Abstract
The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.
Highlights
Reliability and stability are the most important aspects to guaranteeing the safe operation of electrical networks and power systems
An electrical line trip fault usually happens in a power grid system [1,2]; it will produce a long sequence of fault data in the electrical sensor network, eventually cause power outages and economic losses
Many deep neural network (DNN) methods have been applied in power fault prediction [23,24]; the popularly used models are the recurrent neural network (RNN) [23,25] and long short-term memory (LSTM) network [26]
Summary
Reliability and stability are the most important aspects to guaranteeing the safe operation of electrical networks and power systems. (1) The long time-series line trip fault prediction method using the improved stackedInformer network is adopted; it exploits more comprehensive temporal information of long sequence input measurement data, includes normal and abnormal current and voltage data of power lines, and predicts the short sequence output fault This method achieves a superior generalization performance. GC can be viewed as a projected gradient descent method with a constrained loss function; it operates directly on gradients by centralizing the gradient vectors in order to have zero means This technology improves the training time of the long sequence time-series fault prediction markedly and improves the accuracy and efficiency of the presented methodology.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
