A Three-Level Distributed Architecture for the Real-Time Monitoring of Modern Power Systems

Papadopoulos, Theofilos A.; Kontis, Eleftherios O.; Papadopoulos, Panagiotis

doi:10.1109/access.2022.3159340

Cited by 10 publications

(1 citation statement)

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“…Following the tightening of the rules of the electricity market, there is an active implementation of digital systems that allow for increasing the flow of intersystem active power flows [13][14][15], which reduces the stability margin and increases the likelihood of developing an accident with EPS division.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements

Senyuk,

Safaraliev,

Pazderin

et al. 2023

Mathematics

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Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions’ value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems.

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Section: Introductionmentioning

confidence: 99%