Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system

Abdalla, Ahmed N.; Nazir, Muhammad Shahzad; Jiang, Mingxin; Kadhem, Athraa Ali; Wahab, Noor Izzri Abdul; Cao, Su-Qun; Ji, Rendong

doi:10.1177/0144598720959749

Cited by 22 publications

(3 citation statements)

References 35 publications

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“…For example, the mean wind speed of no more than 5 m/s with the most frequent wind velocity of around 3 m/s is studied for wind energy application in Malaysia [39]. Considering the sunny climate in the region with minor precipitation, our result suggest that a hybrid power system [40,41] with both wind and solar energy can suits the Gulf coast (Qatar) environment.…”

Section: Site Location and View Of The Instrument Towermentioning

confidence: 94%

Diurnal Wind Pattern and Climate Condition on the Coastal Region of Qatar

Sadr

2021

JSRR

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Climate pattern in the Persian Gulf is of great interest due to the strategic geographical location of the gulf as a waterway of major oil transportation and its increasing regional economic importance. However, long term and continues climate observations in this region are rare, especially for high resolution data. High resolution wind pattern and climate conditions were measured at 3 heights on a 9 m tower on the shoreline north of Qatar (26.08 N, 51.36 E) from August 2015 to September 2016. In this work, the annual wind and climate patterns (wind velocity, temperature, relative humidity, and air pressure) are first presented. Drag coefficient, turbulent kinetic energy and sensible heat flux are calculated using the high speed measured data to explain the observed climate pattern. The results show the wind in the southern part of the gulf is dominant by a northwest stream with a diurnal average speed of 4.7 m/s. During the test year, the diurnal average temperature and relative humidity were 27°C and 70%, respectively. The drag coefficient is much higher for the wind from 270o-330o, corresponding to the wind coming mainly from northwest. The Turbulent Kinetic Energy (TKE) is strong during the daytime, especially around noon when the diurnal value is at its peak, and weak during the night. The result of this analysis may be used for better understanding of the local climate, allowing for further assessment of wind energy and pollution diffusion in the region.

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Section: Site Location and View Of The Instrument Towermentioning

confidence: 94%

Diurnal Wind Pattern and Climate Condition on the Coastal Region of Qatar

Sadr

2021

JSRR

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“…In actual engineering applications, reactive power compensation devices inevitably produce active power loss and operation and maintenance costs. In addition, when the reactive power resources of the distribution network are insufficient, we need to add reactive power compensation equipment, which generates additional investment [4]. Therefore, the system needs to comprehensively consider the minimum total investment of the reactive power compensation device while pursuing the minimum active power loss to establish the objective function:…”

Section: The Reactive Power Optimisation Modelmentioning

confidence: 99%

Distribution network monitoring and management system based on intelligent recognition and judgement

Xue

Sun

et al. 2021

Applied Mathematics and Nonlinear Sciences

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Based on the shortcomings of the current intelligent management of distribution networks, the article designs and implements a remote intelligent detection system for live distribution networks. The article constructs and realises an algorithm based on a genetic algorithm (GA) and agent system. The algorithm is applied to energy-saving intelligent supervision of equipment in the distribution network. The simulation experiment shows that the integrated algorithm based on GA and agent system can accurately detect power quality in real time. At the same time, the algorithm can monitor the energy consumption of equipment in the distribution network under multiple disturbances.

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“…Scientists worldwide have conducted numerous research efforts to realise wind power prediction by developing various models in recent years. Numerous researchers, e.g., Rahman et al [6], Ray et al [7], and Abdalla et al [8], have undertaken significant investigations to develop refined software models that are intended to predict power generation via the utilisation of RES. Some researchers have found that the amount of variable data affects wind power forecasting performance [9,10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wind Power with Machine Learning Models

Karaman

2023

Applied Sciences

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Wind power is a vital power grid component, and wind power forecasting represents a challenging task. In this study, a series of multiobjective predictive models were created utilising a range of cutting-edge machine learning (ML) methodologies, namely, artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks, and long short-term memory (LSTM) networks. In this study, two independent data sets were combined and used to predict wind power. The first data set contained internal values such as wind speed (m/s), wind direction (°), theoretical power (kW), and active power (kW). The second data set was external values that contained the meteorological data set, which can affect the wind power forecast. The k-nearest neighbours (kNN) algorithm completed the missing data in the data set. The results showed that the LSTM, RNN, CNN, and ANN algorithms were powerful in forecasting wind power. Furthermore, the performance of these models was evaluated by incorporating statistical indicators of performance deviation to demonstrate the efficacy of the employed methodology effectively. Moreover, the performance of these models was evaluated by incorporating statistical indicators of performance deviation, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) metrics to effectively demonstrate the efficacy of the employed methodology. When the metrics are examined, it can be said that ANN, RNN, CNN, and LSTM methods effectively forecast wind power. However, it can be said that the LSTM model is more successful in estimating the wind power with an R2 value of 0.9574, MAE of 0.0209, MSE of 0.0038, and RMSE of 0.0614.

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Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system

Cited by 22 publications

References 35 publications

Diurnal Wind Pattern and Climate Condition on the Coastal Region of Qatar

Diurnal Wind Pattern and Climate Condition on the Coastal Region of Qatar

Distribution network monitoring and management system based on intelligent recognition and judgement

Prediction of Wind Power with Machine Learning Models

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