Abstract
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.
Highlights
The increasing penetration of renewable energy sources, coupled with the liberalized energy markets, gives rise to new challenges for safe, secure and stable power system operation
The loss value reaches a plateau after certain number of epochs, which is detected by the early stopping [51], indicating convergence of the model
A data-driven machine learning (ML) approach to solving the power system transient stability assessment (TSA) was proposed in this paper
Summary
The increasing penetration of renewable energy sources, coupled with the liberalized energy markets, gives rise to new challenges for safe, secure and stable power system operation. Some of the principal operational challenges include: (1) reduced system inertia, (2) over-generation, (3) adequacy risks, (4) poor reactive power for voltage control, (5) grid congestion, (6) reduced frequency regulation, (7) steep residual load ramps,. With the increased proportion of renewables in the generation mix, problem of the reduced system inertia will increase, with significant repercussions on the transient stability assessment (TSA) of the power system. Aforementioned issues, among other things, exert new (and intensify existing) stresses on the stability and security of the (already strained) power systems. They may exacerbate negative effects of severe faults, severe weather, and other internal and external power system disturbances [2,4]. According to the Electrical Power Research Institute, power supply outages crisis create globally an economic loss estimates of $104 billion to
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