Design and implementation of a fuzzy logic computer-controlled sun tracking system

Yousef, Hassan

doi:10.1109/isie.1999.796768

Cited by 44 publications

(36 citation statements)

References 4 publications

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“…In [7] a type of on-off control was utilized for two-axis sun tracking. On the other hand, common closed-loop control methodologies include robust proportional (P) control, proportional-integral (PI) control, derivative (D)-like control, proportional-integral-derivative (PID) control [8][9][10][11], fuzzy control [12][13][14][15], Linear-Quadratic Regulator(LQG) control [16] or H-infinity(H∞) control [17,18]. Various controllers have individual advantages and disadvantages [17].…”

Section: Open Accessmentioning

confidence: 99%

The Logic-Based Supervisor Control for Sun-Tracking System of 1 MW HCPV Demo Plant: Study Case

Yeh

Lee

2012

Applied Sciences

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This paper presents a logic-based supervisor controller designed for trackers for a 1MW HCPV demo plant in Taiwan. A sun position sensor on the tracker is used to detect the sun position, as the sensor is sensitive to the intensity of sun light. The signal output of the sensor is partially affected by the cloud, which has a hard control position with the traditional PID control. Therefore we have used logic-based supervisor (LBS) control which permits switching the PID control to sun trajectory under sunny or cloudy conditions. To verify the stability of the proposed control, an experiment was performed and the results show that the proposed control can efficiently achieve stabilization of the trackers of the 1MW HCPV demo plant.

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Section: Open Accessmentioning

confidence: 99%

The Logic-Based Supervisor Control for Sun-Tracking System of 1 MW HCPV Demo Plant: Study Case

Yeh

Lee

2012

Applied Sciences

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“…The literature contains many solar irradiation geometry models for solar devices [4,6,18,[29][30][31]. In the present study, a spherical coordinate system is used to model the geometrical relationship between the sun and each sensor in the sensing array.…”

Section: Validity Testmentioning

confidence: 99%

“…Various researchers have proposed the use of numerical optimization schemes for the development of accurate solar tracking systems. Typical examples of such schemes include neural networks [17], fuzzy logic algorithms [18], adaptive neuro-fuzzy control schemes [19], optical vernier [20] and nonlinear compensation [21] [23].…”

Section: Introductionmentioning

confidence: 99%

Solar Orientation Measurement Systems with Integrated Solar Cells

Chiang¹,

Lee²,

Chou³

2009

TOBCTJ

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This paper presents a novel approach for estimating the position of the sun by measuring the output voltage signals of two solar cell arrays orientated orthogonally to one another. Each array comprises a symmetrical arrangement of six cells inclined at angles of either 15°, 45° or 75° to the horizontal, respectively. As the position of the sun varies over the course of the day, a change is induced in the output voltage of each of the solar cells within the array. It is shown that the output voltage of any cell (V) varies as a linear function of the included angle between it and the solar cell with the maximum output voltage (V max ). A mathematical correlation is derived to express the relationship between the comparative output voltage of each solar cell (V/V max ) and the solar position. The sun's position is then computed by averaging the elevation angles derived from the comparative output voltages of all of the cells within the array. It is shown that both the zenith angle and the latitude angle of the sun can be accurately determined by deploying the measurement system such that one solar cell array is aligned in the East-West (E-W) direction while the other is orientated in the North-South (N-S) direction.

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“…By contrast to standard methods, greater accuracy of systems, particularly non-linear systems, could be ensured with AI methods. The MPP has been frequently tracked with neural networks (NN) and fuzzy logic (FL) methods (Yousef, 1999;Chaouachi et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

ANFIS-based PI controller for maximum power point tracking in PV systems

Hussein¹

2018

Int. j. adv. appl. sci.

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This paper presents a maximum power point tracking (MPPT) control system which is designed to increase the energy generation efficiency of Photovoltaic (PV) arrays. Usually Maximum power point tracking control system uses dc-to-dc converters to compensate for the output voltage of the PV array in order to keep the voltage at the value, which maximizes the output power. The purpose of the work is to develop an adaptive neuro-fuzzy inference system (ANFIS)-based proportional integral controller. The operating temperature and level of irradiance constitute inputs for the ANFIS controller, allowing it to determine the maximum available power that the PV array possesses. The error between the reference power from the ANFIS controller and the measured voltage and current of the PV array enables the proportional integral controller to generate the duty cycle. It is shown that ANFIS-based PI controller gives better performance criteria, unlike conventional techniques which usually give associations at steady state operating conditions. Eventually, the proposed MPPT control system based on ANFIS could provide better results than conventional techniques in terms of performance, accuracy and stability.

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Design and implementation of a fuzzy logic computer-controlled sun tracking system

Cited by 44 publications

References 4 publications

The Logic-Based Supervisor Control for Sun-Tracking System of 1 MW HCPV Demo Plant: Study Case

The Logic-Based Supervisor Control for Sun-Tracking System of 1 MW HCPV Demo Plant: Study Case

Solar Orientation Measurement Systems with Integrated Solar Cells

ANFIS-based PI controller for maximum power point tracking in PV systems

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