Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm

Chen, Zhihuan; Yuan, Xiao; Yuan, Yanbin; Lei, Xiaohui; Zhang, Binqiao

doi:10.1016/j.renene.2018.10.061

Cited by 32 publications

(18 citation statements)

References 31 publications

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“…This is because, the imperialistic competition process provides the ability to develop a more promising information distribution among the empires compare with other meta‐heuristic methods . Because of the excellent convergence ability of ICA algorithm, it is suggested to solve engineering problems in industry, such as the management of renewable energy sources, flexible job shop scheduling, and parameter estimation of photovoltaic systems …”

Section: Proposed Clustering Model and The Solution Methodologymentioning

confidence: 99%

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Samadi

Aghamohamadi

Mahmoudi

2019

Int Trans Electr Energ Syst

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Summary In time‐of‐use (TOU) schemes, most similar clusters to load demand can properly map the consumption pattern. However, the performance of such scheme is affected by the load responsiveness to the obtained clusters. This paper presents a time period clustering approach for TOU schemes to find the optimal clusters, using not only the load patterns but also its modified version obtained by responsiveness of elastic loads. A responsive load model is employed to simulate the load pattern modifications considering the hourly prices, load elasticities, and customers' benefit function. An imperialist competitive algorithm (ICA) has been designed to iteratively solve the problem. The proposed approach maximizes the load factors pertaining to the modified seasonal load patterns that are obtained on the basis of load responsiveness to the allocated clusters in each iteration of ICA. A comprehensive seasonal case study has been conducted to evaluate the performance of the proposed model. Results and comparisons show that the proposed clustering technique can significantly enhance the performance of the existing TOU schemes in terms of load factor improvement and peak reduction as it considers both load patterns and the actual load responsiveness to the obtained clusters.

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Section: Proposed Clustering Model and The Solution Methodologymentioning

confidence: 99%

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Samadi

Aghamohamadi

Mahmoudi

2019

Int Trans Electr Energ Syst

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“…Different control algorithms such as PID, 46,47 fuzzy logic, 48–49 generalized super-twisting algorithm 50 and fuzzy-PID position 51 are already well established and are in use in real-life applications. In recent times, the Electro-Hydraulic Actuators (EHA) control and Digital Displacement Technology are evolved, which are directly related to improving the energy efficiency of a power hydraulic system.…”

Section: Different Strategies Of Energy Savingsmentioning

confidence: 99%

“…This stated concept increases the controllability of the system and energy consumption of the system. The dynamic performance of a hydraulic system can be improved by applying different control algorithms such as proportional integral derivative (PID), 46,47 fuzzy logic, 48–49 generalized super-twisting algorithm, 50 and fuzzy-PID position. 51 All these control algorithms are essential to improve the energy efficiency of a power hydraulic system.…”

Section: Introductionmentioning

confidence: 99%

Energy-saving strategies on power hydraulic system: An overview

Mahato

Ghoshal

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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Different strategies for improving the energy efficiency of a power hydraulic system have been reviewed in this article. The energy-saving scheme is classified into three categories: S ystem design, Improving components or product functions and Loss reduction. The sub-categories of energy-saving strategies are discussed briefly. Also, different energy-saving potentials of power hydraulic system are presented in tabular form for clear understanding on the chronological development toward energy-efficient fluid power system.

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“…At present, the control methods of HTGS are mainly concentrated on proportional integral derivative (PID) control [14,15], intelligent control [16][17][18], Hamiltonian control [19], finitetime control [20], and sliding mode control [21]. A fractional order PID controller which parameters are optimised by gravitational search algorithm combined with the Cauchy and Gaussian mutation to improve the control performance for the hydraulic turbine governing system is proposed in Ref.…”

Section: Introductionmentioning

confidence: 99%

Takagi–Sugeno fuzzy generalised predictive control of a time‐delay non‐linear hydro‐turbine governing system

Tian

Wang

Zhu

et al. 2019

IET Renewable Power Generation

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This study focuses on a fuzzy generalised predictive control (FGPC) method for a time-delay hydro-turbine governing system (HTGS). First, based on the time-delay Takagi-Sugeno fuzzy model, a time-delay HTGS and its fuzzy prediction model are given. Second, with the help of delay fuzzy linearisation and a fourth-order Runge-Kutta algorithm, a transformed controlled auto-regressive integrated moving average model is obtained. Then, a new FGPC scheme for the time-delay HTGS is proposed. Finally, numerical simulations are implemented to verify the validity and superiority of the proposed method. It also provides a reference for the stability control of relevant hydropower station systems.

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Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm

Cited by 32 publications

References 31 publications

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Energy-saving strategies on power hydraulic system: An overview

Takagi–Sugeno fuzzy generalised predictive control of a time‐delay non‐linear hydro‐turbine governing system

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