A fractional order fuzzy-proportional-integral-derivative based pitch angle controller for a direct-drive wind energy system

Pathak, Diwaker; Gaur, Prerna

doi:10.1016/j.compeleceng.2019.07.021

Cited by 41 publications

(37 citation statements)

References 15 publications

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“…It is necessary to apply a new, powerful, and suitable tuning algorithm to select the proposed MPPT controller gains in an optimal manner. 35 After the inclusive literature review and simulations, TLBO has been decided to apply for the optimal controller parameters selection. 38 TLBO is a recent and self-regulating algorithm that is independent of algorithmspecific tuning constraints and hence, becomes a standard tuning algorithm for WECS.…”

Section: Tuning Of the Proposed Mppt Methodsmentioning

confidence: 99%

“…The electrical modeling of the stator side of PMSG in the d-q reference frame can be stated as in 35 With the assumption of equal stator inductances in the d-q reference frame (L d = L q ), the electromagnetic torque equation can be reduced to Equation (5).…”

Section: Pmsg Dynamic Modelmentioning

confidence: 99%

“…FOPID is the modified version of the conventional PID controller and one of the most commonly used controllers designed to further enhance the performance of the conventional PID controller as discussed in 35 . In the discrete-time domain, controller output u FOPID (z) related as follows:…”

Section: Fractional Order Pid Controller Based Tip Speed Ratio Maximentioning

confidence: 99%

“…where k(e) is a nonlinear error function formulated as given in Equation (23 To get an approximate model of FOPID controller and realization in terms of integer order Laplace function using appropriate techniques like "Oustaloup approximation" 35 due to the multiple transformations that is, continuous to the discrete-time domain, leads to an unavoidable mismatch between the continuous and discrete transfer function responses of the overall system, causing the loss of "long memory feature" of the integral term in the FOPID control output. 37 The control action of the proposed controller is derived from a long memory discrete PID (LDPID) controller, which according to 37 , not only provides similar high performance as that of FOPID but also eliminates the aforementioned limitations.…”

Section: Nonlinear Pid Controller Based Tip Speed Ratio Maximum Powmentioning

confidence: 99%

“…TLBO is used to find the optimal value of a defined fitness function by allowing variation in system variables within permissible bounds. TLBO can be explained as in 35 TLBO updates its new solutions based on the difference between the previous and newly attained values which are analogous to grades attained by learners. This statement can be formulated by (31).…”

Section: Teaching Learning Based Optimizationmentioning

confidence: 99%

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Fractional‐order nonlinear PID controller based maximum power extraction method for a direct‐driven wind energy system

Pathak

Bhati

Gaur

2020

Int Trans Electr Energ Syst

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Objective To maintain the operation of a wind turbine at its maximum efficiency and for the extraction of maximum power from the wind. Method In this paper, at first, a novel application of fractional‐order nonlinear proportional‐integral‐derivative (FONPID) controller is proposed in the machine side converter (MSC) control loop of a 2 MW grid‐connected wind energy system. Then, the grid side converter (GSC) is controlled to ensure the unity power factor of the understudy system. To extract the maximum power from the wind, the application of the proposed controller is applied to a well‐established TSR MPPT control method, which compares optimal and actual values of rotor speed, and generated error is given to the proposed controller to vary the rotor speed accordingly. Moreover, improved MPPT performance offered by the proposed FONPID controller has also been compared to well‐established existing controllers based TSR MPPT methods. Furthermore, parameters of the proposed and existing controllers in the TSR MPPT control loop are also tuned using teaching‐learning based optimization (TLBO) and obtained performance is compared with the performance of particle swarm optimization (PSO) to show effectiveness. Results The generator speed offered by the proposed FONPID based TSR MPPT method effectively tracks its reference values far better than the extant controller based TSR MPPT method, whereas extant NPID has less overshoot as compared to the extant PID based TSR MPPT method. Conclusion To perform the effective operation of achieving MPP, q‐axis current of the stator has been controlled by the proposed FONPID based TSR MPPT method in such a style that rotor speed can be operated at its optimal value where the generator has the maximum power for given wind speed. DC‐link voltage is controlled at its rated value to guarantee the unity power factor operation using the GSC controller. FONPID based TSR MPPT method provides a better and robust MPPT operation than the PID, NPID, and FOPID controllers based TSR MPPT method. The performance has been assessed in terms of minimized fitness function value, steady‐state error, settling time, and percentage overshoot.

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Section: Tuning Of the Proposed Mppt Methodsmentioning

confidence: 99%

Section: Pmsg Dynamic Modelmentioning

confidence: 99%

Section: Fractional Order Pid Controller Based Tip Speed Ratio Maximentioning

confidence: 99%

Section: Nonlinear Pid Controller Based Tip Speed Ratio Maximum Powmentioning

confidence: 99%

Section: Teaching Learning Based Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

Fractional‐order nonlinear PID controller based maximum power extraction method for a direct‐driven wind energy system

Pathak

Bhati

Gaur

2020

Int Trans Electr Energ Syst

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A novel fractional‐order nonlinear PID ‐based pitch angle control strategy for a PMSG ‐based wind energy system

Pathak

Gaur

2020

Int Trans Electr Energ Syst

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The mechanical power of the wind turbine has a cubic relationship with wind speed. Therefore, during high wind speed, the output power of wind turbine driven permanent magnet synchronous generator (PMSG) needs to be maintained at rated value effectively. This can be achieved by the pitch angle control of the wind turbine. In this paper, a new application of the fractionalorder nonlinear proportional-integral-derivative (FONPID) controller is pro

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ANN‐PID based automatic braking control system for small agricultural tractors

Pradhan,

Sahoo,

Kushwaha

et al. 2024

Journal of Field Robotics

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Braking system is a crucial component of tractors as it ensures safe operation and control of the vehicle. The limited space availability in the workspace of a small tractor exposes the operator to undesirable posture and a maximum level of vibration during operation. The primary cause of road accidents, particularly collisions, is attributed to the tractor operator's insufficient capacity to provide the necessary pedal power for engaging the brake pedal. During the process of engaging the brake pedal, the operator adjusts the backrest support to facilitate access to the brake pedal while operating under stressed conditions. In the present study, a linear actuator‐assisted automatic braking system was developed for the small tractors. An integrated artificial neural network proportional–integral–derivative (ANN‐PID) controller‐based algorithm was developed to control the position of the brake pedal based on the input parameters like terrain condition, obstacle distance, and forward speed of the tractor. The tractor was operated at four different speeds (i.e., 10, 15, 20, and 25 km/h) in different terrain conditions (i.e., dry compacted soil, tilled soil, and asphalt road). The performance parameters like sensor digital output (SDO), force applied on the brake pedal (), and deceleration were considered as dependent parameters. The SDO was found to good approximation for sensing the position of the brake pedal during braking. The optimized network topology of the developed multilayer perceptron neural network (MLPNN) was 3‐6‐2 for predicting SDO and deceleration of the tractor with a coefficient of determination () for the training and testing datasets of the SDO and deceleration were obtained as 0.9953 and 0.9854, and 0.9254 and 0.9096, respectively. The Ziegler–Nichols (Z‐N method) method was adopted to determine the initial optimal gains of the PID controller and later these coefficients were optimized using response surface methodology. The optimized proportional (), integral (), and derivative () coefficient values were 4.8, 6.782, and 3.15, respectively. The developed integrated ANN, that is, MLPNN and PID‐based algorithm could successfully control the position of the brake pedal during braking. The stopping distance and slip of the tractor during automatic braking increased with an increase in the forward speed for the tractor from 10 to 25 km/h in all the selected terrain conditions.

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A fractional order fuzzy-proportional-integral-derivative based pitch angle controller for a direct-drive wind energy system

Cited by 41 publications

References 15 publications

Fractional‐order nonlinear PID controller based maximum power extraction method for a direct‐driven wind energy system

Fractional‐order nonlinear PID controller based maximum power extraction method for a direct‐driven wind energy system

A novel fractional‐order nonlinear PID ‐based pitch angle control strategy for a PMSG ‐based wind energy system

ANN‐PID based automatic braking control system for small agricultural tractors

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