Recent Advances of Edge Computing for Smart Grid

In the construction of smart microgrids for petrochemical enterprises, the generating unit is an important part, and the rolling bearings are one of the key components of the generator. The condition of the rolling bearing directly affects the safe operation of the entire generating unit and an accurate fault diagnosis of the bearing not only can improve the stability of the smart microgrid, but also can reduce the risk of loss of the factory. This study proposes an improved fault diagnosis method based on variational modal decomposition (VMD) and a convolutional neural network (CNN). The VMD algorithm was used to remove random noise in the original signal and a CNN was used to extract useful data from the vibration signal processed by VMD. Since the modal number and penalty parameter of the VMD are difficult to choose and they have a profound impact on the decomposition results, differential evolution (DE) was used as the optimization method and envelope entropy was used as the fitness function to optimize the VMD parameters. Since it is difficult to ensure the best fit of the hyper-parameters of CNN, this study proposes a method for using the DE algorithm to obtain suitable hyper-parameters for the CNN, and then used the CNN to diagnose a fault. The test results using the vibration data of Case Western Reserve University show that the combination of VMD and CNN can improve the convergence speed more than 10% and the accuracy to over 99.6%.

With the increasing development of smart grid technology, short-term load forecasting becomes particularly important in power system operation. However, the design of accurate and reliable short-term load forecasting methods and models is challenging due to the volatility and intermittency of renewable energy sources, as well as the privacy and individual characteristics of electricity consumption data from user data. To overcome this issue, in this paper, a novel cloud-edge collaboration short-term load forecasting method is proposed for smart grid. In order to reduce the computational load of edge nodes and improve the accuracy of node prediction, we use the method of building a model pre-training pool to train multiple pre-training models in the cloud layer at the same time. Then we use edge nodes to retrain the pre-trained model, select the optimal model and update the model parameters to achieve short-term load forecasting. To assure the validity of the model and the confidentiality of private data, we utilize the model pre-training pool to minimize edge node training difficulty and employ the approach of secondary edge node training. Finally, extensive experiments confirm the efficacy of our proposed method.