16 December 2020

6.2% increase in PV plant production with TeamTrack™, Soltec's backtracking algorithm

Field test results and simulations carried out by TÜV Rheinland® confirm that TeamTrack™, Soltec’s backtracking algorithm, increases photovoltaic power plant production to 6.2%.

01. Abstract

Tracking algorithms seek the closest angle to the sun’s perpendicular position, being the backtracking algorithms are responsible for adjusting the angles as required to prevent shading in between trackers. Since the position of the tracker affects its shade cast, surveys play a mayor role in the backtracking angle assessment. In addition, backtracking algorithms make trackers work together to ensure each tracker “sacrifices” part of its natural position with the aim of reducing cast shadows and, hence, maximizing overall power production.

In this whitepaper we present the results of a full study carried out by TÜV Rheinland®, certifying Soltec’s TeamTrack® Backtracking algorithm behaviour over 9 sites with different terrain and latitude surveys. This study on TeamTrack® shows that the yearly energy yield increase in ranges between 3.6% and 7.5% compared to standard Tracking. Energy yield optimized with respect to Standard Backtracking oscillates over the tested sites between 1.2% and 3.5%.

TeamTrack® is compared to other backtracking strategies in the Mediterranean region with regular and irregular terrain cases. It presents improvements in ranges between 0.23% and 1.45%, compared to 1P trackers with central sensors, and up to 1.43% compared to 32-linked-row trackers.


As opposed to fixed structures, solar single axis trackers can prevent photovoltaic module “self-shading”. During sunrise and sunset hours, when the sun is low, trackers can position themselves at an angle preventing shadow cast onto the back-row panel, as shown in figure 1. This functionality, known as backtracking, has a clear purpose: To prevent shading and maximize production.

Figure 1a: Standard Tracking keeps tracker at maximum angle for low solar elevation angles, causing self-shading.
Figure 1b: Backtracking consists in rotating tracker to lower inclination angles until shading is avoided. Example applying only to closest tracker.

Tracking algorithms seek the angle closest to the sun’s perpendicular position, being the backtracking algorithms responsible for adjusting this angle as required not to shade the remaining trackers. Since the position of a tracker affects the others, backtracking algorithms have to get them to work together to ensure each tracker “sacrifices” part of its natural position with the aim of reducing cast shadows and, hence, maximizing overall power generation.

In this sense, it is advised to balance irradiation received on different planes (i.e. to balance solar tracker angles) to reduce mismatch losses between strings, as shown in figure 1c.

Commonly, standard backtracking angle is calculated according to the following formula [1]:

W = width of the tracker

Figure 1c: Standard Backtracking sets all rows to the same angle, according to the formula, which does not consider land survey.

Even if the application of these basic geometric principles may seem simple, obtaining a backtracking algorithm that actually maximizes power generation requires taking into consideration factors or challenges which determine the difference between standard backtracking systems and a fully sophisticated backtracking system such as Soltec’s TeamTrack®.

02. The terrain challenge
02.1. East-West Slopes

The topography of the terrain has a direct impact on shadow projection because absolute module height varies according to terrain level curves [2,3]. For example, standard backtracking is insufficient in continuous slopes because it does not take them into account, thus resulting in power generation losses, as shown in figure 2.

Figure 2a: Standard Backtracking causes self-shading on Eastward slopes land during sunset.
Figure 2b: Optimized Backtracking Algorthm (BTA) avoids self-shading.

During sunset, in case of an Eastward slope (figure 2a), the corrected angle (figure 2b) should be less inclined than the angle calculated by standard backtracking, as the latter would result in increased shade-induced power losses. On the contrary, in case of a Westward slope (figure 2c), trackers could be more inclined (figure 2d) than calculated by standard backtracking formulas, leading to strips of sunlight on the ground and introducing losses due to misorientation or decreased optimization. The same thing would occur during sunrise, but the other way around, as described in table 1:

Table 1: Non-optimized effects of Standard Backtracking over land slopes.
Phenomena caused by the application of Standard Backtracking
Eastward slopeMisorientationShading
Westward slopeShadingMisorientation
Figure 2c: Standard Backtracking promotes ‘strips of sunlight’ on Westward Slopes land at sunset.
Figure 2d: Optimized Backtracking moves tracker onto a more oriented angle.

In other words, to prevent this situation of non-optimized plant generation, it is necessary to use an evolved backtracking algorithm that takes into account terrain slopes. In this case, this algorithm would be able to maximize generation during sunrise and sunset, as shown in figures 2b and 2d.

02.2. Irregular Slopes

We know most terrains are not flat, but it is also necessary to consider that slopes can have more than one direction and inclination within the photovoltaic plant site. Slopes tend to have various inclination angles, hence making things more complicated.

In the case of East-West slopes, “sunken” trackers, that is, trackers at a lower height than adjacent trackers due to changes in slope, tend to be shaded when a standard backtracking algorithm is used. As for “peak” trackers, meaning those sitting higher, their generation will never be optimized, as shown on figure 3a.

Figure 3a: Standard Backtracking at a peak tracker does not optimize oriented angle and cast a shadow in the back-row tracker.
Figure 3b Backtracking algorithms that considers irregular slopes avoid shading casted by peak trackers.

In such cases, power generation is considerably increased if more sophisticated backtracking algorithms are used, as shown on figure 3b. Such algorithms should consider the slope of all trackers to determine optimal positions and solve configurations prone to shading.

02.3. North-South Slopes

In the case of North-South slopes, changes in slope along tracker axis are also an issue which worsens the longer the tracker is. As shown in figure 4b, the standard backtracking formula calculated for the most Southern tracker end, cannot take the other tracker end into consideration, thus projecting triangular shades along the tracker.

Figure 4a: Standard Backtracking estimate the tracker angle on flat land.
Figure 4b: Standard Backtracking on North-South tilted trackers casts shading in lower tracker extreme.
03. Layout or plant design challenge
03.1. Alignment

The backtracking strategy is usually applied to East-West tracker alignments. However, when actuation is fully independent, shading may occur on the North-South direction in trackers of the same row and of adjacent rows (figure 5). This inconvenience is more relevant at higher latitudes (larger azimuth), where this type of shading occurs.

This is even more complicated by the fact that layout also has a significant impact on rows when these are misaligned due to typically irregular plant contours, as shown in figure 5.

To address these inconveniences, calculation techniques are used to ensure tracker positioning prevents shading also between adjacent rows N-S and power generation is optimized. These calculations are part of the so-called ‘backtracking algorithms’, which are more effective depending on their level of sophistication.

Figure 5: Shades in North-South direction cast by trackers with different angles. Angles of groups a, b and c are assessed according to different survey East-West slopes.
Figure 6. Example of Backtracking Algorithm that gets trackers work together.
04. TeamTrack, Soltec's algorithm to maximize power generation
04.1. Soltec’s TeamTrack Algorithms were validated by TÜV Rheinland®

To address all above-mentioned challenges, Soltec worked for years to develop and perfect TeamTrack, a tracking algorithm which models actual plant topography and determines all time the best set of angles that prevents shading and maximize generation.

In this section, the results of an in-depth analysis carried out by TÜV Rheinland® of TeamTrack® are presented through the evaluation of different plants and designs.

04.2. TeamTrack Overview

The TeamTrack algorithm uses 3D-Survey geometric analyses to consider the relative position of each tracker in the layout, with the aim to continuously checks the possibility that a tracker might be shading an adjacent tracker. Based on expected sun position by NREL’s SPA, the algorithm assesses various angle combinations previously to command the trackers position, confirming that the adopted position is more suitable to prevent shading and maximize production of the plant as a whole.

04.3. TÜV Rheinland®Independent Third Party Verification

TÜV Rheinland®, leading technical service organization worldwide, drafted an independent third-party report to assess Soltec’s TeamTrack® algorithm effectiveness.

Scenario Description

o evaluate algorithm robustness, TÜV Rheinland analyzed 9 different scenarios: 3 sites with different climate conditions and latitudes (shown in table 2) and 3 different survey types, which are as follows:

Very irregular = variable slopes in East-West (Ơ2 = 4.8 deg2) and North-South direction (Ơ2 = 4.1 deg2)
Irregular = variable slopes in East-West (Ơ2 = 4.6 deg2), constant North-South direction (Ơ2 = 0.9 deg2)
Regular = constant slopes in East-West (Ơ2 = 0.7 deg2), constant North-South direction (Ơ2 = 0.2 deg2)

Table 2: Sites Characteristics
RegionLocationLatitudeG Horizontal
ClimateDaylight Temperature January (ºC)Daylight Temperature July (ºC)
MediterraneanSpain41.01 N1700Semi-arid1232
NorthernGermany52.55 N1000Cold maritime424
EquatorialBrazil6.87 S1900Hot and humid2727
Table 3: System description of PV plant
System Information
Size of plant8064 kWp
Module tilted / Orientation1-axis tracker E / W
Type of installationGround
Module typeHanwha Q Cells Q.PLUS L-G4.2 350
Inverter typePower Electronics FS3000
Strings per tracker3
Modules per string30

For this comparison, an 8064-kWp photovoltaic plant with 350Wp modules and 2 centralized inverters was used. 30-Module strings were grouped in DC combiner boxes. (Table 3). The model considered the shade-induced electric losses of modules, including module bypass diodes. The plant is comprised of a total of 256 SF7 trackers by Soltec, 2×45, 2-in-portrait modules installed with a 10-meter pitch (GCR: 40%).


For each scenario the individual tracking angles of the different tracker are provided by the client and serve together with the tracker position as input for the shading analysis of the system. After calculating the position of the sun, the shaded area of the tracker can be determined for each time step (1min) over a whole year. Taking the shading situation and the electrical behavior of the PV modules into consideration, the power output for each tracker and the summarized yield at the inverter are calculated.

The 9 sites (combination of 3 layouts at 3 latitudes) has been simulated for a full year with a 1 minute resolution, taking the set of angles determined by TeamTrack®, standard tracking (defined in figure 1a ) and standard backtracking (figure 1c). Results are compared as follows.

Comparative Results

TeamTrack: Shadow-Free and Maximum Generation.

As previously mentioned, the TeamTrack backtracking algorithm processes NREL’s solar position data together with terrain irregularity to obtain backtracking angle positions which optimize power generation; eliminate shading between rows during sunrise and sunset and ensure utmost production.

Figure 7a illustrates the percentage of module surface shades calculated for a specific date (June 21st), in regular terrain for Mediterranean latitude. Graph shows the different behavior of TeamTrack®, Standard Backtracking, and Standard Tracking. TeamTrack algorithm effectiveness becomes evident when compared to standard strategies, which do not take slopes into consideration (as previously illustrated in figure 3).

Figure 7a: Percentage of shaded module area for June 21st (1-minute resolution)

The graph shows significant shade reduction compared to standard tracking and backtracking strategies due to terrain westward slope, especially during sunrise, when trackers with standard tracking and standard backtracking are shaded, whereas TeamTrack keeps solar trackers shade-free1. This improvement is translated directly into greater power generation, represented in figure 7b. Energy Yield that day was 3.4 kWh/kWp under standard tracking, 3.5 kWh/kWp under standard backtracking and up to 3.7 kWh/kWp using TeamTrack, meaning a 9% increase.

While the tracker with standard tracking and even backtracking are shaded in the morning, the tracker with TeamTrack are mainly shade free.

Figure 7b: Specific AC Power for June 21st (1-minute resolution)

Comparisons of the 9 cases conclude that the use of TeamTrack results in a yearly energy yield increase ranging of 3.6% to 7.5% with respect to Standard Tracking. Energy yield optimization with respect to Standard Backtracking at the different sites ranges from 1.2% to 3.5%.

The results for the case of regular terrain in Mediterranean latitudes (41º) are presented below. Three strategies of backtracking have been analyzed to get the results: standard tracking, standard backtracking and TeamTrack.

Energy yield by Team Track increases in 6.2% and 2.3% with respect to Standard Tracking and Standard Backtracking. This increase is consistent over the months, as shown in figure 8.

Figure 8: Monthly specific AC yield of TeamTrack® and common tracking algorithms.
Table 4: TeamTrack energy yield. Monthly comparison with common tracking algorithms.
Spec. YieldTeamTrack-Std Diff[%]TeamTrack-StdBT Diff [%]

It is interesting to analyze the month of December, when under average latitude and regular terrain conditions, power generation increases up to 9.5% compared to standard tracking and 5.1% when compared to standard backtracking. The highest monthly increasement is reached in December in northern latitude, with 14.1% and 18.9%, respectively.

Baseline case summary Mediterranean region Semiarid climate
YEARLY improvement +6.2%
Maximum monthly improvement +9.5%

Results for the 9 defined scenarios are shown in the table below and confirm that Soltec’s TeamTrack algorithm leads to higher annual yield gains ranging between 3.6% and 7.5% in comparison to a standard tracking algorithm.

Table 5: Annual yield gain with Soltec’s TeamTrack compared to Standard Tracking.
05. Tracker topologies and advanced backtracking algorithms: Selecting the most suitable strategy

As seen above, the main drawbacks of backtracking algorithms are linked to terrains of complex topography. In fully flat and horizontal terrains standard backtracking can provide acceptable results. However, considering the increasingly larger size of photovoltaic plants, it is uncommon to find terrains with such characteristics. Furthermore, optimized backtracking avoids the need for large earthworks during the project’s civil phase, thus offering economic and environmental benefits.

Backtracking is also affected by the tracker type used. Although there are many types of trackers, two of them easily stand out: decentralized or independent and centralized.

Although most Tier 1 solar tracker manufacturers use backtracking algorithms to try to prevent the inconveniences described in the first section, the type of tracker selected determines the backtracking algorithm strategy that can be implemented onsite. Below we analyze the strengths and weaknesses of key strategies, comparing them to irradiance optimization achieved with TeamTrack.

05.1. BTA Strategies using Sensors

Trackers following sensor-based backtracking strategies are equipped with cells in strategic spots to detect shades. As it is shown in figure 9, when shade reaches the sensor, the tracker corrects the angle and controls its position based on sensor feedback. This type of strategy does not require knowing terrain topography beforehand but has several inconveniences.

Firstly, this type of control only ensures correct performance at the point where the sensor is placed. For example, a sensor installed in the middle would only prevent shading in the middle. However, in the case of long trackers with North-South inclination there would be shades in the extreme closest to the ground, as shown in figure 10.

Backtracking control systems of this type tend to include a buffer, applied to all trackers to prevent this type of shading and correct installation inaccuracies. This buffer positions trackers at an angle lower than the calculation angle, making it possible to prevent shading on solar trackers installed on terrains with steep slopes. However, that will also causes an increase of power losses due to misorientation on the trackers installed on the areas with a less steep slope.

Figure 9: Backtracking algorithm based on sensors allows strips of sunlight.
Figure 10: Example of shading in BTA equipped with middle sensors in trackers with North slope.

As above mentioned, a visual indicator to evaluate backtracking algorithms optimization and quality is to observe the level of radiation that reaches the ground (‘strips of sunlight’). This translates into energy not reaching the tracker and, therefore, into power generation loss and terrain misuse.

The inaccuracy of these systems can be mitigated by means of machine-learning techniques and software. However, that takes a long time and never quite reaches the reliability level of topography modeling strategies. Furthermore, these control systems depend on sensor reliability, thus increasing the need for periodic cleaning, inspections and maintenance requirements to ensure proper performance, as well as increasing operations and maintenance costs. Sensor-based positioning strategies increase the risk of failure and, therefore, of energy losses.

The base case described in section 2 (Mediterranean latitude and regular terrain) was used to implement a layout in which 1-in-vertical configuration trackers are equipped with a sensor in the middle to simulate backtracking strategy operation.

Evaluation is carried out by assessing the following indicators:

Shading (SH) is a percentage average of the shaded area, which does not include energy.

Misorientation (MSO) quantifies the lack of orientation induced. It is calculated as the relationship between irradiance on non-shaded areas (Gplane ) and the maximum irradiance that would theoretically be available, i.e. with a standard tracking strategy (trackers perfectly oriented) and without shading.

Finally, BT Effectiveness (BTEf) is an index that includes MSO and SH effects.

The following table shows those indicator results:

Table 6: Evaluation of BT strategies with middle sensor. (G objective = 2331 kWh/m2).
BT StrategyLayoutG effective (kWh/m2)Misorientation Losses (MSO)Shading (SH)BT Effectiveness (BTEf)Improvement Vs StdTrack
Middle sensorregular2.207-5,3%0,66%94,10%6,10%
Middle sensor +buffer 25cmregular2.182-6,4%0,55%93,07%4,88%
Figure 11. Comparison of SH and MSO of Middle sensor with and without margin with TeamTrack.
BTA Strategies in Linked-Row Trackers

Linked trackers are trackers in which two or more rows are linked mechanically, meaning their tilting angle is the same. Movement restriction in this type of trackers limits their capacity to optimize backtracking adaption to terrain topography. (figures 12.a and 12.b).

Although shades can be prevented by positioning trackers more horizontally, such position is not favorable in terms of energy yield (figure 12.b).

Figure 12a Standard Backtracking in linked trackers on East-West slopes casts shades and induces strips of sunlight.

On the contrary, the positioning of decentralized or independent trackers is better adapted to terrain characteristics. The level of algorithm sophistication determines the level of optimized power generation. Shades and strips of sunligth can be almost avoided with a sophisticated backtracking algorithm, as seen in figures 12c and 12d.

Figure 12b Backtracking algorithm in linked trackers on East-West slopes induces strips of sunlight.
Figure 12c Backtracking algorithm in independent trackers on East-West slopes induces minimal strips of sunlight.
Figure 12d Soltec’s TeamTrack in independent trackers on East-West slopes completely avoids shading and strips of sunlight.

The larger the number of linked trackers, the more detrimental this lack of optimization due to misorientation. The following table shows the percentage of annual shading for various linked or central trackers configurations for the same base case (described in section 2), ranging from dual-row trackers to others connecting up to 32 trackers driven by a single engine.

This lack of optimization will be quantified using the “misorientation index”, which annual results for both regular and irregular layouts are shown in table 7.

Table 7: Comparison of annual shading for linked trackers *Percentages with respect to the non-shading case. (G objective = 2331 kWh/m2).
BT StrategyG effective (kWh/m2)Misorientation Losses (MSO)Shading (SH)BT Effectiveness (BTEf)Improvement Vs StdTrack
Single row 1P2.2012204-5,6%-5,4%0,55%1,08%93,92%93,59%5,84%5,92%
Dual-row 2P2.2092204-5,2%-5,4%0,64%1,03%94,19%93,58%6,23%5,91%

As expected, we can see how misorientation impact increases when additional trackers are linked. Besides, this impact is deeper when survey include irregularities.

Figure 13: Comparison of tracker linked Row BT misorientation and shading with TeamTrack reference.

This problem does not apply to independent trackers that in figures 2d & 2c can be positioned to better adapt to terrain characterisitics, as described on 3b.

Figure 12d includes a comparison with Soltec’s TeamTrack, which allows for the optimization power generation.

Centralized trackers all move with the same angle, meaning they have the inherent limitation of being incapable to adapt to terrain conditions. On the contrary, independent trackers do adapt, favoring the optimization of radiation levels during backtracking periods.

05.2. Comparison

Using the baseline case (Mediterranean latitude and regular layout), the energy capture by different types of trackers and/or algorithm strategies along the year has been compared, and yield estimated assuming a PR of 85%.

Table 8: Comparison for different types of trackers and algorithm strategies.
MAX G objective 2331 kWh/m2Misorientation Index Losses (%)Efective Insolation kWh/m2Energy Yield (PR=85%) kWh/kWpEnergy Yield Comparison Vs StdTrackEnergy Yield Comparison Vs TeamTrack
Middle sensor-5,3%220718766,10%-0,23%
Middel sensor+buffer-6,4%218218544,88%-1,45%
Dual-row 2P-5,2%220918786,23%-0,10%
06. Conclusions

The study of TÜV Rheinland® verifies that Soltec’s TeamTrack obtains 6.2% more energy than standard tracking in regular terrains and average latitudes. Optimization compared to standard backtracking is 2.3%. Furthermore, the study concludes that compared to standard tracking, Soltec’s TeamTrack obtains production gains ranging between 3.6% and 7.5%, depending on terrain and climate conditions. If a comparison is made to standard backtracking, annual optimization ranges between 1.2% and 3.5%.

Algorithm gains depend on terrain regularity and latitude. For example, in highland latitudes (Germany), TeamTrack’s gain reaches 14.1% compared to standard tracking and 18.9% when compared to standard backtracking in December.

Figure 14: Evaluation of different backtracking strategies. *To visually represent the shading percentage on the graph, the bar value has been multiplied by 3.

ref. 1 K. Anderson, “Maximizing Yield with Improved Single-Axis Backtracking on Cross-Axis Slopes,” pp. 1–9, 2020

ref. 2 E. Lorenzo, L. Narvarte, and J. Muñoz, “Tracking and backtracking,” Progress in Photovoltaics: Research and Applications, vol. 19, no. 6, pp. 747–753, 2011. [Online]. Available: https: //doi.org/10.1002/pip.1085

ref. 4 K. Anderson and M. Mikofski, “Slope-Aware Backtracking for Single-Axis Trackers,” pp. 1–24, 2020, [Online]. Available: https://www.nrel.gov/docs/fy20osti/76626.pdf.

Javier Guerrero-Perez holds a Ph.D. with honors in Renewable Energy. His professional activity spans over ten years in the solar industry within multinational EPC operations. He has published several papers on modelling electrical behavior of both PV modules and inverters, oriented to large scale simulation. Since 2015, when he was part of the team of La Silla PV plant (2015), current research lines are focused on modelling the bifacial PV modules behavior while he is leading tracking algorithms and Soltec’s Bifacial Tracker Evaluation Center reasearch.
Irene Muñoz Benavente holds an International Ph.D. with special honors in Renewable Energy from the Technical University of Cartagena, working in the renewable energy sector since 2016, specifically in Photovoltaic. Focused in optimizing tracking algorithms for solar trackers.
Antonio Fabián Ros Gómez holds a bachelor’s degree in Industrial Electronics and Automation Engineering from the Technical University od Cartagena. Focused in optimizing tracking algorithms and perform solar plants modelling, simulations and data analysis.