Temporal and Spatial Distribution of Power System Voltage based on Generalized Regression Neural Network

Fan, Shixiong; Lin, Jinghuai; Zhang, Tiance; Dong, Genyuan; Liu, Xingwei

doi:10.1109/ispec48194.2019.8974895

Cited by 1 publication

(2 citation statements)

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Section: Introductionmentioning

confidence: 99%

“…Besides fuzzy logic, other AI methods have been applied in literatures [15][16][17][18][19] regarding power system security awareness. Fan et al [15] proposed a data-driven system voltage prediction model based on the generalized regression neural network to dig in-depth power system operation big data to enhance system situation awareness. Literature [16] proposed a real-time safety assessment tool based on PMU and a decision tree to estimate potential safety hazards after failure: Voltage amplitude fluctuation, temperature limit violation, voltage stability, and transient stability.…”

Section: Introductionmentioning

confidence: 99%

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

Venayagamoorthy

Gao

2020

Energies

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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.

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