Control of active and reactive power of grid connected inverter using adaptive network based fuzzy inference system (ANFIS)

Ahuja, Rajesh Kumar; Maity, Tanmoy; Kakkar, Sushma

doi:10.1109/poweri.2016.8077329

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…A T is the transpose of the matrix A. (A T A) 21 is the pseudo-inverse of A (for A T is a non-singular matrix.) Following assumptions have been made:…”

Section: Learning Principle For Anfismentioning

confidence: 99%

“…ANFIS-based controller is used to track the currents to the reference values, which in turn control the active and reactive powers. 21 The proposed system is developed and simulated in MATLAB environment. As the variation in the parameters of filter and transformer may affect the performance of the system, it is important to validate the performance of the system with the parametric uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Performance enhancement of grid-interfaced inverter using intelligent controller

Kakkar

Ahuja

Maity

2020

Measurement and Control

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The high-performance grid-interfaced inverters are in demand as they are rapidly used in renewable energy systems. The main objective of grid-interfaced inverters is to inject high-quality active and reactive power with sinusoidal current. Many control schemes have been proposed earlier in the literature, but the operation under parametric uncertainties has not been given much attention. In this article, an adaptive network–based fuzzy inference control algorithm for a three-phase grid-interfaced inverter under parametric uncertainties is proposed. The main purpose of the proposed technique is to enhance the response time, decrease the steady-state oscillation in the injected active and reactive power and enhance the power quality even with parametric uncertainties. For assessment and evaluation reason, the conventional proportional–integral control is compared with the proposed controller. For a fair comparison, the gain setting for the proportional–integral control is obtained by Particle swarm optimization algorithm. The suggested system is developed and simulated in MATLAB/Simulink. Simulation results demonstrate that both the controllers work well to regulate the powers to required values, even with parametric variations. However, the proposed control demonstrates superiority in comparison to conventional proportional–integral control in terms of speedy response, decreased steady-state fluctuations, better power quality and increased robustness. The rise time and fluctuations in the per-unit active and reactive power are much less with the proposed control. Total harmonic distortion of the injected current and grid current are significantly better than the conventional proportional–integral control.

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“…A T is the transpose of the matrix A. (A T A) 21 is the pseudo-inverse of A (for A T is a non-singular matrix.) Following assumptions have been made:…”

Section: Learning Principle For Anfismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: Performance enhancement of grid-interfaced inverter using intelligent controller

Kakkar

Ahuja

Maity

2020

Measurement and Control

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“…R. K. Ahuja and A. Mahesh has carried out past research on the application of ANFIS's new evolutionary algorithm to the problem of power system stability and satisfactory results have been reported [23], [24].…”

Section: Introductionmentioning

confidence: 99%

“…This study uses the Adaptive Neuro Fuzzy Inference System (ANFIS) method [23], [24] as the main method that has the ability to analyse objective functions and also has the advantage of optimizing coordination in different systems.…”

Section: Introductionmentioning

confidence: 99%

Overcurrent Relay Coordination using an Adaptive Neuro Fuzzy Inference Systems (ANFIS)

Rizal¹,

Adriansyah²,

Budiyanto³

et al. 2020

EEA

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OCR is the most widely used for the protection of distribution systems and protection backups in transmission systems. Maloperations can occur if the primary relay and backup relay are not adequately coordinated. It is essential that during abnormalities or disorders, OCR must be able to isolate only the wrong parts of the power system from a healthy system. Relay operation time can be reduced by proper coordination between primary and backup relays by selecting the optimum TMS and PS values. And because of that, relay coordination is an important part of power system protection. OCR time coordination is significant to reduce power outages by avoiding the mal-operative reserve relay. PS and TMS values with the help of MATLAB programming. In this study on the PLN system, Suralaya 150kV IEEE 23 Bus. 4 CM, NLP, GA, and ANFIS methods compared to 31 OCR, 38 main relay pairs, and backups produced 152∆t, 304 main and backup relay operation times. By looking at negative polarity optimization, NLP is superior to 64.340%, while ANFIS is superior to 94.875%.

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Utilising Feedforward Compensation, Active and Reactive Power Control of a Grid-Tied Three-Phase Inverter

Fatima,

Siddiqui,

Sinha

2024

2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)

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Control of active and reactive power of grid connected inverter using adaptive network based fuzzy inference system (ANFIS)

Cited by 6 publications

References 17 publications

RETRACTED: Performance enhancement of grid-interfaced inverter using intelligent controller

RETRACTED: Performance enhancement of grid-interfaced inverter using intelligent controller

Overcurrent Relay Coordination using an Adaptive Neuro Fuzzy Inference Systems (ANFIS)

Utilising Feedforward Compensation, Active and Reactive Power Control of a Grid-Tied Three-Phase Inverter

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