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Unnoticed breakdowns in capacitor voltage transformers (CVTs) result in accuracy degeneration, even explosions. Thus, detecting breakdowns in time is critical. Existing methods have made unsatisfactory progress as labeled data is insufficient. We propose an unsupervised end-to-end method, including domain knowledge-assisted feature extraction, problem formulation, and optimization. First, an estimator is proposed to obtain the extra ratio error with detailed breakdown information. Second, a new feature, i.e., distance to the clustered voltage (DCV), is introduced. Third, the detection task is reformulated into an optimization problem. Details about breakdowns are the decision variables, aiming to minimize the differences between DCV and the estimated ratio error. An optimization algorithm called the DCV-lead grid search (DCV-GS) is designed for acceleration. Experiments and practical implementations demonstrate its accuracy and efficiency. This method could detect both existing and new breakdowns timely without labeled data, which can reduce the cost and time of maintenance. • Domain knowledge is utilized in feature extraction, problem formulation, and optimization. • A feature (DCV) is developed with voltages measured by same-phase CVTs are the same. • A ratio error estimator with details of breakdowns in a CVT is proposed. • The detection task is formulated into an optimization problem. • A DCV-guided algorithm is developed to accelerate the optimization. [ABSTRACT FROM AUTHOR]

Understanding long-term monitoring steady-state errors of capacitor voltage transformers (CVTs) is critical for acquiring accurate and continuous voltage signals from the power grid. However, existing methods cannot maintain good performance over the long term because they cannot update the initial model while primary voltages fluctuate nor maintain stability while steady-state error deteriorates. To remedy this problem, this paper proposes a physics-information-enabled self-updating method. Differences in fluctuations between primary voltages and steady-state error are illustrated based on the physical structure of CVTs. This information is then used to select samples that only contain fluctuations from primary voltages and update the model. At the same time, the mapping relationship between statistics and errors is derived, which makes the evaluation of error possible. Simulations on data from real substations show that its recognition accuracy reaches 97%, and the evaluation deviations of ratio error and phase displacement are within ± 0. 085 % and ± 2. 5 ′ . • Detect CVT with abnormal long-term measurement in real-time without power outages. • Theoretical analysis of error variation characteristics caused by the CVD in the CVT. • Construct an index that describes the degree of change in complex periodic data. • Give a physics-information-enabled self-updating method to update monitoring models. [ABSTRACT FROM AUTHOR]

Uncalibrated capacitive voltage transformers (CVTs) may significantly degrade measurement accuracy, because of the undetected excessive measurement error (ME). In this article, an online detection method is proposed which combines multi-source heterogeneous data composed of CVT measurements, acceptance test errors, and error limits. By measuring the same voltage with multiple CVTs, the monitoring statistics are generated and the statistic thresholds for the excessive ME detection are set according to the acceptance test errors and the error limits. To further ensure accuracy, the monitoring statistics and acceptance test errors for the CVTs surpassing the thresholds are used to estimate the ME. This estimation is then compared with the error limits as a cross-check to the detection result. Simulation shows that the difference between the ME estimated from the proposed method, and the actual ME is less than 0.01 % and the faulty CVT recognition accuracy exceeds 99%. • Detect the CVT with abnormal measurement in real-time without power outages. • The regulation for determining abnormal CVT is its error surpass the limits. • Can apply to all voltage transformers with different accuracy levels. • Experiments verify this method by Monte Carlo method. [ABSTRACT FROM AUTHOR]

• Actual ratio error of phase B CVT is lower than phase A and phase C. • Only the actual phase error of phase B CVT stay the same as calibration results. • The secondary load plays a decisive role to the error characteristics of CVTs. • The actual secondary load in substations should be applied to any kind of calibration of CVTs. The capacitor voltage transformer (CVT) is one of the most important measurement equipment in the power system, and its measurement accuracy is a key factor to ensure the safety of the power system and the fairness of electricity trade. To ensure the steady-state measurement accuracy, the conventional calibration is carried out to CVTs before operating in substations, and the operating CVTs must be calibrated periodically. However, excessive measurement error often occurs to CVTs which have passed the calibrations. In this paper, the reasons for the inconsistency between calibration results and actual measurement errors are proposed. The calibration in the laboratory, on-site calibration and three-phase operating CVTs are simulated according to actual operating conditions. Both the theoretical analysis and simulation results demonstrate that significant differences exist between the actual measurement errors and the calibration results of CVTs. Some experiments corresponding to the simulations verify the conclusions above. Finally, some suggestions to improve the reliability of calibration results are given. [ABSTRACT FROM AUTHOR]

As the key metering equipment of the high-voltage transmission network, the high-voltage potential transformer (HVPT) needs manual maintenance to ensure that its operating status complies with relevant regulations. Due to the requirement of power outages, the manual maintenance could only be conducted at regular intervals and the measurement error of HVPT remains unknown during the operation. To address the issue, this paper proposes an on-line anomaly detection method for the measurement error of HVPT with high applicability to the complex power system. By taking advantage of the multi-bus structure, the abnormal HVPTs can be detected and located based on the in-phase relation between the HVPTs connected to the same phase and the three-phase relation within the HVPT groups. This method is data-driven and will not affect the operation of the power system. The effectiveness of the proposed method is verified in a 220 kV substation. • Detect the abnormal HVPTs in real time without power outages. • Applied to any structure where one voltage is measured with at least two HVPTs. • Not affected by voltage fluctuations and active voltage adjustment. • Performance is better than the previous three-phase relation method. • Simulation results and field application results in the substation validate the method. [ABSTRACT FROM AUTHOR]