Maximization of instantaneous wind penetration using particle swarm optimization

Sreedharan, Sasidharan; Ongsakul, Weerakorn; Singh, Jaspreet

doi:10.4314/ijest.v2i5.60099

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…However, the selected connection bus should be the number 3 due to reactive losses can be compensated. This result agrees with the obtained in [9].…”

Section: A Case Study: Modified 14-bus Test Systemsupporting

confidence: 96%

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Methodology for optimal bus placement to integrate wind farm optimizing power flows

Molina-Moreno

Medína

Cisneros-Magana

2015

2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

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This paper proposes an alternative algorithm to locate the optimal bus placement to integrate wind farms based on the criterion of minimizing the electrical losses using the solution of the power flow problem to identify the best connection point. The wind farm is modeled as a negative load being possible to connect this power generation to the possible connection buses and obtaining the power flow solution for each different connection point as a generation change or displacement. The IEEE 14, 30 and 57 bus test systems have been used to validate the proposed methodology. The results have shown the optimal bus among a possible set of them that causes minimum losses when a wind farm is integrated to it.Keywords-losses; negative load; point of common coupling; power flow; wind farm integration; wind power generator.

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“…However, the selected connection bus should be the number 3 due to reactive losses can be compensated. This result agrees with the obtained in [9].…”

Section: A Case Study: Modified 14-bus Test Systemsupporting

confidence: 96%

“…Reference [9] presented a methodology to obtain the maximum instantaneous wind penetration by the optimal grid control parameters using particle swarm optimization. The methodology consists on placing the DFIG based wind farm at suitable location and utilizing a suitable algorithm to enable maximum grid penetration.…”

Section: Introductionmentioning

confidence: 99%

Methodology for optimal bus placement to integrate wind farm optimizing power flows

Molina-Moreno

Medína

Cisneros-Magana

2015

2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

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“…This methodology comprises two stages, in which the particle swarm optimization (PSO) algorithm is implemented in the first stage for maximizing the penetration of the renewable resources such as wind and solar by keeping all parameters based on the grid requirements. Then, the small signal stability of the system is optimized in the second stage with maximum renewable energy penetration in which the best location is identified by using WFPI for connecting the wind farm (Sreedharan et al 2010), and the solar generation is fixed by considering the limiting values of voltage and bus load absorption capability. These two stages are applied on an IEEE 14-bus system and Kerala grid with solar and wind power.…”

Section: Proposed Methodsmentioning

confidence: 99%

An enhanced particle swarm optimization algorithm for improving the renewable energy penetration and small signal stability in power system

Renuka¹,

Reji²,

Sreedharan³

2018

Renewables

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In power systems, increasing the renewable energy penetration with small signal stability is one of the demanding and critical tasks in recent days. This research work aims to develop a multistage optimization technique, namely particle swarm optimization (PSO), for improving both the energy penetration and small signal stability. Here, the wind and solar power sources are considered, and its penetration is maximized by satisfying the grid requirements such as the bus voltage, line flows, and real and reactive power generation within the limit. This work includes two stages: in the first stage, PSO algorithm is implemented for maximizing the renewable energy penetration to the test systems. Then, in the second stage, the small signal stability of the systems is improved with maximum renewable energy penetration in which the best locations for connecting the wind farm are identified by using the calculation of wind farm placement index and solar generation is fixed by considering voltage and bus load absorption capability. During simulation, the proposed method is tested and validated by using IEEE 14-bus standard system, and the 220 kV Kerala (India) grid practical system with the solar and wind power. Moreover, various measures such as power generation, load and bus voltage are evaluated for two different case studies. In this evaluation, it is proved that the renewable energy sources are safely integrated with the power system with increased energy penetration and improved small signal stability. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…The RNN output is given to the inverter current controller. Here, the input layer to the hidden layer weights are specified as (w 11 ,w 22 ,…,w 1n and w 21 ,w 22 ,…,w 2n ). The arbitrary weights of recurrent layer and the output layer neurons are generated at the specified interval [w min , w max ].…”

Section: Process For Recurrent Neural Network Trainingmentioning

confidence: 99%

Maximum Power Point Tracking for Wind Power Generation System at Variable Wind Speed using a Hybrid Technique

Vijayalakshmi¹,

Ganapathy²,

Vijayakumar³

et al. 2015

IJCA

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Maximization of instantaneous wind penetration using particle swarm optimization

Cited by 12 publications

References 13 publications

Methodology for optimal bus placement to integrate wind farm optimizing power flows

Methodology for optimal bus placement to integrate wind farm optimizing power flows

An enhanced particle swarm optimization algorithm for improving the renewable energy penetration and small signal stability in power system

Maximum Power Point Tracking for Wind Power Generation System at Variable Wind Speed using a Hybrid Technique

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