Capacitor Voltage Transformer Research Articles

On-line error monitoring of capacitive voltage transformer based on substation topology and distribution analysis

Abstract The capacitor voltage transformer (CVT) is widely used in power systems with high voltage levels. The operation state of CVT is related to the accuracy of electric energy metering. Because the internal structure of CVT is complex and vulnerable to the external environment, the error may be out of tolerance in the actual operation process. The error is composed of system error and random error. The change of error is mainly the change of system error. In general, random errors satisfy specific distributions and will not change easily. In this paper, the voltage amplitude and phase effective value output by the CVT acquisition device at the same voltage level are used to compare the amplitude effective value of the in-phase output to obtain the ratio characteristic quantity K, and the phase effective value is subtracted to obtain the difference characteristic quantity θK . K and θK contain the information of two CVT ratio differences and angle differences. The probability density distribution obeyed by K and θK during the normal operation of CVT is taken as the reference probability density distribution and compared with the probability density distribution to be measured during online monitoring. If the probability density distribution changes significantly, it shows that the ratio difference and angle difference have exceeded the difference under the premise of this paper. Finally, the contribution method is used to locate the out-of-tolerance CVT. In this paper, the online monitoring method is verified by simulation data. The test results show that the ±0.1% range ratio difference and the ±5′ range angle difference can be identified, and the online monitoring of the 0.1-stage CVT error is realized.

Influence of electromagnetic transient physical models considering characterization characteristics on capacitive voltage transformers

Influence of electromagnetic transient physical models considering characterization characteristics on capacitive voltage transformers

Study of Problem and Effect Mitigation Technique of Unbalance Capacitance in 500 kV Single Phase Transformers

This article presents the study of the effect of unbalanced stray capacitance in single-phase 500 kV power transformers and establishes the mitigation technique. The power transformers were operated in Wang Noi Substation for transmitting the electrical power. There are 3 units of single-phase power transformer. Previously, a transformer of phase B failed after being serviced for several years. Thus, the new power transformer was ordered instead of the failed one. However, once the new system had been operated, the hotspots at the surge arrester of tertiary winding (22 kV) were found and the unbalance of phase voltages was observed, resulting in overvoltage as a root cause of the problem. Once a thorough investigation was done, it was found that there was an effect of unbalanced stray capacitance between tertiary winding and ground core since the new power transformer has some different designs compared with the remaining. Hence, the mitigation technique was performed by adding the capacitor parallel to the tertiary winding until the voltage balance was achieved. Simulation with the PSCAD program was performed to determine the optimum value for additional capacitors, i.e., voltage balance, using an iterative method. In this article, the capacitors used for additional were capacitor voltage transformers, which have been installed. As a result of the troubleshooting, the voltage at the 22 kV bus was more balanced as well as not causing overvoltage, and the hotspots at the surge arrester of tertiary winding were not detected resulting in gaining the stability and reliability of using these transformers.

CVT error prediction method based on improved attention mechanism transformer model

In order to enhance the performance of capacitive voltage transformer (CVT) error prediction, we propose a transformer model (AM-transformer) error prediction method based on an improved attention mechanism. The method starts from both input data and model improvement, adopting Pearson’s correlation coefficient to calculate the environmental factors that have a greater impact on the CVT measurement error, and enhancing the ability of local learning of the attention mechanism by adding causal convolution and data segmentation operations. The experimental results show that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the method reach 0.016, 4.21% and 0.021, respectively, realizing a high prediction accuracy.

Error state prediction of capacitor voltage transformer based on TimesNet and gate control unit

The stability and reliability of the error state of capacitor voltage transformer (CVT) are poor, which affects the fairness of electricity trade settlement and the safe operation level of power grid. However, the CVT error data is superimposed by different periodic information such as daily period, monthly period and quarterly period, which makes the prediction of transformer error state challenging. In this paper, a CVT error state prediction method based on TimesNet and gate control unit (GRU) is proposed. The TimesNet network is employed to capture the intraperiod-variation and interperiod-variation characteristics of the ratio difference data of a-phase, b-phase and c-phase, and the feature data and GRU model are employed to predict the output. The simulation results demonstrate that the mean square error (MSE) and mean absolute error (MAE) of the proposed model are 0.0002 and 0.0101, respectively, indicating that this model has lower prediction error and higher prediction accuracy.

Error prediction of a capacitor voltage transformer using dilated causal convolution and LSTM

To timely and accurately complete the error prediction of capacitor voltage transformers, this paper proposes an error prediction algorithm for capacitor voltage transformers that combines dilated causal convolution and LSTM. The algorithm is divided into two parts: one part updates the state of the input sequence using LSTM, while the other part extracts features through dilated causal convolution, maintaining data causality about the original sequence. Finally, the outputs of these two parts are combined to make the obtained sequence feature information more accurate and enriched. Experiments show that the MSE between the error prediction results obtained by this method and the actual error samples of capacitor voltage transformers is only 0.0003, which can accurately and efficiently complete the error prediction of capacitor voltage transformers.

Self-detection method for measurement error of capacitor voltage transformer considering conversion error.

The measurement error of capacitor voltage transformers (CVTs) has poor stability under the complex environment of substations. Conventionally, error detection is performed by regularly comparing the output of standard transformers, which lacks real-time performance. Moreover, CVTs are prone to operating in an out-of-tolerance state. Thus, this study first analyzes the basic principle of the CVT measurement error self-detection method based on principal component analysis under the constraints of three-phase symmetrical operating characteristics of a power system. Then, the impact of the signal conversion error of analog-to-digital conversion equipment on the self-detection results of CVT measurement errors in engineering applications is analyzed, and a multi-layer wavelet analysis signal denoising method is proposed to enhance the self-detection ability of CVT measurement errors. Finally, simulations show that the proposed method can identify an error change of 0.1%, meeting the self-detection requirements for the measurement error at 0.2 class.

Research on Data-Driven Self-Diagnosis for Measurement Errors in Capacitor Voltage Transformers

Research on Data-Driven Self-Diagnosis for Measurement Errors in Capacitor Voltage Transformers

Online Capacitor Voltage Transformer Measurement Error State Evaluation Method Based on In-Phase Relationship and Abnormal Point Detection

Online Capacitor Voltage Transformer Measurement Error State Evaluation Method Based on In-Phase Relationship and Abnormal Point Detection

Error Evaluation Method of Capacitive Voltage Transformer Based on Improved Principal Component Analysis

The capacitive voltage transformer is an important data source for electricity trading and is easily influenced by environmental factors during operation, resulting in decreased metering accuracy. Because the voltage data does not satisfy the Gaussian distribution, the accuracy of the traditional principal component analysis method is poor when evaluating the error of capacitive voltage transformers. This paper proposes an improved principal component analysis method based on the local outlier factor. First, principal component analysis is utilized to separate the primary voltage fluctuation and error change. Then, a new statistic is established by using the local outlier factor instead of the traditional statistic as an evaluation standard, which reduces the missed diagnosis and misdiagnosis caused by the data distribution characteristics. The experimental results show that the improved method is better than the traditional method in terms of the detection rate of transformer anomaly detection, can effectively detect the abnormal change of transformer error and is more suitable for capacitive voltage transformers in operation.

FAULT DETECTION AND LOCATION BASED SVM FOR THREE PHASE TRANSMISSION LINES APPLYING POSITIVE SEQUENCE FAULT COMPONENTS

Transmission lines are an imperative element of the modern power systems. Any faults in them can cause an undesirable interruption in power supply. Precise analysis of these faults is important in-order to ensure an incessant supply of power. For this purpose, fault detection and classification are needed to clear any such faults and re-establish the system to maintain its normal operation. In this paper, a novel integrated approach of protective relaying with enhanced support vector machine algorithm has been adopted for detecting faults and its location estimation in long transmission line. The proposed scheme is successfully able to detect and classify different symmetrical and unsymmetrical faults along with some peculiar cases related to High Impedance Faults (HIF) and evolving faults, current transformer (CT) saturation/ capacitive voltage transformer (CVT) transient, close-in faults, swing condition, source strength variation, etc. The comparative analysis with recent proposed techniques declared the potentiality and robustness of the scheme

Studies on the use of genetic algorithms to improve radial basis neural networks to monitor capacitive voltage transformers online

The metering accuracy and stability of the power grid transformer devices are of great concern to all parties of the power grid, power plants and large users, as well as the relevant government departments, with the further deepening of the electric power system reform and the gradual establishment of the electric power spot trading market. The outcomes demonstrate the viability and efficacy of the employed diagnosis method. The completed research can serve as a reference point for China’s development of a capacitive voltage transformer line monitoring system.

Research on adaptive state prediction method for the metering error of capacitor voltage transformer

A capacitor voltage transformer (CVT) is widely used in high voltage power systems because of its good insulation performance. However, the structure of CVT is more complex and the stability of its metering error is poor, which easily causes the loss of power metering. The conventional evaluation method for the metering error of CVT is to compare and calibrate with a standard transformer under off-line condition in a fixed period. Because of the long evaluation period, it is impossible to accurately predict the state change of CVT metering error, which is of more significance practically. To solve this problem, this paper proposes an adaptive state prediction method: Analyze the measurement data of CVT using principal component analysis method under the constraint of electrical physical relationship, the metering of CVT is mapped to residual and score (CRS) statistic. For this way, the self-evaluation of CVT metering error in real-time is realized without a standard transformer to get the high frequency time series of error data. According to the measurement data of CVT in process, the CRS statistics are batch processed adaptively, and the prediction model of CRS statistics is established based on the time series analysis. Experiments show that the method can accurately predict the state change of CVT metering error, and the prediction error is better than 15%. It is helpful to promote the development of CVT metering error detection into on-demand detection.

Analysis of the Very Fast Transient Overvoltage in a Nuclear Plant 500kV GIL-GIS System

The simulation models of the 500kV gas-insulated transmission lines (GIL) and gas-insulated substation (GIS) systems in a realistic nuclear plant are presented in the paper. The Very Fast Transient Overvoltage (VFTO) which is generated during switching operations, is analyzed. Some affecting factors on the level of the VFTO are investigated. These factors are the location of the power source, the entrance capacitance of the potential transformer (PT), and the capacitive voltage transformer (CVT). At last, some suggestions on overvoltage calculations or calculations of electromagnetic interference sources are proposed. The conclusions can be used in insulation designing and analyzing the electromagnetic interference on the secondary system in nuclear plants and substations.

Online measurement of capacitor voltage transformer metering errors based on GRU and MTL

The metering accuracy of capacitive voltage transformers (CVTs) affects the fairness of electricity settlement, and the online measurement of their metering errors has become a research hotspot. This error consists of amplitude and phase errors of three-phase voltages. In this paper, because the error data is characterized by time series, nonlinearity, and large volumes, the Gate recurrent unit is selected as a deep learning framework to predict it, the Batch normalization is proposed to prevent gradient disappearance, and the self-attentive mechanism is proposed to help learn long-time information. To measure all six errors simultaneously, multitask learning is employed to mine the shared information and coupling among multiple tasks, and the loss function is reconstructed to ensure simultaneous convergence of the six tasks. Experimental results show that, compared with the traditional single-task model, the accuracy indexes MSE, RMSE, and MAE of the proposed model are reduced by 95.1%, 81.1%, and 89.5% on average for all tasks, and the total training time is reduced by 57.8%; compared with other multitask models, the accuracy indexes are improved by orders of magnitude. This shows that the proposed model fully meets the requirements of both accuracy and speed for online measurement of CVT error.

CVT on-line error measurement hybrid-driven by domain knowledge and Stacking Model

The performance of Capacitive Voltage Transformer (CVT) degrades over time, making measurement error monitoring a research hotspot in the field of smart grid. At present, these are several challenges such as complex data features, a lack of criteria for selecting optimal measurement models, and low precision. CVT measurement errors can be classified into ideal error and additional one. The former is typically evaluated via mutual information and redundancy within the topology-level transformer group. Considering that a single model cannot process the time series, strong randomness and nonlinearity of the additional error, the Stacking model is selected. Based on the principle of heterogeneity and high-quality, correlation coefficient and feature contribution degree, Random Forest, eXtreme Gradient Boosting, Ridge Regression, K Nearest Neighbors, Support Vector Regression, and Long Short-Term Memory are chosen as base learners through correlation and feature contribution analysis; while extra-trees with strong generalization and robustness is chosen as the meta learner. To improve the measurement precision, the attention-like mechanism is used to scale time and accuracy weights. Finally, according to the power domain knowledge, a linear superposition model is developed to fuse the ideal and additional errors, and thus realize online error measurement for CVTs. The experimental results indicate that the improved Stacking model outperforms mainstream measurement models by an average reduction of 59.47%, and 52.58% in the root mean squared error and the mean absolute error with the best R2 closest to 1. It not only effectively improves the accuracy but also meets speed requirement for online error measurement.

Analysis of an Abnormal Secondary Voltage Accident of Capacitive Voltage Transformer

Capacitive Voltage Transformer (CVT) is an important measurement and protection equipment in power system, which has the characteristics of high electrical strength, large insulation margin and reliable operation. During the operation of CVT, its upper and lower capacitors, compensation reactors, lightning rods and other components often have faults. The faults of CVT can be found in time by judging the secondary voltage value. In this paper, an abnormal secondary voltage fault of CVT is introduced. Combined with test and disassembly analysis, it is determined that the fault is caused by breakdown of capacitor unit. Through fault simulation, this paper studies the typical CVT faults and the change law of secondary voltage, and puts forward the threshold of the secondary voltage abnormal alarm of CVT, providing important reference for CVT online monitoring.

Phasor correction of coupling capacitor voltage transformers for high-performance protection

Phasor correction of coupling capacitor voltage transformers for high-performance protection

Modeling of a capacitive voltage transformer for evaluation of transient response in conformity with the IEC 61869-5 Standard

This paper presents the implementation of a nonlinear model of a Capacitive Voltage Transformer (CVT) to evaluate the transient response in conformity with the international standard IEC 61869-5. The work is divided into three main steps: electrical tests to measure the CVT parameters and frequency response; implementation of linear mathematical modeling and an optimization algorithm to estimate unmeasured parameters; and implementation of nonlinear modeling in a simulation program for electromagnetic transients. First, the requirements of the IEC standard about the transient response of CVT are presented. Then, some important aspects of CVT modeling are discussed. Afterward, a linear model of a CVT is used to estimate the parameter not measured in the laboratory. Then, a nonlinear model is implemented in ATP. The model is validated in the time domain by comparing the simulation results with some signals measured in the laboratory. Finally, simulations of the transient response tests are made following the IEC 61869-5. The results show that the model developed in this work is reliable and can be used for the simulation of ferroresonance and transient response tests following the IEC 61869-5 when no laboratory structure is available.

On the Remote Calibration of Instrumentation Transformers: Influence of Temperature

The remote calibration of instrumentation transformers is theoretically possible using synchronous measurements across a transmission line with a known impedance and a local set of calibrated voltage and current transformers. In this paper, an extension of the research results presented in our paper “On the Remote Calibration of Voltage Transformers: Validation of Opportunity” published in AMPS 2018 was undertaken by reporting on how temperature could increase the measurement uncertainty contributed by voltage and current transformers. This was needed to better understand if remote calibration could be realized under real-world conditions where temperatures at different geographical locations could be significantly different. This paper describes the influence of magnetic voltage, current transformers, and capacitive voltage transformers. Based on the research in this paper, one could conclude that the influence of magnetic voltage transformers (MVTs) is low, but, especially with current measurement transformers and capacitive voltage transformers, errors should be considered when performing remote calibrations on measurement transformers over long distances with different environmental conditions.