An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems

Liu, Yichen; Ćetenović, Dragan; Li, Haiyu; Gryazina, Elena; Terzija, Vladimir

doi:10.1016/j.ijepes.2021.107764

Cited by 64 publications

(24 citation statements)

References 45 publications

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“…From the perspective of communication delay, the neural network algorithm is used for large-scale scheduling calculation of distributed behavior recognition, and the dynamic and random cluster analysis [ 10 ] is used to prove that the algorithm has high accuracy. (3) Compared with classical theories, such as Bayesian theory, grey theory, chaos theory, and rough set, the improved neural network method has strong time-series sensitivity to data [ 11 ]. The above research is based on this, but neural network algorithm's calculation accuracy, search time, and overall convergence are still not ideal.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition. Firstly, genetic algorithm is used to cluster the redundant data, so that the data are in fragment order, and then it is used to reduce the data redundancy of different behaviors and weaken the influence of dimension on behavior recognition. Then, the genetic algorithm clusters the data to form subgenetic particles with different dimensions and carries out coevolution and optimal location sharing for subgenetic particles with different dimensions. Through simulation test, the algorithm constructed in this paper is better than genetic algorithm and neural network algorithm in terms of calculation accuracy and convergence speed. Finally, the prediction model is constructed by setting the initial value and threshold to predict the behavior recognition, and the results show that the accuracy of the model constructed in this paper is improved in the analysis of behavior recognition.

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Section: Introductionmentioning

confidence: 99%

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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“…In the literature, genetic algorithm is preferred for solving many problems. Some of those; In solving the multi-mode multi-objective problem [18], it is used as a solution sequencing problem [18], in the solution of the effect maximization problem in social networks [19], in the solution of the dualobjective routing problem in dynamic networks [21], in the solution of the multi-objective reactive power distribution strategy problem for wind energy integrated systems [20], shape optimization [23], biomedicine [24]. Genetic algorithm is frequently preferred in the feature selection process, especially in recent years [26][27][28][29][30]…”

Section: Genetic Algorithmmentioning

confidence: 99%

Machine Learning-Based Comparative Study For Heart Disease Prediction

Gullu

Akçayol

Barışçı

2022

Advances in Artificial Intelligence Research

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Heart disease is one of the most common causes of death globally. In this study, machine learning algorithms and models widely used in the literature to predict heart disease have been extensively compared, and a hybrid feature selection based on genetic algorithm and tabu search methods have been developed. The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module. The models have been tested using different datasets, and detailed comparisons and analysis were presented. The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

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“…The confidence interval (CI) width represents the certainty of probability prediction, which is also an important objective to consider. As one of the most popular multiobjective optimization algorithms, the non-dominated sorting genetic algorithm (NSGA-II) reduces the complexity of genetic algorithms with fast calculation and good convergence [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

Duan

Liu

Zhou

et al. 2022

Sensors

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The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

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An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems

Cited by 64 publications

References 45 publications

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

Machine Learning-Based Comparative Study For Heart Disease Prediction

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

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