Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to the unavailability of reliable quantitative criteria. Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics. Finally, the trained assessment model is employed to determine the systems stability status in real time. The test results on the IEEE 39-bus system suggest that the proposed approach manages to assess the stability status of the system accurately and timely. Furthermore, the superiority of the proposed method over traditional shallow learning-based assessment methods has also been proved.

[1]  Rui Zhang,et al.  Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Yimin Hou,et al.  A novel approach of decoding EEG four-class motor imagery tasks via scout ESI and CNN , 2020, Journal of neural engineering.

[3]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[4]  V Ajjarapu,et al.  A Novel Online Load Shedding Strategy for Mitigating Fault-Induced Delayed Voltage Recovery , 2011, IEEE Transactions on Power Systems.

[5]  Kang Li,et al.  Incorporating Demand Response of Electric Vehicles in Scheduling of Isolated Microgrids With Renewables Using a Bi-Level Programming Approach , 2019, IEEE Access.

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  Yuchen Zhang,et al.  A Hybrid Randomized Learning System for Temporal-Adaptive Voltage Stability Assessment of Power Systems , 2020, IEEE Transactions on Industrial Informatics.

[8]  Chao Lu,et al.  Imbalance Learning Machine-Based Power System Short-Term Voltage Stability Assessment , 2017, IEEE Transactions on Industrial Informatics.

[9]  Chunhui Zhao,et al.  Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification , 2020, IEEE Transactions on Industrial Informatics.

[10]  Yang Li,et al.  Feature selection for transient stability assessment based on kernelized fuzzy rough sets and memetic algorithm , 2018, 1808.08790.

[11]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[12]  Chao Lu,et al.  Wordbook-based light-duty time series learning machine for short-term voltage stability assessment , 2017 .

[13]  Chen Chen,et al.  A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making , 2018, Energy.

[14]  Zhen Yang,et al.  Application of EOS-ELM With Binary Jaya-Based Feature Selection to Real-Time Transient Stability Assessment Using PMU Data , 2017, IEEE Access.

[15]  Yang Li,et al.  Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering , 2018, IEEE Access.

[16]  Dongbo Zhao,et al.  Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings , 2020, Energy Conversion and Management.

[17]  Yang Li,et al.  Social cognitive optimization with tent map for combined heat and power economic dispatch , 2018, International Transactions on Electrical Energy Systems.

[18]  Ruoyu Zhang,et al.  A Hierarchical Self-Adaptive Method for Post-Disturbance Transient Stability Assessment of Power Systems Using an Integrated CNN-Based Ensemble Classifier , 2019, Energies.

[19]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[20]  K. Kawabe,et al.  Analytical Method for Short-Term Voltage Stability Using the Stability Boundary in the P-V Plane , 2014, IEEE Transactions on Power Systems.

[21]  Yuchen Zhang,et al.  A Hierarchical Self-Adaptive Data-Analytics Method for Real-Time Power System Short-Term Voltage Stability Assessment , 2019, IEEE Transactions on Industrial Informatics.

[22]  K. L. Praprost,et al.  An energy function method for determining voltage collapse during a power system transient , 1994 .

[23]  Yang Li,et al.  Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process , 2018, 1808.05708.

[24]  Yang Li,et al.  Optimal distributed generation planning in active distribution networks considering integration of energy storage , 2018, 1808.05712.

[25]  Zhen Yang,et al.  Optimal Scheduling of an Isolated Microgrid With Battery Storage Considering Load and Renewable Generation Uncertainties , 2018, IEEE Transactions on Industrial Electronics.

[26]  Chao Lu,et al.  Time Series Shapelet Classification Based Online Short-Term Voltage Stability Assessment , 2016, IEEE Transactions on Power Systems.