Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit

Zheng, Yuan; Zhou, Jianzhong; Zhu, Weijie; Zhang, Chu; Li, Chaoshun; Fu, Wenlong

doi:10.1016/j.neucom.2016.05.007

Cited by 30 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…us, the corresponding online optimization computation increases exponentially. In order to limit the dimension of decision variables in optimization problems, the idea of stair-like control [38][39][40] is introduced into model predictive control. By constraining the increment of each step, the trend of change is stair-like.…”

Section: Asa-based Prior-knowledge Learningmentioning

confidence: 99%

Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor

Feng

et al. 2021

Complexity

Self Cite

View full text Add to dashboard Cite

With renewable energy (RE) being increasingly connected to power grids, pumped storage plants (PSPs) play a very important role in restraining the fluctuation of power grids. However, conventional control strategy could not adapt well to the different control tasks. This paper proposes an intelligent nonlinear model predictive control (NMPC) strategy, in which hydraulic-mechanical and electrical subsystems are combined in a synchronous control framework. A newly proposed online sequential extreme learning machine algorithm with forgetting factor (named WOS-ELM) is introduced to learn the dynamic behaviors of the coupling system. Specifically, the initial learning parameters are optimized by prior-knowledge learning and a new self-adaptive adjustment strategy is also put forward. Subsequently, the stair-like control strategy and artificial sheep algorithm (ASA) are used in rolling the optimization mechanism to replace the existing complex differential geometric solutions. Comparative experiments are carried out under different working conditions based on a PSP in China. The results show that the influence from coupling factors can be considerable and the proposed MPC strategy indicates superiority in voltage and load adjustment as well as the frequency oscillation suppression.

show abstract

Section: Asa-based Prior-knowledge Learningmentioning

confidence: 99%

Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor

Feng

et al. 2021

Complexity

Self Cite

View full text Add to dashboard Cite

show abstract

“…The role of hydropower stations in the frequency control and peak regulation of the power grid has become increasingly important. Thus, the security and stable operation of the hydropower generator unit (HGU) is crucial to the stability of the power system [1,2]. In practical engineering, the vibration signal is a typical indicator for evaluating the health status and stability of the HGU [3].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Objective Optimization Model for Vibration Tendency Prediction of Hydropower Generators

Zhang

Shan

et al. 2019

Sensors

View full text Add to dashboard Cite

The hydropower generator unit (HGU) is a vital piece of equipment for frequency and peaking modulation in the power grid. Its vibration signal contains a wealth of information and status characteristics. Therefore, it is important to predict the vibration tendency of HGUs using collected real-time data, and achieve predictive maintenance as well. In previous studies, most prediction methods have only focused on enhancing the stability or accuracy. However, it is insufficient to consider only one criterion (stability or accuracy) in vibration tendency prediction. In this paper, an intelligence vibration tendency prediction method is proposed to simultaneously achieve strong stability and high accuracy, where vibration signal preprocessing, feature selection and prediction methods are integrated in a multi-objective optimization framework. Firstly, raw sensor signals are decomposed into several modes by empirical wavelet transform (EWT). Subsequently, the refactored modes can be obtained by the sample entropy-based reconstruction strategy. Then, important input features are selected using the Gram-Schmidt orthogonal (GSO) process. Later, the refactored modes are predicted through kernel extreme learning machine (KELM). Finally, the parameters of GSO and KELM are synchronously optimized by the multi-objective salp swarm algorithm. A case study and analysis of the mixed-flow HGU data in China was conducted, and the results show that the proposed model performs better in terms of predicting stability and accuracy.

show abstract

“…The hydraulic turbine governing system (HTGS) is the core control system of hydroelectric generating units (HGUs), which undertakes the tasks of load and frequency adjustment. The HTGS is a non-linear and non-minimum phase system [1][2][3][4][5][6][7]. Traditional identification methods, such as least squares method [8], input response method [9], and maximum likelihood estimate (MLE) [10], have been applied in parameter identification of HTGS in the past years, but there are too many restrictions on these methods.…”

Section: Introductionmentioning

confidence: 99%

A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect

Ding

et al. 2018

Energies

Self Cite

View full text Add to dashboard Cite

For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite’s memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method.

show abstract

Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit

Cited by 30 publications

References 31 publications

Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor

Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor

A Hybrid Multi-Objective Optimization Model for Vibration Tendency Prediction of Hydropower Generators

A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect

Contact Info

Product

Resources

About