Field Current Waveform-Based Method for Estimation of Synchronous Generator Parameters Using Adaptive Black Widow Optimization Algorithm

Micev, Mihailo; Ćalasan, Martin; Petrović, Dragan; Ali, Ziad M.; Quynh, Nguyen Vu; Aleem, Shady H. E. Abdel

doi:10.1109/access.2020.3037510

Cited by 23 publications

(8 citation statements)

References 51 publications

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“…Furthermore, the short-circuit test has been presented in [6], considering field voltage variations. The authors in [7] and [8] identified the SG's parameters using simplified analytical waveforms of the field and armature shortcircuit current, respectively. In [9], the simplified expressions are substituted by the full field and armature current expressions, which do not neglect any parameter of the generator, and therefore it needs to be expressed by the inverse Laplace transformation.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

Micev

Ćalasan

Radulović

et al. 2023

IEEE Trans. Ind. Inf.

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This paper deals with black-box modeling of synchronous generators based on artificial neural networks (ANN). ANN is applied to define the relationship between the excitation and terminal generator voltage values, while the Levenberg-Marquardt algorithm is used for determining the ANN's weight coefficients. The relation is made based on generator response on reference voltage step changes. The proposed approach is checked using the experimental results obtained from the measurements on a real 120 MVA generator from hydroelectric power plant Piva in Montenegro. Further, a fair comparison with the nonlinear auto-regression model with the exogenous input (NARX) and Hammerstein-Wiener (H-W) model is made. For the validation, different experiments were conducteddifferent values of step disturbances, other controller parameters, and different rotating speeds. Based on the presented results, it can be noted that the proposed ANN model is very accurate and provides a very high degree of matching with the experimental results and outperforms the other considered nonlinear models. Furthermore, the proposed test procedure and model is easy to implement and do not require disconnection of the generator from the grid or additional equipment for experimental realization. Such obtained models can be used for different testing types related to the excitation system.

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Section: Related Workmentioning

confidence: 99%

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

Micev

Ćalasan

Radulović

et al. 2023

IEEE Trans. Ind. Inf.

Self Cite

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“…In the literature, there are several studies that use BWO for some research areas such as image segmentation [46], designing and modelling electronic elements [47], developing wind tribune emulator [48], estimation of synchronous generator parameters [49]. To the best of our knowledge, there is one study that uses BWO for discrete problems [50].…”

Section: A Black Widow Optimization Algorithm (Bwo)mentioning

confidence: 99%

Binary Black Widow Optimization Approach for Feature Selection

Keleş

Kilic

2022

IEEE Access

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Feature selection is a process of reduction of irrelevant, negligible, noisy features from data sets so as to obtain better performance measurements with fewer features. Throughout the literature, various methods are presented that use different approaches to get through this difficult problem, prevalently. In this study, a binary variant of the Black Widow Optimization (BWO) is proposed in a wrapper mode for the purpose of feature selection. The BWO algorithm has early convergence ability on continuous problems and that characteristic is also effective for finding an optimum solution in feature selection problem. The proposed approach compared with state-of-the-art and widely used approaches such as Binary Particle Swarm Optimization (BPSO and VPSO), Binary Grey Wolf Optimization (BGWO1 and BGWO2). The performance of these algorithms is assessed over 20 benchmark data sets from the UCI repository. The results show that the proposed binary method can be utilized effectively in discrete problems such as feature selection.

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“…Furthermore, BWO algorithm has the advantages of reliability and fast convergence speed, which promote its wide application in various fields. Micev et al (2020) proposed adaptive black widow optimization algorithm to estimate synchronous machine parameters. Mukilan and Semunigus (2021) used deep convolution neural network (DCNN) based on the enhanced BWO algorithm for human object detection.…”

Section: Introductionmentioning

confidence: 99%

An RBF neural network based on improved black widow optimization algorithm for classification and regression problems

Liu

Zhou²,

Zhou³

et al. 2023

Front. Neuroinform.

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IntroductionRegression and classification are two of the most fundamental and significant areas of machine learning.MethodsIn this paper, a radial basis function neural network (RBFNN) based on an improved black widow optimization algorithm (IBWO) has been developed, which is called the IBWO-RBF model. In order to enhance the generalization ability of the IBWO-RBF neural network, the algorithm is designed with nonlinear time-varying inertia weight.DiscussionSeveral classification and regression problems are utilized to verify the performance of the IBWO-RBF model. In the first stage, the proposed model is applied to UCI dataset classification, nonlinear function approximation, and nonlinear system identification; in the second stage, the model solves the practical problem of power load prediction.ResultsCompared with other existing models, the experiments show that the proposed IBWO-RBF model achieves both accuracy and parsimony in various classification and regression problems.

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Field Current Waveform-Based Method for Estimation of Synchronous Generator Parameters Using Adaptive Black Widow Optimization Algorithm

Cited by 23 publications

References 51 publications

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

Binary Black Widow Optimization Approach for Feature Selection

An RBF neural network based on improved black widow optimization algorithm for classification and regression problems

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