Adaptive Neuro Fuzzy Inference System PID Controller for AVR System Using SNR-PSO Optimization

Yavarian, Kamal; Mohammadian, Amir; Hashemi, F.

doi:10.15676/ijeei.2015.7.3.3

Cited by 10 publications

(6 citation statements)

References 19 publications

(11 reference statements)

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“…In this context, the firing level of node "i" is represented by w i: Grid partitioning having 2 inputs with k = 3 [42].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Solar Radiation Prediction Using an Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) Optimization Ensemble

Muhammad Ibrahim,

Muhammad Lawan,

Abdulkadir

et al. 2024

Adaptive Neuro-Fuzzy Inference System as a Universal Estimator [Working Title]

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A dependable design and monitoring of solar energy-based systems necessitates precise data on available solar radiation. However, measuring solar radiation is challenging due to the expensive equipment required for measurement, along with the costs of calibration and maintenance, especially in developing countries like Nigeria. As a result, data-driven techniques are often employed to predict solar radiation in such regions. However, the existing predictive models frequently yield unsatisfactory outcomes. To address this issue, this study proposes the creation of intelligent models to forecast solar radiation in Kano state, Nigeria. The model is developed using an ensemble machine learning approach that combines two Adaptive Neuro-Fuzzy Inference Systems with sub-clustering optimization and grid-partitioning optimization. The meteorological data used for model development include maximum temperature, minimum temperature, mean temperature, and solar radiation from the previous 2 days as predictors. To evaluate the model’s performance, various metrics like correlation coefficient, determination coefficient, mean-squared error, root-mean-squared error, and mean-absolute error are employed. The simulation results demonstrate that the ANFIS ensemble outperforms the individual ANFIS models. Notably, the ANFIS-ENS exhibits the highest accuracy. Consequently, the developed models provide a reliable alternative for estimating solar radiation in Kano and can be instrumental in enhancing the design and management of solar energy systems in the region.

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“…In this context, the firing level of node "i" is represented by w i: Grid partitioning having 2 inputs with k = 3 [42].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Solar Radiation Prediction Using an Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) Optimization Ensemble

Muhammad Ibrahim,

Muhammad Lawan,

Abdulkadir

et al. 2024

Adaptive Neuro-Fuzzy Inference System as a Universal Estimator [Working Title]

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“…In 2015 (Yavarian et al, 2015), an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to improve the performance of the AVR system under tunning the controller parameters by using a combination of Signal-to-Noise Ratio (SNR) and Particle Swarm Optimization (PSO) algorithms and comparing the results with the Genetic Algorithm (GA). In 2016 (babu and Chiranjeevi, 2016), a method was developed to calculate the FOPID controller parameters using the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of AVR controller performance using exponential distribution and transit search optimization techniques

Amin,

Attia,

Khamees

et al. 2024

Front. Energy Res.

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This paper attempts to obtain the optimal solution to enhance the performance of the Automatic Voltage Regulator (AVR) Controller, as it is an essential tool to control the synchronous generator output voltage. The controller improves AVR system stability and response time; moreover, it is demonstrated that the Proportional Integral Derivative (PID) controller achieves the goal by applying two artificial intelligence techniques to design the optimal values of the Automatic Voltage Regulator (AVR) PID controller for a single area model. The first is the Exponential Distribution Optimization Algorithm (EDO), and the second is the Transit Search Optimization Algorithm (TS). EDO and TS are used to determine the best PID controller parameters and have also recently been developed in the breadth of optimization problems and associated computational complexities field. The objective function, Integrated Square Error (ISE), minimizes the error voltage for improved stability and response. The outcomes are compared to various optimization techniques to prove the validation of the two proposed methods. The results show that the EDO and TS proved their superiority through their stability level to the AVR system and their steady-state error improvement. Moreover, the dominant effect of damping frequency decreases the oscillation and the reduced maximum overshoot that protects the loads from being subjected to non-permissible over-voltage levels. Finally, a robustness test is applied to the two proposed optimization methods to prove their reliability and effectiveness.

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“…It is possible to see studies in which advanced controllers such as sliding mode control [5], fuzzy logic control [6], and adaptive neuro-fuzzy inference system (ANFIS) [7] are used in the control of the AVR. However, some difficulties are encountered during the application of such controllers due to insufficient expert knowledge, high computational load, and difficulty identifying the source of the problem.…”

Section: Literature Surveymentioning

confidence: 99%

Novel TIλDND2N2 Controller Application with Equilibrium Optimizer for Automatic Voltage Regulator

Tabak

2023

Sustainability

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Sustainability is important in voltage regulation control in grids and must be done successfully. In this paper, a novel tilt-fractional order integral-derivative with a second order derivative and low-pass filters controller, referred to as TIλDND2N2 controller, is proposed to enhance the control performance of an automatic voltage regulator (AVR). In this article, the equilibrium optimizer (EO) algorithm is used to optimally determine the eight parameters of the proposed controller. In this study, a function consisting of time domain specifications is used as the objective function. To evaluate the performance of the proposed controller, it is compared with the proportional-integral-derivative (PID), fractional order PID (FOPID), PID accelerator (PIDA), PID plus second order derivative (PIDD2), and hybrid controllers used in previous studies. Then, Bode analysis is performed to determine the achievement of the proposed controller in the frequency domain. Finally, the robustness test is realized to assess the response of the proposed controller against the deterioration of the system parameters. As a result, the proposed controller demonstrates outstanding control performance compared to studies in terms of settling time, rise time and overshoot. The proposed controller shows superior performance not only in frequency domain analysis but also in perturbed system parameters.

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Adaptive Neuro Fuzzy Inference System PID Controller for AVR System Using SNR-PSO Optimization

Cited by 10 publications

References 19 publications

Solar Radiation Prediction Using an Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) Optimization Ensemble

Solar Radiation Prediction Using an Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) Optimization Ensemble

Development of AVR controller performance using exponential distribution and transit search optimization techniques

Novel TIλDND2N2 Controller Application with Equilibrium Optimizer for Automatic Voltage Regulator

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