Online Correction Research Articles

A Bi-Level Model for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

Power system state estimation is an important component of the status and healthiness of the underlying electric power grid real-time monitoring. However, such a component is prone to cyber-physical attacks. The majority of research in cyber-physical power systems security focuses on detecting measurements False-Data Injection attacks. While this is important, measurement model parameters are also a most important part of the state estimation process. Measurement model parameters though, also known as static-data, are not monitored in real-life applications. Measurement model solutions ultimately provide estimated states. A state-of-the-art model presents a two-step process towards simultaneous false-data injection security: detection and correction. Detection steps are χ2 statistical hypothesis test based, while correction steps consider the augmented state vector approach. In addition, the correction step uses an iterative solution of a relaxed non-linear model with no guarantee of optimal solution. This paper presents a linear programming method to detect and correct cyber-attacks in the measurement model parameters. The presented bi-level model integrates the detection and correction steps. Temporal and spatio characteristics of the power grid are used to provide an online detection and correction tool for attacks pertaining the parameters of the measurement model. The presented model is implemented on the IEEE 118 bus system. Comparative test results with the state-of-the-art model highlight improved accuracy. An easy-to-implement model, built on the classical weighted least squares solution, without hard-to-derive parameters, highlights potential aspects towards real-life applications.

Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications.

Iterative learning NARMA-L2 control for turbofan engine with dynamic uncertainty in flight envelope

Nonlinear control of turbofan engines in the flight envelope has attracted much attention in consideration of the inherent nonlinearity of the engine dynamics. Most nonlinear control design techniques rely on the correction theory of reference model parameter to extend the typical flight operations from ground operation. However, dynamic uncertainties in flight envelope lead to the deviation of operating state, and it is negative to control performance. This article is to develop online correction neural network–based speed control approaches for the turbofan engine with dynamic uncertainty in the flight envelope. Two improved online correction nonlinear ways combined with nonlinear autoregressive moving average (NARMA) are proposed, such as gradient search nonlinear autoregressive moving average with feedback linearization (NARMA-L2) control and iterative learning NARMA-L2 control. The contribution of this article is to provide better control quality of fast regulation and less steady errors of engine speed by the proposed methodology in comparison to the conventional NARMA-L2 control. Some important results are reached on both turbofan engine controller design and dynamic uncertainty tolerance at the typical flight operations, and the numerical examples demonstrate the superiority of the proposed control in the flight envelope.

MODEL ORDER REDUCTION METHODS FOR COUPLED MACHINE TOOL MODELS

Thermal effects are the most dominant source for displacements in machine tools and thus work-piece inaccuracies during the manufacturing process. A promising strategy to meet the ever-increasing accuracy requirements is the use of predictive models for, e.g., parameter and design op-timizations or online correction of the thermally induced error at the tool center point (TCP) in the pro-duction process. However, these techniques require fast but precise simulations. The need for high model accuracy is in direct contrast to the desired real-time capabilities. Model order reduction (MOR) is an attractive tool to overcome this problem. A modeling toolchain, which is tailored for the effective construction of fast and accurate models is proposed and demonstrated, emphasizing the involved MOR step.

An improved on-line measurement method of particulate matter concentration using tri-wavelength laser light scattering

Continuous monitoring of particulate matter (PM) from stationary source emissions is essential to reduce air pollution. The measurement accuracy of the widely used light scattering method is seriously affected by the particle characteristics, especially particle size. Based on the dependence of the scattered light on the particle size and incident wavelength, an improved on-line measurement method of PM mass concentration using tri-wavelength laser light scattering was proposed. Laboratory measurements on silica particles, Arizona dust, and fly ash from coal-fired power plants using this method were performed with a coupled laser. The experimental and theoretical results of silica particles showed an apparent power-law relationship between the PM mass concentration sensitivity (PMCS) and the average particle size. A linear function can approximate the relationship between the ratio of scattered light intensities at different wavelengths (RSDW) and the average particle size. The relationship between PMCS and RSDW established by particle size was introduced into the on-line correction of particle mass concentration measurement. The measurements of silica particles, Arizona dust, and fly ash obtained excellent correction effects. Compared with the uncorrected light scattering method, the average measurement deviations of mass concentration for three samples are reduced from 49.75% ± 1.11%, 48.62% ± 2.62% and 46.48% ± 0.83% to 8.11 ± 1.99%, 4.34 ± 2.26%, and 2.99 ± 0.55%, respectively.

Cortex-dependent corrections as the tongue reaches for and misses targets.

Precise tongue control is necessary for drinking, eating and vocalizing1-3. However, because tongue movements are fast and difficult to resolve, neural control of lingual kinematics remains poorly understood. Here we combine kilohertz-frame-rate imaging and a deep-learning-based neural network to resolve 3D tongue kinematics in mice drinking from a water spout. Successful licks required corrective submovements that-similar to online corrections during primate reaches4-11-occurred after the tongue missed unseen, distant or displaced targets. Photoinhibition of anterolateral motor cortex impaired corrections, which resulted in hypometric licks that missed the spout. Neural activity in anterolateral motor cortex reflected upcoming, ongoing and past corrective submovements, as well as errors in predicted spout contact. Although less than a tenth of a second in duration, a single mouse lick exhibits the hallmarks of online motor control associated with a primate reach, including cortex-dependent corrections after misses.

Complex lane detection based on dynamic constraint of the double threshold

In order to adapt to various complex lane environments, a general fast lane extraction algorithm was proposed. Firstly, according to the mutability of the local gray value and the characteristics of image saliency, the collected road image was preprocessed, and the region of interest was obtained by the proposed double threshold algorithm. Only in the significant region of the road were the image gray value change and image smoothing carried out to solve the problems of consuming too much time and poor noise resistance in lane detection. The lane line edge was then extracted based on the improved Canny operator. When the Otsu threshold was selected, the Kalman filter algorithm was introduced to quickly predict the optimal threshold of the subsequent image sequence in accordance with the characteristics of optimized autoregressive data processing.. Finally, for the straight line fitted by the Hough transform, an effective multi-layer evaluation function was established to realize the online correction of lane lines. Compared with the traditional lane line extraction algorithm, the experimental results show that the proposed algorithm has better accuracy, real-time performance and robustness.

Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm

Conventionally, the transition probabilities in the interacting multiple model (IMM) are often fixed based on the prior information. However, this conservative setting may result in inaccurate state estimations. To solve this problem, a Bayesian-based online correction function is proposed in this paper, which can adaptively adjust the transition probabilities. To deal with the response lag and the short-term peak estimation error problem during the respond to model jump, a model jumping threshold is defined, so that the current information of the models can be fully utilized by the IMM algorithm and the correction function of the transition probabilities can be further improved. Subsequently, an adaptive transition probability-based parallel IMM algorithm is proposed in this paper. Finally, three maneuvering target tracking simulations are conducted to verify the performance of the proposed algorithm, the results show that the proposed algorithm can improve the response speed of the system model jump and the state estimation accuracy. The effectiveness and feasibility of the algorithm are proven.

Dosimetric influence of deformable image registration uncertainties on propagated structures for online daily adaptive proton therapy of lung cancer patients

PurposeA major burden of introducing an online daily adaptive proton therapy (DAPT) workflow is the time and resources needed to correct the daily propagated contours. In this study, we evaluated the dosimetric impact of neglecting the online correction of the propagated contours in a DAPT workflow. Material and methodsFor five NSCLC patients with nine repeated deep-inspiration breath-hold CTs, proton therapy plans were optimised on the planning CT to deliver 60 Gy-RBE in 30 fractions. All repeated CTs were registered with six different clinically used deformable image registration (DIR) algorithms to the corresponding planning CT. Structures were propagated rigidly and with each DIR algorithm and reference structures were contoured on each repeated CT. DAPT plans were optimised with the uncorrected, propagated structures (propagated DAPT doses) and on the reference structures (ideal DAPT doses), non-adapted doses were recalculated on all repeated CTs. ResultsDue to anatomical changes occurring during the therapy, the clinical target volume (CTV) coverage of the non-adapted doses reduces on average by 9.7% (V95) compared to an ideal DAPT doses. For the propagated DAPT doses, the CTV coverage was always restored (average differences in the CTV V95 < 1% compared to the ideal DAPT doses). Hotspots were always reduced with any DAPT approach. ConclusionFor the patients presented here, a benefit of online DAPT was shown, even if the daily optimisation is based on propagated structures with some residual uncertainties. However, a careful (offline) structure review is necessary and corrections can be included in an offline adaption.

A Geometrical Interpretation of Current Transient Responses to Predict Current Gradients for IPMSM Model Predictive Control

The ability to use actuator and load models online in combination with experimental data becomes increasingly important in future electrical drives. Among the applications of such online model in torque and flux controllers, model predictive control (MPC) has gained increasing interest, allowing to deal in real time and in a suboptimal way with drive variations captured by the model used. For such ability, sufficient processing power is required to handle the model in real time, which appears to be less of a problem in recent drives. This article contributes to the current prediction in MPC, applied to an interior permanent-magnet synchronous machine (IPMSM) drive. The study aims at improving the prediction, through an online estimation of the current time gradient coefficients, and this by considering consecutive space vector step responses. Through measuring the resulting current responses, an online correction of the current gradients is realized. A two-step method, which consists of an estimation step and a prediction step, will be described in order to improve the current prediction. Experimental results obtained from a 3.7-kW IPMSM drive will verify that the proposed geometrical approach effectively improves the conventional short window current predictions.

Stereotactic Body Radiation Therapy (SBRT) for Liver Oligometastases: Outcomes and Safety.

The aim of this study was to investigate adverse effects, progression free survival (PFS), one-year local control (LC) and one-year overall survival (OS) of patients with liver oligometastases treated with stereotactic body radiotherapy (SBRT), and whether there was a significant difference in these parameters in patients with primary colorectal cancer compared to other tumor localizations. Patients were simulated using four-dimensional computed tomography (4DCT). Using volumetric modulated arc therapy (VMAT) technique, SBRT was performed on 16 patients with <3 liver metastases. The prescribed dose was 60 Gy in 8 fractions (BED 105 Gy). Cone beam CT (CBCT) was used for image guidance before each fraction with online correction. There were no adverse effects. Median PFS for all patients, patients with primary colorectal cancer, and patients with primary non-colorectal cancer was 11 months (SE 2.1), 16 months (SE 2.8), 6 months (SE 2.4), respectively. There was no significant difference in the PFS for these two observed groups (P=0.09). The one-year LC was 62.5%. Patients with primary colorectal cancer had one-year LC of 87.5%, while the group of patients with primary non-colorectal cancer had one-year LC of 37.5% (P=0.063). The total one-year OS was 87.5%. In the group of patients with primary colorectal cancer, the one-year OS was 100%, while in the group of patients with primary non-colorectal cancer, the one-year OS was 75% (P=0.317). SBRT with 8 × 7.5 Gy can be safely delivered and is effective method of treating liver oligometastases.

Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network

The performance of predictive energy management (PEM) strategy depends greatly on the precise prediction. Inevitably, the imprecise prediction occurs under changeable driving conditions. This study proposes an online correction predictive energy management strategy to address the above issue. The first is development of a PEM using the sequential quadratic programming (SQP) combined with back-propagation neural network-based velocity prediction. Second, a correction algorithm based Fuzzy Neural Network (FNN) is designed to evaluate the correction factor. The Q-learning based Swarm Optimization (QSO) algorithm is implemented to optimize the parameters of the FNN and evaluate accurately the correction factor. Finally, in combination with the above efforts, a novel correction algorithm based on the SQP backup control strategy and integrated the QSO algorithm with the FNN has been further established accordingly. The numerical validation results demonstrate that the superior performance of the proposed strategy in economic compared with the benchmark strategies, and the effectiveness of the proposed strategy is validated by Hardware-in-the-loop (HIL) experiments. Both the numerical validation and HIL results indicate that the combination of QSO and FNN made it possible to develop the online correction algorithm capable of significantly further improving the hydrogen consumption of the fuel cell electric vehicle.

Assessment of setup errors of IGRT combined with a six degrees of freedom bed for patients with primary rectal cancer

Objective: To investigate the effect of six degree of freedom (6-DOF) bed combined with cone beam computed tomography (CBCT) in the on-line correction of setup errors in patients with primary rectal cancer. Methods: The clinicopathological data of 17 patients with primary rectal cancer in Department of Radiotherapy, Third Hospital of Peking University from July 2013 to January 2014 were collected. There were 14 males and 3 females, a median age of 65 years. The difference of CBCT and 6-DOF bed combined with CBCT online correction of patients with positioning error were retrospectively analyzed. Results: Before position correction, the first CBCT verification of setup errors in the three translation directions including X (left and right), Y (in and out) and Z (up and down) directions were (0.06±0.25) cm, (0.13±0.40) cm and (-0.28±0.31) cm, respectively. The setup errors of RX (rotation pitch), RY(rolling) and RZ (left and right rotation) directions were (0.62±1.15)°, (-0.19±0.99)°, and (-0.34 ± 0.84)°, respectively . After correction of IGRT combined with six freedom of bed, the setup errors of translation X, Y and Z were (0.01±0.09) cm, (-0.01±0.05) cm and (-0.03±0.08) cm, respectively, and the setup errors of rotation RX, RY and RZ directions were (-0.16±0.40)°, (0.36±0.31)°and (-0.01±0.25)°, respectively. There were significant differences in translation direction (X, Y and Z direction) and rotation direction (Rx, RY and RZ) before and after 6-DOF bed combined with CBCT correction (all P<0.05). In the translation direction, the higher frequency range of Z-direction error value was 0.20-0.79 cm. In the rotation direction, the frequency range of error in Rx direction was 0.20°-2.99°. There was no significant difference between bone mode and gray scale model registration (P>0.05). With the progress of radiotherapy, the setup errors of X, Z, Rx, RY and RZ directions increased except Y direction. Conclusions: In radiotherapy, six freedom bed combined with CBCT is helpful to correct the setup errors of patients with primary rectal cancer. Six freedom bed may be used to correct the setup errors of patients with primary rectal cancer online. Image-guided radiation therapy (IGRT) is recommended for bone pattern registration in patients with rectal cancer.

Thermal error modeling and compensation of multilink high-speed precision press system

It is essential to develop the predictive model of thermal error for multilink high-speed precision press system (MHSPPS) in order to construct its compensation on the slider’s position accuracy at the bottom dead point (BDP). In this work, based on the principal factor strategy and the clustering methodology, the locations of thermal sensitive points (TSPs) on the MHSPPS are determined and the corresponding temperature is taken as the feed in parameter of the thermal error model (TEM). An improved adaptive genetic algorithm (IAGA) incorporated with a back-propagation neural network (BPNN) is then presented so as to simulate and predict the thermal error of MHSPPS through the collected temperature of TSPs. Compared with the experimental results, accuracy of the predictive model forward goes beyond that of the traditional TEMs, such as multiple linear regression (MLR), genetic algorithm with BPNN (GA-BPNN), and particle swarm optimization with BPNN (PSO-BPNN) models, which proves effectiveness of the proposed model. In order to implement the compensation experiment, a novel adjustment mechanism of slider’s BDP position for MHSPPS is designed and the compensation algorithm with online correction based on the IAGA-BPNN model is also proposed. Test results indicate that the maximum errors and the RMS errors of the slider’s BDP position after compensation with online correction based on the proposed IAGA-BPNN model can be reduced to 93.33% and 98.75%, respectively, which verifies the authenticity of thermal error compensation based on the IAGA-BPNN model and online correction algorithm.

Цифровая коррекция показаний измерительных каналов уровня жидких сред с областью нечувствительности датчиков на атомных электрических станциях

The introduction and development of computerized digital systems in modern power engineering has led in Russia to a full modernization of domestically produced nuclear power plant measurement systems. In particular, digital methods for correcting the readings of hydrostatic level sensors have been widely put in use since 2013. The experience gained from putting them in use was generalized in the reference and organizational and methodological literature in 2017. However, the subsequent results of preoperational adjustments carried out at newly designed nuclear power units and at those under construction have shown that some issues still remain unresolved in this application field. In particular, there is a need is to develop digital methods for correcting the readings produced by channels measuring the level of liquid media in pressurized process tanks if there occurs a sensor dead zone. The complexity of this problem is stemming from the fact that it is not possible to take the sensor dead zone into account by introducing a constant correction to the measurement channel readings in view of the technological process peculiarities. Thus, during the operation of nuclear power units, the water density in pressurized process tanks and vessels decreases from 980 to 590 kg/m3, and the vapor density increases from 0 to 100 kg/m3. Such changes give rise to complex thermal and physical interphase processes that occur under the conditions of large mass transfers of the vapor--water mixture. As a result, the sensor's dead zone boundary undergoes an essential change in the tank longitudinal section depending on the current values of the working fluid thermal and physical characteristics. To solve this problem, digital methods for online correction of measured level readings are proposed that take into account changes in the two-phase working medium (vapor--water) in the course of a continuous technological process. Nowadays, the introduction of digital methods for correcting the hydrostatic level sensor readings is especially relevant in connection with the development of industry programs aimed at increasing the capacity of newly designed and operating nuclear power units, which pose more demanding requirements to the accuracy of thermal and physical measurements and operational safety as a whole.

Spatial and dosimetric evaluation of residual distortions of prostate and seminal vesicle bed after image-guided definitive and postoperative radiotherapy of prostate cancer with endorectal balloon.

PurposeTo quantify daily residual deviations from the planned geometry after image‐guided prostate radiotherapy with endorectal balloon and to evaluate their effect on the delivered dose distribution.MethodsDaily kV‐CBCT imaging was used for online setup‐correction in six degrees of freedom (6‐dof) for 24 patients receiving definitive (12 RTdef patients) or postoperative (12 RTpostop patients) radiotherapy with endorectal balloon (overall 739 CBCTs). Residual deviations were evaluated using several spatial and dosimetric variables, including: (a) posterior Hausdorff distance HDpost (=maximum distance between planned and daily CTV contour), (b) point Pworst with largest HDpost over all fractions, (c) equivalent uniform dose using a cell survival model (EUDSF) and the generalized EUD concept (gEUDa with parameter a = −7 and a = −20). EUD values were determined for planned (EUDSFplan), daily (EUDSFind), and delivered dose distributions (EUDSFaccum) for plans with 6 mm (=clinical plans) and 2 mm CTV‐to‐PTV margin. Time series analyses of interfractional spatial and dosimetric deviations were conducted.ResultsLarge HDpost values ≥ 12.5 mm (≥15 mm) were observed in 20/739 (5/739) fractions distributed across 7 (3) patients. Points Pworst were predominantly located at the posterior CTV boundary in the seminal vesicle region (16/24 patients, 6/7 patients with HDpost ≥ 12.5 mm). Time series analyses revealed a stationary white noise characteristic of HDpost and relative dose at Pworst. The EUDSF difference between planned and accumulated dose distributions was < 5.4% for all 6‐mm plans. Evaluating 2‐mm plans, EUDSF deteriorated by < 10% (<5%) in 75% (58.5%) of the patients. EUDSFaccum was well described by the median value of the EUDSFind distribution. PTV margin calculation at Pworst yielded 8.8 mm.ConclusionsAccumulated dose distributions in prostate radiotherapy with endorectal balloon are forgiving of considerable residual distortions after 6‐dof patient setup if they are observed in a minority of fractions and the median value of EUDSFind determined per fraction stays within 95% of prescribed dose. Common PTV margin calculations are overly conservative because after online correction of translational and rotational errors only residual deformations need to be included. These results provide guidelines regarding online navigation, margin optimization, and treatment adaptation strategies.

Deep correction of breathing-related artifacts in real-time MR-thermometry

Real-time MR-imaging has been clinically adapted for monitoring thermal therapies since it can provide on-the-fly temperature maps simultaneously with anatomical information. However, proton resonance frequency based thermometry of moving targets remains challenging since temperature artifacts are induced by the respiratory as well as physiological motion. If left uncorrected, these artifacts lead to severe errors in temperature estimates and impair therapy guidance.In this study, we evaluated deep learning for on-line correction of motion related errors in abdominal MR-thermometry. For this, a convolutional neural network (CNN) was designed to learn the apparent temperature perturbation from images acquired during a preparative learning stage prior to hyperthermia. The input of the designed CNN is the most recent magnitude image and no surrogate of motion is needed. During the subsequent hyperthermia procedure, the recent magnitude image is used as an input for the CNN-model in order to generate an on-line correction for the current temperature map.The method's artifact suppression performance was evaluated on 12 free breathing volunteers and was found robust and artifact-free in all examined cases. Furthermore, thermometric precision and accuracy was assessed for in vivo ablation using high intensity focused ultrasound. All calculations involved at the different stages of the proposed workflow were designed to be compatible with the clinical time constraints of a therapeutic procedure.

Daily patient geometry correction: application of NAL and eNAL protocols

Abstract Purpose: To test the NAL and eNAL correction protocols using daily patient setup displacements. Methods and material: In total, the analysis was performed for 749 and 797 kV CBCT images for gynecological and prostate patients, respectively, each of 30 patients. After the planning procedure, patients were set up on the treatment table in the treatment position every day. The on-line correction protocol was applied. KV CBCT images were acquired by means of x-ray lamp mounted orthogonally on Linac. Patient setup displacement was assigned. NAL and eNAL corrections protocols were simulated using daily data from online corrections for these two groups of patients. The overall systematic error and random error were calculated for each direction. Results: For the prostate group, the random errors for daily Raw data (no correction) in LAT, LONG, and VERT directions were 2.0 mm, 1.6 mm, and 3.2 mm, respectively. For NAL and eNAL protocols, they were in the range of 1.8 mm to 3.2 mm. For the gynecological group, the random errors were: for daily Raw data 2.2 mm, 1.7 mm, and 3.2 mm, respectively. For NAL and eNAL protocols, they were in the range of 2.0 to 3.4 mm. For the prostate group, values of systematic errors 1.8 mm, 1.8 mm, and 3.3 mm, respectively for Raw data. For NAL and eNAL protocols, these values were less than 1.8 mm. For the gynecological group, the systematic errors were 2.6 mm, 2.3 mm, and 2.8 mm, respectively, for Raw data. For NAL ana eNAL protocols less than 1.8 mm. For the gynecological group, for Raw data, 45% of the total displacement vectors exceeded 5 mm, whereas only 25% did after the NAL procedure and 29% after the eNAL procedure. For the prostate group, for Raw data, 34% of the total displacement vectors exceeded 5 mm, whereas only 22% did after NAL procedure and 28% after eNAL procedure Conclusions: For gynecological and prostate cancer patients, the NAL and eNAL correction protocols can be safely applied to substantially reduce setup errors.

An Effective Correction Method for AFM Image Distortion due to Hysteresis and Thermal Drift

Hysteresis and thermal drift cause image distortion of atomic force microscopy (AFM). To address this issue, a hysteresis compensation algorithm based on B-spline curve fitting is first designed to correct the image distortion due to hysteresis, so as to provide a basis for further thermal drift correction. Afterward, a novel off-line drift correction algorithm based on cross diagonals' scanning is proposed to reconstruct high-quality AFM images. More precisely, according to the distorted image and the leading diagonal profile obtained by initial scanning, the drift is first calculated by template matching with an increasing sliding window. Furthermore, since the calculated drift contains a part of incorrect data, an adaptive filter is designed to generate more accurate horizontal thermal drift, based on which an undistorted image is constructed by bilinear interpolation. Finally, the performance of the proposed method is verified by convincing experimental and application results. Compared with online correction methods, the proposed approach is easier to implement since it does not require hardware improvement. Besides, compared with other off-line methods, the proposed method is simpler with less time consumption since it only needs two additional scanning lines to correct image distortion.

An online correction system for electronic voltage transformers

As a vital piece of equipment, electronic voltage transformer is widely used in the power system measurements. The current calibration techniques of this asset class exhibit several issues including complex real time implementation. In contrary with existing calibration techniques, this paper proposes a new correction system for electronic voltage transformers, which can be conducted online without causing any interruption to the electricity grid. The new proposed correction system utilizes an SF6 cylinder capacitor and employs an advanced signal processing technique based on the improved Rife frequency shift algorithm. The improved Rife frequency shift algorithm is a combination of the Hanning self-convolution window and the conventional Rife algorithm, which features simple calculation and high accuracy. Results show that the accuracy of the proposed algorithm is higher than the conventional commonly used interpolation algorithms. Compared with the existing traditional calibration techniques, the proposed system features easy online implementation, simple calculation and high accuracy. The robustness of the proposed system is validated through simulation analysis and practical field measurements.