Neurogenetic approach for real-time damping of low-frequency oscillations in electric networks

Shahriar, Mohammad Shoaib; Shafiullah, Md; Rana, Juel; Ali, Amjad; Ahmed, Ashik; Rahman, Syed Masiur

doi:10.1016/j.compeleceng.2020.106600

Cited by 18 publications

(10 citation statements)

References 12 publications

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“…Dashti et al [16] In electrical energy distribution networks, a study of fault prediction and localisation methods is presented. Shahriar et al [17] Low-frequency oscillations in electric networks can be dampened in real time using a neurogenetic technique. Ghasemi-Marzbali [18] A coordinated technique for the stability of multi-machine power systems.…”

Section: Grdenić Et Al [15]mentioning

confidence: 99%

“…To this end, the representation of the electromagnetic coupling of the two axes d and q by equivalent diagrams of inductors and resistances is of great interest for the analysis of the operation of the machine [4][5][6] and for the identification parameters [7,8]. Important studies have been done recently in the scope of improving stability in different modalities [9][10][11][12][13][14][15][16][17][18][19][20] as shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Energy Transfer from an Electrical Network-Connected Synchronous Machine

Sari-Ali¹,

Chikh-Bled²,

Ikumapayi³

et al. 2022

JESA

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Any alternating electric power supply is always characterized by three reference quantities, which are voltage, electric power and frequency. The generators of power stations are synchronous machines. The present work is based on the modelling of the synchronous machine for a study of the stability of the electricity transmission network. The example considered is a thermal power station of the national network. i.e., Algiers port power station. We have developed a computer program based on MATLAB to simulate the calculations of our multi-variable system. The study presented in this article examines the static stability of a thermal power plant. To make this study a reality, it is necessary to model the synchronous machine. We present the three models of the synchronous machine by emphasizing the role of the shock absorbers and the smoothness of the model with three windings. This work is supported by a spectral analysis based on the eigenvalues of the system.

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Section: Grdenić Et Al [15]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Transfer from an Electrical Network-Connected Synchronous Machine

Sari-Ali¹,

Chikh-Bled²,

Ikumapayi³

et al. 2022

JESA

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“…Figure 1a shows the PSS integrated SMIB electric power network, where the synchronous generator is connected to an infinite bus system through a transmission line of specific reactance. The fourth-order nonlinear model of the SMIB system and its linearized form can be found in [36][37][38][39]. The incorporated PSS is one of the commonly used single-stage lead-lag controllers, as shown in Figure 1b.…”

Section: Example 1: Smib Electric Network Without Upfcmentioning

confidence: 99%

“…The state-space model of the overall network after inclusion of the PSS is available in Ref. [36] and represented as (1) where the number of states is six.…”

Section: Example 1: Smib Electric Network Without Upfcmentioning

confidence: 99%

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Real-Time LFO Damping Enhancement in Electric Networks Employing PSO Optimized ANFIS

et al. 2020

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In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for various applications. Apparently, a nonthreatening, but slow-burning issue of the electric power systems is the low-frequency oscillations (LFO), which, if not dealt with appropriately and on time, could result in complete network failure. This paper addresses the role of a prominent ML family member, particle swarm optimization (PSO) tuned adaptive neuro-fuzzy inference system (ANFIS) for real-time enhancement of LFO damping in electric power system networks. It adopts and models two power system networks where in the first network, the synchronous machine is equipped with only a power system stabilizer (PSS), and in the other, the PSS of the synchronous machine is coordinated with the unified power flow controller (UPFC), a second-generation flexible alternating current transmission system (FACTS) device. Then, it develops the proposed ML approach to enhance LFO damping for both adopted networks based on the customary practices of statistical judgment. The performance measuring metrics of power system stability, including the minimum damping ratio (MDR), eigenvalue, and time-domain simulation, were used to analyze the developed approach. Moreover, the paper presents a comparative analysis and discussion with the referenced works’ achieved results to conclude the proposed PSO-ANFIS technique’s ability to enhance power system stability in real-time by damping out the unwanted LFO.

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