Fuzzy Model for Real-Time Reservoir Operation

Dubrovin, Tanja; Jolma, Ari; Turunen, Esko

doi:10.1061/(asce)0733-9496(2002)128:1(66)

Cited by 79 publications

(45 citation statements)

References 6 publications

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“…Estos conjuntos y reglas borrosas constituyen la base del conocimiento del sistema borroso denominado Memoria Asociativa Borrosa (MAB o FAM) (Figuras 1 y 2), que puede ser construida sobre la base del conocimiento del experto o a partir de datos observados (Dubrovin et al, 2002).…”

Section: Sistemas De Control Borroso Y Aplicación De Ordenador Nubeciunclassified

Implementación de sistemas de control borroso en actividades académicas dirigidas en las Escuelas de Ingeniería

Calvo

Gutiérrez‐Estrada

Martí

et al. 2015

Model. sci. educ. learn.

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Como propuesta de trabajo práctico dirigido se ha planteado el diseño, desarrollo e implementación de un sistema experto que permite la simulación de modelos de lógica borrosa o difusa. Este sistema experto se ha utilizado en la resolución de un problema real de ingeniería, la regulación automática de los caudales de entrada de agua en balsas de plantas de acuicultura semi-intensiva (esteros mejorados). El objetivo fundamental de esta regulación es controlar la calidad del agua en las balsas de producción. El desarrollo de esta actividad académica se ha incluido en los programas docentes de las materias 'Mecánica de Fluidos' e 'Impacto Ambiental' de 3er curso de algunos Grados de Ingeniería impartidos en la Universidad de Huelva. De forma global se puede afirmar que esta metodología docente, basada en el estudio y solución de un problema práctico, ha supuesto un estímulo para la promoción de la autonomía y el aprendizaje significativo del alumno. The design, development and implementation of an expert system that allows the simulation of fuzzy logic models was proposed as supervised practical work. This expert system was used in solving a real engineering problem: the automatic regulation of the water inflow of semi-intensive aquaculture plants. The main objective of this regulation is to control the water quality in the aquaculture ponds. The development of this academic activity has been included in the subject programs 'Fluid Mechanics' and 'Environmental Impact' of 3rd course of some Degrees of Engineering of Huelva University. Globally, we can say that this teaching methodology, based on the study and solution of a practical problem, has been an incentive for the promotion of autonomy and significant learning of students.

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Section: Sistemas De Control Borroso Y Aplicación De Ordenador Nubeciunclassified

Implementación de sistemas de control borroso en actividades académicas dirigidas en las Escuelas de Ingeniería

Calvo

Gutiérrez‐Estrada

Martí

et al. 2015

Model. sci. educ. learn.

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“…Therefore the FIS has been applied to different subjects, such as reservoir operation (Russel and Camplell 1996;Shrestha et al 1996;Dubrovin et al 2002;Ponnambalam et al 2003;Akyilmaz and Kutterer 2004), and wave study (Kazeminezhad et al 2005;Őzger and Sen 2007). This paper employs an adaptive-network-based FIS (ANFIS), which combines ANN and FIS, to predict sea level and tides.…”

Section: An Adaptive Neuro-fuzzy Inference System For Sea Level Predimentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Inference System for Sea Level Prediction Considering Tide-Generating Forces and Oceanic Thermal Expansion

Li¹,

Chang²

2008

Terr. Atmos. Ocean. Sci.

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The paper presents an adaptive neuro fuzzy inference system for predicting sea level considering tide-generating forces and oceanic thermal expansion assuming a model of sea level dependence on sea surface temperature. The proposed model named TGFT-FN (tide-Generating Forces considering sea surface temperature and Fuzzy neuro-network system) is applied to predict tides at five tide gauge sites located in Taiwan and has the root mean square of error of about 7.3 -15.0 cm. The capability of TGFT-FN model is superior in sea level prediction than the previous TGF-NN model developed by Chang and Lin (2006) that considers the tide-generating forces only. The TGFT-FN model is employed to train and predict the sea level of Hua-Lien station, and is also appropriate for the same prediction at the tide gauge sites next to Hua-Lien station.

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“…A variety of case studies has been published in which the decision-making phase of the selected control model involves the use of artificial intelligence methods, including genetic algorithms, fuzzy logic and neural networks, e.g. Chang and Chang (2001), Dubrovin et al (2002) and Chang et al (2005). The application of the above methods in the development of control algorithms is also tested during normal operation of the reservoir, with the main criteria being to ensure the necessary volume and quality of water (Butcher, 1971;Chandramouli and Nanduri, 2011;Chaves et al, 2004), especially during flood situations (Chang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A stochastic approach to the operative control of flood flows through a reservoir

Jaroš

Starý

Březková

2016

Journal of Hydrology and Hydromechanics

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Abstract:The contribution focuses on the design of a control algorithm aimed at the operative control of runoff water from a reservoir during flood situations. Management is based on the stochastically specified forecast of water inflow into the reservoir. From a mathematical perspective, the solved task presents the control of a dynamic system whose predicted hydrological input (water inflow) is characterised by significant uncertainty. The algorithm uses a combination of simulation model data, in which the position of the bottom outlets is sought via nonlinear optimisation methods, and artificial intelligence methods (adaptation and fuzzy model). The task is written in the technical computing language MATLAB using the Fuzzy Logic Toolbox.

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Fuzzy Model for Real-Time Reservoir Operation

Cited by 79 publications

References 6 publications

Implementación de sistemas de control borroso en actividades académicas dirigidas en las Escuelas de Ingeniería

Implementación de sistemas de control borroso en actividades académicas dirigidas en las Escuelas de Ingeniería

An Adaptive Neuro-Fuzzy Inference System for Sea Level Prediction Considering Tide-Generating Forces and Oceanic Thermal Expansion

A stochastic approach to the operative control of flood flows through a reservoir

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