Abstract
This paper proposes a novel unified prediction approach for both small-signal and transient rotor angle stability as opposed to other studies that have only addressed transient rotor angle stability. Deep learning techniques are employed in this paper to train an online prediction model for rotor angle stability (RAS) using the voltage phasor measurements which are collected across the entire system. As a result, the trained model provides a fast yet accurate prediction of the transient stability status when a power system is subjected to a disturbance. Also, if the system is transiently stable, the prediction model updates the power system operator concerning the damping of low-frequency local and inter-area modes of oscillations. Therefore, the presented approach provides information concerning the transient stability and oscillatory dynamic response of the system such that proper control actions are taken. To achieve these objectives, advanced deep learning techniques are employed to train the online prediction model using a dataset which is generated through extensive time domain simulations for wide range of operating conditions. A convolutional neural network (CNN) transient stability classifier is trained to operate on the transient response of the phasor voltages across the entire system and provide a binary stability label. In tandem, a long-short term memory (LSTM) network is trained to learn the oscillatory response of a predicted stable system to capture the step-by-step dynamic evolution of the critical poorly damped low-frequency oscillations. The superior performance of the proposed model is tested using the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area and IEEE 50-machine, 145-bus test systems and is verified with time domain simulation.
Highlights
P OWER system stability refers to its ability to return to an acceptable state of equilibrium operating condition after being subjected to a disturbance
The dynamic response of a power system is governed by a set of highly nonlinear differential and algebraic equations (DAE) which describe the behavior of the synchronous generators and its associated control systems, loads, renewable power generation, flexible AC transmission devices (FACTs) in addition to the transmission network [2], [3]
While the literature [22] has opted for mean absolute percentage error (MAPE) to quantify long-short term memory (LSTM) network prediction errors, due to the near-zero values of R(λ) the mean arctangent absolute percentage error (MAAPE) is a preferable measure while maintaining the same semantic value [36]
Summary
P OWER system stability refers to its ability to return to an acceptable state of equilibrium operating condition after being subjected to a disturbance. Two separate classifiers based on LSTM networks are presented to predict the stability status when a power system is subjected to a disturbance [18]. These classifiers deploy voltage measurements and rate-of-change-of-frequency (RoCoF) during the first five cycles of the post-fault period. Fast and accurate predictions are achieved by the proposed models, transient stability assessment is only considered and no attempts have been made to provide more information concerning other dynamic attributes of the system. The main contributions of the proposed work can be summarized as follows: 1) Unified model is proposed in this paper for predicting both transient and small-signal stability using real-time measurements of the system voltages. A LSTM network is constructed to learn the oscillatory response of the stable system over time after classification
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call