Advanced Control of Three-Phase PWM Rectifier Using Interval Type-2 Fuzzy Neural Network Optimized by Modified Golden Sine Algorithm

Açıkgöz, Hakan; Çöteli, Resul; Tanyıldızı, Erkan; Dandil, Besir; Kayışlı, Korhan

doi:10.1080/15325008.2023.2185838

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…The membership function (MF) of a general T2FLC is three-dimensional. The principal reason for the proposal of two-dimensional interval T2FLC (IT2FLC) is the ensuring of a reduced degree of computational complexity in comparison with general T2FLC (Acikgoz et al, 2023). Fuzzy neural networks (FNNs) refer to combinations of artificial neural networks (ANNs) and FL.…”

Section: It2fnn Based On Tsk Logic Modelmentioning

confidence: 99%

Model Predictive Controlled IM Drive based on IT2FNN Controller

Demir,

Yildiz,

Gani

2023

Power Electronics and Drives

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In this paper, the predictive torque control (PTC) based induction motor (IM) drive using an interval type-2 fuzzy neural network (IT2FNN) controller in the speed control loop is designed and tested in simulations. The states required for the proposed motor drive are estimated by extended complex Kalman filter (ECKF). The ECKF performs online estimations of stator currents, rotor fluxes, rotor mechanical speed, and rotor resistance. Compared to conventional extended Kalman filter (EKF), which estimates the same states/parameters, the designed ECKF has less computational burden because it does not contain matrix inverse and the matrix dimensions have been reduced. In addition, the rotor resistance estimated by ECKF is updated online to the PTC system. Thus, the performance of the PTC-based IM drive is improved against variations in the rotor resistance, whose value changes with operating conditions such as frequency and temperature. In order to force both the ECKF observer and the proposed IM drive, a challenging scenario containing the wide speed range operation of the IM is designed. Simulation results confirm the performance of the proposed speed-sensorless PTC-based drive that uses an IT2FNN controller in the speed control loop and the estimation performance of the ECKF observer.

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Section: It2fnn Based On Tsk Logic Modelmentioning

confidence: 99%

Model Predictive Controlled IM Drive based on IT2FNN Controller

Demir,

Yildiz,

Gani

2023

Power Electronics and Drives

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“…The GoldSA-II algorithm is an effective metaheuristic algorithm that optimizes using the decreasing pattern of the sine wave and the golden ratio [20,21]. GoldSA-II algorithm is a modified version of the Gold-SA algorithm and uses the sine function and golden ratio just like GoldSA [20][21][22][23].…”

Section: B Modified Golden Sine Algorithmmentioning

confidence: 99%

Extreme Learning Machine-Based Power Forecasting in Photovoltaic Systems

Duranay

2023

IEEE Access

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With the consuming of fossil fuels, sustainable alternative sources are needed for energy production. Renewable energy is an important source in the fight against climate change and provides economic benefits by increasing energy security. Solar energy is one of the popular renewable energy sources. The photovoltaic system is a technology that produces electrical energy from solar radiation. These systems, which are clean, renewable, and environmentally friendly, are important in terms of meeting our future energy needs. Photovoltaic system power forecasting is an important tool for both energy planning and energy management, as it increases the predictability of bulk power system. In addition, power estimates also allow improving the system efficiency and planning the maintenance date. In this study, due to its high accuracy, fast computation, and easy applicability, Extreme Learning Machine (ELM) method was used to estimate the daily average energy production of a solar power plant located in Elazig/Turkey. To improve the performance of the model, ELM's hyperparameters were optimized using the Modified Golden Sine Algorithm (GoldSA-II). To show the power generation change that occurs in parallel with the seasonal weather change, graphs of the real power of different months from the four seasons and the power estimated by ELM are presented in the study. The radiation, temperature, wind speed, real power, and ELM predicted power values for the tenmonth period in which the operating data were obtained are given in the table. By examining this table, the effect of weather conditions on production can be observed more clearly. The R and RMSE values of the ELM model, which are calculated separately for each month, are presented in the form of a radar chart. The average daily R and RMSE values calculated for the ten-month period were calculated as 0.95 and 0.0716, respectively, and the performance of the ELM model used in the study was proven by the calculated R and RMSE values.INDEX TERMS Extreme learning machine, photovoltaic, power forecasting, renewable energy.

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“…Compared with type-1 fuzzy control, interval type-2 fuzzy control uses interval number to describe the relationship between input variables and output variables. It has better fault tolerance, accuracy and interpretability, and has better performance in solving some complex problems, such as Microgrid frequency regulation [25], local model control [26], three-phase PWM rectifier control [27], Type-2 fuzzy C-means algorithm [28]. Dirik et al [29] used interval type-2 fuzzy logic for the path planning, but the algorithm is based on known environment.…”

Section: Introductionmentioning

confidence: 99%

Robot Path Planning Based on Interval Type-2 Fuzzy Controller Optimized by an Improved Aquila Optimization Algorithm

Li,

Zhang,

Han

2023

IEEE Access

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Uncertainty and complexity in the local path planning are hot topics. In this paper, a novel IAOFC algorithm is proposed for local path planning in the complex environment. Considering the uncertainty and complexity of local path planning, this paper uses interval type-2 fuzzy control to design path planning method, which can respond more quickly to the uncertainty of the environment and improve the computation speed and efficiency. To further improve the performance of the fuzzy controller, this paper uses an improved Aquila Optimizer (AO) algorithm to optimize the membership function of the interval type-2 fuzzy controller (renamed by IAOFC). By using the optimized fuzzy controller, the time cost and path cost can be reduced. In simulation experiments, the path planning in static environment is designed to verify the basic performance and efficiency of the algorithm, and the path planning in dynamic environment is validated to verify the robustness of the algorithm. Finally, the superiority of the IAOFC algorithm is proved by comparing it with some other algorithms. According to experiment results, IAOFC has an average cost reduction of 15% and 6% than other algorithms in static and dynamic environments, respectively.

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Advanced Control of Three-Phase PWM Rectifier Using Interval Type-2 Fuzzy Neural Network Optimized by Modified Golden Sine Algorithm

Cited by 6 publications

References 41 publications

Model Predictive Controlled IM Drive based on IT2FNN Controller

Model Predictive Controlled IM Drive based on IT2FNN Controller

Extreme Learning Machine-Based Power Forecasting in Photovoltaic Systems

Robot Path Planning Based on Interval Type-2 Fuzzy Controller Optimized by an Improved Aquila Optimization Algorithm

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