Model reference self-tuning fractional order PID control based on for a power system stabilizer

Ghany, M. A. Abdel; Shamseldin, Mohamed Α.

doi:10.11591/ijpeds.v11.i3.pp1333-1343

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…To improve the performance of the conventional PSSs, many approaches for tuning the parameters have been proposed such as classical methods, variable structure and adaptive control method (Ghany and Shamseldin, 2020), intelligent control methods (Baadji et al, 2019), robust control method (Sharma and Mishra, 2018) and gradient methods for optimization (Guo et al, 2019). However, designing a PSS system is a difficult assignment because it involves a heavy volume of system modeling and a substantial optimization computational time on the system (Ibrahim et al, 2019; Setiadi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations

Sabo

Wahab

Othman

et al. 2021

Transactions of the Institute of Measurement and Control

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Generally, power systems experience a variety of disturbances that can result in low frequency electromechanical oscillations. These low frequency oscillations (LFOs) take place among the rotors of synchronous generators connected to the power system. These oscillations may sustain and grow to cause system separation if no adequate damping is provided. Power system stabilizers (PSSs) are one of the alternative devices used to solve this rotor oscillation problem. The major limitation of using PSSs at the excitation system of synchronous machine is that the conventional PSS is a permanent parameter type operating under a particular system operating condition, and its parameters are acquired through trial and error. An efficient way of operating the PSS has been by designing the PSS parameters using a powerful optimization procedure. However, designing PSS damping controller is a cumbersome task as it needs a comprehensive test system modeling and a heavy computational burden on the system. In this research, a novel, model-free neuro-fuzzy controller (NFC) is designed as the LFOs’ damping controller to substitute the traditional PSS system making the power system simple without complications in PSS design and parameter optimization. The proposed controller application implements the LFOs’ control without a linearized mathematical model of the system, as such it makes the system less complex and bulky. Single machine infinite bus (SMIB) test system was simulated in SIMULINK domain with the PSSs and with the proposed controller to compare the NFC effectiveness. The simulation outcome for the eigenvalue study with NFC stabilizer yields steady eigenvalues that enhanced the damping status of the system greater than 0.1 with decreased overshoots and time to rise via the proposed NFC process than with the conventional FFA-PSS. Similarly, the generator transient reaction also presents the ω and δ based on the time to settle was improved by 64.66% and 28.78%, respectively, via the proposed NFC process than with the conventional FFA-PSS. Finally, the conventional PSS was found to be complicated in its design, parameter optimization and less effective than the proposed controller for the LFOs’ control.

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Section: Introductionmentioning

confidence: 99%

Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations

Sabo

Wahab

Othman

et al. 2021

Transactions of the Institute of Measurement and Control

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A Modified Model Reference Adaptive Control for High-Performance Pantograph Robot Mechanism

El-Τehewy¹,

Shamseldin

Sallam

et al. 2021

Wseas Transactions on Applied and Theoretical Mechanics

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For the minimization problem with pointwise observation governed by a one-dimensional parabolic equation with a free convection term and a depletion potential, we formulate a result on the existence and uniqueness of a minimizer from a prescribed set. We use a weight quadratic cost functional showing the temperature deviation. We obtain estimates for the norm of control functions in terms of the value of the quality functional in different functional spaces. It gives us a possibility to estimate the required internal energy of the system. To prove these results we establish the positivity principle.

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Sliding Mode Self-Tuned Single Neuron PID Controller for Power System Stabilizer

Ghany

Shamseldin

2021

Wseas Transactions on Computers

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In this paper, a modified technique based on the combination of the Single Neuron PID (SNPID), as the main controller and Sliding Mode Control (SMC), as an adaptation technique, to design an optimized self-tuned for SNPID controller that may overcome difficulties faced when a change in system operating points occurs. The proposed approach has been implemented as a power system stabilizer (PSS) for a synchronous generator connected to an infinite bus. The Flower Pollination (FP) optimization is based on an appropriate objective function. To demonstrate the effectiveness of the combination obtained controllers, PSS, is tested under different operating conditions. The combination controllers are shown through uncertainties system parameters changes under different disturbances. The results show the ability of the suggested controllers to enhance well the system performances

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Model reference self-tuning fractional order PID control based on for a power system stabilizer

Cited by 3 publications

References 19 publications

Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations

Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations

A Modified Model Reference Adaptive Control for High-Performance Pantograph Robot Mechanism

Sliding Mode Self-Tuned Single Neuron PID Controller for Power System Stabilizer

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