Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions.
The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.
Within the standard IEEE C37.118 applications and proposed hardware structure of a phasor measurement unit (PMU) are described. This paper presents the concept of the system for measuring and transferring synchrophasors from a theoretical aspect. Synchrophasor algorithms are developed in MATLAB/Simulink for the purpose of easier verification and hardware deployment on today’s market available and affordable real time development kits. Analysis of the synchrophasor measurement process is performed gradually. Firstly, by defining the synchrophasor based on three-phase to αβ-transformation and then introducing a discrete Fourier transform (DFT) based on synchrophasor estimation algorithm. Later, accompanying adverse effects resulting from its application are analyzed by means of simulation. To increase accuracy and improve estimation algorithm interpolated discrete Fourier transform (IpDFT) with and without windowing technique is used. To further optimize algorithm performance convolution sum in recursive form has been implemented instead of classical DFT approach. This study was carried out in order to validate described measurement system for the monitoring of transients during island operation of a local power electric system. Finally, simulation and experimental results including error analysis are also presented.
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