Journal
2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2018)
Volume -, Issue -, Pages 1123-1127Publisher
IEEE
DOI: 10.1109/ICISCE.2018.00231
Keywords
component; Finite-state machine; Fault Risk Prevention; Hidden Markov; Distribution Network
Funding
- State Grid Corporation of China Research Program: Distribution network big data analysis technical study and platform development for supporting lean management [52020116000G]
- National Key Research and Development Program of China-Basic Theories and Methods of Analysis and Control of the Cyber Physical Systems for Power Grid [2017YFB0903000]
Ask authors/readers for more resources
Because of the factors such as the unknown or accidental fault causes and the few factors such as the number of records before and after the distribution network fault, in view of the difficulty of the fault analysis of the distribution network, the risk prediction is inaccurate, the risk exclusion or fault isolation scheme can not be formed systematically and scientifically, so it is difficult to improve the emergency handling capacity of the distribution network. A risk control model of distribution network fault based on the non deterministic finite state machine is proposed, and the migration path of each state in the risk process of the distribution network is analyzed. The non deterministic finite state machine and the hidden Markov algorithm are combined to calculate the state transfer probability through the sample training, and the various input causes are considered comprehensively and comprehensively. In order to minimize the impact of data on the risk prediction results, a series of control schemes are formed, and the correctness of the model is verified by using a more perfect fault data set of typical distribution network in large city.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available