阅读说明：本技术 用于定位太阳能面板阵列中的太阳能面板以高效地捕获日光的系统和方法 (System and method for positioning solar panels in an array of solar panels to efficiently capture sunlight ) 是由 马玉东 弗朗切斯科·博雷利 艾伦·达利 刘洋 于 2018-07-06 设计创作，主要内容包括：一种太阳能跟踪系统(200)包括：多个太阳能面板模块SPMi,其形成太阳能面板模块网格,其中所述多个太阳能面板模块SPMi可彼此独立地定向到太阳能源；以及控制系统SPCi,其经配置以基于性能模型将所述多个太阳能面板模块SPMi中的每一者彼此独立地定向到所述太阳能源以优化从所述太阳能面板模块网格输出的能量,其中所述性能模型基于含有所述太阳能面板模块网格的区域的地表形态以及所述太阳能面板模块SPMi中的每一者局部的天气条件来预测从所述太阳能面板模块网格输出的能量。(A solar tracking system (200) comprising: a plurality of solar panel modules SPMi forming a grid of solar panel modules, wherein the plurality of solar panel modules SPMi are orientable independently of each other to a solar energy source; and a control system SPCi configured to direct each of the plurality of solar panel modules SPMi independently of one another to the solar energy source to optimize the energy output from the grid of solar panel modules based on a performance model, wherein the performance model predicts the energy output from the grid of solar panel modules based on a terrain of an area containing the grid of solar panel modules and weather conditions local to each of the solar panel modules SPMi.)

1. A solar tracking system, comprising:

a plurality of rows of solar panel modules forming a grid of solar panel modules, wherein the rows of solar panel modules are orientable independently of each other to a solar energy source; and

a control system configured to direct each of the plurality of rows of solar panel modules to the solar energy source independently of one another based on a performance model to optimize energy output from the grid of solar panel modules, wherein the performance model predicts energy output from the grid of solar panel modules based on the orientation of each of the plurality of rows of solar panel modules to the solar energy source and first data comprising weather conditions local to each of the plurality of rows of solar panel modules.

2. The solar tracking system of claim 1, wherein the first data further comprises a pre-measurement of irradiance on each of the plurality of rows of solar panel modules.

3. The solar tracking system of claim 2, wherein the irradiance comprises diffuse light.

4. The solar tracking system of claim 2, wherein the amount of irradiance is a function of: global horizontal irradiance GHI, direct normal irradiance DNI, diffuse horizontal irradiance DHI, ground reflected radiation, or any combination thereof.

5. The solar tracking system of claim 1, wherein the performance model is calibrated based on outputs of the solar panel modules and based on occlusions between the rows of solar panel modules.

6. The solar tracking system of claim 5, wherein the obscuration is determined by a small solar panel coupled to each of the plurality of rows of solar panel modules.

7. The solar tracking system of claim 5, wherein the occlusion is determined from second data comprising a terrain map indicating relative positions between adjacent ones of the plurality of rows of solar panel modules.

8. The solar tracking system of claim 7, wherein the relative position corresponds to a height relative to a fixed position.

9. The solar tracking system of claim 1, wherein the plurality of rows of solar panel modules SPM_iEach of which comprises a corresponding self-powered controller SPC_iDrive assembly DA_iAnd a photovoltaic device PV_iWherein i is 1 to X.

10. The solar tracking system of claim 9, wherein the control system comprises a plurality of Network Control Units (NCUs)_jSaid plurality of network control units being each coupled to said self-powered controller SPC_iWherein i is 1 to X, j is 1 to Y, Y<X。

11. The solar tracking system of claim 10, wherein the ratio X: Y is at least 50: 1.

12. The solar tracking system of claim 10, wherein each SPC_iWirelessly coupling to a corresponding NCU_j。

13. The solar tracking system of claim 12, wherein the plurality of network control units NCUs_iCoupled to the central controller.

14. The solar tracking system of claim 13, wherein the central controller adjusts the performance model using a machine learning algorithm based on energy sensed on the solar tracking system.

15. The solar tracking system of claim 14, wherein the performance model is adjusted periodically.

16. The solar tracking system of claim 1, wherein the performance model comprises a polynomial with variables weighted by gain.

17. The solar tracking system of claim 16, wherein the gains include a conventional tracking gain and a time of day dependent backtracking gain.

18. The solar tracking system of claim 1, wherein the performance model comprises a diffuse table.

19. The solar tracking system of claim 1, wherein the weather condition is determined from a current weather condition, a forecasted weather condition, or both.

20. The solar tracking system of claim 10, wherein the network control unit NCU_jAre configured to exchange data and commands between each other and are coupled to form a mesh network.

21. The solar tracking system of claim 1, wherein the plurality of rows of solar panel modules SPM_iIs SM_iNormal to and from SM_iAn angle of incidence θ i between lines to the solar energy source, where i is 1 to X.

22. The solar tracking system of claim 9, wherein each SPC_iConfigured to respond to a loss of a corresponding network control unit NCU_jOperates under default operation.

23. The solar tracking system of claim 22, wherein the default operation includes associating SM with_iOriented to a default angle with the zenith.

24. The solar tracking system of claim 12, wherein the central controller comprises:

a weather finding module for receiving the row solar panel module SM_iWeather data local to each of;

a global horizontal irradiance and diffuse horizontal irradiance DHI-GHI module;

a line-to-line tracking module for determining the plurality of lines of solar panel modules SM_iA slope of each of the to horizontal lines;

a diffusion angle adjuster for adjusting the performance model based on the slope, time of year, and time of day;

a first transmission module for pushing orientation commands to the self-powered controller SPC based on the performance model_iEach of (a); and

a second transmission module for transmitting a signal from the network control unit NCU_jEach of which receives data, wherein i 1 to X, and j 1 to Y.

25. A solar tracking system, comprising:

a plurality of solar panel modules forming a grid of solar panel modules, wherein the plurality of solar panel modules are orientable independently of each other to a solar energy source; and

a control system configured to direct each of the plurality of solar panel modules to the solar energy source independently of each other based on a performance model to optimize energy output from the grid of solar panel modules, wherein the performance model predicts energy output from the grid of solar panel modules based on a terrain of an area containing the grid of solar panel modules and weather conditions local to each of the solar panel modules.

26. A method of updating a performance model for optimizing energy output from a solar tracking system, the solar tracking system comprising a plurality of rows of solar panel modules forming a grid of solar panel modules, each of the plurality of rows of solar panel modules having a corresponding photovoltaic device, each of the plurality of rows of solar panel modules being independently orientable to a solar energy source, the method comprising:

determining a terrain morphology model indicative of relative positions between the rows of solar panel modules;

determining an occlusion between the rows of solar panel modules to calibrate the terrain model to account for the occlusion; and

updating the performance model based on the terrain model and weather data comprising a predicted amount of radiation on each of the plurality of rows of solar panel modules, thereby optimizing the energy output from the solar tracking system.

27. The method of claim 26, wherein the radiation comprises diffuse light, direct light, or any combination thereof.

28. The method of claim 26, wherein the topographical model is derived from a topological map.

29. The method of claim 28, wherein determining the topological map comprises sensing energy incident on each of the plurality of photovoltaic devices while changing an orientation of each of the plurality of photovoltaic devices to the solar energy source.

30. The method of claim 28, wherein the topological map comprises relative heights between the rows of solar panel modules.

31. The method of claim 29, wherein the topological map indicates relative heights between the rows of solar panel modules and an ordering (sequence) of the rows of solar panel modules relative to each other.

32. The method of claim 31, wherein the relative heights and the ordering are determined by orienting a small solar panel on each of the plurality of rows of solar panel models relative to a solar energy source and sensing radiation impinging thereon.

33. The method of claim 28, wherein determining the topological map comprises sensing energy incident on each of the plurality of rows of solar panel modules.

34. The method of claim 28, wherein the topological map indicates a slope of the plurality of rows of solar panels relative to a fixed horizontal line.

35. The method of claim 26, wherein the obscuration between rows of solar panel modules comprises obscuration between adjacent rows of the rows of solar panel modules.

36. The method of claim 26, wherein the weather data comprises a time of year, a time of day, and weather conditions local to each of the plurality of solar panel modules.

37. A method of initializing a solar tracking system comprising a plurality of rows of solar panel modules forming a grid of solar panel modules, each of the plurality of rows of solar panel modules having a corresponding photovoltaic device, each of the plurality of rows of solar panel modules being independently orientable to a solar energy source, the method comprising:

determining a performance model for maximizing energy output from the grid of solar panel modules, the performance model having a control parameter generated from first data, wherein the control parameter is adjusted based on a diffusion score over a predetermined time period; and

transmitting a directional command to each of the plurality of rows of solar panel modules based on the performance model.

38. The method of claim 37, wherein the first data comprises a terrain morphology of an area containing the plurality of rows of solar panel modules.

39. The method of claim 37, wherein the diffusion score is based on an occlusion between adjacent ones of the plurality of rows of solar panel modules.

40. The method of claim 38, further comprising determining the topography.

41. The method of claim 40, wherein the topography is determined by a laser survey, an airborne survey, tracking the solar energy source using energy sensed on the plurality of photovoltaic devices, tracking the solar energy source using energy sensed on the plurality of rows of solar panel modules, or any combination thereof.

42. The method of claim 41, wherein the aerial survey comprises an airplane survey, an unmanned aerial survey, a satellite survey, a helicopter survey, or any combination thereof.

Technical Field

The present invention relates to energy conversion systems. More particularly, the present invention relates to controlling a solar tracking system to efficiently capture solar radiation for conversion to electrical energy.

Background

With the growing recognition of the environmental impact and associated costs of burning fossil fuels, solar energy has become an attractive alternative. Solar tracking systems track the trajectory of the sun to more efficiently capture radiation, which is subsequently converted to electrical energy. Solar tracking systems are less efficient when weather conditions change or when they do not consider local terrain that reduces the amount of light captured.

Disclosure of Invention

In accordance with the principles of the present invention, a solar tracking system is controlled by a global performance model based on weather and the local surface morphology of the solar tracking system. In one embodiment, the performance model uses a machine learning algorithm that periodically updates its parameters to learn from weather and terrain morphology data. In one embodiment, a solar tracking system includes a plurality of rows of solar panel modules forming a grid of rows of solar panel modules, wherein each row may be independently oriented to a solar energy source (e.g., the sun) relative to the other rows. As one example, the angle of incidence at which each row of solar panel modules is oriented to a solar energy source may be different than the angle of incidence at which each of the other rows of solar panel modules is oriented to a solar energy source. The performance model optimizes the overall output of the mesh, which does not necessarily correspond to optimizing the output from each individual row due to interactions (coupling) between adjacent rows.

In one embodiment, the performance model is characterized by a polynomial that determines the orientation of each individual row of solar panel modules to optimize (e.g., maximize) the total energy output from the grid of solar panel modules. Preferably, the parameters of the performance model are determined based on the morphology of the earth. The parameters are periodically updated based on weather conditions, such as forecasts and historical weather data. In this way, the performance model is a learning model that continuously optimizes the solar tracking system to account for changing weather conditions.

In another embodiment, the performance model includes a diffusion table that relates the energy output of the solar tracking system to weather conditions.

In accordance with the present invention, laser site surveys, learned surveys using energy readings on photovoltaic devices coupled to solar panel modules, energy readings on solar panel modules, aircraft and drones imaging the position of the sun and resulting obscuration into topographical locations are used to determine surface morphology, to name a few examples. Weather conditions are determined using satellite weather forecasts (ground truth), cameras aimed at the sky, power measurements on solar panel modules, and voltage measurements on photovoltaic devices informed by local data.

A solar tracking system according to an embodiment uses a mesh network that provides fault protection functionality.

Drawings

The drawings are included to illustrate embodiments of the invention. The same reference numbers will be used throughout the drawings to refer to the same or like elements.

FIG. 1 illustrates a portion of a solar tracking system including a plurality of rows of solar panel modules.

FIG. 2 illustrates a solar tracking system according to one embodiment of the present invention.

FIG. 3 is a block diagram of a diffusion control architecture NX monitoring and data acquisition (SCADA) according to one embodiment of the invention.

Fig. 4 shows the steps of a process for determining parameters of a global optimal performance model of a solar grid (enhanced tracking algorithm) according to one embodiment of the invention.

FIG. 5 is a scatter table generated from annual data according to one embodiment of the present invention.

FIG. 6 is a graph of a diffusion ratio table trace plotting tracker angle ratio coefficients versus DHI/GHI ratio according to one embodiment of the present invention.

FIG. 7 is a graph plotting tracker angular ratio coefficients versus DHI/GHI ratio according to a trace back of a diffusion ratio table in accordance with an embodiment of the present invention.

FIG. 8 illustrates the components of a SCADA according to one embodiment of the invention.

FIG. 9 illustrates a configuration including a row of solar panel modules and a "small solar panel" to determine the relative height of multiple SPMs using shading, according to one embodiment of the invention.

Fig. 10-23 illustrate an algorithm and the results of using the algorithm to determine relative altitude according to one embodiment of the present invention.

Detailed Description

A solar tracking system according to the principles of the present invention captures radiation more efficiently for conversion to electrical energy. It will be appreciated that for high capacity systems, such as those generating hundreds of megawatts, a small percentage of efficiency gain translates into a large gain in energy output.

According to one embodiment, a solar tracking system including individual rows of solar panel modules adjusts each row independently of the other rows to provide more finely tuned tracking and also efficiently captures diffuse radiation to increase the total energy output by the system. Preferably, the solar tracking system is based on a performance model that is periodically tuned based on a learning algorithm that compares predicted values (e.g., radiation incident on the solar panel or output generated at the solar panel) to actual values and updates the performance model accordingly. In one embodiment, a performance model is generated by plotting weather conditions (e.g., the ratio of the diffusion fraction index to the optimal diffusion gain or the ratio of diffuse radiance to direct radiance) and fitting a curve (performance model) to the data using regression. In another embodiment, this data is stored in a diffusion table.

FIG. 1 shows a portion of a solar tracking system 100 including a plurality of solar panels 110A-D forming a grid of solar panel modules to illustrate the principles of the present invention. Each of the solar panel modules 110A-D has a light collecting surface for receiving solar radiation that is later converted to electricity for storage in a battery and for distribution to a load. Embodiments of the invention determine a performance model that predicts the output of the grid, the performance model being used to direct each of the rows of solar panel modules to the sun or other radiation source to optimize the total energy output from the grid. Preferably, the performance module is determined from the terrain of the area containing the grid, the local weather conditions of each of the solar panel modules, or both. As one example, the performance model takes into account dependencies (coupling) between rows (adjacent and otherwise) of solar panels. For example, if the solar panel module row 110A obscures or partially obscures the solar panel module row 110B, then the two rows are said to be coupled. In other words, due to occlusion at a particular time of day or other relationship between rows 110A and 110B, maximizing the global energy output by the entire grid does not necessarily correspond to maximizing the energy output by rows 110A and 110B. In practice, it is possible to maximize the global energy output by coordinating the outputs, e.g., by orienting row 110A to generate 80% of its maximum value and orienting row 110B to generate 10% of its maximum value. The performance model determines these coefficients or gains (and thus the angle of orientation to the sun) for each of all rows in the system 100, including rows 110A and 110B.

As used herein, "orienting" means, in one embodiment, changing the angle between the normal of the solar panel module and the line to the sun ("angle of incidence"), changing any combination of x-y-z coordinates of the solar panel module relative to a fixed position (e.g., GPS position), rotating the solar panel module along any of the x-y-z coordinate axes, or any combination of these. After reading this disclosure, one skilled in the art will recognize other methods of orienting rows of solar panel modules to vary the amount of radiation impinging thereon and converted into electrical energy.

FIG. 2 illustrates a solar tracking system 200 according to one embodiment of the invention. Solar tracking system 200 is a distributed peer-to-peer network. Solar tracking system 200 includes a plurality of rows of Solar Panel Modules (SPM)₁...SPM₈Together forming a grid of solar panel modules. Each SPM_i(where i is 1 to 8, but other values are also contemplated) is coupled to a corresponding self-powered controller (SPC)_i) And a Drive Assembly (DA)_iNot shown). Each SPC_iHaving means for orienting their corresponding Drive Assemblies (DA) based on an orientation command_i) And thus directed SPM_iThe logic of (1). As an example, SPC_iReceiving a steering command from a network control unit (described below) to steer the SPC_iAngle of incidence with the sun thetai. Corresponding drive assembly DA_iWill SPM_iIs positioned at an angle thetai. Line SPM_i205i can be oriented independently of the other rows.

Solar panel module SPM_iEach of the rows receives light, converts the light to electrical power, and stores the electrical power in a corresponding data storage medium SM_iWherein i is 1 to 8. Storage medium SM₁...SM₈Are ganged together and electrically coupled to customer loads 220 through distribution panel 215. Network Control Unit (NCU) NCU₁And NCU₂Each wirelessly coupled to one or more of the SPMs. As shown in FIG. 1, the NCU₁Wireless coupling to SPC₁To SPC₄And NCU₂Wireless coupling to SPC₅To SPC₈。NCU₁And NCU₂Both of which are coupled to the NXFP switch 250 over ethernet cables. The switch 250 switches the NCU₁And NCU₂Coupled to NX monitor and data acquisition (SCADA)260, which in turn is coupled to switch 270, which is coupled to remote host 296 over a network, such as a cloud network. In some embodiments, the remote host 296 performs processes such as generating performance models, retrieving weather data, etc., to name a few such tasks. For ease of reference, the NCU₁、NCU₂The combination of the NX SCADA260 and the NXFP switch 250 is referred to as the "SCU" system controller 265. The components enclosed by the dotted line 280 togetherAlso referred to as a "grid" or "zone" 280.

Preferably, each NCU in region 280 is coupled to each of the remaining NCUs in region 280, thereby forming a mesh architecture. Thus, if for any reason, the NCU₁Lose communication to the NX SCADA260, then the NCU₁Can pass through NCU₂Communicating with NXSCADA 260. In other words, each NCU in zone 280 acts as a gateway to the NX SCADA260 for any other NCU in zone 280. This increased redundancy provides a failsafe network. In one embodiment, the NCUs in zone 280 are wirelessly coupled to each other.

Each NCU in region 280 has increased functionality. As some examples, the NCUs in region 280 together ensure that the performance model is globally optimized and that the components in region 280 operate properly. If, for example, SPC₁Indicating NCU₁It is occluded, but according to a performance model, SPC₁Should not be occluded, then NCU₁It is determined that an error has occurred. Each SPC also informs its associated NCU when the SPC has changed its orientation. Using this information, the NCU can thus track the solar panel module SPM_iIn the orientation of (c).

According to one embodiment, if a row of solar panel modules experiences a catastrophic failure and cannot communicate with its associated SCADA, the solar panel modules enter a default mode. As an example, in the default mode, the SPM_iThe energy conversion of the entire grid is optimized independently of its energy conversion.

It will be appreciated that fig. 2 has been simplified to facilitate illustration. In other embodiments, region 280 contains less than 8 SPMs and 2 NCUs, but preferably more than this number. In one embodiment, the ratio of SPC to NCU is at least between 50:1 and 100: 1. Thus, as an example, during normal operation, the NCU₁By SPC₅₀With SPC₁Communication, NCU₂With SPC₅₁To SPC₁₀₀Communications, and the like.

In operation, a performance model is generated for each of the solar panel modules based on the terrain morphology of the area containing the particular solar panel module, the weather local to the particular solar panel module, or both. In one embodiment, the weather includes an amount of direct light, an amount of Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), any combination of these, a ratio of any two of these (e.g., DHI/GHI), or any function of these. After reading this disclosure, one skilled in the art will recognize functions of DNI, GHI, and DHI that may be used to generate a performance model in accordance with the principles of the present invention. The base performance model is determined using regression or other curve fitting techniques by fitting weather conditions to the output. It will be appreciated that each SPM has its own performance model based on, among other things, its terrain and local weather conditions. Each base performance model is then updated based on the diffuse fractional sky, as described below.

As an example, parameters of the base performance model are pushed to the solar panel module SPM_iAssociated SPC. These parameters reflect the orientation of the solar panel module if no "diffuse fractional" sky based adjustment is required. To account for diffuse radiation, parameters adjusted based on the angle of diffusion are also sent to specific SPCs_i. As one example, the parameters for the base performance model indicate that the solar panel module should be oriented at an angle of incidence of 10 degrees for global optimization of the performance model. The diffusion angle adjustment factor data indicates that 10 degrees is not optimal for this SPM, but 70% (a factor of 0.7) of this angle should be used. Therefore, a diffusion angle adjustment factor (gain factor) of 0.7 is pushed to a particular solar panel. When a particular SPC receives two parameters, it orients its associated solar panel to an angle of incidence of (0.7) × (10 degrees) ═ 7 degrees. Preferably, the diffusion angle adjustment is performed periodically, for example once per hour, but other periods can also be used.

Some embodiments of the present invention avoid morning occlusion by using backtracking. The performance model thus generates some gains (e.g., target angles for directional SPM) for early morning tracking (to avoid occlusion) and another gain for other times. Systems according to these embodiments are referred to as operating in two modes: conventional tracking and backtracking. That is, the system uses a backtracking algorithm (performance model) at a specified time in the morning and a conventional tracking algorithm at all other times.

The performance model distinguishes between forecasted weather and instantaneous weather. For example, a transient weather change (e.g., a brief drop in radiation) may be due to passing clouds rather than an actual weather change. Therefore, the performance model preferably gives more weight to the forecasted weather.

FIG. 3 is a block diagram of a diffusion control architecture NX SCADA300, according to one embodiment of the invention. The NX SCADA300 receives as input the weather forecast (e.g., DFI), NCU and SPC data (unmodified tracking angle), field configuration parameters (e.g., SPC yield states and diffusion tables), and outputs the tracking and backtracking optimal ratios for each SPC. A diffusion table according to one embodiment of the present invention plots an optimal diffusion gain versus diffusion fraction index for determining a performance model.

FIG. 4 shows a step 400 of a process for determining parameters of a performance model according to one embodiment of the invention. While the process is performed for each row of solar panels in the grid, the explanation below describes a process for a single row of solar panel modules in the grid. It will be appreciated that the process will be performed for the remaining rows of solar panel modules. First, in step 405, a Sun Position Angle (SPA) is calculated from the latitude and longitude of a particular solar panel module row and the time of day. Next, in step 410, it is determined whether Bit0 in the yield state is on. Here, Bit0 and Bit1 are two-Bit sequences (Bit0Bit1) to describe in which of the 4 possible modes the solar tracker is in: bit 0-0/1 corresponds to row-to-row (R2R) tracking off/on, and Bit 1-0/1 corresponds to diffuse tracking off/on. Thus, for example, Bit0Bit1 ═ 01 corresponds to R2R tracking off and diffuse tracking on, Bit0Bit1 ═ 10 corresponds to R2R tracking on and diffuse tracking off, and so on. In other embodiments, Bit 0-1/0 corresponds to R2R tracking off/on and Bit 1-1/0 corresponds to diffuse tracking off/on. The designation is arbitrary.

If Bit0 is not on, the process proceeds to step 415, where SPA _ Tracker is set to SPA _ Site, and continues to step 425. If it is determined in step 410 that Bit0 in the yield state is on, then the process continues to step 420, where the SPA for the tracker is converted, whereupon the process continues to step 425. In step 425, a "backtrack" is calculated. From step 425, the algorithm proceeds to step 430 where it is determined whether Bit1 in the yield state is on. If Bit1 is on, then the process continues to step 435; otherwise, if Bit1 in the yield state is disconnected, the process continues to step 455.

In step 435, the process determines whether a diffusion ratio has been received in the last 70 minutes. If a diffusion ratio has been received within the last 70 minutes, the process continues to step 440; otherwise, the process continues to step 455. In step 440, the process determines whether the particular SPC is in a trace-back mode. If the SPC is determined not to be in the trace-back mode, then the process continues to step 445; otherwise, the process continues to step 450. In step 445, the tracker target angle is set to (tracker target angle) × diffusion ratio. From step 445, the process continues to step 455. In step 450, the target tracker angle is set to (target tracker angle) × (dispersed _ backtrack _ ratio). From step 455, the process continues to step 455. In step 455, the tracker is moved to a target tracker angle.

As shown in fig. 4, steps 415, 420, and 425 form the R2R algorithm; step 435 forms a "time abandon" algorithm; and steps 440, 445, 450, 455 form a diffusion algorithm. In FIG. 4, for the time abandonment, if the diffusion ratio was not received within the last 70 minutes, the ratio is set to 1.

Those skilled in the art will recognize that step 400 is merely illustrative of one embodiment of the present invention. In other embodiments, some steps may be added, other steps may be deleted, steps may be performed in a different order, and the time period may be changed (e.g., 70 minutes between diffusion adjustments).

FIG. 5 is a diffusion table generated for yearly data plotting optimal diffusion gain versus diffusion fraction index, according to an example.

FIG. 6 is a graph plotting tracker angular ratio coefficients versus DHI/GHI ratio for a final diffusion ratio table for tracking according to one embodiment of the present invention. FIG. 7 is a graph of a final diffusion ratio table for backtracking plotting tracker angle ratio coefficients versus DHI/GHI ratio according to one embodiment of the present invention.

FIG. 8 shows a SCADA 700. SCADA800 includes a row-to-row (R2R) tracking module 801, a storage device 805, a diffusion angle adjuster 810, a first transmission module 815 and a second transmission module 820, a DHI-GHI module 825, a weather lookup module 835, and a reporting engine 830. R2R tracking module 801 is coupled to storage device 805, diffusion angle adjuster 810, and first transmission module 815. R2R tracking module 801 tracks the slope of a solar panel module at its location, stores the slope in storage device 805, sends a target tracking angle (for a given date and time) to diffusion angle adjuster 810, and transmits the slope to first transmission module 815 for pushing to an SPC. the lookup module 835 collects weather data for DHI-GHI module 825. the DHI-GHI module provides the weather data to diffusion angle adjuster 810. the diffusion angle adjuster 820 transmits the second transmission module 820, the second transmission module pushes the weather data to SPC 35SD module 835, and also pushes the associated weather data from the local weather sensor module L, the diffusion angle adjuster 830, the local weather sensor module 830, and the local weather sensor module receives the weather data such as the local weather sensor data 830, the local weather sensor module 830, the diffusion angle adjuster 840, the local weather sensor module, the sensor module, and the sensor module 830.

The terrain morphology module 802 is configured to store maps and communicate terrain morphology information to the R2R tracking module 801. The information may be used to compute a row-to-row table. It is contemplated that the R2R tracking module 801 may include a terrain morphology module 802. The information stored in the terrain morphology module 802 may be updated on a periodic basis. For example, laser field surveys, surveys learned using photovoltaic devices on SPC, closed loop readings on solar panel modules, or airplane or drone imaging may be used to determine topographical information.

As explained above, preferably the SCADA800 does not push the "best" angle for each individual SPA, but rather the angle that optimizes the overall global energy output. The diffusion angle adjuster 810 does not push an angle, but a ratio (e.g., 70%, "gain factor"). In a preferred embodiment, SCADA800 is configured to transmit two gains: gain for regular tracking, and gain for "backtracking", i.e. gain to avoid occlusions during early morning hours. Thus, according to one embodiment, the SCADA800 determines the time of day and thus whether to generate a regular tracking gain or a backtracking gain, which is pushed to the SPC.

As explained above, in one embodiment, the topology of each SPM is determined from the shading between SPMs (proximity and otherwise) using small solar panels ("small solar panels") that are each coupled to or integrated with a self-powered controller (SPC) on the SPM as described above or otherwise coupled to the SPM. As used herein, a small solar panel, similar to an individual solar panel in an SPM, is capable of reading the amount of radiation (e.g., solar radiation) that strikes its surface. Similar to SPM, this amount of radiation can be related to the orientation (e.g., angle of incidence) of the surface to the solar energy source. Fig. 9 shows a torsion tube supporting both a small solar panel 910 and a row of solar panel modules 901, SPM 901 comprising individual solar panels 901A-J. The torsion tube is coupled to a drive assembly (not shown) for orienting (here, rotating) the SPM 901 and the radiation collection surface of the small solar panel 910 to the solar energy source.

In one embodiment, the small solar panel 910 determines the occlusion between SPMs and thus its relative height in this way, "height distribution" may be estimated in the following, β events refer to the panel no longer being occluded, for example, when a first one of the SPMs moves, β events may be triggered to show that the other panels are no longer occluded.

Fig. 11-17 show, among other things, how simple trigonometry can be used to determine the relative height (dh). Fig. 18 shows a simple recursive algorithm for determining the relative height. Fig. 19-23 show the results of using this algorithm according to one embodiment of the present invention.

In various embodiments, the small solar panel is the same as or forms part of the SPC powered photovoltaic device or is a separate component from the SPC powered photovoltaic device. Thus, photovoltaic devices other than small solar panels may be used according to fig. 9-23 to determine the relative heights and ordering between SPMs described herein.

In a preferred embodiment, the logic of the solar tracking system according to the invention is distributed. For example, referring to FIG. 2, the base performance model is generated at SCADA260 or at a central location coupled to SCADA260 through a cloud network. The diffusion adjustment (e.g., gain) is determined at SCADA 260. The actual target angle for each SPC is determined at the associated SPC based on the gains.

Using a cloud network, SCADA260 can receive weather forecasts, share information from the NCUs and SPCs in cloud-to-zone 280, offload computing functionality to a remote processing system, or any combination of these or any other tasks.

In operation of one embodiment, a global optimal performance model is generated in two phases for a solar tracking system. In the first stage, the detailed site geometry (terrain morphology) of the area containing the solar tracking system is determined. This can be determined using laser field surveys, surveys learned using photovoltaic devices on SPC, closed loop readings on solar panel modules, or airplane or drone imaging.

As some examples, the topography of the area containing the SPC is determined by orienting the photovoltaic devices on the SPC to known locations of the sun. Energy readings compared to the known location of the sun may be used to determine the location of the associated solar panel, including any one or more of its x-y-z coordinates relative to a fixed point (i.e., its GPS coordinates) or its slope/slope relative to the normal or another fixed angle, to name a few such coordinates. The solar panels may be oriented in a similar manner and their local terrain morphology similarly determined. In yet another embodiment, a separate sensing panel is mounted on each row of solar panel modules. By adjusting the orientation of the sensing panel relative to the sun, based on the time of day (i.e., the angle of the sun) and the output generated on the sensing panel, the relative position of adjacent rows of solar panel modules can be determined. In yet another embodiment, the x-y-z coordinates of the edges of the rows of solar panel modules are physically measured.

In the second phase, periodic adjustments to the parameters of the performance model are made, for example, by using weather conditions (e.g., forecasts and historical conditions), such as using satellite weather forecasts, skyward cameras, power measurements on solar panel modules, and voltage measurements from SPCs.

It will be appreciated that each of the SPC, NCU, and SCADA described herein includes a memory containing computer-executable instructions and a processor for executing those instructions, such as disclosed herein.

It will be appreciated that the solar grid is able to span a large area, such that different portions of the solar grid experience different weather conditions. According to embodiments of the invention, a performance model is generated for each solar panel module and updated based on each local weather condition.

Those skilled in the art will recognize that various modifications may be made to the disclosed embodiments without departing from the scope of the present invention. As one example, while the embodiments disclose multiple rows of solar panel modules, each row may be replaced by a single elongated solar panel module. Further, while the examples describe the radiation source as the sun, the principles of the present invention contemplate other radiation sources, such as thermal radiation sources.

Systems and Methods for generating Performance models are disclosed In U.S. patent application 14/577,644 entitled "Systems and Methods for modeling, Step Testing, and adaptive control of In-Situ Building components" filed on 19.12.2014, entitled "Systems and Methods for modeling, Step-Testing, and adaptive control In-Situ Building components" that requires filing on 20.12.2013 and entitled "Systems, Methods, and platforms for Characterizing the Performance of In-Situ Building and System components and Sub-components by Using general Performance Data, Utility Meter Data, and automated Step Testing" (filed on 20.12.2014, and entitled "Systems, Methods, and platforms for Characterizing the Performance of In-Situ Building and System components and Sub-components" filed on 358.8.7. and entitled "temporary Building Data for use, Step-Testing" filed on 358.7.4 Priority of U.S. provisional patent application No. 62/022,126 to System, method and Platform (System) for Automated testing in Commercial Buildings in articles, the above applications are hereby incorporated by reference in their entirety.

Systems and methods for Self-Powered Solar trackers are disclosed in U.S. patent application No. 14/972,036, filed on 12, 16, 2015 and entitled "Self-Powered Solar tracker Apparatus," which is hereby incorporated by reference herein.

Systems and methods for line-to-line Tracking are disclosed in U.S. patent application No. 62/492,870, entitled "line-to-line Sun Tracking Method and System," filed on 1/5 in 2017, which is hereby incorporated by reference.

The tracking system is described in U.S. patent application No. 14/745,301 entitled "Clamp Assembly for Solar Tracker" (filed on 19.6.2015), the application is a continuation of U.S. patent application No. 14/489,416, entitled "Clamp assembly for Solar Tracker" filed 9/17 2014, which is a partial continuation of U.S. patent application No. 14/101,273, filed 12/9/2013 and entitled "Horizontal Balanced Solar Tracker Apparatus", which claims priority of U.S. patent application No. 61/735,537, filed 12/10/2012 and entitled "Fully Adjustable Tracker Apparatus (full Adjustable Tracker Apparatus)", which is hereby incorporated herein by reference in its entirety.