Supervisory Power System Stability Control using Neuro-fuzzy system and particle swarm optimization algorithm

Sallama, Abdulhafid; Abbod, Maysam F.; Taylor, Gareth

doi:10.1109/upec.2014.6934678

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…In [188] authors proposed 3-D principal component analysis (PCA) along with NFS based classifiers for automatic classification of the PQDs. Designing of an advanced supervisory power system stability controller (SPSSC) using NFS has been presented in [189]. ICA for classifying the single and multiple PQDs [190] and classification of neuro-fuzzy systems based on their learning algorithm, fuzzy method, and structure from 2000 to till date has been explained in [191].…”

Section: D: Neuro-fuzzy System Based Pqds Classificationmentioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This paper presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

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Section: D: Neuro-fuzzy System Based Pqds Classificationmentioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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“…The spatial command grades from external movement of the ring and the native releases of the attractant; the cells offer an attraction signal to each other so they swarm together. The swarming exemplified by [30]: (8) Elimination and Dispersal step: like in the residential location, exists of residents of bacteria modifications either progressively or unexpectedly owed to some other effect. Actions befell such that all the bacteria in an expanse die or a set spread into a new fragment of the setting.…”

Section: Optimization Techniquementioning

confidence: 99%

“…Using artificial intelligence (AI) techniques in designing PSS used in two ways. Firstly, by implementing the AI as the PSS similar to advanced power system stability controller (SPSSC) using Neuro-fuzzy system [8], optimize a fuzzy logic-PSS by an adaptive neuro-fuzzy inference system (ANFIS) [9], and deciding a fuzzy PSS parameter using the particle swarm optimization (PSO) [10].…”

Section: Introductionmentioning

confidence: 99%

Modified Particle Swarm Optimization Based on Lead-Lag Power System Stabilizer for Improve Stability in Multi-Machine Power System

Ibrahim¹,

Elnaghi²,

Ibrahem³

et al. 2018

IJEEI

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Inter-area oscillations not only limit the transferred bulk power but can extend to isolate the areas and may cause the blackout in some parts of the system or all the system. This paper depicts the improvement process of power system stability by using the modified particle swarm optimization (PSO) technique to optimize the lead-lag power system stabilizer (PSS) parameters offline to improve its performance. PSO modified by adjusting the damping boundary condition to prevent the particles from an outing of the searching space which improves the optimization process. Optimized PSS structure is a conventional lead-lag PSS (IEEE type-PSS1A) with speed deviation input signal. Proposed PSS performance compared with bacterial foraging based lead-lag PSS, and a simplified multi-band PSS: IEEE® type PSS4B. A comparison process applied to the system divided into two areas 11-bus 4-generators. Furthermore, performance indices as Eigenvalue, damping ratio, participation factor, maximum overshoots, settling time, and steady-state error used to utilize the analysis. The simulation results clarify the strength of the proposed PSS over the other compared PSSs. Simulation results in mathematical analysis prove that the proposed PSS improves the overall system stability better than the BG based lead-lag PSS by (23.02835%) and the MB-PSS by (94.14835%).

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Intelligent control of fixed capacitor - thyristor controlled reactor for power quality improvement

Khan

Bharath

Mishra

et al. 2016

2016 7th India International Conference on Power Electronics (IICPE)

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Supervisory Power System Stability Control using Neuro-fuzzy system and particle swarm optimization algorithm

Cited by 3 publications

References 16 publications

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Modified Particle Swarm Optimization Based on Lead-Lag Power System Stabilizer for Improve Stability in Multi-Machine Power System

Intelligent control of fixed capacitor - thyristor controlled reactor for power quality improvement

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