An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks

The security of energy supplies requires the depletion of potential fault events in power transmission networks. To achieve this, sufficient lead time before the happening of a fault event is indispensable for preparing countermeasures. With this inspiration, this paper establishes the fuzzy inference with rare association rule learning system. This ensemble system is designed for the long-term prediction of the spatiotemporal distribution of such energy security weaknesses, that is, to predict when and where these events are more expected to appear. In this system, merely the environmental features rather than the electrical features are needed as inputs. All the selected input features are divided into discrete and continuous features, and are evaluated separately. For the discrete features, the rare association rule learning model is implemented so that the rarely distributed environmental elements are extracted and diagnosed specifically. The risk indices of each element on the overall reliability are worked out as well. For the continuous features, a hierarchical fuzzy inference system along with the rare association rule learning model is deployed to calculate the corresponding risk indices of all the elements. In the hierarchical fuzzy inference system, the probabilistic fuzzy risks are employed instead of the direct fuzzy risks. Then the relative weights of these two sides are optimized. At last, an empirical case based on a practical transmission network is conducted, and the flexibility and the robustness of the proposed system during real applications can be validated consequently.