scholarly journals Transient stability analysis of power grids with admissible and maximal robust positively invariant sets

2020 ◽  
Vol 68 (12) ◽  
pp. 1011-1021
Author(s):  
Tim Aschenbruck ◽  
Willem Esterhuizen ◽  
Stefan Streif
Keyword(s):  
Power Systems ◽  
Transient Stability ◽  
Energy Transition ◽  
Power Grids ◽  
Invariant Sets ◽  
Admissible Sets ◽  
Major Fault ◽  
Bounded Disturbances ◽  
One Machine ◽  
Positively Invariant Sets

AbstractThe energy transition is causing many stability-related challenges for power systems. Transient stability refers to the ability of a power grid’s bus angles to retain synchronism after the occurrence of a major fault. In this paper a set-based approach is presented to assess the transient stability of power systems. The approach is based on the theory of barriers, to obtain an exact description of the boundaries of admissible sets and maximal robust positively invariant sets, respectively. We decompose a power system into generator and load components, replace couplings with bounded disturbances and obtain the sets for each component separately. From this we deduce transient stability properties for the entire system. We demonstrate the results of our approach through an example of one machine connected to one load and a multi-machine system.

scholarly journals Stability of Power Grids: State-of-the-art and Future Trends

2019 ◽  
Vol 217 ◽  
pp. 01017
Author(s):  
Nikita Tomin ◽  
Daniil Panasetsky ◽  
Alexey Iskakov
Keyword(s):  
Steady State ◽  
Lyapunov Function ◽  
Power Systems ◽  
Transient Stability ◽  
State Of The Art ◽  
Test System ◽  
Power Grids ◽  
Small Signal ◽  
Small Signal Stability ◽  
Signal Stability

The state of the art of transient stability and steady-state (small signal) stability in power grids are reviewed. Transient stability concepts are illustrated with simple examples; in particular, we consider two machine learning-based methods for computing region of attraction: ROA produced by Neural Network Lyapunov Function; estimation of the ROA of IEEE 39-bus system using Gaussian process and Converse Lyapunov function. We discuss steady state stability in power systems, and using Prony’s modal analysis for evaluating small signal stability for the 7 Bus Test system and real French power system.

scholarly journals Study of power system stability: Matlab program processing data from Zahrani power plant (Beirut, Lebanon)

2019 ◽  
Vol 124 ◽  
pp. 05011
Author(s):  
Fouad Alhajj Hassan ◽  
Alexander Sidorov
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Power System Stability ◽  
System Stability ◽  
Main Attention ◽  
Matlab Program ◽  
Processing Data ◽  
One Machine ◽  
Great Damage

Stability is of great importance in power systems; instability can cause fluctuations in many parameters of a power system. Since the purpose is to keep feeding the load when a fault or disturbance of overload occurs, the main attention will be focused on over-voltage and frequency because they might make a great damage and shutdown the system. In this study, calculations were performed for one machine connected to infinite bus, considering steady state and transient stability using Matlab to achieve a stable system.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6953
Author(s):  
Yixing Du ◽  
Zhijian Hu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Short Term Memory ◽  
Risk Function ◽  
Stability Margin ◽  
Risk Level ◽  
Stability Assessment ◽  
Lstm Network ◽  
Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

scholarly journals Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Lijuan Li ◽  
Yongdong Chen ◽  
Bin Zhou ◽  
Hongliang Liu ◽  
Yongfei Liu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Dynamic Model ◽  
Threshold Model ◽  
Security Evaluation ◽  
Stochastic Dynamic ◽  
Threshold Condition ◽  
Stochastic Excitation ◽  
Predictor Corrector ◽  
One Machine

AbstractWith the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. There is a problem of establishing an effective stochastic dynamic model and algorithm under different stochastic excitation intensities. A Milstein-Euler predictor-corrector method for a nonlinear and linearized stochastic dynamic model of a power system is constructed to numerically discretize the models. The optimal threshold model of stochastic excitation intensity for linearizing the nonlinear stochastic dynamic model is proposed to obtain the corresponding linearization threshold condition. The simulation results of one-machine infinite-bus (OMIB) systems show the correctness and rationality of the predictor-corrector method and the linearization threshold condition for the power system stochastic dynamic model. This study provides a reference for stochastic modelling and efficient simulation of power systems with multiple stochastic excitations and has important application value for stability judgment and security evaluation.

scholarly journals Special Issue: “Wind Power Integration into Power Systems: Stability and Control Aspects”

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3680
Author(s):  
Lasantha Meegahapola ◽  
Siqi Bu
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Renewable Energy Sources ◽  
Power Grids ◽  
Energy Sources ◽  
Low Carbon ◽  
Carbon Neutrality ◽  
Stability And Control ◽  
Wind Power Integration ◽  
And Control

Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to the clean and low-carbon renewable energy sources [...]

scholarly journals Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1090
Author(s):  
Charilaos Latinopoulos ◽  
Aruna Sivakumar ◽  
John W. Polak
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Power Grids ◽  
Local Distribution ◽  
Technical Problems ◽  
Vehicle To Grid ◽  
Distribution Transformers ◽  
Demand Models ◽  
Multiple Vehicles ◽  
Infrastructure Investments

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.

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