Harmonics elimination in multilevel inverter with unequal DC sources by fuzzy-ABC algorithm

Malarvizhi, M.; Gnanambal, I.

doi:10.1080/0952813x.2014.930596

“…Celik et al (2010) discuss the routing protocol designed on swarm intelligence to a WSN. Malarvizhi and Gnanambal (2015) propose a hybrid method incorporated with fuzzy inference system (FIS) as well as ABC algorithm. FIS is a rule supported artificial intelligent approach that is applied for the generation of dataset with respect to shifting angles, voltage harmony and harmonic distortion.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy rule selection using artificial bee colony optimisation algorithm

Kiran

¹

,

Dethe

²

2019

IJAIP

0

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Wireless sensor network (WSN) brings an innovative model with embedded system with restrictions of computing ability, intercommunication, storage capacity, and energy resource which is applied for a high range of applications in the situations when constructing the network based on conventional infrastructure is not feasible. Clustering with WSN is a successful technique to reduce the rate of energy use of sensor node. The fuzzy logic calculates the cluster head (CH) selection probability depending on the node's earlier communication history to choose the CH. The set of rules applied to the fuzzified input is the fuzzy rule base. The output of the inference engine is changed to crisp output by defuzzification. Artificial bee colony (ABC) is an optimisation protocol owes its inspiration to the exploration behaviour of honey bees. It is a comparatively innovative optimisation algorithm which has proven to be on par with classical bio-inspired protocols. This work on ABC optimisation algorithm is suggested for selecting fuzzy rules. Rule selection methods combine different rules from fuzzy rule set to decrease the rules while maintaining the performance of the system. The rules that decrease the performance of the system are removed, to get a fuzzy rule set with improved performance.

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“…Celik et al (2010) discuss the routing protocol designed on swarm intelligence to a WSN. Malarvizhi and Gnanambal (2015) propose a hybrid method incorporated with fuzzy inference system (FIS) as well as ABC algorithm. FIS is a rule supported artificial intelligent approach that is applied for the generation of dataset with respect to shifting angles, voltage harmony and harmonic distortion.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy rule selection using artificial bee colony optimisation algorithm

Kiran

¹

,

Dethe

²

2019

IJAIP

0

View full text Add to dashboard Cite

Wireless sensor network (WSN) brings an innovative model with embedded system with restrictions of computing ability, intercommunication, storage capacity, and energy resource which is applied for a high range of applications in the situations when constructing the network based on conventional infrastructure is not feasible. Clustering with WSN is a successful technique to reduce the rate of energy use of sensor node. The fuzzy logic calculates the cluster head (CH) selection probability depending on the node's earlier communication history to choose the CH. The set of rules applied to the fuzzified input is the fuzzy rule base. The output of the inference engine is changed to crisp output by defuzzification. Artificial bee colony (ABC) is an optimisation protocol owes its inspiration to the exploration behaviour of honey bees. It is a comparatively innovative optimisation algorithm which has proven to be on par with classical bio-inspired protocols. This work on ABC optimisation algorithm is suggested for selecting fuzzy rules. Rule selection methods combine different rules from fuzzy rule set to decrease the rules while maintaining the performance of the system. The rules that decrease the performance of the system are removed, to get a fuzzy rule set with improved performance.

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“…This technique is studied to obtain different points in a half cycle with different modulation indexes to get effective areas that give the best THD. In 2015 [19], a 7-level CHBVSI with unequal separated DC sources was used. A fuzzy logic algorithm is used to select switching angles and an artificial bee's colony (ABC) system optimises the selection of these angles.…”

Section: Introductionmentioning

confidence: 99%

Asymmetrical Multilevel Inverter with Modified Absolute Sinusoidal PWM Technique for Sensorless Control of Induction Motor

Antar¹,

Sadiq²,

Saleh³

2020

Proceedings of the Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart

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Sensorless speed and torque control of a three-phase induction motor utilizing cascaded H-bridge inverter with unequal selected DC voltage sources (USDCVS) is presented in this study. Several output voltages with minimum distortions and IGBTs are achieved with DC input voltage sources equal to (1, 3, 9) Vdc. The induction motor speed and torque are estimated depending on the instantaneous output phase voltages and currents of the drive circuit. A novel controller circuit is built depending on a modified absolute sinusoidal PWM (MASPWM) technique. Embedded s-function is coded to regenerate switching pulses and get various output levels. Simulation results demonstrate that the USDCVS of the multilevel inverter based on MASPWM technique produces different output levels with acceptable distortion. Also, show an acceptable sensorless speed and torque responses at steady-state and dynamic operation conditions under different speeds and mechanical load torques that explained the strength of the drive and control circuits.

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Harmonics elimination in multilevel inverter with unequal DC sources by fuzzy-ABC algorithm

Cited by 7 publications

References 15 publications

Fuzzy rule selection using artificial bee colony optimisation algorithm

Fuzzy rule selection using artificial bee colony optimisation algorithm

Fuzzy rule selection using artificial bee colony optimisation algorithm

Asymmetrical Multilevel Inverter with Modified Absolute Sinusoidal PWM Technique for Sensorless Control of Induction Motor

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