Selection of Suitable Feedforward Neural Network Based Power System Stabilizer for Robust Excitation Control System

Ansari, Jamshed Ahmed; Soomro, Suhail Ahmed; Memon, Aftab Ahmed; Soomro, Jahangeer Badar; Shah, Madad Ali; Fayazuddin,

doi:10.1109/eeeic.2018.8494469

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…After generating a neural network containing these responses to the DFIG model, this newly-born file runs in conjunction with the PID controller in order to set the responses. Initially, these PFFNNs work in a similar way to PID, as it is part of the training [25]. Once training is completed, this controller can now be replaced with PI and PID for DFIG power control.…”

Section: Implementation Of Probabilistic Feedforward Neural Networkmentioning

confidence: 99%

“…The PFFNN is prepared with an overseen/monitored preparing procedure of artificial neural networks [25]. In this article, PFFNN is created as a unique sort of trained and ingenious controller.…”

Section: Implementation Of Probabilistic Feedforward Neural Networkmentioning

confidence: 99%

“…In this article, PFFNN is created as a unique sort of trained and ingenious controller. The input and target data for preparing the PNN are acquired from the input and output of the ordinary controller [25]. The layers, weights and biases of the simulated PFFNN network are shown in Figures 2 and 3 respectively.…”

Section: Implementation Of Probabilistic Feedforward Neural Networkmentioning

confidence: 99%

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Power Optimization Control Scheme for Doubly Fed Induction Generator Used in Wind Turbine Generators

et al. 2020

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Scientists and researchers are exploring different methods of generating and delivering electrical energy in an economical and reliable way, enabling them to generate electricity focusing on renewable energy resources. All of these possess the natural property of self-changing behavior, so the connection of these separate independent controllable units to the grid leads to uncertainties. This creates an imbalance in active power and reactive power. In order to control the active and reactive power in wind turbine generators with adjustable speed, various control strategies are used to allay voltage and current variations. This research work is focused on the design and implementation of effective control strategies for doubly fed induction generator (DFIG) to control its active and reactive power. A DFIG system with its control strategies is simulated on MATLAB software. To augment the transient stability of DFIG, the simulation results for the active and reactive power of conventional controllers are compared with three types of feed forward neural network controllers, i.e., probabilistic feedforward neural network (PFFNN), multi-layer perceptron feedforward neural network (MLPFFN) and radial basic function feedforward neural network (RBFFN) for optimum performance. Conclusive outcomes clearly manifest the superior robustness of the RBFNN controller over other controllers in terms of rise time, settling time and overshoot value.

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Section: Implementation Of Probabilistic Feedforward Neural Networkmentioning

confidence: 99%

Section: Implementation Of Probabilistic Feedforward Neural Networkmentioning

confidence: 99%

Section: Implementation Of Probabilistic Feedforward Neural Networkmentioning

confidence: 99%

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Power Optimization Control Scheme for Doubly Fed Induction Generator Used in Wind Turbine Generators

et al. 2020

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“…The commissioning of the controller must be finalized as quickly as possible and must also endorse adequate operation regarding different operating conditions of the electrical system 5 . Researchers developed a large quantity of PSS design techniques that performed quite well in stabilizing the system, such as adaptive control techniques, 6 fuzzy logic, 5 sliding mode control techniques, 7 robust control techniques, 8 optimization methods using LQR, 9 H∞ techniques, 10 artificial intelligence techniques 11‐13 and linear control techniques, such as pole placement 13 . Therefore, using conventional methods under inconstant operating conditions is a complex process 14 .…”

Section: Introductionmentioning

confidence: 99%

“…Some approaches of NNs based on machine learning for rotor angle stability are used to model, identify and control the plant itself as in 11,12,20,21 . The difficulty in implementing this kind of practice arises from the need for extreme confidence in the methodology to properly damp oscillations, since NNs must work perfectly in all situations.…”

Section: Introductionmentioning

confidence: 99%

The nonlinear autoregressive network with exogenous inputs (NARX) neural network to damp power system oscillations

Carbonera

Bernardon

Karnikowski

et al. 2020

Int Trans Electr Energ Syst

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A Comprehensive Review of Power System Stabilizers

Nocoń

Paszek

2023

Energies

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This paper presents a current literature review (from the years 2017–2022) on issues related to the application of power system stabilizers (PSSs) for damping electromechanical swings in power systems (PSs). After the initial selection of papers found in the databases used, over 600 publications were qualified for this review, of which 216 were subjected to detailed analysis. In the review, issues related to the following problems are described: applications of classic PSSs, applications of new stabilizer structures based on new algorithms (including artificial intelligence), development of new methods for tuning PSSs, and operation of PSSs in PSs with high power generation by renewable sources. Describing individual papers, the research methods used by the authors (simulations, measurement methods, and a combination of both) are specified, attention is paid to the waveforms presented in the papers, and reference is made to the types of PSs in which PSSs (large multimachine, reflecting real systems, smaller standard multimachine New-England type, and simplest single-machine) operate. The tables contain detailed comments on the selected papers. The final part of the review presents general comments on the analyzed papers and guidelines for future PS stability studies.

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Selection of Suitable Feedforward Neural Network Based Power System Stabilizer for Robust Excitation Control System

Cited by 4 publications

References 6 publications

Power Optimization Control Scheme for Doubly Fed Induction Generator Used in Wind Turbine Generators

Power Optimization Control Scheme for Doubly Fed Induction Generator Used in Wind Turbine Generators

The nonlinear autoregressive network with exogenous inputs (NARX) neural network to damp power system oscillations

A Comprehensive Review of Power System Stabilizers

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