A review of data-driven short-term voltage stability assessment of power systems: Concept, principle, and challenges
arXiv:2112.12240 · doi:10.1155/2021/5920244
Abstract
With the rapid growth of power market reform and power demand, the power transmission capacity of a power grid is approaching its limit, and the secure and stable operation of power systems becomes increasingly important. In particular, in modern power grids, the proportion of dynamic loads with fast recovery characteristics such as air conditioners, refrigerators, and industrial motors is increasing. As well as the increasing proportion of different forms of renewable energy in power systems. Therefore, short-term voltage stability (STVS) of power systems cannot be ignored. This article comprehensively sorts out the STVS problems of power systems from the perspective of data-driven methods and discusses existing challenges.
arXiv commentAccepted by Mathematical Problems in Engineering
References in corpus (16)
- Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach
- Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties
- Optimal distributed generation planning in active distribution networks considering integration of energy storage
- Optimal Scheduling of Integrated Demand Response-Enabled Integrated Energy Systems with Uncertain Renewable Generations: A Stackelberg Game Approach
- Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings
- Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: a bi-level programming approach via real-time pricing
- Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting
- Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach
- Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game
- Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments
- A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems
- Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data
- Deep Learning for Short-Term Voltage Stability Assessment of Power Systems
- Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors
- Method for Quantitative Estimation of the Risk Propagation Threshold in Electric Power CPS Based on Seepage Probability
- Rule extraction based on extreme learning machine and an improved ant-miner algorithm for transient stability assessment