A transient stability assessment method based on the trajectory in the dimension-reduced power-angle space

Author(s):  
Fei Tang ◽  
Qixin Wang ◽  
Bingcheng Cen ◽  
Qingfen Liao ◽  
Yu Liu ◽  
...  
Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 121 ◽  
Author(s):  
Feilai Pan ◽  
Jun Li ◽  
Bendong Tan ◽  
Ciling Zeng ◽  
Xinfan Jiang ◽  
...  

With the interconnection between large power grids, the issue of security and stability has become increasingly prominent. At present, data-driven power system adaptive transient stability assessment methods have achieved excellent performances by balancing speed and accuracy, but the complicated construction and parameters are difficult to obtain. This paper proposes a stacked-GRU (Gated Recurrent Unit)-based transient stability intelligent assessment method, which builds a stacked-GRU model based on time-dependent parameter sharing and spatial stacking. By using the time series data after power system failure, the offline training is performed to obtain the optimal parameters of stacked-GRU. When the application is online, it is assessed by framework of confidence. Basing on New England power system, the performance of proposed adaptive transient stability assessment method is investigated. Simulation results show that the proposed model realizes reliable and accurate assessment of transient stability and it has the advantages of short assessment time with less complex model structure to leave time for emergency control.


2021 ◽  
Vol 2121 (1) ◽  
pp. 012012
Author(s):  
Jian Chai ◽  
Xihuai Wang ◽  
Jianmei Xiao

Abstract Machine learning algorithms have been widely used in power system transient stability evaluation. The combined application of data analysis and evaluation and neural network provides a new direction for power system transient stability analysis. After the actual power grid is running, there is obviously an imbalance between stable samples and unstable samples. The current deep learning network realizes the power system transient stability assessment method with too many redundant attributes, and the characteristics will inevitably be lost during the data transmission process. This leads to serious problems with the tendency of the training of the data-driven transient stability assessment model. The rough set theory algorithm is introduced to reduce the redundant attributes of power system transient data sets, which simplifies the difficulty of data training. At the same time, as the neural network deepens, the deep residual neural network model has a higher accuracy rate and effectively avoids the “gradient explosion” and “gradient dispersion” problems. Compared with the traditional neural network, it has better Evaluate performance.


Author(s):  
Theodoros Kyriakidis ◽  
Guillaume Lanz ◽  
Rachid Cherkaoui ◽  
Maher Kayal

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2273 ◽  
Author(s):  
Meiyi Li ◽  
Wentao Huang ◽  
Nengling Tai ◽  
Moduo Yu

Inverter-interfaced distributed generators (IIDGs) have been widely applied due to their control flexibility. The stability problems of IIDGs under large signal disturbances, such as large load variations and feeder faults, will cause serious impacts on the system. The virtual synchronous generator (VSG) control is an effective scheme for IIDGs to increase transient stability. However, the existing linearized stability models of IIDGs are limited to small disturbances. Hence, this paper proposes a Lyapunov approach based on non-linearized models to assess the large signal stability of VSG-IIDG. The electrostatic machine model is introduced to establish the equivalent nonlinear model. On the basis of Popov’s theory, a Lyapunov function is derived to calculate the transient stability domain. The stability mechanism is revealed by depicting the stability domain using the locus of the angle and the angular frequency. Large signal stability of the VSG-IIDG is quantified according to the boundary of the stability domain. Effects and sensitivity analysis of the key parameters including the cable impedance, the load power, and the virtual inertia on the stability of the VSG-IIDG are also presented. The simulations are performed in PSCAD/EMTDC and the results demonstrate the proposed large signal stability assessment method.


2013 ◽  
Vol 427-429 ◽  
pp. 1390-1393
Author(s):  
Bo Wang ◽  
Ke Wang ◽  
Da Hai You ◽  
Wei Hua Chen ◽  
Gang Wang

In this paper an genetic algorithm-extreme learning machine (ELM) based real-time transient stability assessment method is proposed. This method uses genetic algorithm (GA) to search optimal input weights and hidden biases in the principle of cross validation to establish GA-ELM classifier. In order to do real-time transient stability assessment, generator trajectories of rotor angle, rotor speed, voltage magnitude, electromagnetic power and imbalance power in-and post-disturbance are chosen as original features for the quick access based synchronously sampled values. Simulation results of New-England 39-bus system show that this method has good performance in power system transient stability assessment.


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