An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy

Zhang, Yuxian; Xia, Qian; Huideng, Peng; Wang, Jianhui

doi:10.1155/2016/9891382

Cited by 4 publications

(9 citation statements)

References 15 publications

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“…where rij is the j-th attribute in the i-th rule, and ci is the fault label of the i-th rule. ij r′ ′ represents the attribute of the i-th rule after observation [23]. where r ij is the j-th attribute in the i-th rule, and c i is the fault label of the i-th rule.…”

Section: Multi-population Quantum Codingmentioning

confidence: 99%

“…The updating of individuals is carried out for optimizing the rule set S 1 . Considering the mining of fuzzy classification rules is a combination optimization problem, an updating strategy based on allele [23] is proposed to improve the accuracy of optimization and accelerate the convergence speed.…”

Section: Hybrid Updating Strategymentioning

confidence: 99%

“…The optimal population pop * and corresponding antecedents r ij * is recorded after each fitness evaluation, and compare the distances of r ij and r ij ' to r ij * , respectively. The allele corresponding to the shorter distance is defined as the "superior allele", and the other one as the "inferior allele" [23]. Each allele is updated according to the difference of relative superiority, then H ε gate [24] is utilized to update the probability amplitudes corresponding to each allele.…”

Section: Hybrid Updating Strategymentioning

confidence: 99%

“…Each allele is updated according to the difference of relative superiority, then H ε gate [24] is utilized to update the probability amplitudes corresponding to each allele. The above evolution process is referred to as the hybrid updating strategy [23]. The specific updating operation is as follows:…”

Section: Hybrid Updating Strategymentioning

confidence: 99%

“…The superior allele employs a reasonable choice of the initial step length and a dynamic adjustment of the search step length, thereby guiding the updating for the superior allele to search for the optimal solution. In the initial phase, the updating strategy is accelerated by the guiding of searching direction [23]:…”

Section: Hybrid Updating Strategymentioning

confidence: 99%

See 4 more Smart Citations

State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

2019

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Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In this paper, a data-driven method for fault identification of offshore WTs is presented. The main idea is to improve fault identification accuracy and facilitate the probabilistic sorting of possible faults with critical variables so as to provide abundant and reliable reference information for maintenance personnel. In the stage of state rule mining, representative initial rules are generated via the combination of a clustering algorithm and heuristic learning. Then, a multi-population quantum evolutionary algorithm is utilized to optimize the rule base. In the stage of fault identification, abnormal states are identified via a fuzzy rule-based classification system, and probabilistic fault sorting with critical variables is realized according to the fuzzy reasoning of state rules. Ten common sensor and actuator faults in 5 MW offshore WTs are taken to verify the feasibility and superiority of the proposed scheme. Experimental results demonstrate that the proposed method has higher identification accuracy than other identification methods and thus prove the feasibility of the proposed probabilistic fault analysis scheme. data mining technologies have made considerable progress [11]. Consequently, data-based methods have received increasing attention in the area of fault detection and location of WTs [12].In data-based research, substantial improvements have been proposed to improve the accuracy of fault identification. Chen et al. [13] proposed a fault prediction method for WTs using an adaptive neuro-fuzzy inference system with a priori knowledge. This method provides a fault instruction and allows the operator to determine the repair plan before the failure worsens. Hu et al. [14] proposed a fault diagnosis method that combines SVM and domain knowledge to improve the identification accuracy for faults in WTs. However, the aforementioned methods depend excessively on expert knowledge, and their identification results are simplistic. Wang et al. [15] proposed an FDI scheme based on a variable selection method using principal component analysis; in this scheme, the result of fault identification is determined by the signal contribution, but the method can only identify an abnormal signal rather than a fault type. Ruiz et al. [16] proposed a fault classification method for WTs that transforms the domain signals of WTs into two-dimensional matrices. Certain similar faults are merged for identification in the experiment, and the separation of similar faults is unwarrantable. Pashazadeh et al. [17] obtained the fault data of WTs from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) simulator and Simulink (in a MATLAB environment) and developed an FDI scheme on the basis of classifier fusion, in which four classifiers are implemented in parallel. However, this m...

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Section: Multi-population Quantum Codingmentioning

confidence: 99%

Section: Hybrid Updating Strategymentioning

confidence: 99%

Section: Hybrid Updating Strategymentioning

confidence: 99%

Section: Hybrid Updating Strategymentioning

confidence: 99%

Section: Hybrid Updating Strategymentioning

confidence: 99%

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State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

2019

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Parameters optimization of ANFIS using quantum-inspired evolutionary algorithm

Xia

Zhang

Awad

et al. 2017

2017 29th Chinese Control and Decision Conference (CCDC)

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Interval Prediction Method for Wind Speed Based on ARQEA Optimized by Beta Distribution and SWLSTM

et al. 2022

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The interval prediction of wind speed is crucial for the economic and safe operation of wind farms. To overcome the probability density function parameter optimization and long-term correlation of time series problems in an interval prediction method, a hybrid model based on the beta distribution of an allele real-coded quantum evolutionary algorithm (ARQEA) and a shared weight long short-term memory (SWLSTM) neural network is proposed for predicting the interval of short-term wind speed, which is beta–ARQEA–SWLSTM. Input variables are determined via autocorrelation functions, and the shape and position parameters in the beta distribution function are optimized by the ARQEA algorithm. An interval-divided multi-distribution function aggregation is proposed to deal with the fluctuation of wind speed series. Lastly, case studies are provided to demonstrate the effectiveness of the proposed method.

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An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy

Cited by 4 publications

References 15 publications

State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

Parameters optimization of ANFIS using quantum-inspired evolutionary algorithm

Interval Prediction Method for Wind Speed Based on ARQEA Optimized by Beta Distribution and SWLSTM

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