Improved security and stability of grid connected the wind energy conversion system by unified power flow controller

Wartana, I Made; Agustini, Ni Putu; Sreedharan, Sasidharan

doi:10.11591/ijeecs.v27.i3.pp1151-1161

Cited by 3 publications

(6 citation statements)

References 33 publications

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“…However, a well-generalized model should achieve the best solution despite the empirical risks and structural risks, which in turn constitute the real danger of prediction by learning statistics theory. By presenting a weighting factor 𝛾 for the empirical risk, which is depicted by the sum of the squares of the errors i.e., ‖𝜀‖ 2 , their proportions can be regularized, and the structural risk can be depicted by ‖𝛽‖ 2 which is a value to maximize the distance to the edge disconnecting between boundary categories. In addition, to obtain a robust estimate that attenuates the anomalous interferences, the error 𝜀 𝑖 is weighted by the variable 𝑣 𝑖 .…”

Section: Regularized Extreme Learning Machinementioning

confidence: 99%

“…In addition, to obtain a robust estimate that attenuates the anomalous interferences, the error 𝜀 𝑖 is weighted by the variable 𝑣 𝑖 . Thus, ‖𝜀‖ 2 is prolonged to ‖𝐷 𝜀 ‖ 2 , where 𝐷 = 𝑑𝑖𝑎𝑔(𝑣 − 𝑡 𝑖 = 𝜀 𝑖 (6)…”

Section: Regularized Extreme Learning Machinementioning

confidence: 99%

“…In recent years, the environmental problems caused by climate change and traditional fossil fuels have become increasingly serious. Using sustainable renewable energy (RE) sources guards against environmental deterioration and shields the atmosphere from the hazards and disruptions of nuclear power [1], [2]. The fact that RE is powered by renewable resources such as the sun, wind, and water results in lower costs.…”

Section: Introductionmentioning

confidence: 99%

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Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Mansoury,

El Bourakadi,

Yahyaouy

et al. 2024

Bulletin EEI

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Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.

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Section: Regularized Extreme Learning Machinementioning

confidence: 99%

Section: Regularized Extreme Learning Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Mansoury,

El Bourakadi,

Yahyaouy

et al. 2024

Bulletin EEI

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show abstract

“…However, the magnetization of an induction generator requires reactive power. The useful power generated due to wind-related variations can have a considerable impact on the device's reactive power absorption and terminal voltage [5], [6], [7]. When wind installations are integrated into the grid, power quality concerns need to be addressed [9].…”

Section: Introductionmentioning

confidence: 99%

An Initiative Towards Efficient and Sustainable V2G Technology Fed STATCOM for Grid connected Wind Energy System

S. Rudhra

2024

jes

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The proliferation of renewable energy sources, particularly wind energy, has significantly contributed to the diversification of the global energy mix and the mitigation of greenhouse gas emissions. However, the intermittent nature of wind power generation poses challenges to the stability and reliability of the power grid. This paper explores the integration of Vehicle-to-Grid (V2G) technology with Static Synchronous Compensator (STATCOM) for grid-connected wind energy systems. Novelty. Vehicle-to-Grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and the power grid, allowing EV batteries to serve as energy storage devices. This capability can be leveraged to support grid stability and reliability by utilizing EV batteries to store surplus wind energy during periods of high generation and release it back to the grid during periods of low generation or high demand. Purpose. This effectively mitigates the intermittency and variability of wind power, smoothing out fluctuations and improving grid stability. Methods. By employing V2G technology and leveraging the dual-purpose battery solution can compensate the grid voltage fluctuations and improve power factors in grid-connected wind energy systems, enhancing overall system stability and reliability. Results. The Hystersis current controller (HCC) scheme is used for controlling STATCOM to enhance the dynamic performance. Practical value. The system is simulated in its entirety using the matrix laboratory (MATLAB) . The proposed sustainable model will pave the way for maintaining the true power supply under varying wind conditions.

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“…To increase system performance, RES must be integrated into the network on a broad scale. A UPFC is connected at voltage point of mutual pairing in order to improve WECS' dynamic performance and system's transfer capacity [34]. Generally, power systems operate under varying and often unpredictable conditions.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

2023

IJATEE

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Power flow optimization is a critical aspect of power and control system design, aiming to minimize running costs while ensuring control variable tolerances and maximizing power delivery. The utilization of flexible alternating current transmission system (FACTS) particularly unified power flow controllers (UPFCs), enhances a system's power transfer capacity. UPFCs offer versatile adjustments for active and reactive power lines and voltage levels simultaneously, contributing to improved system performance and stability. A novel approach for enhancing flexibility and control in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) was proposed. The technique involved the integration of a fuzzy-based control strategy for UPFC and the utilization of a cascaded adaptive neuro fuzzy inference system (ANFIS) to manage UPFC's series and shunt converters. Additionally, an improved butterfly optimization algorithm (IBOA) was introduced to ensure stability for DFIG-WECS. The methods were implemented and validated through simulations using MATLAB software. The effectiveness of the proposed approach was demonstrated through experimental results. The integration of cascaded ANFIS-based UPFC control and IBOA with WECS resulted in increased system power quality and stability. The total harmonic distortion (THD) of the system is reduced by 1.8%, indicating an enhancement in power quality. The application of UPFC and innovative control methodologies results in optimal power flow (OPF), maintaining stringent control variable tolerances in power systems. This research presents a systematic approach to enhancing flexibility and control in a DFIG-based WECS through the innovative use of UPFC and advanced control techniques. The proposed fuzzy-based UPFC control, in conjunction IBOA, significantly improves power quality and stability, thereby enhancing the overall efficiency of the system.

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Improved security and stability of grid connected the wind energy conversion system by unified power flow controller

Cited by 3 publications

References 33 publications

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

An Initiative Towards Efficient and Sustainable V2G Technology Fed STATCOM for Grid connected Wind Energy System

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

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