Control and maximum power tracking operation of hybrid excited variable speed induction generator

Sowmmiya, U.; Uma, G.

doi:10.1016/j.epsr.2016.10.011

Cited by 15 publications

(3 citation statements)

References 36 publications

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“…Growth and challenges in the digital network were introduced by Muruganantham et al (2017). In a variable speed wind-driven induction generator feeding discontinuous-continuous mode, a method of voltage regulation, maximum power extraction, and power transfer operation was described by Sowmmiya and Uma (2017). Indragandhi et al (2017) discussed a detailed review of demand of power, power production and the renewable energy source status across the globe and predict worldwide share of renewable energy source in the production of electricity.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

RETRACTED: Hybrid renewable energy systems for power flow management in smart grid using an efficient hybrid technique

Kumaravel¹,

Vijayakumar

2020

Transactions of the Institute of Measurement and Control

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Efficient approach for power flow management of hybrid renewable energy system connected smart grid system is proposed in this paper. Here, the proposed approach is the combination of both the modified elephant herding optimization algorithm with tabu search algorithm named as MEHOTSA. In the proposed technique, the modified elephant herding optimization algorithm plays out the assessment procedure to establish the exact control signals for the system and builds up the control signals database for the offline way in light of the power variety between source side and the load side. The multi-objective function is shaped by the grid required active power and reactive power varieties generated based on the accessible source power. The accomplished dataset is used to work the Tabu search algorithm on the online way and it leads the control procedure in less execution time. The proposed technique-based control model enhances the control parameters of the power controller in light of the power flow varieties. By utilizing the proposed methodology, the power flow management of the smart grid system is controlled dependent on the source side and load side parameters varieties. Additionally, the proposed methodology is in charge of controlling the energy sources so as to produce the power demanded by the grid, utilizing optimally both renewable energy sources and energy storage devices. Finally, the proposed model be actualized in MATLAB/Simulink platform and the performance are compared with other techniques.

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Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

RETRACTED: Hybrid renewable energy systems for power flow management in smart grid using an efficient hybrid technique

Kumaravel¹,

Vijayakumar

2020

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Moreover, control of fuzzy deals with ideal SOC that controls the charge and discharge currents to extend the battery life cycle. Sowmmiya and Uma 31 have presented a technique of voltage regulation, maximal extraction power, and operation of power transfer at vary rapid wind-driven induction generator, which feeds the DC MG. Indragandhi et al 32 have suggested fully analysis of power requirement. Here, the power production with state of generation due to renewable energy sources worldwide and predict global participation of renewable energy in the production of electricity.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

IH ² OA based on intelligent power flow management of HRES in smart grid

et al. 2021

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This manuscript proposes a hybrid strategy for power flow management under a smart grid system connected to a hybrid renewable energy system (HRES).The HRES is the combination of photovoltaic, wind turbine, fuel cell and energy storage system or battery. The proposed technique is the execution of the Integrated Harris Hawk Optimization algorithm (IH 2 OA). Here, Harris Hawk Optimization is the consolidation of crossover and mutation function, hence it is called IH 2 OA. Moreover, IH 2 OA is enhancing the voltage source inverter of control signals under the variation of power replace among source and load side. The numerous characteristics are made up of necessary grid active with reactive power variations created under the view of available power source. The IH 2 OA method ensures the location of control signals using parallel implementation as opposed to active with reactive power variation. In the IH 2 OA method, the control scheme-based optimizes the power controller under the power flow variation. The proposed system performed on MATLAB/ Simulink platform and the efficiency are compared with diverse existing techniques.

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“…Utilizing a comprehensive nonlinear model of the plant through computer simulation the execution of the control methodology was surveyed broadly. Based on four heuristic algorithms a home energy management (HEM) controller was structured by Sowmiya and Uma (2017). The heuristic algorithms were bacterial foraging optimization algorithm (BFOA), GA, binary particle swarm optimization (BPSO) and wind driven optimization (WDO).…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Optimum utilization of grid connected hybrid renewable energy sources using hybrid algorithm

Muthusamy¹,

Meenakumari²

2020

Transactions of the Institute of Measurement and Control

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An optimal utilization of smart grid connected hybrid renewable energy sources is proposed in this paper. The hybrid technique is the combination of recurrent neural network and adaptive whale optimization algorithm plus tabu search, called AWOTS. The main objective is the RES optimum operation for decreasing the electricity production cost by hourly day-ahead and real time scheduling. Here, the load demands are predicted using AWOTS to develop the correct control signals based on power difference between source and load side. Adaptive whale optimization algorithm searching behaviour is adjusted by tabu search. The proposed technique is executed in the MATLAB/Simulink working platform. To test the performance of the proposed method, the load demand for the 24-hour time period is demonstrated. By then the power generated in the sources, such as photovoltaic, wind turbine, micro turbine and battery by the proposed technique, is analyzed and compared with existing techniques, such as genetic algorithm, particle swarm optimization and whale optimization algorithm. Furthermore, the state of charge of the battery for the 24-hour period is compared with existing techniques. Likewise, the cost of the system is compared and error in the sources also compared. The comparison results affirm that the proposed technique has less computational time (35.001703) than the existing techniques. Moreover, the proposed method is cost-effective power production of smart grid and effective utilization of renewable energy sources without wasting the available energy.

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Control and maximum power tracking operation of hybrid excited variable speed induction generator

Cited by 15 publications

References 36 publications

RETRACTED: Hybrid renewable energy systems for power flow management in smart grid using an efficient hybrid technique

RETRACTED: Hybrid renewable energy systems for power flow management in smart grid using an efficient hybrid technique

IH ² OA based on intelligent power flow management of HRES in smart grid

Optimum utilization of grid connected hybrid renewable energy sources using hybrid algorithm

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Control and maximum power tracking operation of hybrid excited variable speed induction generator

Cited by 15 publications

References 36 publications

RETRACTED: Hybrid renewable energy systems for power flow management in smart grid using an efficient hybrid technique

RETRACTED: Hybrid renewable energy systems for power flow management in smart grid using an efficient hybrid technique

IH 2 OA based on intelligent power flow management of HRES in smart grid

Optimum utilization of grid connected hybrid renewable energy sources using hybrid algorithm

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Resources

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IH ² OA based on intelligent power flow management of HRES in smart grid