Abstract
Power system steady-state security relates to its robustness under a normal state as well as to withstanding foreseeable contingencies without interruption to customer service. In this study, a novel cellular computation network (CCN) and hierarchical cellular rule-based fuzzy system (HCRFS) based online situation awareness method regarding steady-state security was proposed. A CCN-based two-layer mechanism was applied for voltage and active power flow prediction. HCRFS block was applied after the CCN prediction block to generate the security level of the power system. The security status of the power system was visualized online through a geographic two-dimensional visualization mechanism for voltage magnitude and load flow. In order to test the performance of the proposed method, three types of neural networks were embedded in CCN cells successively to analyze the characteristics of the proposed methodology under white noise simulated small disturbance and single contingency. Results show that the proposed CCN and HCRFS combined situation awareness method could predict the system security of the power system with high accuracy under both small disturbance and contingencies.
Highlights
With the development of grid interconnection, the structure of modern power systems is expanding
In order to see the performance of echo state network (ESN)-based cellular computation network (CCN), the multi-layer perceptron (MLP)- andfrom wereofapplied in this paper for comparison
This paper has presented how to implement power system security awareness using a CCN and hierarchical cellular rule-based fuzzy system (HCRFS) combined methodology
Summary
With the development of grid interconnection, the structure of modern power systems is expanding. The CCN has good performance in solving the problems of transient stability prediction [21], load flow inferencing [22], state estimation [23], dynamic state prediction [24], wide area measurement (WAM) [25], and situation awareness (SA) [26] using measurements from PMUs. In the previous research by the author [27,28], different kinds of neural network-based CCN were applied as an effective tool for power system state estimation. This paper proposed an online situation awareness method considering power system static security using echo state network (ESN)-based CCN and fuzzy logic.
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