Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm

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

doi:10.3390/en11113108

Cited by 13 publications

(14 citation statements)

References 31 publications

(52 reference statements)

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“…Further works will comprise designing a T-S fuzzy model suitable for industrial control systems. Later on, the LSSVM method was proposed on the basis of IHBSA, and it was used for model identification of a pumped turbine governing system [96]. The model was evaluated on two benchmark functions and an application for the pumped turbine governing system.…”

Section: Bsancsmentioning

confidence: 99%

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Hassan

Rashid

2020

Applied Mathematics and Computation

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Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems.This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are twofold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and meta-analysis guide for the scholars who are working on improving BSA.In artificial intelligence (AI), besides the existing traditional symbolic and statistical methods [1], computational intelligence (CI) has emerged as a recent research area [2]. According to [3], CI deals with collections of nature-inspired algorithms and approaches that in turn have been used to deal with complex practical problems that cannot be feasibly or effectively solved by traditional methods. CI comprises three subfields: fuzzy logic, evolutionary computation, and neural network.The concept of swarm intelligence (SI) is a part of evolutionary computation. It deals with artificial and natural systems that comprise several individuals and possess the ability of self-organisation and decentralised control.This concept, which was primarily initiated by Gerardo Beni and Jing Wang in 1989 in the context of cellular robotic systems, is used in AI [4]. Typically, the SI system comprises a collection of agents. The agents interact with their own environment or with each other. SI syst...

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Section: Bsancsmentioning

confidence: 99%

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Hassan

Rashid

2020

Applied Mathematics and Computation

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“…Regression. Suykens et al proposed a weighted LSSVM model based on the standard LSSVM model, which can eliminate the influence of outliers in the training sample on the predictive performance and guarantee good generalization performance [20][21][22]. e optimization problem can be rewritten as…”

Section: Weighted Least Squares Support Vector Machinementioning

confidence: 99%

“…However, LSSVM solutions have some potential drawbacks, including the sparseness being lost and the use of a sum squared error cost function that might obtain less robustness. Suykens applied a weighted version of the LSSVM to obtain robust estimates for regression [20], inspired by the application of a sparse robust least squares support vector machine model in the model identification of a pumped turbine governing system [21]. Meanwhile, because the dam prototype monitoring data are easily affected by outliers, the accuracy and reliability of the dam deformation prediction model are directly affected.…”

Section: Introductionmentioning

confidence: 99%

“…End for(14) Construct the LSSVM model and evaluate the fitness values of all the ants;(15) Update all the position of the antlions using equation(40);(16) Update the elite antlion if an antlion becomes fitter than the elite; (17) End while(18) Return elite antlion, namely, the optimal parameter combination (c best , σ best ).ALGORITHM 1: Parameter optimization for the LSSVM model using the MALO algorithm.Construct the normalized training set and test set by equation(49)MALO algorithm optimizes (γ, σ) for LSSVM model Construct the data sets of the dam deformation time series, and divide the data set into a training set and a test setMALO algorithm optimizes u for adaptive weight functionInitialize the parameter (Vemin, T max , t = 1), construct the LSSVM model in equation(21) Calculate the initial fitting errors ζ i 0 and the initial weight values v i 0 in equation(26) Construct the WLSSVM model in equation(25), t = t + 1Calculate the fitting errors ζ i t and update the weight values v i t in equation(26) Meet the criteria in equation(50) Construct the WLSSVM model for dam deformation prediction based on the final weight values v i T Antinormalization of the output data in equation (51) Estimate the prediction accuracy in equation ((52)-(55)) No Yes…”

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confidence: 99%

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An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

Chen

Shao

et al. 2020

Mathematical Problems in Engineering

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A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

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“…Pumped storage units (PSUs) have played an important role in maintaining the balance of power supply and demand because of their fast start-up and shutdown speed, flexible working condition conversion, excellent peak-load regulation, and frequency regulation ability [1][2][3]. Pump turbine governing system is the core control system of the pumped storage power station which is responsible for stabilizing the unit frequency and regulating the unit power [4,5]. Due to the huge flow inertia of the long-distance water pipeline and the existence of the unstable "S" characteristic area, the optimal control of PTGS is highly complex [6].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Tian

et al. 2019

Complexity

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In order to make the pump turbine governing system (PTGS) adaptable to the change of working conditions and suppress the frequency oscillation caused by the “S” characteristic area running at middle or low working water heads, the traditional single-objective optimization for fractional-order PID (FOPID) controller under single working conditions is extended to a multiobjective framework in this study. To establish the multiobjective FOPID controller optimization (MO-FOPID) problem under multiworking conditions, the integral of the time multiplied absolute error (ITAE) index of PTGS running at low and high working water heads is adopted as objective functions. An improved nondominated sorting genetic algorithm III based on Latin hypercube sampling and chaos theory (LCNSGA-III) is proposed to solve the optimization problem. The Latin hypercube sampling is adopted to generate well-distributed initial population and take full of the feasible domain while the chaos theory is introduced to enhance the global search and local exploration ability of the NSGA-III algorithm. The experimental results on eight test functions and a real-world PTGS have shown that the proposed multiobjective framework can improve the Pumped storage units’ adaptability to changeable working conditions and the proposed LCNSGA-III algorithm is able to solve the MO-FOPID problem effectively.

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Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm

Cited by 13 publications

References 31 publications

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

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

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