Particle Swarm Optimization-Based MPPT Controller for Wind Turbine Systems
With the increased concerns about environment due to reduction in conventional energy sources, it has become very important to search for new kinds of clean and renewable energy. Wind energy system is widely preferred as a renewable energy source due to i
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1 Introduction The maximum power which can be extracted from a wind turbine mainly depends on two factors, viz., velocity of wind and operating point of the system. Therefore, MPPT is an important step to increase the efficiency of the system [1]. The study of these MPPT techniques shows that they may actually fall in five categories, namely optimal torque control (OP), hill climbing search control (HCS), power signal feedback control (PSF), tip-speed ratio control (TSR), and power mapping control [1, 2]. Many papers have been published to study these techniques with their advantages and disadvantages [1–3]. In this paper, TSR control method is implemented (Fig. 1) using particle swarm optimization (PSO), which reduces the oscillations around the optimal point and increases the efficiency of wind turbine [4, 5]. This artificial intelligence technique is found to be more efficient and faster than other techniques. One of the important requirements of TSR control is an anemometer which measures the wind velocity. The preknown values of the optimal TSR are stored in lookup table. Then, the measured wind velocity is converted to its corresponding optimal speed reference with the help of these values [6]. This method offers faster control and it is possible to yield more energy. PSO-based TSR control method helps to eliminate the requirement of any prior information about the system and calculates the optimal value online, and thus helps in extracting the peak power from the turbine.
S. Jagwani (B) · L. Venkatesha BMSCE, Bangalore, India e-mail: [email protected] L. Venkatesha e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 R. K. Shukla et al. (eds.), Data, Engineering and Applications, https://doi.org/10.1007/978-981-13-6351-1_25
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Fig. 1 MPPT control for wind turbine system
WIND TURBINE POWER CHARACTERISTICS
8000 7000
Turbine power (W)
6000 5000 4000 3000 2000 1000 0 0
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Turbine rotor speed (rps)
Fig. 2 Wind turbine characteristics
2 Wind Turbine Characteristics The mechanical power extracted from the wind generators Pm (as shown in Fig. 2) is expressed as [1, 2] Pm
1 ρ Av 3 C p (λ, β) 2
(1)
Particle Swarm Optimization-Based MPPT Controller …
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Fig. 3 Power coefficient curve
where ρ v A C p (λ, β)
air density (kg/m3 ), wind speed (m/s), area swept by the rotor blades (m2 ) coefficient of performance
The parameter C p defines the power extraction efficiency. It is a nonlinear function of TSR (λ) and pitch angle of the blade (β). In this work, a variable speed fixed pitch turbine is considered, hence β is fixed. The maximum theoretical value of performance coefficient C p is 0.59 according to the Betz limit. TSR is the ratio of rotational speed of turbine (ω) to linear speed of blade tips and is given by [7] λ ω R/v
(2)
where R radius of blade. Different versions of the equation for obtaining C p have been defined by the authors in previously published papers [1–3, 8, 9]. In this p
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