Performance improvement of SEIG based WECS using Artificial Neural Network

Navin, Kumar; Dyanamina, Giribabu

doi:10.1109/sceecs57921.2023.10063131

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…ANN is a computational model that draws inspiration from the design and operation of the human brain [31]. It has also been previously applied to boost wind energy conversion systems' efficiency [32] and power tracking purposes [33]. ANN is a machine learning algorithm that can be taught to spot data patterns and generate predictions using those patterns.…”

Section: Cost Assessment and Optimization Using Annmentioning

confidence: 99%

Cost-Effective Hybrid Wind-Photovoltaic Generation System for Isolated Critical Loads: A Case Study

Chatterjee,

Banerjee

2023

J. Electron. Electric. Eng.

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This study provides a technical and economic analysis of integrating a microgrid structure for power generation with wind energy and photovoltaic (PV) generation. The study mainly focuses on providing optimal generation from renewable energy sources for supplying isolated critical loads with optimized cost. Using HOMER software, a financial study of the proposed wind-PV-based microgrid is presented. A model is built with artificial neural network (ANN) to further tune the simulated economic optimization results obtained for further economic optimization and validation analysis. A reduction of about 22% in cost of energy is obtained from the ANN based optimization of system cost. This indicates that the proposed hybrid generation system is a profitable one serving localized loads in isolation with 100% renewable energy utilization. The proposed system can modelled and established in an onshore area in eastern India with optimized utilization of renewable sources for generation with minimal cost as observed.

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Section: Cost Assessment and Optimization Using Annmentioning

confidence: 99%

Cost-Effective Hybrid Wind-Photovoltaic Generation System for Isolated Critical Loads: A Case Study

Chatterjee,

Banerjee

2023

J. Electron. Electric. Eng.

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Grid-secluded Induction Generator with ANN and Intreval Type-2 Fuzzy based Controller for Wind Power Generation with Smart Load Control

Chatterjee¹,

Banerjee

2023

Preprint

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Three-phase Induction generators are widely used to extract power from wind both in grid-connected and isolated conditions. This paper proposes an induction generator for standalone operation which is suitable for microgeneration schemes in remote and grid inaccessible areas as a means of extracting electric power from wind. An inverter connected across the generator acts as a source of variable excitation and regulates the load voltage during changing loads or low wind speed conditions. The obtained power is converted to DC and the same is again fed to loads via a three-phase inverter run at fixed frequency. Optimal power generation is ensured using an artificial neural network (ANN) and an interval Type-2 Fuzzy inference system enabled maximum power point tracking (MPPT) based controller. A smart load controller is also proposed based on ANN which can also isolate loads with incipient faults. The novelty of the scheme lies in the ease of implementation, proposal of a new MPPT strategy with smart load control. Appropriate simulation and experimental results validate the proposed strategy along with suitable comparisons.

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Grid-secluded Induction Generator with ANN and Interval Type-2 Fuzzy based Controller for Wind Power Generation with Smart Load Control

Chatterjee¹,

Banerjee

2023

Preprint

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Three-phase Induction generators are widely used to extract power from wind both in grid-connected and isolated conditions. This paper proposes an induction generator-based generation scheme with innovative power point tracking capability for standalone operation. The scheme is suitable for use in remote and grid inaccessible areas as a means of extracting electric power from wind. An inverter connected across the generator acts as a source of variable excitation and regulates the load voltage during changing loads or low wind speed conditions. The obtained power is converted to DC and the same is again fed to loads via a three-phase inverter run at a fixed frequency. Optimal power generation is ensured using an artificial neural network (ANN) and an interval Type-2 Fuzzy inference system enabled maximum power point tracking (MPPT)-based controller. For the wind turbine system, ANN is used to estimate the maximum output voltage value and interval type-2 fuzzy logic is used for the generation of optimum duty cycle for pulses of the converter. A smart load controller is correspondingly proposed based on ANN which can consistently isolate loads with incipient faults. The novelty of the scheme lies in the ease of implementation, proposal of a new MPPT strategy with smart load control. Appropriate simulation and experimental results validate the proposed strategy, along with suitable comparisons.

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Performance improvement of SEIG based WECS using Artificial Neural Network

Cited by 3 publications

References 6 publications

Cost-Effective Hybrid Wind-Photovoltaic Generation System for Isolated Critical Loads: A Case Study

Cost-Effective Hybrid Wind-Photovoltaic Generation System for Isolated Critical Loads: A Case Study

Grid-secluded Induction Generator with ANN and Intreval Type-2 Fuzzy based Controller for Wind Power Generation with Smart Load Control

Grid-secluded Induction Generator with ANN and Interval Type-2 Fuzzy based Controller for Wind Power Generation with Smart Load Control

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