Prediction model of measurement errors in current transformers based on deep learning.

The long-term monitoring stability of electronic current transformers is crucial for accurately obtaining the current signal of the power grid. However, it is difficult to accurately distinguish between the fluctuation of non-stationary random signals on the primary side of the power grid and the gradual error of the transformers themselves. A current transformer error prediction model, CNN-MHA-BiLSTM, based on the golden jackal optimization (GJO) algorithm, which is used to obtain the optimal parameter values, bidirectional long short-term memory (BiLSTM) network, convolutional neural networks (CNNs), and multi-head attention (MHA), is proposed to address the difficulty of measuring error evaluation. This model can be used to determine the operation of transformers and can be widely applied to assist in determining the stability of transformer operation and early faults. First, CNN is used to mine the vertical detail features of error data at a certain moment, improving the speed of error prediction. Furthermore, a cascaded network with BiLSTM as the core is constructed to extract the horizontal historical features of the error data. The GJO algorithm is used to adjust the parameters of the BiLSTM model; optimize the hidden layer nodes, training frequency, and learning rate; and integrate MHA mechanism to promote the model to pay attention to the characteristic changes of the data in order to improve the accuracy of error prediction. Finally, this method is applied to the operation data of transformer in substations, and four time periods of data are selected to verify the model effectiveness of the current transformer dataset. The analysis results of single step and multi-step examples indicate that the proposed model has significant advantages in terms of accuracy and stability in error prediction.

Development of a Wideband Precision Electric Field Measuring Sensor.

High-voltage electric field measurement technology has certain applications in electric field measurement of power systems, but due to the limitation of its measurement accuracy and bandwidth, it cannot be used for the measurement of lightning-impulse voltage. In order to calibrate the nonlinearity of the MV-level lightning-impulse voltage measurement system, this paper proposes the design and implementation of a high-precision inductive wideband electric field measuring sensor (EFMS). The influence of the metal shell on the electric field distribution was simulated, and the influence of the electric field non-uniformity coefficient was studied. The characteristics of the EFMS were tested, and the results showed that the EFMS can accurately reproduce the waveform of lightning-impulse voltage and power-frequency voltage, with a proportionality coefficient of 0.05664 V/(kV/m). In mostly uniform and extremely non-uniform fields, the nonlinearity of the EFMS for impulse voltage is less than ±0.25%, and the nonlinearity of the EFMS for power-frequency voltage is less than 0.1%. It is shown that the EFMS can be used for the nonlinearity calibration of ultra-high voltage impulse measurement devices.