Simulation of neuro-fuzzy controlled grid interactive inverter

Altın, Necmi; Sefa, İbrahim

doi:10.1109/icat.2011.6102109

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…By combining ANFIS with traditional PID controller the adaptive fuzzy neural PID control can be implemented in a PV inverter system to address the issues of system instability and long response times [94,95]. The ANFIS-based PID control in the PV inverter system is given in Figure 13.…”

Section: Adaptive Neuro-fuzzy Optimization For Pv Inverter With Pq Co...mentioning

confidence: 99%

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Zhang,

Zhai,

Mao

et al. 2024

Energies

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PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system.

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Section: Adaptive Neuro-fuzzy Optimization For Pv Inverter With Pq Co...mentioning

confidence: 99%

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Zhang,

Zhai,

Mao

et al. 2024

Energies

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“…The design of a fuzzy controller is often done by simulation or by performing inputoutput experiments on a prototype of the existing system [4], [5]. As fuzzy controller applications, they used the interactive inverter in the controlled network [6], the speed control of the permanent magnet synchronous motor [7] in their studies of Real Time Control of an Automated Guided Vehicle with Fuzzy Logic [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Adjustment of Fuzzy Controller Based Evolutionary Algorithms For Driving Electric Motor with Computer Interface

Bulut¹

2020

Preprint

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This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC motor. The design is based on optimization of rule bases of fuzzy controller. In the learning stage, the obtained rule base fitness values are measured by working the rule base on the controller. The learning stage is repeated the termination criteria. The proposed fuzzy controller is performed on the dc motor from a PC program using a interface circuit.

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“…In fuzzy‐PI controllers, fuzzy logic defines the proportional ( K P ) and integral ( K I ) gains of the PI controller according to operation point of the system simultaneously. Consequently, dependence of the controller on the parameter changes the system and external effects can be minimised [30].…”

Section: Introductıonmentioning

confidence: 99%

Fuzzy PI controlled inverter for grid interactive renewable energy systems

Sefa

Altın

Özdemir

et al. 2015

IET Renewable Power Generation

Self Cite

100

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In this study, a fuzzy-PI controlled grid interactive inverter has been designed and implemented. The proportional and integral gains of the PI controller are decided and tuned by the fuzzy logic controller (FLC) according to required operation point of the system. Thus, adaptive nature of the FLC and robust structure of the PI controller are synthesised. Eventually, an adaptive PI controller which can adopt changes because of different operation conditions, grid disturbances and natural effects with fast transient response is obtained. Simulation studies are validated with experimental results. Both simulation and experimental results show that proposed system has fast dynamic response and tracks reference current with a low overshot and short settling time. In addition, the waveform of the inverter output current is sinusoidal and also the current is in same phase and frequency with the line voltage. Furthermore, the total harmonic distortion level of the inverter current meets the international standards. In addition, the proposed inverter system is compared with conventional PI controlled grid interactive inverter with various proportional and integral gains.

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Simulation of neuro-fuzzy controlled grid interactive inverter

Cited by 7 publications

References 14 publications

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Optimal Adjustment of Fuzzy Controller Based Evolutionary Algorithms For Driving Electric Motor with Computer Interface

Fuzzy PI controlled inverter for grid interactive renewable energy systems

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