Risk Assessment Method of Solar Smart Grid Network Security Based on TimesNet Model

Smart grids have enormous potential in terms of reliability and sustainability, but with the large-scale integration of distributed energy like solar energy, the network security risks of smart grids have also increased. In response to the physical and information network threats faced in the network security risk assessment of solar powered smart grids, this study develops a smart grid theft detection model based on TimesNet and a smart grid intrusion detection model based on bidirectional long short-term memory networks. The results indicated that when the proportion of electricity theft data was 25%, the false detection rate of the proposed model was 3.52. The area under the curve of the proposed model was 0.98, and the detection rate, false negative rate, F1 value, and accuracy were 97.04%, 1.21%, 92.69%, and 97.15%, respectively. The loss value of the proposed intrusion detection model was stable at around 0.012 in the NSL-KDD dataset and around 0.02 in the CICIDS2017 dataset, with a detection accuracy of 97.54% and a false positive rate of 1.21%. The experiment demonstrated the electricity theft behavior and network intrusion detection performance of the proposed model, which can effectively detect security threats faced by solar smart grids and provide practical basis for network security risk assessment. The research results can help reduce the economic losses of power companies, maintain a good order of electricity consumption, and ensure the safe and stable operation of solar smart grids.