical model for the timing skew and developing two methods based on the model to realize in-situ direct magnetic loss measurement. The mathematical model is first established to analyze the effects of timing skew on the measured magnetic loss in a dc–dc converter. The first method, called intersection method, illustrates how to unravel the entangled relationship between the actual timing skew and real magnetic loss in the circuit with the aid of the mathematical model. The second method, called derivative method, builds on the intersection method and greatly enhances the accuracy and the repeatability of the loss measurement. Our novel approach for in-situ magnetic loss measurement is verified in simulation and experiment with a conventional dc–dc step-down buck converter under various operating conditions and design parameters. [ABSTRACT FROM AUTHOR]

Phasor measurement unit (PMU) testing is desired to ensure the continued power system monitoring and the maneuvering precision of synchrophasor-based control. IEEE Std C37.118 and IEEE synchrophasor measurement test suite specification (TSS) clarify the different requirements for both steady-state and dynamic test scenarios with varied error ranges. Existing PMU testing requires taking PMUs out of service and test in the lab typically manually. This article presents an architecture for remote PMU testing utilizing the PMU performance analyzer (PPA) test suite, which can considerably reduce the cost while achieving the substation automation. The proposed field-programmable gate array (FPGA) programming method is demonstrated to meet the TSS requirements for signal source generators (SSG). The accuracy of the frequency and harmonic distortion factors by SSG using the proposed method are compared with the built-in signal generation module of the national instruments (NI) cRIO system. The steady-state and dynamic test results of remote PMU testing based on the proposed architecture are presented in this article. The satisfactory performance test results demonstrate the feasibility of the architecture. [ABSTRACT FROM AUTHOR]

This study addresses the validation of harmonic models for power generation units considering practical aspects associated with test‐bench and on‐site measurements. A new method is proposed for the harmonic model validation. This method has been developed within the joint research project 'NetzHarmonie,' which deals with the study of harmonic emissions of renewable energy sources in the power system. The main idea for model validation is to compare the harmonic voltage–current characteristic of the model with the measured harmonic voltage–current pairs. This idea is implemented in a practical process by considering measurement uncertainties as well as the time variation of harmonics. In the validation process, measurement data should meet the given requirements to verify whether they are suitable for the task of validation. The application of the proposed model validation process to photovoltaic and wind power generation units with different topologies and power classes is thoroughly addressed as well. The application of the proposed process is illustrated in detail for selected harmonic orders from these measurement campaigns. This study aims to provide a scientific foundation for the consideration of the harmonic model validation of power generation units in future standards. [ABSTRACT FROM AUTHOR]

Distribution system state estimation (DSSE) has been enabled by the deployment of smart meters and is currently the subject of active research, focused mainly in medium-voltage distribution grids (MVDGs). This article proposes a modified weighted least-squares (WLS) DSSE for low-voltage distribution grids (LVDGs) where the neutral conductor is grounded only at the MV–LV substation. DSSE methods developed for MVDGs are not applicable in such systems due to the significant voltage drop across the neutral conductor. The proposed DSSE includes the neutral voltage in the state vector, and the measurement functions are modified accordingly. To address any convergence issues and to enhance the accuracy of the proposed DSSE, virtual measurements are introduced for the neutral voltage. The effectiveness of the proposed DSSE is illustrated in a real LVDG and in the IEEE European low-voltage test feeder under different operating conditions, smart meter classes, and system layouts. In addition, a Monte Carlo analysis is performed for highlighting the importance of the proposed modifications to the WLS DSSE. Among others, the analysis indicates that the proposed method converged in all trials, despite including the neutral voltage in the state vector. [ABSTRACT FROM AUTHOR]

New global energy efficiency classification standard IEC 61800-9-2 for frequency converters and motor systems has been recently launched. It classifies the energy efficiency of the converters and motor-drive systems and introduces three different methods to determine voltage-source converter (VSC) losses for the classification. In here, experimental tests are carried out using the input–output method and the calorimetric method to determine the losses and efficiency of a 160-kW commercial VSC. The measurement results with these two independent methods are compared and the uncertainty of the methods is analyzed. The converter electric input and output power distributions are analyzed in the frequency domain and are used further to estimate the electric power measurement uncertainty. The measurement uncertainty related to losses obtained with the input–output method is compared with the loss difference between the two methods. This is the first research work to accurately evaluate the experimental methods for the determination of the power losses and examine the validity of the energy efficiency classes of the frequency converters according to new IEC61800-9-2. An urgent need to update the loss boundaries of the IE-classes has been found and the measurement uncertainty effect on the IE-classification is discussed. [ABSTRACT FROM AUTHOR]

Based on laser power meter calibration requirements, through the purchase of standard equipment and field monitoring equipment developed, standard laser detector, optical path related equipment, such as construction of laser radiation power meter calibration test system, implementation of laser power meter of laboratory test and vehicle, large equipment form a complete set of laser power meter field test and field test of the radiated power of the laser equipment. Calibration system is introduced in detail the composition and working principle of the uncertainty of the system are analysed in detail, and through the system stability test, laser power, beam monitoring and correction device performance test, ate test for power meter calibration experiment data analysis, proved in this study, the laser power meter calibration method provided will enable calibration system, the expanded uncertainty of relative reached 2% (k = 2), which can realise wavelength of 532 and 1064 nm, power measuring the range of 0.1 mW–20 W laser power meter calibration.

Power system state estimation is heavily subjected to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. Traditional weighted least square (WLS), as the most universal state estimation method, attempts to minimize the residual between measurements and the estimation of measured variables, but it is unable to handle the measurement error. To solve this problem, based on random matrix theory, this paper proposes a data-driven approach to clean measurement error in matrix-level. Our method significantly reduces the negative effect of measurement error, and conducts a two-stage state estimation scheme combined with WLS. In this method, a Hermitian matrix is constructed to establish an invertible relationship between the eigenvalues of measurements and their covariance matrix. Random matrix tools, combined with an optimization scheme, are used to clean measurement error by shrinking the eigenvalues of the covariance matrix. With great robustness and generality, our approach is particularly suitable for large interconnected power grids. Our method has been numerically evaluated using different testing systems, multiple models of measured noise and matrix size ratios. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of integrity of the state estimation in presence of a novel bad data injection threat. We consider persistent adversaries who inject attack vectors “silently,” i.e., they drift the results of the state estimation gradually in multiple steps, with each step bypassing the bad data detection step of state estimation. Prior works on the topic have shown how an adversary can bypass the bad data detection by constructing attack vectors as linear combinations of the column vectors of the measurement Jacobian matrix. We show here that the attack surface is much broader than implied by this assumption. We demonstrate our attack strategy using realistic load patterns from NYISO in the IEEE 14-bus system. We also propose a detection method that uses the expected energy of normalized residues and a paired t-test, and we show its effectiveness. The generality of the proposed attack and detection strategy implies that it can be used in other cyber-physical systems involving state estimation based on linearized state perturbation. [ABSTRACT FROM AUTHOR]

In the modern age, the Internet of Things forms the basis of all the infrastructures for improving efficiency, reliability, and comfort. As applied to the power system, increasing the penetration of information and communication technologies at the device and process level is enabling devices to communicate with each other, thereby, facilitating wide-area situational awareness of the power grid. Dependence on the communication infrastructure has also made the power grid vulnerable to cyber-intrusions. Cybersecurity of the power system is a huge concern, which must be addressed with topmost priority. Securing all the entry points for cyber-attacks may not be economically feasible; hence, a critical set of sensors must be identified and secured to protect the grid against malicious actors. In this article, we propose a defense strategy to select the most critical measurements for securing the power system operation against false data injection attacks. The proposed approach is compared with the existing methodologies in the literature and validated using standard IEEE test cases. [ABSTRACT FROM AUTHOR]

This letter proposes a novel and highly efficient distribution system state estimation (DSSE) method with nonlinear measurements from supervisory control and data acquisition (SCADA) systems. Conventional DSSE based on the weighted least square (WLS) criterion requires multiple Monte Carlo simulations for overall accuracy evaluation and high calculation cost due to a nonlinear iterative process. The proposed method uses the Taylor series of voltages for constructing a linear DSSE model in the interval form and then solves this model by interval arithmetic. This method obtains accurate and robust estimates via a single random sampling of measurements and is computationally efficient. The comparative analysis in the IEEE 34-bus distribution system points to improved estimation results relative to the nonlinear WLS-based method. [ABSTRACT FROM AUTHOR]

State estimation plays a critical role in maintaining operational stability of a power system, which is however vulnerable to attacks. False data injection (FDI) attacks can manipulate the state estimation results through tampering the measurement data. In this paper, a cyber-physical model is proposed to defend against FDI attacks. It judiciously integrates a physical model which captures ideal measurements, with a generative adversarial network (GAN) based data model which captures the deviations from ideal measurements. To improve computation efficiency of GAN, a new smooth training technique is developed, and an online adaptive window idea is explored to maintain the state estimation integrity in real time. The simulation results on IEEE 30-bus system and IEEE 118-bus system demonstrate that our defense technique can accurately recover the state estimation data manipulated by FDI attacks. The resulting recovered measurements are sufficiently close to the true measurements, with the error lower than $1.5e^{-5}$ and $2e^{-2}$ p.u. in terms of voltage amplitude and phase angle, respectively. [ABSTRACT FROM AUTHOR]

Based on laser power meter calibration requirements, through the purchase of standard equipment and field monitoring equipment developed, standard laser detector, optical path related equipment, such as construction of laser radiation power meter calibration test system, implementation of laser power meter of laboratory test and vehicle, large equipment form a complete set of laser power meter field test and field test of the radiated power of the laser equipment. Calibration system is introduced in detail the composition and working principle of the uncertainty of the system are analysed in detail, and through the system stability test, laser power, beam monitoring and correction device performance test, ate test for power meter calibration experiment data analysis, proved in this study, the laser power meter calibration method provided will enable calibration system, the expanded uncertainty of relative reached 2% (k = 2), which can realise wavelength of 532 and 1064 nm, power measuring the range of 0.1 mW–20 W laser power meter calibration. [ABSTRACT FROM AUTHOR]

An enormous challenge for state estimation (SE) in power distribution networks with high penetration of photovoltaic generators lies in how to deal with measurement uncertainty. Driven by this motivation, an interval arithmetic (IA) based SE is proposed in this paper, which considers interval modeling for measurement uncertainty. The proposed SE is formulated as two interval optimization models based on the unknown-but-bounded theory, and solution bounds of state variables are obtained using a two-stage linear programming approach. In particular, the proposed SE is extended to realistic power distribution networks with mixed microphasor measurement units (μPMU), voltage magnitude, smart meters and feeder terminal units. Case studies and results are illustrated and discussed to demonstrate the performance of the proposed IA-based SE. It could provide the operators intuitive and effective bounds of system states for advanced power industrial applications under consideration of uncertainty. [ABSTRACT FROM AUTHOR]

Power system operator executes the unit commitment (UC) program to determine the optimal generation schedule for a daily or weekly horizon. In smart power systems, cyber-attacks such as false data injections (FDIs) may resemble a kind of contingency with only manipulating measurements data and without physical initiating events. This paper proposes a network constrained UC model in the presence of cyber-attacks named cyber-secured UC (CSUC) model. Normally the operating point obtained by UC program is on its conventional schedule, however in case of cyber-attack risk, it can depart from the conventional schedule toward the cyber secured schedule without violating unit and network constraints during such transition. The CSUC schedule is secured against a type of cyber-attacks called load redistribution (LR) attacks. Moving to this new operating point, in case of cyber-attack risk, may incur a small and reasonable additional cost to the cost of conventional generation schedule. In order to guarantee the optimality of solution, the proposed CSUC model is formulated as a mixed integer linear programming (MILP) model. The benders decomposition (BD) method is utilized to solve the model in a master and sub-problem hierarchy. The proposed method is implemented in the IEEE 118-bus test system. [ABSTRACT FROM AUTHOR]

In power transmission systems, the false data injection (FDI) attacks against state estimation (SE) have been well studied. However, due to the unique features of power distribution systems including: the low x/r ratio, existence of one-and/or two-phase branches, unbalanced load distributions, and unsymmetrical line parameters; the research on FDI attacks against distribution system SE (DSSE) is still open. In this paper, we investigate the vulnerability of DSSE to FDI attacks. In particular, we first propose a local state-based linear DSSE for multiphase and unbalanced smart distribution systems, which can facilitate the construction of FDI attacks numerically with the least information of system states. Then, the construction of three-phase coupled FDI attacks is introduced. The consideration of the coupling among phases by the three-phase coupled FDI attacks may require the modification of a large number of measurements by the attackers. To reduce the number of required measurements, the perfect three-phase decoupled FDI attacks, which consider the weak couplings among phases, is investigated. The probabilities of successful three-phase decoupled FDI attacks in strongly three-phase coupled systems are also derived numerically. The performance of the proposed FDI attacks against DSSE is evaluated based on IEEE test feeders. The case study results indicate the feasibility of the FDI attacks against DSSE in practical multiphase and unbalanced smart distribution systems. Future research directions including potential countermeasures are also highlighted. [ABSTRACT FROM AUTHOR]

The increasing complexity of distribution grids due to widespread deployment of renewable resources and/or power electronic devices, e.g., voltage source converters, has necessitated the needs of distribution system state estimation (DSSE) for efficient control relying on an accurate picture of the system states. This paper therefore explores the application of using the deep belief network (DBN) for pseudo measurements modeling in the context of DSSE. Two DBNs are trained respectively, for active and reactive power injection outputs, with load profiles and limited number of real measurements. Given the non-Gaussian behavior of the estimated pseudo measurements, we model the error by Gaussian mixture distribution and accordingly design a state estimator based on the Gaussian component combination method (GCCM). This method is able to handle the non-Gaussian measurement uncertainty while retaining the framework of the classic weighed least square (WLS) algorithm. The effectiveness of the proposed DSSE is demonstrated on a modified US PG&E69 distribution network in terms of the accuracy for both the estimated quantities and the associated uncertainty distributions. [ABSTRACT FROM AUTHOR]

The effective management of future smart grids is strictly related to the accurate knowledge of the network operating conditions via distribution system state estimation (DSSE). To achieve this target, the measurement infrastructure of the distribution systems needs a substantial upgrade. However, obvious economic limitations prevent a massive deployment of measurement instruments on the field in short times. As a consequence, ad hoc meter placement techniques have to be applied to define location and type of a minimum set of measurements required to obtain the desired accuracy performance. This paper presents a mathematical analysis showing the impact of current and power measurements on the accuracy of DSSE results. The goal is to provide the analytical tools to identify the best placement for additional current or power measurements when applying an incremental deployment of meters in the distribution system. The analysis has a general validity but has also a clear impact on practical situations when the monitoring systems are upgraded starting from an existing infrastructure. Tests on a 95-bus sample grid are presented in order to validate the found mathematical results and to highlight the associated benefits in a meter placement perspective. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid state estimation (SE) model where the supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements are decoupled. The proposed hybrid SE can effectively handle different accuracy levels of PMU and SCADA measurements which is a problem for conventional hybrid SEs due to increasing the condition number of SE gain matrix. In addition, the proposed hybrid SE model is fully compatible with PMU measurements, particularly current measurements, which increase the complexity of conventional hybrid SE formulation. The proposed hybrid SE model is tested on the IEEE 118-bus test system as well as on the real-world Iran’s power system. Moreover, the proposed hybrid SE is compared with conventional hybrid SE using a large number of scenarios modeling uncertain telemetry noises. The comparative results illustrate better performance of the proposed hybrid SE than conventional hybrid SE in terms of the gain matrix condition number, SE run time, and SE accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a robust parameter estimator to deal with the problem of inaccurate power losses calculated from the estimated state variables. These power loss inaccuracies result from some incorrect branch impedance due to non-stationary ambient conditions or geometrical constraints unreported in the database. Specifically, we use the projection statistics and the coupling relationship between parameter errors and the measurements to detect incorrect branch parameters associated with leverage points, which are proved to show the largest influence on the state estimates. Then these suspicious parameters are augmented with the original system state variables for the generalized estimation. Finally, the generalized robust estimator is executed as a nonlinear constrained optimization problem. The necessary condition for the primal-dual interior point method-based solver is derived. Our proposed method is able to enforce each suspicious parameter within its validity domain expressed as inequality constraints. Thanks to the robustness of the proposed Schweppe-type Huber (SHGM)-estimator and the enhanced measurement redundancy using multiple night off-peak snapshots of a nearly constant power system state, incorrect parameter values and erroneous zero injections are reliably detected and effectively corrected and validated. The performance of the weighted least squares estimator, the generalized least absolute value estimator, and the SHGM-estimator are evaluated and compared with regards to the estimated values of impedances and losses. Field test results on the realistic 2000-bus French power system show that the SHGM-estimator outperforms the other two estimators. [ABSTRACT FROM AUTHOR]

Microcalorimeters are used by National Metrology Institutes (NMIs) for the realization of the radio frequency and microwaves (RF&MW) primary power standard. Since they are not available on the market, NMIs have to design their own systems. It involves the design of complex mechanical structures and of low-noise data acquisition systems. Resulting setups allow precision measurements of RF&MW power by applying difficult and time-consuming measurement techniques. Weaknesses to be addressed in order to improve the performance, whether in the mechanical structure or in the data acquisition electronics, are not easy to identify. Anyway, they can be found by analyzing the microcalorimeter measurement uncertainty. In recent years, several improvements have been made on the [Istituto Nazionale di Ricerca Metrologica (INRIM), Italy] coaxial microcalorimeter with the aim of making its operation more stable and to reduce the measurement uncertainty. Its weak points are here analyzed by means of a comprehensive uncertainty budget. Details are also given on how uncertainty contributions have been determined. The method here applied is general and can be conveniently used in many other fields or applications, provided that the analytical model is known. It is useful while designing an experiment and/or a posteriori to identify the parts of the apparatus and of the measurement setup that needs to be optimized. [ABSTRACT FROM AUTHOR]

Radiation pressure has recently been shown to have practical application for multikilowatt continuous wave (CW) laser power measurement. One key advantage lies in its ability to measure without absorbing the laser beam. This enables a new measurement paradigm where laser power can be measured traceable to the SI without perturbing the beam. Combining this measurement scheme with a laser constitutes a “traceable source” where laser output power is traceable to the SI in real time. This greatly simplifies the calibration process for multikilowatt laser power meters and yields a path to high-accuracy laser-based material processing. Here, we discuss the state of the art of this approach by describing recent results from calibrations of laser power meters performed using a radiation-pressure-enabled traceable source at CW powers from 1 to 50 kW. We describe measurement results and uncertainty contributions with expanded uncertainties at or below 1.7% for powers above 10 kW. We also briefly discuss the status of development of a radiation-pressure-based technology designed to provide source traceability in the laser manufacturing environment. [ABSTRACT FROM AUTHOR]

This paper describes the new single-phase harmonic power standard developed at VTT MIKES. The standard is based on a custom digitizer, which is designed for both laboratory and on-site power metrology. Test signals are generated using a dual-channel arbitrary function generator, a voltage amplifier, and a transconductance amplifier. The reference voltage and current measurements are, respectively, based on a capacitive–resistive voltage divider and two different types of resistive shunt designs. Thorough characterization of the components is presented. Finally, a tentative uncertainty budget is drawn up for frequencies 53 Hz, 1 kHz, 10 kHz on power factors 1.0 and 0.0. The standard has an expanded uncertainty of 32 and $157~\mu \text{W}$ /VA at 10 kHz for power factors of 1.0 and 0.0, respectively. [ABSTRACT FROM AUTHOR]

Prototype distributed digitizers have been developed with the metrological precision of the individual measurement nodes. A master controller aggregates the data of the nodes and performs all computational tasks. A crucial part of the system is the synchronization of the individual nodes at the nanosecond level using white-rabbit technology. Fiber-optic communication is used to reduce interference and to obtain high isolation. When operated on battery power, this allows for special applications such as the measurement of currents in high-voltage lines. First, results show that a phase difference between nodes of a few microradians can be achieved with a resolution of better than $0.1~\mu $ rad. [ABSTRACT FROM AUTHOR]

Minimization of drive test (MDT) allows coverage to be estimated at the base station using user equipment measurement reports with the objective of eliminating the need for drive tests. In this letter, we quantify various types of errors in MDT-based autonomous coverage estimation that stem from inaccurate user positioning, for example, as a result of GPS measurement uncertainties and quantization due to dividing the coverage area into bins. By investigating the interplay between quantization and positioning error to estimate coverage, we show that there exists an optimal bin width for coverage estimation and determine it as a function of positioning error and user density. This can enable network operators to configure the bin size for given positioning accuracy that results in the most accurate MDT-based coverage estimation. [ABSTRACT FROM AUTHOR]

A method for remote wattmeter (RW) adjustment is introduced in this paper. The estimated adjustment gain is intended for corrections of wattmeter’s indications. Currently, legal metrology regulations do not allow such type of adjustment of certified instruments in the field. However, the proposed adjustment could be applicable in the areas of private or industrial subaccounting, home automation, and so on. If the achievable uncertainty could be specified, the method might be suggested for the wattmeter’s calibration or verification. The implementation of remote adjustment becomes possible if both remote and reference wattmeters are connected to the same electrical grid and are equipped with a data communication channel. The main idea of the method is based on a temporary injection of the additional load at the location of remote device and acquisition of synchronized power readings by both reference and remote devices. The acquired readings are then processed to derive an adjustment gain of the RW. The proposed method aims to cancel an influence of the measurement unit propagating medium between the reference and remote devices. It is assumed that the characteristics of the medium are constant during the short period that is necessary to acquire readings. It is demonstrated that using the simulation of the proposed setup in the case of typical power losses in distribution lines, the adjustment gain can be estimated precisely. It is shown that in the case of remote measurement, the injected load power uncertainty can be reduced by averaging the acquired power samples. [ABSTRACT FROM AUTHOR]

This paper describes the metrological characterization of a new type-N coaxial microcalorimeter and its associated thermistor-type transfer standard, which together are used as the primary standard for microwave power from 0.05 to 18 GHz at the Centro Nacional de Metrología, Queretaro, Mexico. The design characteristics, evaluation method, and measurement uncertainties of the primary standard are given and compared to recent realizations found in the open literature. The metrological characterization of the microcalorimeter is based on a comparison method to indirectly obtain its correction factor. The effective efficiency of the transfer standard, directly calibrated in the microcalorimeter by means of calorimetric measurements, is shown and compared against measurements performed by National Institute of Standards and Technology, showing good agreement within the estimated expanded uncertainties (0.21%–0.47%). The developed data acquisition system to automate the effective efficiency measurements and the proposed software leveling loop to keep the microwave power level constant are also discussed. This approach allows to keep an RF power stability of better than 100 ppm over several hours and is a straightforward solution to replace analog leveling systems which require obsolete hardware. Finally, the analysis followed to evaluate the uncertainty for the effective efficiency measurements is thoroughly described. [ABSTRACT FROM AUTHOR]

We propagate for the first time correlated measurement uncertainties into a nonlinear behavioral model of a millimeter-wave amplifier. We make use of the National Institute of Standards and Technology Microwave Uncertainty Framework to evaluate the uncertainties in large-signal electromagnetic wave measurements of an amplifier, followed by the extraction of $X$ -parameters using an industry standard algorithm. This extracted model is included as a component in a circuit simulator to evaluate gain and efficiency incorporating measurement uncertainty. [ABSTRACT FROM AUTHOR]

This paper considers the problem of placing a limited number of measurements to improve the estimation accuracy in distribution system state estimation (DSSE). In distribution systems, intermittent distributed energy resources and volatile loads will result in a wide variation of system operating conditions. The proposed measurement placement problem is to decide the optimal locations and types of measurements to be placed in the distribution systems that minimize the worst-case estimation errors for DSSE over different system operating conditions. Four indices of the estimation error covariance matrix are chosen as the criteria of accuracy. The proposed measurement placement problem is formulated as a mixed-integer semidefinite programming (MISDP) problem. To avoid the combinatorial complexity, a convex relaxation, followed by a local optimization method, is employed to solve the MISDP problem. The proposed problem and the effectiveness of the proposed solution method are numerically demonstrated on the 33-bus distribution system. [ABSTRACT FROM AUTHOR]

This paper describes the way that white noise (including quantised input section sampling) imparts errors onto frequency and rate-of-change-of-frequency (ROCOF) measurements. The main paper focus concerns the use of filtered heterodyned (i.e., Fourier) analyses for single-phase and three-phase systems, and the filtered Clarke transform for three-phase systems. The rules and equations governing the effect of white noise on frequency and ROCOF are formulated for these techniques, explaining the subtle effects of aliasing, splitting signals and noise into their positive and negative frequency components, and the correlation or decorrelation of noise. It is shown that—as expected—for three-phase ac measurements, averaging three single-phase Fourier measurements produces the same performance against noise as using a method based on Clarke’s transform, if identical filtering is used. Furthermore, by understanding the theory behind the frequency and ROCOF measurement processes, it is shown that to achieve the lowest RMS errors, in the presence of front-end white noise (alone, ignoring other dynamic signal and power quality aspects), a filter which provides ~40 dB/decade attenuation (i.e., a two-boxcar cascade) is recommended for a frequency measurement, but a filter which rolls off at ~60 dB/decade (i.e., a three-boxcar cascade) is recommended for a ROCOF measurement. [ABSTRACT FROM AUTHOR]

The well-known largest normalized residual (LNR) test for bad data identification becomes computationally inefficient for large-scale power systems containing a large volume of bad data, given the fact that it identifies and removes bad measurements sequentially, one at a time. In this paper, a highly efficient alternative implementation of the LNR test will be presented where the computational efficiency will be significantly improved. The main idea is based on the classification of suspect measurements into groups, which have negligible interaction. Then, the LNR test can be applied simultaneously to each individual group, allowing simultaneous identification of multiple bad data in different groups. Consequently, the number of identification/correction cycles for processing a large volume of bad data will be significantly reduced. Simulations carried out on a large utility system show drastic reductions in the CPU time for bad data processing while maintaining highly accurate results. This work is expected to facilitate implementation and more effective use of the LNR test for identifying and correcting measurement errors in very large power systems. [ABSTRACT FROM AUTHOR]

Errors in network parameters can cause serious bias in state estimation solution and make good measurements identified and discarded as bad data. This paper addresses this issue by developing a state estimator that remains robust against parameter errors. This is accomplished by modifying the formulation of the well-known least absolute value state estimator in order to identify and reject not only gross measurement errors, but also parameter errors in the network model. The resulting state estimate will not only be free from the impact of erroneous parameters, but will also reliably estimate and correct the erroneous parameters at the same time. The proposed approach can be formulated as a linear programming (LP) problem, and solved in a computational efficient manner by existing LP solvers. Simulation results show that it is effective under different types of parameter errors, gross measurement errors, and Gaussian noise. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient and statistically robust linear state estimator for very large scale interconnected power grids that are made observable by synchronized phasor measurements. The developed estimator has some desirable properties such as robustness against gross errors in phasor measurements (bad data), computational performance that remains minimally affected by growing system size or bad data as long as the size of the largest control area remains bounded, and the fact that independent system operators associated with control areas do not have to share majority of their real-time data or measurements while the estimator solves for the entire system. The estimator is implemented and tested on a small (30-bus) and a large (2917-bus) utility test systems and results are provided. [ABSTRACT FROM AUTHOR]