Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network

Ibrahim, Yousry; Kamel, Salah; Rashad, Ahmed; Nasrat, Loai; Jurado, Francisco

doi:10.9781/ijimai.2019.05.001

Cited by 23 publications

(13 citation statements)

References 15 publications

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“…Ibrahim et al [14] show in their study that the medical statements are full of ambiguities. Author have gone through a database of patient queries on medical websites are full of ambiguity and therefore the answers returned may not contain the desired information so.…”

Section: Related Workmentioning

confidence: 94%

Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods

Madaan

Goyal

2020

IEEE Access

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Human Body constitution (prakriti) defines what is in harmony with human nature and what will cause to move out of balance and experience illness. Tridosha defines the three basic energies or principles that determine the function of our body on the physical and emotional levels. The three energies are known as VATT, PITT and KAPH. Each individual has a unique balance of all three of these energies. Some people will be predominant in one, while others will be a mixture of two or more. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags behind. A careful and appropriate analysis leads to an effective treatment. To collect a meaningful data set, a questionnaire with 28 different characteristics is validated by Ayurveda experts. Authors calculate Cronbach alpha of VATT-Dosha, PITT-Dosha and KAPH-Dosha as 0.94, 0.98 and 0.98, respectively to check the reliability of the questionnaire. Authors analyzed questionnaires of 807 healthy persons aged 20-60 years and found 62.1% men and 37.9% women. The class imbalance problem is resolved with oversampling and the equally distributed data set of randomly selected 405 persons is used for the actual experiment. Using computer algorithms, we randomly divide the data set (8:2) into a training set of 324 persons and a test data set of 81 persons. Model is trained using traditional machine learning techniques for classification analysis as Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT). System is also implemented using ensemble of several machine learning methods for constitution recognition. Evaluation measures of classification such as root mean square error (RMSE), precision, recall, F-score, and accuracy is calculated and analyzed. On analyzing the results authors find that the data is best trained and tested with CatBoost, which is tuned with hyper parameters and achieves 0.96 precision, 0.95 recall, 0.95 F-score and 0.95 accuracy rate. The experimental result shows that the proposed model based on ensemble learning methods clearly surpasses conventional methods. The results conclude that advances in boosting algorithms could give machine learning a leading future. INDEX TERMS Ayurveda, human body constituents, hyper parameter tuning, KAPH, optimized training model, PITT, VATT.

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Section: Related Workmentioning

confidence: 94%

Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods

Madaan

Goyal

2020

IEEE Access

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“…Enhancing the transient stability of SCIG-WF by improving the control system of STAT-COM was introduced in [6]. The artificial neural network (ANN) was proposed to find the optimal value of STATCOM controller in [7]. The optimal place of integrated unified power quality conditioner (i-UPQC) was investigated in [8] to improve the power quality of distribution system interconnected to SCIG-WF.…”

Section: Literature Surveymentioning

confidence: 99%

Optimal design of SSSC and crowbar parameters for performance enhancement of Egyptian Zafrana wind farm

et al. 2021

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Zafarana Wind Park is considered the largest wind farm in Middle East. By the year 2022, Zafarana wind farm will inject 545 MW to Egypt national electric network (ENEN). This wind farm has been connecting to ENEN in stages (eight stages) from ten years ago. Each stage represents a project of wind farm. Hence, the impact of the emergency condition of ENEN on the performance of the installed stage of Zafarana Wind Park should be studied. Moreover, determining the possible method to reduce the side effects of the emergency condition of ENEN on the installed stage of Zafarana Wind Park becomes an important issue. In this paper, a combination between Static Synchronous Series Compensator (SSSC) and crowbar (Cro-SSSC) is used to enhance the performance of the one installed stage of Zafarana Wind Park. The studied stage of Zafarana Wind Park is Zafarana Z1 wind farm (Z1). Zafarana Z1 wind farm represents that first stage of Zafarana Wind Park that was connected to ENEN. Hence, the paper takes Zafarana Z1 wind farm as the studied case to examine the impact of Cro-SSSC. The crowbar is used to protect the power components of the SSSC from the high current during grid fault condition. Also, in this paper multi-objective lightning attachment procedure optimization algorithm (LAPO) and the sine cosine algorithm (SCA) are used to evaluate and tune the control gains of SSSC and the value of crowbar's resistance. This step is important in order to determine the values of the control's gains of SSSC and the value of crowbar resistance which suit the studied system (one installed stage of Zafarana Wind Park). The sliding mode control (SMC) is used as an example of nonlinear control of SSSC. Also, a hybrid of LAPO and SMC is used to improve the performance of Zafarana Z1 during faults. The results of LAPO, SCA and SMC are compared during three-phase fault and single line to ground fault applied to the studied system.

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“…The second one of Ibrahim et al [12] proposes the use of artificial neural networks to improve the performance of static synchronous series compensators (SSSC) integrated into combined wind farms (CWF). Their results illustrate that the performance of CWF can be improved using SSSC adjusted by a neural network, when they compare with CWF with ordinary SSSC and CWF with SSSC tuned by a multiobjective genetic algorithm.…”

Section: Schrepp and Thomaschewskimentioning

confidence: 99%

Editor’s Note

Crespo¹,

Verdú²

2019

IJIMAI

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Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network

Cited by 23 publications

References 15 publications

Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods

Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods

Optimal design of SSSC and crowbar parameters for performance enhancement of Egyptian Zafrana wind farm

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