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Since it is difficult for manual recording to track the rapid change of indication of the power meter, the power meter images are collected by the camera and automatically recognized and recorded to effectively overcome the disadvantages of manual recording. However, the complex scene lighting environment and smearing character shadows make it difficult to transfer captured images directly to convolutional neural networks for character recognition. A smear character recognition method of side-end power meter under complex lighting conditions is proposed in this article. First, the uneven illumination image enhancement algorithm is studied. Through the estimation of the illumination component of the image, the fusion weight is calculated by the principal component analysis for multiscale fusion, and the up-sampling and down-sampling are adopted to reduce the calculation of the algorithm and achieve the rapid image enhancement. A convolution neural network framework based on deep learning is proposed to realize the segmentation of smear characters, and the final segmented individual characters are fed into a network to identify the meter readings. The experimental results show that the proposed smear character recognition method has fast recognition speed, and the recognition rate of samples with the smear character and complex illumination reaches 99.8%, which meets the requirements of power meter character recognition and is better than other algorithms.

The access of new energy improves the flexibility of distribution network operation, but also leads to more complex mechanism of line loss. Therefore, starting from the nonlinear, fluctuating and multi-scale characteristics of line loss data, and based on the idea of decomposition prediction, this paper proposes a new method of line loss frequency division prediction based on wavelet transform and BIGRU-LSTM (Bidirectional Gated Recurrent Unit-Long Short Term Memory Network).Firstly, the grey relation analysis and the improved NARMA (Nonlinear Autoregressive Moving Average) correlation analysis method are used to extract the non-temporal and temporal influencing factors of line loss, and the corresponding feature data set is constructed. Then, the historical line loss data is decomposed into physical signals of different frequency bands by using wavelet transform, and the multi-dimensional input data of the prediction network is formed with the above characteristic data set. Finally, the BIGRU-LSTM prediction network is built to realize the probabilistic prediction of high-frequency and low-frequency components of line loss. The effectiveness and applicability of the method proposed in this paper were verified through numerical simulation. By dividing the line loss data into different frequency bands for frequency prediction, the mapping relationship between different line loss components and influencing factors was accurately matched, thereby improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

Photovoltaic power actively regulates the reactive power of the active distribution network, leading to the increase of output current of the photovoltaic inverter. Consequently, the temperature rise significantly impacts the maximum junction temperature of the Insulated Gate Bipolar Transistor (IGBT), leading to fluctuations in the junction temperature. To realize the active support of the reactive power of the photovoltaic power supply and ensure its reliable operation, a reactive voltage control strategy of the active distribution network considering the reliability of the photovoltaic power supply is proposed. Considering the reactive power support capability of the distributed photovoltaic power, a multi-objective reactive power optimization model for active distribution networks is established based on IGBT maximum junction temperature, distribution network losses, and photovoltaic active power reduction. The effectiveness of the proposed strategy in enhancing the minimum lifetime, average lifetime, and reliability of photovoltaic power sources is verified using the IEEE 33-node standard distribution system. The results show that under the strategy proposed in this paper, the average junction temperature and junction temperature fluctuation of IGBT are effectively reduced, and the minimum lifetime and average lifetime of all IGBTs are increased by 8 years and 4 years respectively.

System kinetic inertia is declining due to the rapid replacement of synchronous generators (SG) by renewable power generations (RPG). To maintain grid inertia, this paper proposes a generic synthetic inertia (GSI) scheme for RPGs, enabling inertial response by a supercapacitor (SC) bank integrated with a voltage source inverter (VSI) DC link via a DC/DC converter. A GSI control loop is developed for the DC/DC converter controller to vary the SC voltage along with the grid frequency for power exchange following the SG swing equation. The GSI scheme realizes plug‐and‐play advantage without modifying the RPG original system. Small‐signal stability analysis is carried out to optimize the GSI controller parameters. The performance of the GSI scheme is evaluated using a modified IEEE 39‐bus test system. [ABSTRACT FROM AUTHOR]