Online Steady-State Security Awareness Using Cellular Computation Networks and Fuzzy Techniques

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.