Solar tracking system

Abstract

A solar tracking system (200) comprises: 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 one another to a solar energy source; and a control system SPCi configured to orient each of the plurality of solar panel modules SPMi 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 a surface morphology of a region containing the grid of solar panel modules and weather conditions local to each of the solar panel modules SPMi.

Description

[0001] Related information of divisional application

[0002] This case is a divisional application of the invention patent application filed on July 6, 2018, with application number 201880053640.5 and entitled "System and method for positioning solar panels in a solar panel array to efficiently capture sunlight". Technical Field

[0003] This invention relates to energy conversion systems. More specifically, this invention relates to controlling a solar tracking system to efficiently capture solar radiation for conversion into electrical energy. Background Technology

[0004] As the environmental impacts and associated costs of burning fossil fuels become increasingly recognized, solar energy has emerged as an attractive alternative. Solar tracking systems track the sun's trajectory to capture radiation more efficiently, which is then converted into electricity. However, solar tracking systems are less efficient when weather conditions change or when they do not account for local landforms that reduce the amount of light captured. Summary of the Invention

[0005] According to the principles of the present invention, the solar tracking system is controlled by a global performance model based on local landforms and weather conditions. In one embodiment, the performance model uses a machine learning algorithm that periodically updates its parameters and learns from weather and landform data. In one embodiment, the solar tracking system comprises multiple rows of solar panel modules forming a grid, wherein each row can be independently oriented to solar energy (e.g., the sun) relative to other rows. As an example, the angle of incidence of each row of solar panel modules oriented to solar energy may differ from the angle of incidence of each of the solar panel modules in other rows. The performance model optimizes the overall output of the grid, which does not necessarily correspond to optimizing the output from each individual row due to the interaction (coupling) between neighboring rows.

[0006] In one embodiment, the performance model is characterized by a polynomial that determines the orientation of each individual row of the solar panel modules to optimize (e.g., maximize) the total energy output from the solar panel module grid. Preferably, the parameters of the performance model are determined based on the landform. The parameters are periodically updated based on weather conditions, such as forecasts and historical weather data. In this way, the performance model is a learned model that continuously optimizes the solar tracking system to account for changing weather conditions.

[0007] In another embodiment, the performance model includes a diffuse table that correlates the energy output of the solar tracking system with weather conditions.

[0008] According to the present invention, landforms are determined using laser field surveying, learned surveying using energy readings from photovoltaic devices coupled to solar panel modules, energy readings from solar panel modules, and aircraft and drone imaging that correlates the position of the sun and the resulting shading with the terrain location, to name just a few. Weather conditions are determined using satellite weather forecasts (“ground conditions”) provided by local data, cameras aimed at the sky, power measurements from solar panel modules, and voltage measurements from photovoltaic devices.

[0009] The solar tracking system according to the embodiment uses a mesh network that provides fault protection functionality. Attached Figure Description

[0010] The accompanying drawings illustrate embodiments of the present invention. In all the drawings, the same reference numerals refer to the same or similar elements.

[0011] Figure 1 This shows a portion of a solar tracking system that includes multiple rows of solar panel modules.

[0012] Figure 2 A solar tracking system according to an embodiment of the present invention is shown.

[0013] Figure 3 This is a block diagram of a diffuse control architecture NX monitoring and data acquisition (SCADA) according to an embodiment of the present invention.

[0014] Figure 4 The steps of a process (enhanced tracking algorithm) for determining the parameters of a globally optimal performance model of a solar grid according to an embodiment of the present invention are shown.

[0015] Figure 5 It is a diffuse table generated from annual data according to an embodiment of the present invention.

[0016] Figure 6 This is a graph of the diffuse ratio table tracking according to an embodiment of the present invention, which plots the tracker angle ratio coefficient against the DHI / GHI ratio.

[0017] Figure 7 It is a curve of the diffuse ratio table backtracking according to an embodiment of the present invention, which plots the tracker angle ratio coefficient against the DHI / GHI ratio.

[0018] Figure 8 The components of SCADA according to an embodiment of the present invention are shown.

[0019] Figure 9 The illustration shows a configuration comprising a row of solar panel modules and a “small solar panel” for determining the relative height of multiple SPMs using shading, according to an embodiment of the invention.

[0020] Figures 10-23 An algorithm according to an embodiment of the present invention is shown, and the result of using the algorithm to determine the relative height is illustrated. Detailed Implementation

[0021] The solar tracking system according to the principles of the present invention captures radiation more efficiently for conversion into electrical energy. It will be understood that for large-capacity systems, such as those generating hundreds of megawatts, a small percentage gain in efficiency translates into a large gain in energy output.

[0022] According to one embodiment, a solar tracking system including individual rows of solar panel modules adjusts each row independently of the others to provide finer-tuned tracking and also efficiently captures diffuse radiation to increase the total energy output of the system. Preferably, the solar tracking system is based on a performance model that is periodically tuned using a learning algorithm that compares predicted values ​​(e.g., radiation incident on the solar panel or output generated at the solar panel) with actual values ​​and updates the performance model accordingly. In one embodiment, the performance model is generated by plotting weather conditions (e.g., the ratio of diffuse fraction index to optimal diffuse gain or the ratio of diffuse radiation to direct radiation) and using regression to fit a curve (performance model) to the data. In another embodiment, this data is stored in a diffuse table.

[0023] Figure 1A portion of a solar tracking system 100, illustrating the principles of the invention, is shown, comprising a plurality of solar panels 110A-D forming a grid of solar panel modules. Each of the solar panel modules 110A-D has a light-collecting surface for receiving solar radiation, which is later converted into electricity for storage in batteries and for distribution to loads. Embodiments of the invention determine a performance model predicting the output of the grid, the performance model being used to orient each of the rows of solar panel modules toward the sun or other radiation sources to optimize the total energy output from the grid. Preferably, the performance module is determined from the topography of the area containing the grid, the local weather conditions of each of the solar panel modules, or both. As an example, the performance model considers the dependencies (couplings) between rows of solar panels (adjacent and otherwise). For example, if solar panel module row 110A shades or partially shades solar panel module row 110B, then the two rows are said to be coupled. In other words, maximizing the global energy output of the entire grid does not necessarily correspond to maximizing the energy output of rows 110A and 110B due to shading at a specific time of day or other relationships between rows 110A and 110B. In practice, it may be possible to maximize the global energy output by coordinating the outputs, for example, 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 therefore the orientation angle to the sun) for each of all rows in system 100, each containing rows 110A and 110B.

[0024] As used herein, in one embodiment, "orientation" means changing the angle ("angle of incidence") between the normal of the solar panel module and the line to the sun, changing any combination of the xyz coordinates of the solar panel module relative to a fixed location (e.g., a GPS location), rotating the solar panel module along any of the xyz coordinate axes, or any combination of these. Upon reading this invention, those skilled in the art will recognize other methods of orienting a solar panel module to change the amount of radiation irradiating it and converting it into electrical energy.

[0025] Figure 2 A solar tracking system 200 according to an embodiment of the present invention is shown. The solar tracking system 200 is a distributed peer-to-peer network. The solar tracking system 200 includes multiple rows of solar panel modules (SPMs) SPM1...SPM8, which together form a grid of solar panel modules. Each SPM... i (Here, i = 1 to 8, but other values ​​are also expected) coupled to the corresponding self-powered controller (SPC). i ) and drive assembly (DA) i (Not shown). Each SPCi It has a drive assembly (DA) for orienting its corresponding drive assembly based on a directional command. i And therefore targeted SPM i The logic. As an example, SPC i Receive a direction command from the network control unit (described below) to directionate the SPC. i The angle of incidence θi between the sun and the surrounding environment. This corresponds to the drive assembly DA. i SPM i Positioned at angle θi. SPM line. i Each row in 205i can be oriented independently of the other rows.

[0026] Solar Panel Module (SPM) i Each row in the system receives light, converts the light into electricity, and stores the electricity in the corresponding data storage medium SM. i In the SPM, where i = 1 to 8. Storage media SM1...SM8 are combined together and electrically coupled to customer load 220 via distribution panel 215. Network control units (NCUs) NCU1 and NCU2 are each wirelessly coupled to one or more of the SPMs. Figure 1 As shown, NCU1 is wirelessly coupled to SPCs SPC1 through SPC4, and NCU2 is wirelessly coupled to SPCs SPC5 through SPC8. Both NCU1 and NCU2 are coupled to NXFP switch 250 via Ethernet cables. Switch 250 couples NCU1 and NCU2 to NX Monitoring and Data Acquisition (SCADA) 260, which in turn couples to switch 270, which in turn couples to remote host 296 via a network such as a cloud network. In some embodiments, remote host 296 performs processes such as generating performance models, retrieving weather data, etc., to name just a few examples of such tasks. For ease of reference, the combination of NCU1, NCU2, NX SCADA 260, and NXFP switch 250 is referred to as the “SCU” system controller 265. The components enclosed by dotted lines 280 are collectively referred to as a “mesh” or “zone” 280.

[0027] Preferably, each NCU in zone 280 is coupled to each of the remaining NCUs in zone 280, thus forming a mesh architecture. Therefore, if, for any reason, NCU1 loses communication with NX SCADA 260, NCU1 can communicate with NX SCADA 260 via NCU2. In other words, each NCU in zone 280 acts as a gateway to NX SCADA 260 for any other NCU in zone 280. This added redundancy provides a fault-protected network. In one embodiment, the NCUs in zone 280 are wirelessly coupled to each other.

[0028] Each NCU in zone 280 has increased functionality. As examples, the NCUs in zone 280 work together to ensure the performance model is globally optimized and the components in zone 280 operate properly. If, for example, SPC1 instructs NCU1 that it is shaded, but according to the performance model, SPC1 should not be shaded, then NCU1 determines that an error has occurred. Each SPC also informs its associated NCU when it has changed its orientation. Using this information, the NCU can thus track the solar panel module (SPM). i Orientation.

[0029] According to one embodiment, if a row of solar panel modules suffers a catastrophic failure and is unable to communicate with its associated SCADA system, the solar panel modules enter a default mode. As an example, in the default mode, the SPM... i Optimize energy conversion independently of the overall grid's energy conversion.

[0030] Will understand, Figure 2 Simplified for ease of illustration. In other embodiments, zone 280 contains fewer than 8 SPMs and 2 NCUs, but preferably more. In one embodiment, the SPC to NCU ratio is at least between 50:1 and 100:1. Thus, as an example, during normal operation, NCU1 passes through the SPCs... 50 Communicating with SPC1, NCU2 communicates with SPC 51 To SPC 100 Communication etc.

[0031] In operation, a performance model is generated for each solar panel module based on the topography of the area containing the specific solar panel module, the local weather conditions of the specific solar panel module, or both. In one embodiment, weather includes the amount of direct sunlight, the 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 invention, those skilled in the art will recognize the functions of DNI, GHI, and DHI that can be used to generate performance models according to the principles of the invention. The underlying performance model is determined using regression or other curve fitting techniques by fitting weather conditions to the output. It will be understood that each SPM has its own performance model, particularly based on its topography and local weather conditions. As described below, each underlying performance model is then updated based on the diffuse fractional sky.

[0032] As an example, the parameters of the basic performance model are pushed to the solar panel module SPM. iThe associated SPC. If no adjustment based on the "diffuse fraction" sky is required, then these parameters reflect the orientation of the solar panel modules. To account for diffuse radiation, parameters based on diffuse angle adjustment are also sent to the specific SPC. i As an example, the parameters used in the basic performance model indicate that, for global optimization of the performance model, the solar panel module should be oriented at an incident angle of 10 degrees. The diffuse angle adjustment factor data indicates that 10 degrees is not optimal for this SPM, but rather 70% of this angle (a factor of 0.7) should be used. Therefore, a diffuse angle adjustment factor (gain factor) of 0.7 is applied to a specific solar panel. When a specific SPC receives both parameters, it orients its associated solar panel to an incident angle of (0.7)*(10 degrees) = 7 degrees. Preferably, diffuse angle adjustment is performed periodically, for example, once per hour, but other cycles are also possible.

[0033] Some embodiments of the invention avoid morning occlusion by using backtracking. The performance model thus generates some gains for morning tracking (to avoid occlusion) (e.g., for the target angle in directional SPM) and another gain for other times. Systems according to these embodiments are said to operate in two modes: regular tracking and backtracking. That is, the system uses the backtracking algorithm (performance model) at a specified time in the morning and the regular tracking algorithm at all other times.

[0034] Performance models distinguish between forecast and transient weather. For example, transient weather changes (e.g., a brief decrease in radiation) may be attributed to passing clouds rather than actual weather changes. Therefore, performance models preferably assign more weight to forecast weather.

[0035] Figure 3 This is a block diagram of the NX SCADA 300 diffuse control architecture according to an embodiment of the present invention. The NX SCADA 300 receives weather forecasts (e.g., DFI), NCU and SPC data (unmodified tracking angles), and field configuration parameters (e.g., SPC output status and diffuse tables) as inputs and outputs the optimal tracking and backtracking ratio for each SPC. The diffuse table according to an embodiment of the present invention plots the optimal diffuse gain against the diffuse fraction index for determining the performance model.

[0036] Figure 4Step 400 of the process for determining parameters of a performance model according to an embodiment of the present invention is shown. Although the process is performed for each row of solar panels in the grid, the following explanation describes the process for a single row of solar panel modules in the grid. It will be understood that the process is performed for the remaining rows of solar panel modules. First, in step 405, the solar position angle (SPA) is calculated from the latitude and longitude and the time of day for a particular row of solar panel modules. Next, in step 410, it is determined whether Bit0 in the production state is on. Here, Bit0 and Bit1 are two-bit sequences (Bit0Bit1) used to describe which of the four possible modes the solar tracker is in: Bit0=0 / 1 corresponds to row-to-row (R2R) tracking off / on, and Bit1=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, etc. In other embodiments, Bit0 = 1 / 0 corresponds to R2R tracking off / on, and Bit1 = 1 / 0 corresponds to diffuse tracking off / on. The designation is arbitrary.

[0037] If Bit0 is not active, 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, which is in the production state, is active, the process continues to step 420, where the SPA used for the tracker is converted, and thus the process continues to step 425. In step 425, the "backtracking" is calculated. From step 425, the algorithm proceeds to step 430, where it is determined whether Bit1, which is in the production state, is active. If Bit1 is active, the process continues to step 435; otherwise, if Bit1, which is in the production state, is deactivated, the process continues to step 455.

[0038] In step 435, the process determines whether a diffuse ratio has been received within the past 70 minutes. If a diffuse ratio has been received within the past 70 minutes, the process continues to step 440; otherwise, the process continues to step 455. In step 440, the process determines whether a particular SPC is in backtracking mode. If it is determined that the SPC is not in backtracking mode, 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) * diffuse ratio. From step 445, the process continues to step 455. In step 450, the target tracker angle is set to (target tracker angle) * diffused_backtrack_ratio. From step 455, the process continues to step 455. In step 455, the tracker is moved to the target tracker angle.

[0039] like Figure 4 As shown, steps 415, 420, and 425 form the R2R algorithm; step 435 forms the "time-give-up" algorithm; and steps 440, 445, 450, and 455 form the diffuse algorithm. Figure 4 In the context of time abandonment, if no diffuse ratio is received within the first 70 minutes, then the ratio is set to 1.

[0040] Those skilled in the art will recognize that step 400 illustrates only one embodiment of the invention. In other embodiments, steps may be added, steps may be deleted, steps may be performed in a different order, and the time period may be changed (e.g., 70 minutes between diffuse adjustments).

[0041] Figure 5 It is a diffuse table generated from an instance of annual data, which plots the optimal diffuse gain against the diffuse fraction index.

[0042] Figure 6 This is a graph of the final diffuse ratio table for tracking according to an embodiment of the present invention, which plots the tracker angle ratio coefficient against the DHI / GHI ratio. Figure 7 This is a graph of the final diffuse ratio table for backtracking according to an embodiment of the present invention, which plots the tracker angle ratio coefficient against the DHI / GHI ratio.

[0043] Figure 8 A SCADA 700 according to an embodiment of the present invention is illustrated. The SCADA 800 includes a line-to-line (R2R) tracking module 801, a storage device 805, a diffuse 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. The R2R tracking module 801 is coupled to the storage device 805, the diffuse angle adjuster 810, and the first transmission module 815. The R2R tracking module 801 tracks the slope of the solar panel module at its location, stores the slope in the storage device 805, sends a target tracking angle (for a given date and time) to the diffuse angle adjuster 810, and transmits the slope to the first transmission module 815 for push to the SPC. The weather lookup module 835 collects weather data for the DHI-GHI module 825, which provides the weather data to the diffuse angle adjuster 810. The diffuse angle adjuster transmits the diffuse angle to the second transmission module 820, which pushes the data to its associated SPC and also to the reporting engine 830. The Local Sensor Data (LSD) module 840 receives locally sensed weather data (e.g., weather, wind, or other locally sensed data) from the NCU and pushes the data to the weather lookup module 835.

[0044] The terrain feature module 802 is configured to store maps and transmit terrain feature information to the R2R tracking module 801. This information can be used to calculate row-to-row tables. The R2R tracking module 801 is expected to include the terrain feature module 802. The information stored in the terrain feature module 802 can be updated periodically. For example, terrain feature information can be determined using laser field surveys, surveys learned using photovoltaic devices on an SPC, closed-loop readings from solar panel modules, or aircraft or UAV imaging.

[0045] As explained above, preferably, the SCADA 800 does not push an "optimal" angle for each individual SPA, but rather optimizes the angle for the total global power output. The diffuse angle adjuster 810 does not push an angle, but rather a ratio (e.g., 70%, a "gain factor"). In a preferred embodiment, the SCADA 800 is configured to transmit two gains: a gain for regular tracking and a gain for "backtracking," i.e., a gain to avoid occlusion during early morning hours. Thus, according to one embodiment, the SCADA 800 determines the time of day and therefore determines whether to generate a regular tracking gain or a backtracking gain, which is pushed to the SPC.

[0046] As explained above, in one embodiment, the topology of each SPM is determined from the shading between SPMs (adjacent and otherwise) using small solar panels (“small solar panels”), each of which is 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, similar to individual solar panels in an SPM, a small solar panel is capable of reading the amount of radiation (e.g., solar radiation) illuminating its surface. Similar to the SPM, this amount of radiation can be related to the orientation of the surface to solar energy (e.g., angle of incidence). Figure 9 A torsion tube is shown supporting both a small solar panel 910 and a row of solar panel modules 901, each SPM 901 comprising individual solar panels 901A-J. The torsion tube is coupled to a drive assembly (not shown) for orienting (here, rotating) the radiation-collecting surfaces of the SPM 901 and the small solar panel 910 toward solar energy.

[0047] In one embodiment, the small solar panel 910 determines the shading between SPMs and therefore their associated heights. In this way, the "height distribution" can be estimated. Hereinafter, a β event refers to a panel no longer being shaded. For example, a β event can be triggered when the first SPM moves to indicate that the other panels are no longer shaded. These shading events determine the relative heights and order (sequence) of the SPMs. Figures 10-23 This determination is used to illustrate one embodiment of the present invention.

[0048] Figures 11-17 In particular, it shows how to determine relative height (dh) using simple trigonometry. Figure 18 A simple recursive algorithm for determining relative height is shown. Figures 19-23 The results of using this algorithm according to an embodiment of the present invention are shown.

[0049] In different embodiments, the small solar panel is the same as or forms part of the photovoltaic device powering the SPC, or it is a separate component from the photovoltaic device powering the SPC. Therefore, according to Figures 9-23 Photovoltaic devices, different from small solar panels, can be used to determine the relative height and order among the SPMs described in this article.

[0050] In a preferred embodiment, the logic of the solar tracking system according to the present invention is distributed. For example, refer to... Figure 2 A basic performance model is generated at SCADA 260 or at a central location coupled to SCADA 260 via a cloud network. Diffuse adjustment (e.g., gain) is determined at SCADA 260. Based on the gain, the actual target angle for each SPC is determined at the associated SPC.

[0051] Using cloud networks, SCADA 260 can receive weather forecasts, share information from the cloud to NCUs and SPCs in Zone 280, offload computational functionality to remote processing systems, or any combination of these or any other tasks.

[0052] In one embodiment of the operation, a globally optimal performance model is generated for the solar tracking system in two phases. In the first phase, the detailed field geometry (surface 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 an SPC, closed-loop readings from solar panel modules, or aircraft or drone imaging.

[0053] As examples, the topography of an area containing an SPC is determined by orienting the photovoltaic devices on the SPC to a known position of the sun. Energy readings compared to the known position of the sun can be used to determine the location of the associated solar panels, including their xyz coordinates relative to a fixed point (i.e., their GPS coordinates) or their slope / inclination relative to the normal or another fixed angle, to name just a few such coordinates. Solar panels can be oriented in a similar manner and their local topography determined in a similar way. 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, the relative position of adjacent rows of solar panel modules can be determined based on the time of day (i.e., the angle of the sun) and the output generated on the sensing panel. In yet another embodiment, the xyz coordinates of the edges of the rows of solar panel modules are physically measured.

[0054] In the second stage, parameters of the performance model are periodically adjusted, for example, by using weather conditions (e.g., forecast and historical conditions), such as satellite weather forecasts, cameras pointing to the sky, power measurements on solar panel modules, and voltage measurements from the SPC.

[0055] It will be understood that each of the SPCs, NCUs, and SCADAs described herein includes memory containing computer-executable instructions and a processor for executing those instructions, as disclosed herein.

[0056] It will be understood that solar grids can span large areas, allowing different parts of the solar grid to experience different weather conditions. According to embodiments of the invention, a performance model is generated for each solar panel module and updated based on the local weather conditions for each.

[0057] Those skilled in the art will recognize that various modifications can be made to the disclosed embodiments without departing from the scope of the invention. As an example, although the embodiments disclose multiple rows of solar panel modules, each row can be replaced by a single elongated solar panel module. Furthermore, while the examples describe the radiation source as the sun, the principles of the invention contemplate other radiation sources, such as thermal radiation sources.

[0058] Systems and methods for generating performance models are disclosed in U.S. Patent Application No. 14 / 577,644, filed December 19, 2014, entitled "Systems for and Methods of Modeling, Step-Testing, and Adaptively Controlling In-Situ Building Components," which claims U.S. Provisional Patent Application No. 61 / 919,547, filed December 20, 2013, entitled "System, Method and Platform for Characterizing In-Situ Building and System Component and Sub-component Performance by Using Generic Performance Data, Utility-Meter Data, and Automatic Step Testing," and U.S. Provisional Patent Application No. 61 / 919,547, filed July 8, 2014, entitled "System, Method and Platform for Automated Commissioning in Commercial Buildings." Priority to U.S. Provisional Patent Application No. 62 / 022,126, “Buildings”, the entire application of which is hereby incorporated by reference.

[0059] Systems and methods for self-powered solar trackers are disclosed in U.S. Patent Application No. 14 / 972,036, filed December 16, 2015, entitled “Self-Powered Solar tracker Apparatus,” which is hereby incorporated herein by reference.

[0060] A system and method for row-to-row tracking is disclosed in U.S. Patent Application No. 62 / 492,870, filed May 1, 2017, entitled “Row to Row Sun Tracking Method and System,” which is hereby incorporated herein by reference.

[0061] The tracking system is described in U.S. Patent Application No. 14 / 745,301, filed June 19, 2015, entitled "Clamp Assembly for Solar Tracker," which is a continuation of U.S. Patent Application No. 14 / 489,416, filed September 17, 2014, entitled "Clamp Assembly for Solar Tracker," which is partially a continuation of U.S. Patent Application No. 14 / 101,273, filed December 9, 2013, entitled "Horizontal Balanced Solar Tracker," which claims priority to U.S. Patent Application No. 61 / 735,537, filed December 10, 2012, entitled "Fully Adjustable Tracker Apparatus." All of these applications are hereby incorporated by reference.

Claims

1. A solar tracking system (100; 200), comprising: Multi-row solar panel modules (110A-110D) forming a grid of solar panel modules, wherein the multi-row solar panel modules (110A-110D) can be independently oriented to solar energy; as well as A control system, configured to independently orient each of the multi-row solar panel modules (110A-110D) to the solar energy source based on a performance model, optimizes the energy output from the grid of solar panel modules. Its features are: The performance model predicts the energy output from the grid of solar panel modules based on the orientation of each of the multi-row solar panel modules (110A-110D) to the solar energy and first data, the first data including local weather conditions for each of the multi-row solar panel modules (110A-110D) and a predicted amount of irradiance on each of the multi-row solar panel modules (110A-110D), wherein the amount of irradiance varies with the ratio of diffuse horizontal irradiance DHI to global horizontal irradiance GHI.

2. The solar tracking system (100; 200) according to claim 1, wherein the amount of irradiance further varies with direct normal irradiance (DNI) or ground-reflected radiation.

3. The solar tracking system (100; 200) according to claim 1, wherein the performance model is calibrated based on the output of the solar panel modules and based on the shading between the multiple rows of solar panel modules (110A-110D).

4. The solar tracking system (100; 200) of claim 3, wherein the shading is determined from second data including a landform map, the landform map indicating the relative positions between neighbors in the multi-row solar panel modules (110A-110D).

5. The solar tracking system (100; 200) according to claim 1, wherein the multi-row solar panel module (SPM) i Each of these includes the corresponding self-powered controller (SPC). i ), drive assembly (DA) i ) and photovoltaic devices (PVi), where i=1 to X.

6. The solar tracking system (100; 200) according to claim 5, wherein the control system comprises a plurality of network control units (NCU i ) each coupled to a corresponding one or more of the self-powered controllers (SPC j ), where i = 1 to X, j = 1 to Y, Y < X, and each SPC i is wirelessly coupled to the corresponding NCUj.

7. The solar tracking system (100; 200) according to claim 6, wherein the plurality of network control units (NCUi) are coupled to a central controller (260; 300; 800).

8. The solar tracking system (100; 200) of claim 7, wherein the central controller (260; 300; 800) uses a machine learning algorithm to adjust the performance model based on the energy sensed on the solar tracking system (100; 200).

9. The solar tracking system (100; 200) according to claim 1, wherein the performance model comprises a polynomial having variables weighted by gain.

10. The solar tracking system (100; 200) of claim 9, wherein the gain includes conventional tracking gain and time-of-day backtracking gain.

11. The solar tracking system (100; 200) of claim 1, wherein the performance model includes a diffuse table, or the weather conditions are determined from current weather conditions, forecast weather conditions, or both.

12. The solar tracking system (100; 200) according to claim 1, wherein the orientation of each of the multi-row solar panel modules (SPMi) is SM. i The normal and from SM i The angle of incidence θi between the line to the solar energy source, where i = 1 to X.

13. The solar tracking system (100; 200) according to claim 7, wherein each SPC i Configured to respond when the corresponding network control unit (NCU) is lost. j Communication is performed under the default settings.

14. The solar tracking system (100; 200) of claim 13, wherein the default operation includes setting the SM... i Oriented to the default angle relative to the zenith.

15. The solar tracking system (100; 200) according to claim 6, wherein the central controller (800) comprises: Weather lookup module (835), which is used to receive the data from the row solar panel module (SM). i Local weather data for each of the following; Global horizontal irradiance and diffuse horizontal irradiance (DHI-GHI) module (825); Row-to-row tracking module (801), which is used to determine the multi-row solar panel modules (SM) i The slope of each of the terms in the equation to the horizontal line; A diffuse angle adjuster (810) is used to adjust the performance model based on the slope, yearly time, and dayly time; The first transmission module (815) is used to push directional commands to the self-powered controller (SPC) based on the performance model. i Each of them; as well as The second transmission module (820) is used to transmit data from the network control unit (NCU). j Each of the following receives data, where i=1 to X and j=1 to Y.

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