Solar energy is the key to clean energy, which can generate large amounts of electricity for the future smart grid. Unfortunately, the randomness and intermittency of solar energy resources bring difficulties to the stable operation and management of the power systems. To reduce the negative impact of photovoltaic (PV) plants accessing on the power systems, it is great significant to predict PV power accurately. In light of this, we propose a hybrid deep learning approach based on convolutional neural network (CNN) and long-short term memory recurrent neural network (LSTM) for the PV output power forecasting. The CNN model is leveraged to discover the nonlinear features and invariant structures exhibited in the previous output power data, thereby facilitating the prediction of PV power. The LSTM is used to model the temporal changes in the latest PV data, and predict the PV power of next time step. Then, the prediction results in the two models are comprehensively considered to obtain the expected output power. The proposed approach is extensively evaluated on real PV data in Limberg, Belgium, and numerical results demonstrate that the proposed approach can provide good prediction performance in PV systems. INDEX TERMS Solar energy, deep learning, photovoltaic (PV) power forecasting, power systems.
As the integration of wind power into electrical energy network increasing, accurate forecast of wind speed becomes highly important in the case of large-scale wind power connected into the grid. In order to improve the accuracy of wind speed forecast and the generalization ability of the model, Extreme Gradient Boosting (XGBoost) as an improvement from gradient boosting decision tree (GBDT) is trained and deployed in the cheaper central processing unit (CPU) devices instead of graphics processing unit (GPU) devices, thus, a wind speed forecast model based on Extreme Gradient Boosting is proposed in this paper. Firstly, the historical data is taken as a part of the input vectors for the model. Moreover, considering the monthly change of wind speed characteristics, the dataset of wind power is divided into four parts by month so that the models are constructed in different complexity by month. Finally, compared with back propagation neural networks (BPNN) and linear regression (LR) models, the experimental results show that the improved XGBoost model can promote the forecast accuracy effectively. INDEX TERMS Short-term wind speed forecasting, XGBoost, time series, historical characteristics, power grid.
A contactless method based on infrared image matching is presented for zero-value insulator detection on outdoor porcelain insulator string. An improved SIFT (scale-invariant feature transformation) method is developed to extract features and accomplish the pre-matching between the insulator string to be detected and the standard string in the image library. An improved RANSAC (random sampling consistency) algorithm is developed to remove mismatching points and achieve more accurate and faster detection. Firstly, an adaptive circle window is designed to improve the sensitivity of the SIFT operator on arc features extraction from insulators. Then, the dimension of the feature is decreased from128 to 32 by dividing the circle into 4 fan-shaped regions and descript the feature on 8 directions to achieve the lower computation burden and high accuracy. For the SIFT method and RANSAC method uses Euclidean distance to measure the similarity between features extracted from the string to be detected and the string in the standard library, mismatching may be caused. Spatial geometric features of the neighborhood around the feature point are used to construct the error function to improve the RANSAC method and remove the mismatching points. Faster matching is obtained by a threshold control for decreasing the number of data checking for the consensus set data models. Testing results show that the presented method is effective in the detection of zero-value resistances for outdoor insulator string. Compare with the SIFT method and SIFT+RANSAC method, the presented method has higher accuracy and faster detection speed.INDEX TERMS Zero-value insulator detection, infrared imaging, image matching, adaptive circle template, spatial geometric features, prosumer infrastructure
In this paper, a movable and self-extensible apparatus is developed for substation construction and electrical infrastructure maintenance. The designed apparatus is equipped with a complete set of pneumatic and electric tools to achieve the integration and fastening of small electrical parts. The developed apparatus structure can be self-extensible and self-propelled by automatically controlling the servo-drive system. The overall framework and control system of the apparatus are designed by taking the actual needs of on-site maintenance of substation electrical equipment into account. The modal analysis and static analysis are implemented to verify stability and rigidity for the assembled apparatus frame. The finite element model of the developed apparatus is built under the circumstance of strong wind, snowstorm, and other extreme weather events. The security evaluation results and on-site application indicate that the apparatus can effectively enhance the security of maintenance personnel and work efficiency with characteristics of low cost, automatic control, excellent insulation, easy operation as well as high reliability.
The grounding electrode is an important part of HVDC transmission system, especially in monopole operation. Based on the actual operation of the grounding electrode of Guanyin Pavilion in the ±500kV Goose-commutation station in Huizhou, Guangdong, this paper analyses in detail the specific influencing factors of the grounding electrode on the surrounding environment, underground metal pipelines and AC grid. Daily maintenance work considering current distribution, temperature, humidity and step voltage is also proposed to strengthen the understanding and attention of operation and maintenance personnel to the grounding electrode.
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