2018 International Conference on Smart Grid and Clean Energy Technologies
Design and Implementation of a Maximum Power Point Tracking Algorithm for Wind Turbines Using PLC-SCADA
Zaid Samer Al Shattle, Senthil Arumugam Muthukumaraswamy School of Engineering and Physical Sciences Heriot-Watt University Dubai, UAE e-mail:
[email protected];
[email protected]
Abstract—Power tracking has been one of the significant fields of improvements for wind turbines. Systems have been implemented using both mechanical and electrical methods to maximize the power output of those systems. This paper discusses the design and implementation of an algorithm for maximum power point tracking using voltage control. The algorithm will be applied using a PLC-SCADA system and results will be discussed. Keywords-maximum power point tracking; programmable logic controller; supervisory control and data acquisition
I.
Figure 1. Example of mechanical MPPT controller [7].
INTRODUCTION
Development of renewable resources is one of the most critical aspects that clean energy technology is working towards. While clean energy is looked upon positivity, there are still some issues with the placement and how people perceive having those methods of generation in their direct proximity [1], [2]. Those issues usually cause incidents where the creation of more wind turbine farms can be more difficult. Moreover, with the limitation of location for both protected areas and areas that do not have enough wind for a sustainable development more issues start to pop up [3], [4]. Because of those issues and other issues, a suggested methodology is to work on enhancing the current system instead of adding new turbines. Research has shown that applying optimum Maximum Power Point tracking (MPPT) methods can improve the efficiency significantly reaching up to 25% [5]. Most MPPT methods for wind turbines currently use mechanical methods for tracking [5]. Mechanical systems for MPPT are efficient but have the issue of needing to modify the hardware of the turbine itself. One other issue is that any issues in the system will need full maintenance for the system which can increase the downtime significantly, that is, why there is a focus on replacing pure mechanical systems with more electrical systems [6]. Fig. 1 shows an example of a mechanical MPPT controller. This controller will depend on the pitch control for controlling the maximum power point of the system [7]. It can be evident that the system is very complicated. Comparing the system to an electrical based system with an MPPT algorithm would make it clear that electrical systems would be much simpler as shown in Fig. 2.
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Figure 2. Example of electrical MPPT system.
This paper will discuss the design and experimentation of the controller design. The system will use a PLC for the implementation of the algorithm for the system. Similar systems with charge controllers are often used for solar systems [8]. In addition to using PLC to control the algorithm, SCADA will be used to both control, monitor, and plot the results for the system. II.
METHODOLOGY
For the algorithm, the goal is to build a system to track the maximum power point of the wind turbine. Since the system is only using an electrical system to both monitor and control the system, only voltage and current can be sensed and controlled. Unlike solar where there are many methods of applying the MPPT, it is much more limited for wind as it is much more dynamic, and the changes might take some time to start giving an effect. Several ideas were considered for the MPPT algorithm. For example, Successive approximation was at first considered and tested. However, it suffers from a lack of continuous tracking as it will reach a result relatively faster but will not progress from that point onwards correctly.
taken to solve any issue of inaccuracy. One hundred steps were chosen as it provided a balance between accuracy and time taken. Two systems were developed, one automatic mode which follows the structure shown in the flowchart to get the maximum power. The other system is the manual system which helps for troubleshooting and getting experimentation data.
On the other hand, direct-conversion is not possible as only one value can be compared at a time in the system due to how wind turbines function. The same issue that faces successive approximation appears when using a counter system. Due to not having a continuous system, it is not very reliable. After testing various methods, it was found out that combining a counter method with the steepest descent would allow a system where the algorithm will try to head towards the point with maximum power and stay at that point until the wind conditions change [9]. The general equation for the steepest descent method is ˆ wk 1 wk E k (1) wk 2E ek xk where wk 1 is the next value to be tested, wk is the current value, β is the learning factor, ek as the error factor and x as the input of the system. A steepest descent algorithm is usually used in filters to find the value of an unknown differential. By using the same method here we are able to obtain the maxima which represents the maximum power point. While that was general equation is used in filters, it can be easily modified to be used in our system take the form of: (2) wk 1 wk r E ek dx
Figure 4. Used SCADA interface.
For the SCADA system, a custom system that was previously developed for previous projects was used. The goal of the developed SCADA system is to simplify the construction and application of SCADA interface when changes need to be applied to the system. And it has thus been used in multiple applications [10]. Fig. 4 shows an example of an interface developed in an initial version of the SCADA interface. The system is customizable and allows for both monitoring and control of the system. Allowing manual and automatic control with fine tuning as well as providing a graph. Experimentation-wise the system has been constructed by having a wind source and a turbine on a rail where the distance can be adjusted to test various wind conditions. It is worth noting that the experimentation does not attempt to calculate the exact specifications of the system but will instead focus on the improvement regarding the standard system due to lack of some critical information. The gap between the generator and the turbine will vary from 2 cm to 10 cm for this experiment. Fig. 5 shows the experimental setup.
where dx is the change due to the step. It must be noted that for this system there is no final value for the system as the system will end up oscillating the moment it reaches the final output. That is done for monitoring any changes in the wind even after the final speed is reached. To apply the system FM1616-10 Programmable Logic Controller (PLC) was used for controlling the algorithm. The general algorithm had a few issues of reliability as small changes can be not easy enough to detect and analyse for the system due to having capacitors in the switching circuit.
Figure 5. Experiment setup.
Figure 3. System flowchart.
Due to that limitation, the prototype used more significant steps with a slightly different implementation to make the system more reliable. Fig. 3 shows a flowchart of the proposed system. Note that numerous samples were
Figure 6. Switch percentage/level.
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Lastly, the load was taken as a modified log-based scaling. Fig. 6 shows the level of the switching PWM duty cycle. Thus, for any level to be used, we will be using an approximated equation for the PWM duty cycle as shown in Fig. 6.
It can be noticed in Fig. 7 that by increasing the load the voltage decreases. That makes sense as increasing the load means increasing the percentage of the time that the transistor is conducting energy, in other words, it is making the transistor as close to be a short circuit as possible. That causes the voltage to be lower as the resistance decreases. For current, however, it is a different story as shown in Fig. 8. It can be noticed that at higher loads the current increases as the resistance becomes lower. However, it will not keep increasing indefinitely as at higher loads the increase in current will be lower than the decrease in speed which will cause the current to stagnant.
RESULTS AND DISCUSSION
III.
The system was tested in both normal load and automatic mode. Results are for automatic mode below: TABLE I. Load (level)
Distance (cm)
EXPERIMENTATION DATA I (mA)
P (mW)
Direction
0
2
18
V
26
468
++
1
2
16
37
592
++
2
2
15
43
645
++
3
2
14
52
728
++
4
2
13
57
741
++
6
2
9
60
540
--
4
2
12
56
672
+
5
2
10
57
570
-
Figure 9. Power and load graph.
Overall Fig. 9 shows the power graph. For the power/load graph, it is noticed that there is a power that gives the maximum power. That point is the MPP (Maximum Power Point), and the goal is to stay around it as much as possible.
It must be noted that in the current experimental setup each stage change takes 2 seconds, which means that the system needed 16 seconds to reach the ideal load. Table I shows the process of the experiment with the load automatically changing to try to achieve the maximum level. In the beginning the load is increasing with a certain rate until it reaches the “maximum” point. Afterwards it decreases to fine-tune the point. After it reached the ideal point however it did not stop, but instead it started oscillating to get the most accurate results possible. Much of that is due to limited test equipment for accuracy. A simple circuit with a big capacitor and a substantial number of samples was used with 16-bit sampling on the ADCs then 100-samples in the system itself. It has been estimated that with proper circuitry design and equipment the changes can be made up to 10ms per stage change. For that set of results, the following are the graphs for voltage, current and power per load.
TABLE II. Load (level) 0 1 2 3 4 5
DATA AT MAX DISTANCE
Distance (cm) 10 10 10 10 10 10
V 11 9 7 2 0 0
I (mA) 24 26 23 14 0 0
P (mW) 264 234 116 28 0 0
Next dataset showcases the power at a maximum distance at various loads. Table 2 shows the results of power at the maximum distance used. Some of the results showcase how the maximum power can vary from certain loads and distances. It might be hinted that there are some issues from the conclusions gathered below as the current at loads 4 and 5 are zero, and the current increases when decreasing the load from 3 to 1. However, the explanation is that at higher distances the airspeed can be insufficient to move the turbine. Those situations are the primary concern for MPPT. Due to the fact that at slower wind speed the fan output might be almost negligible unless the load is decreased significantly, it is essential to use proper methods of Power tracking.
Figure 7. Voltage and load graph.
IV.
COMPARISON OF MPPT SYSTEMS
When comparing the results of the system discussed with existing systems there are two main fields of comparison: - Comparing between the mechanical and electrical MPPT system. - Comparing between existing and proposed MPPT systems.
Figure 8. Current vs load.
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It is to be noted that if we substitute the distance of the wind generator with another variable such as wind speed, it might be possible to create a solution for the system which will automatically adapt to the maximum power point depending on the speed [10]. However, that is beyond the scope of this research which is more of a proof of concept of the algorithm while demonstrating results.
When discussing mechanical MPPT systems they suffer from several issues, the main issue is reliability. Due to having those systems depending on mechanical switching actions such as gear boxes, adjustable blades and moving turbines, they are more likely to breakdown or face an issue. Second is cost. To design a mechanical MPPT system very expensive modifications need to be done. That can be a very limited factor in renovation projects as they can’t be modified and fixed without very major costs. Lastly will be the ease of implementation. The Electrical MPPT system proposed is viewed to be easily implemented without any modifications to the turbine itself and can be easily installed in the system without any issues. Comparing the MPPT system proposed to other existing electrical system MPPTs a number of changes that were proposed and fixed are shown: - The existing MPPT controllers were mostly adjusted and configured for solar power. While it can function correctly possibly, it is not tested and can cause issues. - The MPPT proposed has a much highest theoretical speed which can be reached if correctly designed and implemented. - The system is designed as a standalone that can be implemented without changing any part of the system, but rather by “plugging in” the MPPT controller. Due to those differences the algorithm proposed and developed is advised to be taken further in research as it can theoretically be an improvement to existing systems and is tested and improved with wind turbines in mind. V.
REFERENCES [1]
J. B. Graham, J. Stephenson and I. J. Smith, "Public perceptions of wind energy developments: Case studies from New Zealand," Energy Policy, vol. 37, no. 9, pp. 3348-3357, 2009. [2] P. DevineϋWright, "Beyond NIMBYism: towards an integrated framework for understanding public perceptions of wind energy," Wind Energy, vol. 8, no. 2, pp. 125-139, 2004. [3] F. Wang, D. Liu and L. Zeng, "Modeling and simulation of optimal wind turbine configurations in wind farms," in Proc. of 2009 World Non-Grid-Connected Wind Power and Energy Conf., 2009, DOI: 10.1109/WNWEC.2009.5335756. [4] C. H. Gundling, "Method for enhancement of a wind plant layout with multiple wind turbines," United States Patent 7941304, 2011. [5] M. A. Abdullah, A. H. M. Yatim, C. W. Tan and R. Saidur, "A review of maximum power point tracking algorithms for wind energy systems," Renewable & Sustainable Energy Reviews, vol. 16, no. 5, pp. 3220-3227, 2012. [6] G. Leen and D. Heffernan, "Expanding automotive electronic systems," IEEE Computer, vol. 35, no. 1, pp. 88-93, 2002. [7] M. Allagui, O. Hasnaoui and J. Belhadj, "A 2MW direct drive wind turbine; vector control and direct torque control techniques comparison," Journal of Energy in Southern Africa, vol. 25, no. 2, pp. 117-126, 2014. [8] P.-C. Pasc and C.-D. Dumitru, "SCADA System for Solar MPPT Controller Monitoring," presented at the 9th Int. Conf. Interdisciplinarity in Engineering, Romania, 2016. [9] Z. Kalousek, "Steepest Descent Method with Random Step Lengths," Foundations of Computational Mathematics, vol. 17, no. 2, pp. 359422, 2017. [10] Z. Al Shattle and S. Muthusukamawamy, “On the Comparison of various Wind-turbine Load Control Systems for Maximum power tracking using PLC–SCADA”, IEEE ICISS 2017, Accepted for publication.
CONCLUSION
Overall an MPPT algorithm was developed and tested. Test results are showing that it is possible to expand and use it further with specific improvements. The SCADA implementation is of important note as it is used for increasing the accuracy of the system and gathering data, which have been vital for this project.
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