Using a fuzzy inference system to control a pumped storage hydro plant

King, David J.; Bradley, David; Mansoor, Sumreena; Jones, D. I.; Aris, F. C.; Jones, G. R.

doi:10.1109/fuzz.2001.1009132

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…Liang (2000) proposed a neural network approach for the hydroelectric generation setting up with pumpedstorage units. King et al (2001) discussed the development of a Fuzzy Inference System (FIS) based governor control for a pumped storage hydro power plant. Stokelj et al (2000) described neural networks for water inflow into the head of the hydro power plant reservoir.…”

Section: Ajasmentioning

confidence: 99%

Neural Cascaded With Fuzzy Scheme for Control of a Hydroelectric Power Plant

Priyadharson¹,

Kumar²,

Saravanan³

et al. 2014

American Journal of Applied Sciences

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A novel design for flow and level control in a hydroelectric power plant using Programmable Logic Controller (PLC)-Human Machine Interface (HMI) and neural cascaded with fuzzy scheme is proposed. This project will focus on design and development of flow and level controller for small scale hydro generating units by implementing gate control based on PLC-HMI with the proposed scheme. The existing control schemes have so many difficulties to manage intrinsic time delay, nonlinearity due to uncertainty of the process and frequent load changes. This study presents the design of neuro controllers to regulate level, cascaded with fuzzy controller to control flow in gate valve to the turbine. A prototype model is fabricated in the laboratory as experimental setup for flow and level control and real time simulation studies were carried out using PID and neural cascaded with fuzzy scheme. The designed prototype model is fabricated with 5 levels in the upper tank and 2 levels in the lower tank. Based on the outputs of the level sensors from the upper and lower tanks, the ladder logic is actuated. This project work uses PLC of Bernecker and Rainer (B and R) Industrial Automation inbuilt with 20 digital inputs and provides 12 potential free outputs to control the miniaturized process depicted in this work. Finally, the performance of the proposed neural cascaded with fuzzy scheme is evaluated by simulation results by comparing with conventional controllers output using real time data obtained from the hydro power plant. The advantages of the proposed neural cascaded with fuzzy scheme over the existing controllers are highlighted.

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Section: Ajasmentioning

confidence: 99%

Neural Cascaded With Fuzzy Scheme for Control of a Hydroelectric Power Plant

Priyadharson¹,

Kumar²,

Saravanan³

et al. 2014

American Journal of Applied Sciences

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show abstract

“…The controller is designed using the back-propagation type gradient descent method called ''temporal back propagation''. The comparison between the fuzzy inference system (FIS) governor and PID controller in a pumped storage hydro plant is discussed in King et al (2001). An excellent response to a step increase in load is shown by FIS than PID-based controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive intelligent hydro turbine speed identification with water and random load disturbances

Kishor

Singh

Raghuvanshi

2007

Engineering Applications of Artificial Intelligence

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“…Liang (2000) proposed a methodology derived from neural network in hydro power production using pumped-storage units. King et al (2001) discussed the governor control in a pumped storage hydroelectric plant using Fuzzy Inference System (FIS). Rabelo et al (2012) proposed the implementation of Reservoir Operation Rules (ROR) using fuzzy and Particle Swarm Optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Flow and Level Control in a Hydro Power Plant Using Fuzzy Logic

Priyadharson

Saravanan

Gomathi

et al. 2014

Journal of Computer Science

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The main objective and an innovative design of this work is to improve the energy efficiency by controlling the variables flow and level in a hydroelectric power plant using Programmable Logic Control (PLC)-Human Machine Interface (HMI) and fuzzy logic approach. This project will focus on design and development of flow and level controller for small scale hydro generating units by implementing gate control based on PLC-HMI and Fuzzy Logic Control (FLC). So far there is no other better performing control scheme, with uncomplicated approach, in order to match and satisfy the dynamic changes in load demand. In this project, FLC will be applied to flow and level control for small scale hydro generating units is proposed. A lab scale experimental setup is made-up as prototype model for flow and level control and simulation outputs were achieved, using PLC-HMI based fuzzy controller scheme. The hardware set up is designed with 5 stages in the tank 1 and 2 stages in the tank 2. Based on the outputs of the level sensors from tanks 1 and 2, the ladder logic will perform. B&R Industrial Automation PLC inbuilt with 24 digital inputs and provides 16 potential free outputs is used to perform control action. Finally, the performance of the proposed scheme is evaluated by simulation results by comparing with conventional controllers output using the data collected from the hydroelectric power plant. The merits of the proposed Fuzzy scheme over the conventional method are spotlighted.

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Using a fuzzy inference system to control a pumped storage hydro plant

Cited by 9 publications

References 5 publications

Neural Cascaded With Fuzzy Scheme for Control of a Hydroelectric Power Plant

Neural Cascaded With Fuzzy Scheme for Control of a Hydroelectric Power Plant

Adaptive intelligent hydro turbine speed identification with water and random load disturbances

Energy Efficient Flow and Level Control in a Hydro Power Plant Using Fuzzy Logic

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