Over the last few years, machine learning (ML) tools have acquired significant progress and attracted extensive applications in many parts of contemporary life. The power sect...tor is one of the leading domains implementing such appealing and effective technologies for diverse applications as a part of the digital transformation of electric networks. Essentially, the low-frequency oscillation (LFO) in a power system is a non-threatening but slow-burning problem, which might cause full network failure unless properly handled. This article dis-cusses a state-of-the-art procedure of LFO damping in power system networks via sine co-sine algorithm (SCA) and deep learning (DL) technique. It uses two networks of power systems, in which, the synchronous generator is fitted with a power system stabilizer (PSS) in the case of the first network; in the other, the synchronous machine is connected to the PSS that coordinates with a unified power flow controller (UPFC). The proposal is developed based on the statistical assessment of the addressed networks, to improve the LFO damping by tuning the PSS parameters in real-time. The proposed technique was evaluated using power system stability performance measuring criteria, such as the eigenvalue and minimum damping ratio (MDR). At the end, the effectivity of the stability gaining procedure is also tested by time-domain simulation to implement in real-time. The study also dealt with a comparative investigation and discussion of the findings of some published works to conclude the capability of the proposed DL tool for stability improvement of the system in real-time by removing the undesirable LFOs.
|Effective start/end date
|1/01/23 → 31/12/23
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