Data-driven methods for real-time voltage stability assessment
Licentiate thesis, 2020

Voltage instability is a phenomenon that limits the operation and the transmission capacity of a power system. An operation state close to the security limits enables a cost-effective utilization of the system but it could also make the system more vulnerable to disturbances. The transition towards a more sustainable energy system, with a growing share of renewable generation, will increase the complexity in voltage stability assessment and cause significant planning and operational challenges for transmission system operators.

The overall aim of this thesis is to develop a real-time voltage stability assessment tool which can be used to assist transmission system operators in monitoring voltage security limits and to provide early warnings of possible voltage instability. The thesis first analyzes the difference between static and dynamic voltage security margins, both theoretically and numerically. The results of the analysis show that power systems with a high share of loads with fast restoration dynamics, such as induction motors or power electronic controlled loads, may cause conventional static methods to assess the voltage security margins to become unreliable. Methods relying on a dynamic assessment of the security margin are in these circumstances more reliable.

However, dynamic assessment of voltage security margins is computationally challenging and can in most cases not be estimated in the time frame required by system operators in critical situations. To overcome this challenge, a machine learning-based method for fast and robust computing of the dynamic voltage security margin is proposed and tested in this thesis. The method, based on artificial neural networks, can provide real-time estimations of voltage security margins, which are then validated using a search algorithm and actual time-domain simulations. The two-step approach is proposed to mitigate any inconsistency issues associated with neural networks under new or unseen operating conditions.

Finally, a new method for voltage instability prediction is developed. The method is proposed to be used as an online tool for system operators to predict the system’s near-future stability condition given the current operating state. The method uses a more advanced neural network based on long-short term memory. The results from case studies using the Nordic 32 test system show good performance and the network can accurately, within only a few seconds, predict voltage instability events in almost all test cases.

security assessment

neural networks

Dynamic voltage security margin

machine learning

voltage instability prediction

voltage stability

Opponent: Hjörtur Jóhannsson, Technical University of Denmark, Denmark

Author

Hannes Hagmar

Chalmers, Electrical Engineering, Electric Power Engineering, Power grids and Components

Fast Dynamic Voltage Security Margin Estimation: Concept and Development

IET Smart Grids Special Issue: Machine Learning in Power Systems,; (2020)

Journal article

On-line Voltage Instability Prediction using an Artificial Neural Network

2019 IEEE Milan PowerTech, PowerTech 2019,; (2019)

Paper in proceedings

A Survey of Voltage Stability Indicators Based on Local Synchronized Phasor Measurements

2018 North American Power Symposium (NAPS),; (2019)

Paper in proceedings

H. Hagmar, L. Tong, R. Eriksson, L. A. Tuan, "Voltage Instability Prediction Using a Deep Recurrent Neural Network," submitted to Transactions on Power Systems, 2020.

Advanced visualization of voltage stability limit and system protection based on real-time measurement

Swedish national grid, 2016-06-01 -- 2020-12-31.

Swedish Energy Agency, 2016-06-01 -- 2020-12-31.

Driving Forces

Sustainable development

Areas of Advance

Energy

Subject Categories

Computer Science

Computer Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering

Publisher

Chalmers University of Technology

Online

Opponent: Hjörtur Jóhannsson, Technical University of Denmark, Denmark

More information

Latest update

4/14/2020