Ann-Based Maximum Power Point Tracking of a Variable-Speed Wind Energy Conversion System using Sepic Converter
Abdulhamid Mustapha, Sathish Kumar Selvaperumal, Hazwani Mohd, Ravi Lakshmanan Asia Pacific University Kuala Lumpur, Jalan Teknologi 5, Taman Teknologi Malaysia, 57000
Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
ABSTRACT
The concerns for environment due to the ever-increasing use of fossil fuels and rapid depletion of this resources have led to the development of renewable energy sources such as wind energy. However, wind energy conversion systems face some challenges which make it less efficient compared to non-renewable energies. Some of these problems include efficiency trade-off and divergence from peak power under rapid wind speed variation. Many researchers have sought to mitigate these problems in order to improve the efficiency and maintain the output power even under such conditions. Hence the idea of Maximum Power Point Tracking (MPPT) emerged, yet most of these researches are based on conventional MPPT methods which have many drawbacks like power oscillations. This paper proposes an Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) control strategy for Wind Energy Conversion System (WECS) implemented with a DC/DC converter. The proposed topology utilizes a Nonlinear Autoregressive with External (Exogenous) Input (NARX) based neural network control strategy to extract the maximum available power from different wind velocities.
To validate the performance of this method, the results were compared with Perturb and Observe (P&O) method. In other to stabilize the output, the system is implemented with Single Ended Primary Inductance Converter (SEPIC). The simulation of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.
KEYWORDS:Artificial Neural Network (ANN), Maximum Power Point Tracking (MPPT), Nonlinear Autoregressive with External (Exogenous) Input (NARX), Single Ended Primary Inductance Converter (SEPIC), Wind Energy Conversion System (WECS)
INTRODUCTION
Electricity generation using conventional energy sources leads to the depletion of its reserves and intensifies the release of greenhouse gases and thus result in climate change which brings about environmental problems. Due to these serious repercussions, the power sectors nowadays rely on renewable energy sources (wind, sun, biomass, etc…) to produce electricity.
Among them, Wind Energy Conversion System (WECS) is widely attracting the power producers and is expected to maintain rapid growth in the coming years. Wind energy is free energy that comes directly from the wind and is normally captured and converted into a more useful form, electricity. Wind turbines are used to produce electricity from wind energy and use it as a substitute for non-renewable energy sources. Global Wind Energy Council (GWEC) reported that wind energy systems accounted for 539 GW of power production worldwide. In 2017 alone, 52.492GW of clean, emissions-free wind power have been installed from January to December 2017 alone.
The Variable Speed Wind Turbines (VSWTs) aim to obtain maximum power output to maximize energy conversion efficiency. To ensure high efficiency while minimizing costs, optimal solutions are constantly being developed. One of the focus areas for optimization is Maximum Power Point Tracking (MPPT). Maximum Power Point Tracking (MPPT) is a very important issue in wind turbine industry. Most conventional MPPT methods uses sensors for extracting maximum power from wind turbine. Most MPPT methods require a mechanical sensor to track the MPP, other methods like Perturb and Observe (P&O) and Incremental Conductance (INC) monitor power variation to track MPP [1]. The two control techniques have high flexibility and they are simple to design.However, they fluctuate around the maximum power point which reduces the efficiency of the wind system. Reference [2] noted that Tip Speed Ratio (TSR) algorithm require anemometer which increases the capital, installation, and operational costs of the WECSs. Furthermore, the accuracy of the wind speed measured is very poor due to the turbulence of WT blades and the variation of the wind speed along the length of the blade. Power Signal Feedback (PSF) and Optimal Torque Control (OTC) MPPT algorithm do not require anemometer, but still need the parameters of specific wind turbine to track MPP.
Furthermore, conventional MPPT control strategies are reported to account for 1% - 3% power loss due have 5% error which commonly occurs [3].
This paper intends to develop a suitable Artificial Neural Network (ANN) based MPPT algorithm for maximum power extraction in WECS. The wind turbine is coupled with a generator during simulation and a suitable converter to connect the MPPT algorithm. The permanent Magnetic Synchronous Generator (PMSG) and Singe Ended Primary Inductor Converter (SEPIC) were considered in this project because they offer significant advantages in wind energy applications which will be further discussed in the paper.
WECS CONFIGURATION
The generation of electricity from wind begins with a wind turbine. The blades of the Wind turbine system will capture the wind energy. The Wind turbine is directly coupled to a generator through a shaft at which the wind energy from the wind turbine system is transformed into mechanical energy. The generator will then convert mechanical energy into electrical energy. The output is fed to the source side (AC/DC Rectifier) where the generated AC is directly converted into DC in a single stage. Further, the voltage appearing at the DC-link is fed to the (DC/DC) which can be supplied to supply the load. The block diagram in Figure 1 shows an overview of the entire system.
Wind Speed (Vw)
Wind Turbine Configuration
Output Power SEPIC
SPEIC Configuration
PMSG
MPPT CONTROL Rectifier
Vdc
Idc
1
2
3
PWM Mathematically
Modelled Wind Turbine
Figure 1: Overall Block Diagram
WIND TURBINE MODELLING
The kinetic energy from wind is transformed into mechanical energy by the wind turbine for variable wind speeds. The design of the wind turbine is taken from [4]. The mechanical power produced by the wind turbine is expressed as,
𝑃𝑚 = 1
2𝜌 𝐴𝑉3 (1)
where P is mechanical power in the moving air (watt), ρ is air density (kg/m3), A is area swept of the rotor blades (m2), V is velocity of the air (m/sc) and Cp is the power coefficient.
𝜆 = 𝜔𝑅
𝑉 (2)
Similarly, the mechanical power produced by the wind turbine system is expressed as 𝑃 = 1
2𝜌 𝐴𝐶𝑝(𝜆, 𝛽)𝑉3 (3)
where 𝐶𝑝 is power coefficient, which is a nonlinear function of pitch angle 𝛽 and tip speed ratio (TSR) 𝜆, which is described as
𝐶𝑝 𝜆, 𝛽 = 0.5176 116 1
𝜆𝑖− 0.4𝛽 − 5
−21 1
𝜆 𝑖 + 0.0068𝜆 1
𝜆𝑖 = 1
𝜆 + 0.08𝛽× 0.035 𝛽3+ 1 PMSG MODELLING
In modern wind energy conversion systems (WECS), large turbines with permanent magnet synchronous generators (PMSGs) are considered suitable for producing electrical energy from the mechanical energy obtained from wind. The PMSGs are being adopted due to their advantages over the other generators. PMSGs have high efficiency, high power density, low rotor inertia, less weight (due to the absence of field winding and gearbox), low copper losses and better controllability. Furthermore, PMSGs are more robust with the capability to handle a wide range of rotor speeds with reference to rapidly changing wind velocities [5]. The mechanical torque (Tm) of a three-phase PMSG generator used in this study are expressed as
𝑇𝑚 = 𝑃
𝜔𝑅 (4)
The stator 𝑑 − 𝑞 equations of the PMSM in the rotor reference frame are 𝑉𝑑 = 𝑅𝑠𝑖𝑑 + 𝐿𝑑𝑑𝑖𝑑
𝑑𝑡 − 𝜔𝑟𝐿𝑞𝑖𝑞
𝑉𝑞 = 𝑅𝑠𝑖𝑞 + 𝐿𝑞𝑑𝑖𝑞
𝑑𝑡 + 𝜔𝑟𝐿𝑑𝑖𝑑 + 𝜔𝑟𝜆𝑚
Where 𝑉𝑑 and 𝑖𝑑 are the d-axis stator current and voltage, 𝑉𝑞 and 𝑖𝑞 are the q-axis stator and current and voltage, 𝑅𝑠 is the stator winding resistance of the generator, 𝐿𝑑 is the d-axis inductance of generator, 𝐿𝑞 is the q-axis inductance of the generator, 𝜆𝑚 is Amplitude of the flux induced by the permanent magnets of the rotor in the stator phases.
In the 𝑑𝑞-frame, the expression for electrodynamic torque becomes 𝑇𝑒 = 1.5𝑝[𝜆𝑚𝑖𝑞+ 𝐿𝑞 − 𝐿𝑑 𝑖𝑞𝑖𝑑] The equation for mechanical systems for PMSG can be given as
𝑑
𝑑𝑡𝜔𝑟 = 1
𝑗 (𝑇𝑒 − 𝐹𝜔𝑟− 𝑇𝑚) 𝑑
𝑑𝑡𝜃𝑟 = 𝜔𝑟
Where 𝑝 is number of pole pairs, 𝑇𝑒 is electromagnetic torque, 𝐹 is the combined viscous friction of the rotor and load, 𝜔𝑟 is the rotor speed, 𝐽: the moment of inertia, 𝜃𝑟is the rotor angular position, and 𝑇𝑚 is the shaft mechanical torque.
RECTIFIER
To supply the DC-link, the PMSG is connected to a rectifier that converts the AC voltage into DC voltage. A diode rectifier was considered for this configuration.
SEPIC CONVERTER MODELLING
A converter is necessary in wind energy conversion system. It is important in ensuring the maximum power extraction in WECS. SEPIC converter is a category of the buck‐ boost converter, which follows both the boost and buck operation. However, SEPIC converter is more efficient than a buck boost converter. Since wind speed is intermittent in nature, it may exceed an above‐ rated wind speed or vary with time; thus, there is need to have a converter that can either increase or decrease the output voltage to keep up with the varying input. In other words, a SEPIC converter is operated in buck mode during above-rated wind speed condition, whereas, in a boost converter, WECS is subjected to a stall condition. SEPIC can maintain the output voltage to optimal value irrespective of the input voltage without any polarity reversal A regular SEPIC converter is applied, derived from the buck boost converter. The derivation of a SEPIC converter
mainly adopted from [6]. From Fig 1, it is seen that a SEPIC converter consists of a switch, dual inductors, dual capacitors and a diode.
SEPIC converter operates in balancing mode; when the switch is ON, the diode 𝐷1 gets reversed biased and inductor 𝐿1 starts storing the charge, whereas the capacitor 𝐶𝑠 supports the load where the charge is transferred through inductor 𝐿2 and capacitor 𝐶𝑜𝑢𝑡 as shown in Figure 1(b). When the switch is in OFF state, the diode becomes forward biased. The energy that is stored in inductor 𝐿1 charges the capacitor 𝐶𝑠 as shown in Figure 3.7(c). The duty cycle and parasitic element of a SEPIC converter are designed as follows:
𝐷 = 𝑉𝑜
𝑉𝑖+𝑉𝑜 (5)
However, this does not account for losses due to parasitic elements such as the diode drop 𝑉𝐷. Therefore, the equation is:
𝐷 = 𝑉𝑜 + 𝑉𝐷 𝑉𝑖 + 𝑉𝑜 + 𝑉𝐷 𝐿1 = 𝐿2 = 𝐿 = 𝑉𝑖𝑚𝑖𝑛
∆𝐼𝐿× 𝑓𝑠× 𝐷 𝐶𝑠 = 𝐼𝑜× 𝐷
∆𝑉𝐶𝑆 × 𝑓𝑠 𝐶𝑜𝑢𝑡 = 𝐼𝑜× 𝐷
𝑉𝑟𝑖𝑝𝑝𝑙𝑒 × 0.5 × 𝑓𝑠
Where 𝑉𝑜 and 𝑉𝑖 are the output and input voltages, respectively. 𝑓𝑠 is the switching frequency. 𝐼𝑜 and 𝐼𝐿 are the output current and inductor ripple current. 𝑉𝑟𝑖𝑝𝑝𝑙𝑒 refers to ripple between the voltage and 𝐷 is the duty cycle.
Figure 2: (a)SEPIC Topology (b) Switch Turned on (c) Switch Turned Off
MPPT MODELLING P&O MPPT ALGORITHM
The conventional MPPTAlgorithm built is based on Perturb and Observe (P&O) flowchart shown in figure 3. It is the relationship between change in power with respect to change in voltage. It follows the principle that when the change in power is equal to the previous power, the wind turbine is operating at the MPP voltage 𝑉𝑀𝑃𝑃 therefore the control variable will keep perturbing in the same direction until the power decreased. In Simulink, the algorithm is implemented by reading the voltage and current at the DC-Link. Thereafter, the power is calculated. Then a unit delay is added to the voltage and the calculated power and the result is subtracted from the initial reading of voltage and power to get 𝛥𝑉 and 𝛥𝑃.
ΔP > 0
ΔV > 0 ΔV > 0
YES
Start
Read V(k) and I(k) from the DC-Link
Calculate P(k) = V(k) * I(k)
ΔP = P(k) - (k-1) ΔV = V(k) - (k-1) Delay P(k) and V(k)
by (k-1)
D = D - ΔD D = D + ΔD D = D - ΔD D = D + ΔD
YES NO
NO YES
TO SWITCH
NO
Figure 3: Flowchart of Perturb and Observe MPPT Algorithm
The control variable of the P&O algorithm is the Duty cycle (D). The algorithm aims to extract maximum power in WECS by varying the duty cycle in step-size until the optimal operating point is reached. In order to do this, there are three possible actions that can be
performed; either D stays the same, D is increased, or D is reduced. A ‘Switch’ block is used to implement the required ‘if’ cases. First, the algorithm checks if 𝛥𝑃 > 0. Then it checks if 𝛥𝑉 > 0. If the algorithm detects that the operating voltage is lower than 𝑉𝑀𝑃𝑃, it adjusts the duty cycle by adding a fixed value 𝑑𝐷 to get a higher voltage. It keeps adjusting until the condition is no longer true and the operating voltage is higher than 𝑉𝑀𝑃𝑃 then it subtracts 𝛥𝐷 from the duty cycle. In the conventional P&O MPPT developed, the step size 𝛥𝐷 is fixed for all input voltages.
These changes are then applied to the PWM Generator which controls the switching of the SEPIC. The fixed step size is set as 0.01.
ANN MPPT ALGORITHM
The type of Neural Network (NN) developed for the proposed topology of the wind energy conversion system is Nonlinear Autoregressive Network with Exogenous Inputs (NARX).The parameters considered for the developed ANN configuration are shown in Table 1.
Table 1. Design specifications of the developed ANN Design Parameters Selected value / item for
parameter
Input Variables 𝑉𝑑𝑐 𝑎𝑛𝑑 𝐼𝑑𝑐 (DC Voltage and Current)
Output Variables 𝐷 (Duty Cycle) Activation Function Sigmoid function Training Function trainscg (Scaled
Conjugate Gradient) Input and feedback
delays
2 Hidden Layer
Neurons
4
Neural Network type Nonlinear Autoregressive Network with Exogenous Inputs (NARX)
According to [7], the function that describes the input-output relationship of the dynamics of the ANN using NARX can be explain in the following equation:
𝑦 𝑡 = 𝐹 𝑥 𝑡 , 𝑥 𝑡 − ∆𝑡 , … , 𝑥 𝑡 − 𝑛∆𝑡 , 𝑦 𝑡 , 𝑦 𝑡 − ∆𝑡 , … , 𝑦(𝑡 − 𝑚∆𝑡) (6) Where,
𝑦 𝑡 = Output value 𝑥 𝑡 = Input value
𝑛 = number of time delays in input
𝑚 = number of time delays on the feedback (output).
By applying Equation (6), The NARX architecture was developed by using MATLAB R2018b neural network time series app. 𝐹 is typically a nonlinear function which is initially unknown. This function is estimated by using a standard feedforward network approach. Thus, a sigmoid function was used as transfer function to compute the mathematical calculation of the time series. DC voltage and current v were taken as inputs for the ANN developed.
Input delays and feedback delays for the architecture were selected using trial and error method. The constant delay of 2 was found to perform better than other values tested. The performance was evaluated based on regression plot which plots the target values against output values. Thus, a value of 2-time delays was chosen for input delay as increasing the time delay causes the training process to be longer. Similarly, after the evaluation of regression plot of the network using various feedback delays, the value of 2 was chosen as the optimum feedback time delay to be used in the network chosen because it resulted in highest accuracy among the other values tested.
The hidden layer size of the system was selected to be 4. This value was selected because it yields a better performance than the other values tested. The performance was evaluated using a ‘Structured trial and error’ method, it was found out that 4 neurons in the hidden layer gives the best possible performance. Thus, one hidden layer with 4 neurons were implemented in the system.
The total number of samples collected are 625488 for DC voltage, DC current and duty cycle. The DC voltage and current were taken as the input of the ANN. The corresponding 625488 samples of duty cycle were taken as the target data for training the ANN. In connection with that, all these data were segregated into three sets where 70% will be used for training, 15%
for validation and another 15% for testing. Before the testing process, these raw data were preprocessed using normalization function which actually convert these input values to better fit the training procedure. Thus, these values were normalized in preprocessing in the range [0, 1]
by using feature scaling before being used in the training process of the network.
Training algorithms play a very important role in developing a neural network. This is because each training algorithm have different kind of problems it can solve. There are various commonly used training algorithms in neural network such as Bayesian regularization,
Levenberg-Marquardt, Gauss-Newton, BFGS, Quasi-Newton, Resilient Backpropagation, Scaled Conjugate Gradient and many more. These algorithms have their own advantages and disadvantages compared to each other. Thus, the training algorithm used in the developed AI was Scaled Conjugate Gradient (SCG) algorithm. As one of Conjugate Gradient algorithms which is efficient in finding solution along conjugate directions, this algorithm was used as the training algorithm. In connection with that, SCG also typically produces faster convergences compared to gradient descent methods used by training algorithms such as Levenberg-Marquardt. This algorithm is conjugated with respect to the Hessian Matrix. With respect to that, according to [8], the initial training direction calculation can be summarized as stated in equation (7).
𝑑𝑖+1 = 𝑔𝑖+𝑖+ 𝑑𝑖𝜌𝑖 (7)
𝑤𝑖+1 = 𝑤𝑖 + 𝑑𝑖𝛾𝑖 (8)
Where,
𝑑 = Training direction vector 𝑔 = Conjugate gradient 𝜌 = Conjugate parameter 𝑤 = weights of the network 𝛾 = learning rate
In short, it can be said that through this training algorithm, training direction vector d is constantly altered based on the error in each iteration of training process. From equation (8), it can be seen that, altered training direction vector causes the new weights to change thus constantly changing weights of the network for each iteration. By applying default values of 0 to both conjugate gradient and conjugate parameter, the weight optimization process of Scaled Conjugate Gradient was initiated.
SIMULATION RESULTS AND DISCUSSION
In this research, wind energy conversion system is implemented along with P&O and NARX MPPT techniques based on SEPIC converter configuration for a standalone operation.
For a more realistic approach, the parameters of AEOLOS H-3kW were considered for the simulation. The design parameters from the wind turbine data sheet are shown in Table 2.
Table 2: AEOLOS Wind Turbine 3kW Specification Characteristic Value
Rated Power 3kW Maximum Output
Power
4kW Rated wind speed 12 m/s Cut-in wind speed 3.0 m/s Cut-out wind speed 25 m/s
Voltage 220-240 V
Rotor Diameter 5m
Rotor blade radius 2.4m
Generator type Three Phase PMSG Stator phase Resistance 0.425 Ω Armature Inductance 0.00835 H
Flux Linkage 0.433Wb-t
Number of poles 4
The parameters of the SEPIC converter used in this research are shown in Table 3. The MPPT control strategy adapted in this paper are approximated to overcome the nonlinearity of the system and also to extract the maximum available power at the particular wind speed.
Different wind speed patterns were tested to determine the efficacy of the developed algorithm.
Constant, stepwise and random wind speed patterns were considered in this research. For the constant wind speed, 12m/s was used. The stepwise wind velocity considered for the wind turbine is 8m/s (from 0 to 0.5s), 10m/s (0.5 to 1s) and 12m/s (1 to 1.5s). And finally, the random win speed was set as 12m/s (from 0 to 0.5s), 7m/s (0.5 to 1s) and 12m/s (1 to 1.5s).
Table 3: SEPIC Parameters Parameter Value Switching
Frequency
50kHz Input Capacitor 8μF Output Capacitor 2mF SEPIC Capacitor 2μF
Inductor 5mH
Load Resistor 48.13Ω
Simulation studies for the different wind speed variations are performed for SEPIC converter with ANN and P&O MPPT algorithms to test their accuracy. The results of the different wind speed variations with different MPPT control strategies are shown in figures below.
Figure 4: Output power for constant wind speed test with ANN and P&O MPPT techniques
Figure 4 shows the comparison of the output power for constant wind speed implemented with P&O and ANN MPPT control techniques. Thus, from the results, it can be observed that the ANN MPPT control strategy achieved higher output power gain than the P&O MPPT technique.
The output power of the ANN as obtained as 3000W while the P&O was only able to achieve 2789W.
Figure 5: SEPIC Output voltage for constant wind speed test with ANN and P&O MPPT techniques
Similarly, Figure 5 shows the output voltage of both ANN and P&O techniques under constant wind speed test. It is observed that the WECS delivers a more stable performance with constant rated voltage of 380V when the ANN based MPPT control technique is implemented.
Figure 6: Output power for step wind speed test with ANN and P&O MPPT techniques
Figure 7: SEPIC Output voltage for step wind speed test with ANN and P&O MPPT techniques
Figure 6 and 7 shows the results obtained for step wind speed test. The ANN MPPT strategy delivers an output power of 888.3W at 8m/s, 1736W at 10m/s and 3000W at 12m/s.
Figure 8: Output power for random wind speed test with ANN and P&O MPPT techniques
Figure 9: SEPIC Output voltage for random wind speed test with ANN and P&O MPPT techniques
Also, Figure 8 and 9 shows the results of the random wind speed test. The output power when ANN MPPT technique is implemented was obtained as 604.1W at 7m/s and 3000W at 12m/s.
CONCLUSIONS
A mathematical wind turbine has been developed with a unique approach in Simulink that uses dashboard control. The trained ANN MPPT algorithm was able to predict optimum values for the duty cycle to extract maximum power from wind energy conversion system. The developed conventional P&O MPPT algorithm was compared with the proposed ANN MPPT algorithm to evaluate the performance of the proposed algorithm. The ANN MPPT algorithm achieved a 99.96% efficiency whereas the P&O was only able to achieve 92.8%.
ACKNOWLEDGEMENTS
I would like to take this opportunity to thank ALLAH Almighty for the good health and well-being that were necessary to see me through 4 years of studies until this point when I can submit my final year project. I would like to express my deep sense of gratitude to my supervisor ASSOC. PROF. DR. SATHISH KUMAR SELVA PERUMAL for his patience, guidance and encouragement to proceed with this project. Without his involvement, assistance and attention to detail, this project would have never been accomplished. Very special thanks to MS HAZWANI, my second marker for her enthusiasm, clear instructions and constructive feedbacks.
I am grateful to all the lecturers and staff of the School of Engineering who always work hard to make our department stand out amongst others. Their dedication is a force that motivates us to work harder.
I would like to dedicate this work to my beloved parents who have been consistently supporting me and acting as a backbone of my success. I would also like thank all my friends, and others whose suggestions and encouragement have contributed to the successful completion of this project.
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