Multiobjective Optimization of a Fractional‐Order PID Controller for Pumped Turbine Governing System Using an Improved NSGA‐III Algorithm under Multiworking Conditions

Zhang, Chu; Tian, Peng; Li, Chaoshun; Fu, Wenlong; Xia, Xin; Xue, Xiaoming

doi:10.1155/2019/5826873

Cited by 29 publications

(23 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the proposed IHGWOSCA algorithm could be optimized with better strategies for further enhancing the convergence speed, as well as the global search capability. Furthermore, multi-objective optimization that has been widely utilized in the field of controlling [48] could be implemented in wind speed forecasting, which could contribute to enhancing the performance of the models [49]. Therefore, several perspectives for further investigation directions could be summarized as: (1) the combination of multiple forecasting models would be the focus of our future works, (2) for the proposed IHGWOSCA algorithm, some strategies that could contribute to a jump out of the local optimum could be employed, (3) multi-objective optimization implemented by various algorithms will be investigated in wind speed forecasting in our future studies.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

Wang

Zhou

et al. 2019

Sustainability

Self Cite

View full text Add to dashboard Cite

Accurate wind speed prediction plays a significant role in reasonable scheduling and the safe operation of the power system. However, due to the non-linear and non-stationary traits of the wind speed time series, the construction of an accuracy forecasting model is difficult to achieve. To this end, a novel synchronous optimization strategy-based hybrid model combining multi-scale dominant ingredient chaotic analysis and a kernel extreme learning machine (KELM) is proposed, for which the multi-scale dominant ingredient chaotic analysis integrates variational mode decomposition (VMD), singular spectrum analysis (SSA) and phase-space reconstruction (PSR). For such a hybrid structure, the parameters in VMD, SSA, PSR and KELM that would affect the predictive performance are optimized by the proposed improved hybrid grey wolf optimizer-sine cosine algorithm (IHGWOSCA) synchronously. To begin with, VMD is employed to decompose the raw wind speed data into a set of sub-series with various frequency scales. Later, the extraction of dominant and residuary ingredients for each sub-series is implemented by SSA, after which, all of the residuary ingredients are accumulated with the residual of VMD, to generate an additional forecasting component. Subsequently, the inputs and outputs of KELM for each component are deduced by PSR, with which the forecasting model could be constructed. Finally, the ultimate forecasting values of the raw wind speed are calculated by accumulating the predicted results of all the components. Additionally, four datasets from Sotavento Galicia (SG) wind farm have been selected, to achieve the performance assessment of the proposed model. Furthermore, six relevant models are carried out for comparative analysis. The results illustrate that the proposed hybrid framework, VMD-SSA-PSR-KELM could achieve a better performance compared with other combined models, while the proposed synchronous parameter optimization strategy-based model could achieve an average improvement of 25% compared to the separated optimized VMD-SSA-PSR-KELM model.

show abstract

Section: Discussionmentioning

confidence: 99%

A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

Wang

Zhou

et al. 2019

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, the proposed MSCAPSO algorithm can be optimized through better strategies to further improve the convergence speed and global search ability. In addition, the multi-objective optimization that has been widely utilized in the field of controlling [44][45][46][47] could be implemented in fault diagnosis, which is expected to improve the accuracy and reduce the variance of the outputs of the model [48]. Moreover, although the diagnosis experiments considered various load conditions, fault sizes, and locations, the signal to noise ratio of the CWRU bearing data is relatively high, and the conclusions obtained in the experiment may not be very comprehensive.…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Tan

et al. 2019

Entropy

Self Cite

View full text Add to dashboard Cite

Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.

show abstract

“…Additionally, the parameters of the approaches mentioned above can greatly affect the performance of the combined model. In view of this, various optimization algorithms based on different strategies have been focused for parameters optimization, such as genetic algorithm (GA) [29], region search evolutionary algorithm (RSEA) [30], artificial sheep algorithm (ASA) [31,32], and grey wolf optimizer (GWO) [33]. To achieve better balance between convergence speed and accuracy, an adaptive mutation grey wolf optimizer (AMGWO) algorithm is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Vibration Tendency Prediction Approach for Hydropower Generator Fused with Multiscale Dominant Ingredient Chaotic Analysis, Adaptive Mutation Grey Wolf Optimizer, and KELM

Wang

Tan

et al. 2020

Complexity

Self Cite

View full text Add to dashboard Cite

Accurate vibrational tendency forecasting of hydropower generator unit (HGU) is of great significance to guarantee the safe and economic operation of hydropower station. For this purpose, a novel hybrid approach combined with multiscale dominant ingredient chaotic analysis, kernel extreme learning machine (KELM), and adaptive mutation grey wolf optimizer (AMGWO) is proposed. Among the methods, variational mode decomposition (VMD), phase space reconstruction (PSR), and singular spectrum analysis (SSA) are suitably integrated into the proposed analysis strategy. First of all, VMD is employed to decompose the monitored vibrational signal into several subseries with various frequency scales. Then, SSA is applied to divide each decomposed subseries into dominant and residuary ingredients, after which an additional forecasting component is calculated by integrating the residual of VMD with all the residuary ingredients orderly. Subsequently, the proposed AMGWO is implemented to simultaneously adapt the intrinsic parameters in PSR and KELM for all the forecasting components. Ultimately, the prediction results of the raw vibration signal are obtained by assembling the results of all the predicted prediction components. Furthermore, six relevant contrastive models are adopted to verify the feasibility and availability of the modified strategies employed in the proposed model. The experimental results illustrate that (1) VMD plays a positive role for the prediction accuracy promotion; (2) the proposed dominant ingredient chaotic analysis based on the realization of time-frequency decomposition can further enhance the capability of the forecasting model; and (3) the appropriate parameters for each forecasting component can be optimized by the proposed AMGWO effectively, which can contribute to elevating the forecasting performance distinctly.

show abstract

Multiobjective Optimization of a Fractional‐Order PID Controller for Pumped Turbine Governing System Using an Improved NSGA‐III Algorithm under Multiworking Conditions

Cited by 29 publications

References 46 publications

A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Vibration Tendency Prediction Approach for Hydropower Generator Fused with Multiscale Dominant Ingredient Chaotic Analysis, Adaptive Mutation Grey Wolf Optimizer, and KELM

Contact Info

Product

Resources

About