Intelligent solar racking system and method for monitoring the same

Abstract

According to one or more embodiments, a smart solar panel bracket system is provided. The smart solar panel bracket system includes a bracket frame for receiving and mechanically supporting solar modules. The smart solar panel bracket system includes sensors distributed throughout the bracket frame. Each of the sensors detects and reports parameter data by generating output signals. The sensors include a module sensor positioned to associate with each of the solar modules and detecting the presence of a module as parameter data for the solar modules. The smart solar panel bracket system includes a computing device for receiving, storing, and analyzing the output signals to determine and monitor the operation of the smart solar panel.

Description

[Technical Field]

[0001] This invention relates to solar power generation via a modular solar energy system. More specifically, this invention includes an intelligent solar bracket system for managing and monitoring mechanically stacked solar transmission cells or modules used for solar power generation. [Previous Technology]

[0002] Currently, there is no economically feasible conventional photovoltaic solar cell technology to achieve a power conversion efficiency greater than 25%. Therefore, at least 75% of the energy from the sun impacting the Earth's surface remains unused. [Summary of the Invention]

[0003] According to one or more embodiments, a smart solar bracket system is provided. The smart solar bracket system includes a bracket frame configured to receive and mechanically support one or more solar modules. The smart solar bracket system includes a plurality of sensors distributed throughout the bracket frame. Each of the plurality of sensors detects and reports parameter data by generating output signals. The plurality of sensors includes one or more module sensors positioned to be associated with each of the one or more solar modules and detecting the presence of at least one module as parameter data for the one or more solar modules. The smart solar bracket system includes a computing device for receiving, storing, and analyzing the output signals to determine and monitor the operation of the smart solar bracket.

[0004] According to one or more embodiments, a method is provided. The method includes monitoring output signals generated by a plurality of sensors distributed in a bracket frame of a smart solar bracket system by at least one computing device. Each of the plurality of sensors is configured to detect and report parameter data in the output signals. The bracket frame is configured to receive and mechanically support one or more solar modules. The plurality of sensors includes one or more module sensors positioned to be associated with each of the one or more solar modules. The method includes determining the operation of the smart solar bracket system by the at least one computing device based on the output signals.

[0005] Additional features and advantages are achieved through the technology of the present invention. Other embodiments and configurations of the present invention are described in detail herein. For a better understanding of the advantages and features of the present invention, please refer to the description and drawings.

Implementation Method

[0018] Cross-reference to related applications

[0019] This application claims priority to U.S. Provisional Patent Application No. 63 / 211,263, entitled “MODULAR SOLAR SYSTEM”, filed June 16, 2021, which is incorporated herein by reference as if its entirety were described herein for all purposes.

[0020] This document discloses a modular solar energy system. More specifically, the modular solar energy system relates to an intelligent solar tray system for managing and monitoring mechanically stacked solar transmission cells or modules used for solar power generation. According to one or more embodiments, the intelligent solar tray system may include a tray frame configured to receive and mechanically support solar modules. The intelligent solar tray system may include sensors distributed throughout the tray frame. It should be noted that each of these sensors detects and reports parameter data by generating output signals. The sensors may include module sensors positioned to associate with each of the solar modules to detect the presence of at least one module as a data parameter of the solar modules. The intelligent solar tray system may include a computing device for receiving, storing, and analyzing the output signals to determine and monitor the operation of the intelligent solar tray. Based on one or more technological effects, advantages and benefits, modular solar energy systems achieve a higher power output efficiency than conventional photovoltaic solar cells (e.g., power output efficiency may include ensuring a power conversion efficiency of 25% or more by conventional photovoltaic solar cells).

[0021] Turning now to Figure 1, the figure illustrates environment 100 according to one or more embodiments. As discussed herein, environment 100 may include one or more modular solar energy systems (i.e., solar module bracket systems for solar power generation). Embodiments of environment 100 include apparatus, systems, methods, and / or computer program products at any level of technical detail that may be integrated. It should be noted that other figures are combined by way of example, and the same element symbols in the figures indicate the same elements and will not be repeated for the sake of brevity.

[0022] According to one or more embodiments, environment 100 may represent a modular solar energy system located within field 101 and include one or more systems 102.n (where n is an integer). More specifically, environment 100 may include system 102 (which may represent any of the systems described herein) supporting one or more mechanically stacked solar transmission modules (i.e., one or more modules 105) within one or more frames 110 (which may represent any of the frames described herein) to receive and convert light energy (e.g., from the sun 201 of FIG. 2, although other sources are anticipated). The solar transmission modules include transmittance quality. Transmittance allows light energy (i.e., irradiance) to pass through an object. In this way, embodiments of environment 100 include devices, systems, methods, and / or computer program products at any possible level of technical detail.

[0023] Site 101 can be any terrain or open space to support one or more support structures 102.n, as well as roofs and / or other property areas. For example, any single component of system 102 can be placed within site 101, such as directly on a roof without a column, cover, and / or beam configuration. Each system 102 can be considered as a support structure, an architecture, and a stacked structure. Each system 102 includes at least one inverter 115, a switch 117, a series connection 120, and one or more frames 110 (where m is an integer).

[0024] Each frame 110 may include one or more modules 105 (e.g., modules 210, 230, and 240 of FIG. 2). According to one or more embodiments, one or more modules 105 may receive and convert light energy and may include at least one transmission solar module. Each frame 110 may electrically connect the modules 105 after the solar modules are inserted into the bracket frame 110. Each frame 110 is a system that can be assembled in a factory (i.e., automatically or non-automatically) and may serve as a structural assembly of a custom module. Each frame 110 may include T-shaped frame members and L-shaped frame members as well as flat members. A top surface / area and / or a bottom surface / area of ​​each frame 110 may remain open. A beam configuration of system 102 supports each frame 110. The beam configuration may be a C-shaped channel support. The flat members may support one or more electrical snap-on power sensors, each corresponding to a module position of each frame 110 that can receive a module.

[0025] Inverter 115 may be any power electronic device or circuit system that changes current (such as direct current (DC) to alternating current (AC)). Switch 117 may be a power-off switch that grounds each system 102. According to one or more embodiments, switch 117 provides an electrical latch and prevents popping when system 102 is energized. Series connection 120 may be any electronic configuration for connecting one or more electrical components (e.g., one or more modules 105), whether in series or in parallel to a particular electrical component (e.g., inverter 115). Series connection 120 may include a plurality of pre-wired sockets for quick assembly of one or more modules 105 into frame 110.

[0026] Each module 105 is connected to a corresponding serial connection 120 (e.g., via a pre-wired socket, a pin connection, a pigtail connection or the like) and has a temporary section 129 (e.g., close to or adjacent to) that defines a distance from other modules 105.

[0027] In this context, "adjacent" includes two components that are adjacent, in contact, or adjacent (e.g., in effective contact) but not joined together, and in some cases directly stacked on top of each other. Temporary section 120 may be maintained by a seal or similar on its periphery. The seal may include (but is not limited to) one or more of an adhesive or other fastener, a gasket, a plastic component, and a gap filler. According to one or more embodiments, the seal is a combination of a gap filler and an adhesive or other fastener. According to one or more embodiments, temporary section 120 is sealed on its periphery to support mechanical stacking and to prevent foreign objects (e.g., dust, insects, rodents, or the like) from penetrating between modules 105.

[0028] In this context, proximity includes two components that are close to, near, or at a predefined distance but not joined together. Examples of proximity may include (but are not limited to) 1 mm, 5 mm, 1 cm, 5 cm, 1 dm, 5 dm, or similar. The space (i.e., temporary section 120) may be maintained by one of the seals described herein and / or by the frame 110. According to one or more embodiments, the frame 110 supports and secures mechanical stacking and provides seals on one or more sides of the module 105. Examples of seals for the frame 110 include (but are not limited to) a mesh screen, a waterproof membrane, or an air filter. Examples of mechanical stacking include (but are not limited to) horizontal stacking, stacking parallel to a horizontal plane (i.e., flush with the ground), and stacking parallel to a plane of a solar array (i.e., design decision).

[0029] System 102 may include a plurality of sensors (e.g., any of sensors 136, 139, and 140). Any of sensors 136, 139, and 140 may be a sensor configured to convert one or more conditions of environment 100 into an electrical signal (e.g., an output signal). For example, any of sensors 136, 139, and 140 may include one or more of an electrode, a temperature sensor (e.g., a thermocouple), a current sensor, a light sensor, an accelerometer, a microphone, a radiation sensor, a proximity sensor, a position sensor, and a long-range (LoRa) sensor (e.g., any low-power wide-area network modulation sensor). Any of sensors 136, 139, and 140 may further represent relays, LoRa radios, Global Positioning System (GPS) units, etc., each of which further facilitates the detection and communication of environmental conditions 100.

[0030] According to one or more embodiments, each module 105 may include at least one sensor 136 (e.g., an internal sensor) and a code 138. For example, module 105 may be a smart module that includes an internal sensor (e.g., sensor 136). It should be noted that module 105 may also be a conventional solar energy technology that can be detected and monitored by environment 100.

[0031] In addition, additional sensors 139 and 140 can be located via system 102 and environment 100. Sensor 139 can be considered as a module sensor, and sensor 140 can be considered as a frame sensor (associated with frame 110 and distributed throughout frame 110). Environment 100 and its components (such as any of sensors 136, 139, and 140) can be managed by a device 160 (such as computing device 305, ARM processor, or the like). For example, device 160 can manage and receive output signals from multiple sets of sensors 136, 139, and 140 from different / multiple frames 110 and system 102 (e.g., device 160 can collect output signals from one or more of sensors 136, 139, and 140 associated with and distributed within frame 110.1 of system 102.1 and frame 110.1 of system 102.2). That is, system 102 may include a plurality of sensors (e.g., any of sensors 136, 139, and 140) that communicate with device 160. Device 160 may automatically determine the location of one of the modules 105 within frame 110. Furthermore, environment 100 may be connected to a grid 170 and may be accessible by a maintenance robot, a drone, a maintenance service provider, a technician, etc.

[0032] According to one or more embodiments, system 102 and frame 110 can be assembled in a factory setup (including pre-wiring) to reduce field assembly costs in field 101 while improving quality. Accordingly, a plurality of sensors can be pre-wired throughout frame 110. When frame 110 is shipped to field 101, frame 110 can be connected together and erected (e.g., like a jigsaw puzzle). One or more frames 110 may have dimensions to accommodate modules 105 (e.g., length and width of 1 × 2 meters) and provide spacing to accommodate cooling and bandgap distributions. According to one or more embodiments, 1-inch high modules with 1-inch spacing provide an 8-inch high module (e.g., it can look like a stack of pancakes). System 102 then provides lateral side-by-side stability, while one or more modules 105 provide longitudinal stability. According to one or more embodiments, one or more modules 105 can be adjacent (e.g., they can be directly stacked on top of each other without gaps).

[0033] According to one or more embodiments, environment 100 includes system 102, frame 110, and one or more modules 105 of field 101 having sensor 136. Environment 100 illustrates at a macro level how elements and components are connected within a larger network for alarm purposes, and how power is supplied to grid 170 or other loads (e.g., one or more batteries). Frame 110 can be integrated into any system providing a three-dimensional solar system application. As described herein, one or more modules 105 include a bottom module and one or more upper modules.

[0034] System 102 fixes one or more solar modules 105 in a mechanically stacked manner to vertically align a plurality of solar cells and / or transmission solar cells of the one or more modules 105. The environment 100 may further include one or more solar cell configurations. For example, module 105 includes one or more cells configured in an xy grid, where both x and y are integers greater than 0, or where x is 1 and y is an integer greater than 0. The width, wiring, and configuration of the cells can be managed and operated to control power generation on a per-cell basis.

[0035] Based on one or more technical effects, benefits, and advantages, the frame 110 facilitates the movement, replacement, and / or exchange of the solar modules 105, such as for use in next-generation modules 105. The frame 110 vertically stacks the solar modules 105 to maximize solar energy capture per square meter of surface area. Furthermore, the vertical arrangement of the solar modules 105 within the frame 110 enables capture of band gaps, cooling, spacing, etc. According to one or more embodiments, the frame 110 can utilize a housing or enclosure for holding multiple solar modules fixed on top of another, with temporary sections 130 between them allowing airflow for cooling or eliminating the need for a space. It should be noted that in one example of FIG. 1, the frame 110 stacks four layers of modules 105, each module 105 corresponding to one of the series connections 120. Each series connection 120 corresponds to and is electrically connected to one of the modules 105 to receive power from it. Each series connection 120 is electrically different from the other series connections 120.

[0036] According to one or more embodiments, environment 100 may also include a uniform design in which solar modules 105 are connected in series or in parallel. For example, each cell of module 105 can provide a voltage of 1.5 volts. Furthermore, up to 32 cells can be connected in series within each series connection 120 to each module 105 (e.g., 18 modules 832 connected in series per series connection 120 to provide 864 volts). Inverter 115 can be combined in parallel with the 32 series connections 120 to generate a high current supplied to grid 170. According to one or more embodiments, environment 100 may also include a layered design in which solar modules 105 of one or more series connections 120 are connected in parallel, solar modules 105 of one or more series connections 120 are connected in series, and / or a combination thereof (e.g., a set of layers managed in a hybrid environment).

[0037] System 102 may be a pre-wired modular rack system incorporating one or more of the technical forms described herein (e.g., modular DC optimizers). According to one or more embodiments, system 102 may be an assembly of one or more frames 110. One structure of system 102 may be made of carbon fiber, steel, metal, alloy, wood, plastic, fiberglass, or any combination thereof. According to one or more embodiments, system 102 may be a "smart rack" system providing a plurality of sensors (e.g., sensors 136, 139, and 140) and the ability to communicatively couple to device 160.

[0038] Based on one or more technological effects, advantages, and benefits, frame 110 can layer single pn-junction cells (which are cost-effective compared to other technologies) in a stacked structure along with a stacked structure that mimics a series cell concept without joining solar modules 105 and maintains different electrical properties. Furthermore, based on one or more technological effects, advantages, and benefits, frame 110 provides an improved modular structure that facilitates easy field installation to reduce the cost structure of a battery plant (BOP). For example, in the United States, the current cost of a solar module for a utility-scale solar power project corresponds to approximately US$0.40 per watt, including diminishing returns on solar cell cost reductions. Additionally, BOP cost reductions have made very little progress compared to conventional solar technologies and have not decreased proportionally with advancements in conventional solar technologies. Various government agencies have set a total cost threshold of US$0.50 per watt (DC) for a solar power plant to be cost-competitive with conventional fossil fuels. This objective can only be achieved where there is a significant improvement in BOP cost. One way to achieve this goal is by increasing the density of sunlight captured per unit of land surface, thereby reducing electricity production costs by amortizing BOP costs across higher kWh of electricity production. Furthermore, based on one or more technological effects, advantages, and benefits, frame 110 is more suitable for residential and commercial buildings with limited roof and property areas. Frame 110 then allows buildings to become net-zero electricity consumers and are effectively off-grid 170. Moreover, based on one or more technological effects, advantages, and benefits, environment 100 can save infrastructure while having the flexibility to utilize future technological improvements (e.g., an average solar lifespan of 15 years; conversely, environment 100 can now extend that lifespan to over 50 years).

[0039] Figure 2 depicts a system 200 according to one or more embodiments. The figure in Figure 2 is oriented according to an X1-X2 axis and a Y1-Y2 axis. The X1-X2 axis (as indicated by a left-right double arrow) is typically oriented horizontally across the page. The Y1-Y2 axis (as indicated by an up-down arrow) is typically oriented vertically across the page. The X1 direction is opposite to the X2 direction, and the Y1 direction is opposite to the Y2 direction. Other orientations may be made according to these axes, which may be tilted or angled. A side or surface of a reference component may be described according to these axes. For example, a lower side or bottom side or a downward-facing surface of a component being described may refer to a Y1 side or a Y1 surface.

[0040] System 200 receives light energy or light 202 from at least one sun 201 (from the Y2 direction). Light 202 can be considered as incident light or natural light (although other sources are expected). System 100 receives light 202 at a plurality of solar modules.

[0041] According to one or more embodiments, system 200 includes an optional module 210 having an optional transmissive solar cell 211; a transmissive module 230 having a transmissive solar cell 231; a module 240 having a visible transmissive solar cell 241 (e.g., module 240 may be a transmissive module); an optional reflective module 250; one or more boxes 270; and a bus 280. Note that the optionality of any component or feature is indicated by a dashed border. For example, a top solar module (e.g., optional module 210) may be a concentrator or micro-concentrator, one or more intermediate modules (e.g., transmissive modules 230 and 240) may be one or more transmissive modules, and a bottom module may capture any remaining light energy (e.g., infrared radiation) or reflect it (e.g., reflective module 250).

[0042] Module 210 may be a concentrator, a micro-concentrator, or a transmission module. Each module is connected to one or more boxes 270, which may be electrical combiner boxes (e.g., corresponding to the serial section 120 of the frame 110 as described herein) that supply power generated by one or more modules 210, 230, and 240 to busbar 280 (e.g., PV bus connector and PV connector box).

[0043] One or more boxes 270 may provide wired electrical connections for receiving sockets, connections, or the like of one or more modules 210, 230, and 240. It should be noted that the wired electrical connections may include sensors, and the wired electrical connections may engage wiring from individual modules. For example, the wiring bundles of a module may protrude to the back of one of the sockets for easy installation, repair, and maintenance (e.g., in a plug-and-play manner). These wired electrical connections and sockets may be weatherproof, quick-connect hardware (e.g., for connecting wires to a junction box to simplify installation and reduce field quality errors). Each wired electrical connection may include a fuse with an indicator light to ensure power is cut off during module installation and maintenance. Additionally, as described herein, the operation of system 200 may be monitored by one or more sensors.

[0044] System 200 is an example of a modular solar energy system. More specifically, System 200 is an example of a mechanically stacked solar transmission cell or modular device. According to one or more embodiments, System 200 includes at least two mechanically stacked modules, such as Selective Module 210, Transmission Module 230, and Module 240. According to one or more embodiments, Selective Module 210, Transmission Module 230, and Module 240 may be bifacial (e.g., absorbing light energy from either side) and include clear wiring to allow light energy to pass through multiple times therein. Selective Module 210 and Transmission Module 230 may represent one or more upper modules. Module 240 may represent a module layer, which may be the same as or different from one or more upper modules. Selective module 210 and transmission module 230 include a plurality of transmission solar cells (e.g., selective transmission solar cell 211 and transmission solar cell 231), which convert light energy received on a Y2 side (or a sun or a first side) into electricity and transfer the unconverted portion of the light energy to the next module. For example, selective module 210 transfers the unconverted portion of the light energy in a Y1 direction to transmission module 230 on one Y2 side (or a sun or a second side) of selective module 210. Furthermore, transmission module 230 transfers the unconverted portion of the light energy in a Y1 direction to module 240 on one Y2 side (or a sun or a second side) of transmission module 230.

[0045] It should be noted that module 240 includes a plurality of solar cells (e.g., solar cell 241 may be transmissive), which convert at least a portion of the unconverted portion of light energy into electricity. System 200 mechanically stacks at least two mechanically stacked modules such that the plurality of solar cells of module 240 are perpendicularly aligned with the plurality of transmissive solar cells of each of selected module 210 and transmissive module 230. In one embodiment, the mechanical stack of system 200 may be further sealed on one or more sides, such as by means of a mesh screen, a waterproof membrane, or an air filter as described herein. Examples of mechanical stacking include (but are not limited to) horizontal stacking, parallel stacking with a horizontal plane (i.e., flush with the ground), and parallel stacking with a plane of a solar array (i.e., design decision). By maintaining mechanical and electrical separation of the battery / module, one or more modules 210, 230, and 240 may be designed to work together for electrical aggregation (e.g., it further enables a broader range of electrical components to achieve power aggregation).

[0046] In operation, sunlight 202 passes through one or more modules 210, 230, and 240. Module 210 can absorb light 202 of a first wavelength in a first spectral response. Light 291 of the first spectral response beyond the first wavelength (i.e., its irradiance) is further transmitted to transmission module 230 in the Y1 direction.

[0047] The transmission module 230 can absorb light 291 of a second wavelength in a second spectral response. According to one or more embodiments, the first and second wavelengths may be the same. According to one or more embodiments, the first and second spectral responses may also be the same. Light 293 of the second spectral response beyond the second wavelength (i.e., its irradiance) is further transmitted to the module 240 in the Y1 direction.

[0048] The figure depicts graph 298 according to one or more embodiments. Graph 298 represents CdTe (e.g., transmission solar cell 231 and transmission module 230) and c-Si (e.g., solar cell 241 and module 240). One key 299 identifies the lines within graph 298. Graph 298 includes an x-axis showing a nanometer-level wavelength, a left y-axis showing a spectral intensity, and a right y-axis showing spectral response and transmittance. It should be noted that the approximate absorption range of CdTe is 400 nm to 800 nm (e.g., a second wavelength). For example, in system 200, transmission module 230 absorbs the irradiance of light 291 in the wavelength range of 400 nm to 800 nm and transmits unabsorbed light energy in the wavelength range greater than 800 nm.

[0049] Module 340 can absorb light 293 of a third wavelength in a third spectral response. According to one or more embodiments, the third wavelength may include and / or be wider than the second wavelength. Referring back to Figure 298, it should be noted that the approximate absorption range of c-Si is 400 nm to 1200 nm (e.g., the second wavelength). Module 240 absorbs the irradiance of light 293 in a wavelength range of at least 800 nm to 1200 nm and can transmit light energy in a wavelength range greater than 1200 nm. Module 240 can also absorb the irradiance of light 293 in a wavelength range of 400 nm to 1200 nm, wherein the 400 nm to 800 nm wavelength range includes the remaining light 293 (i.e., its irradiance) across the second wavelength that exceeds the second spectral response. In addition, the remaining light 295 (i.e., its irradiance) beyond the third wavelength and across the third wavelength that exceeds the third spectral response is further transmitted to the reflective module 250 in the Y1 direction.

[0050] The reflective module 250 reflects light 295 back to module 240 in the Y2 direction. Any remaining irradiance of light 295 may be further absorbed by module 240 or transmitted as light 297 to the transmission module 230 (e.g., light 297 continues in the Y2 direction). Then, any remaining irradiance of light 297 may be further absorbed by the transmission module 230 or transmitted as light 299 to module 210 (e.g., light 299 continues in the Y2 direction). Note the faded arrows representing light 202, 291, 293, 295, 297, and 299, illustrating the absorption of irradiance and the description of energy, as light 202 is converted into electricity transmitted to box 270. It should also be noted that a particular irradiance, or a portion thereof, may not be absorbed during the first pass but may be absorbed during the second pass (i.e., in the Y2 direction).

[0051] Turning now to Figure 3, the figure illustrates a computing system 300 according to one or more embodiments. The computing system 300 may represent any computing device, computing apparatus, and / or computing environment including hardware, software, or a combination thereof. Furthermore, the disclosed embodiments of the computing system 300 may include devices, systems, methods, and / or computer program products at any possible level of technical detail.

[0052] The computing system 300 includes a computing device 305 (e.g., device 160 of FIG. 1) comprising one or more central processing units (CPUs) collectively referred to as a processor 310. The processor 310 (also referred to as processing circuitry) is coupled to a system memory 320 and various other components via a system bus 315. The computing system 300 and / or device 305 may be adapted or configured to function as an online platform, a server, an embedded computing system, a personal computer, a console, a personal digital assistant (PDA), a mobile phone, a tablet computer, a quantum computing device, a cloud computing device, a mobile device, a smartphone, a fixed mobile device, a smart display, a wearable computer, or the like.

[0053] The processor 310 may be any type of general-purpose or special-purpose processor, including a central processing unit (CPU), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), graphics processing unit (GPU), controller, multi-core processing unit, three-dimensional processor, quantum computing device, or any combination thereof. The processor 310 may also have multiple processing cores, and at least some cores may be configured to perform specific functions. Multi-parallel processing may also be configured. In addition, at least the processor 310 may be a neuromorphic circuit containing a processing element that simulates a biological neuron.

[0054] Bus 315 (or other communication mechanism) is configured to transmit information or data to processor 310, system memory 320 and various other components, such as adapter 325.

[0055] System memory 320 is an example of a (non-transitory) computer-readable storage medium, wherein software 330 may be stored as a software component, module, engine, instruction, or the like for execution by processor 310 to cause operation of computing device 305, as described herein with reference to FIG1. ​​System memory 320 may include any combination of a read-only memory (ROM), a random access memory (RAM), internal or external flash memory, embedded static RAM (SRAM), solid-state memory, cache, static storage (such as a magnetic disk or optical disk), or any other type of volatile or non-volatile memory. Non-transitory computer-readable storage medium may be any medium accessible by processor 310 and may include volatile media, non-volatile media, or the like. For example, the ROM is coupled to the system bus 315 and may include a basic input / output system (BIOS) that controls certain basic functions of the device 305, and the RAM is a read-write memory coupled to the system bus 315 for use by the processor 310. Non-transitory computer-readable storage media may include any media that is removable, non-removable, or similar.

[0056] According to one or more embodiments, software 330 may be configured in hardware, software, or a hybrid implementation. Software 330 may consist of modules that operate and communicate with each other and exchange information or instructions. According to one or more embodiments, software 330 may provide one or more user interfaces, such as representing an operating system or other application and / or provided directly as needed. User interfaces include (but are not limited to) graphical user interfaces, windowed interfaces, internet browsers and / or applications, operating systems, folders, and other visual interfaces like these. Thus, user activity may include any interaction or manipulation of the user interface provided by software 330. Software 330 may further include custom modules for executing application-specific programs or derivatives thereof, such that computing system 330 may include additional functionality. For example, according to one or more embodiments, software 330 may be configured to store information, instructions, commands, or data to be executed or processed by processor 310 to logically implement the methods described herein (e.g., big data operations relative to machine learning and artificial intelligence). The software 330 in Figure 3 may also represent an operating system, a mobile application, a client application, and / or similar applications used in the computing device 305 of the computing system 300.

[0057] Furthermore, the module of software 330 may be implemented as a hardware circuit, including custom very large-scale integrated circuit (VLSI) circuitry or gate arrays in programmable hardware devices (e.g., field-programmable gate arrays, programmable array logic, programmable logic devices), off-the-shelf semiconductors (such as logic chips, transistors, or other discrete components), graphics processing units, or the like. The module of software 330 may be implemented at least partially in software for execution by various types of processors. According to one or more embodiments, one identification unit of executable code may include one or more entities or logical blocks of computer instructions that can be organized, for example, into an object, program, norm, subnorm, or function. The executable code of an identification module is co-located or stored in different locations such that the module is included when logic is coupled together. One module of executable code may be a single instruction, one or more data structures, one or more data sets, a plurality of instructions, or the like, distributed across several different code segments, different programs, and spanning several memory devices or similar entities. Operational or functional data may be identified and drawn within the module of software 330, and may be represented in an appropriate form and organized within any appropriate type of data structure.

[0058] Furthermore, the module of software 330 may also include (but is not limited to) a location module, an augmented reality module, a virtual reality module, a blockchain module, and a machine learning and / or artificial intelligence (ML / AI) algorithm module. In one example, the module of software 330 may modulate signals onto a power line for use in inter-panel communication and / or to provide wireless communication.

[0059] A location module can be configured to generate, construct, store, and provide algorithms and models for determining the position and relative distance of a computing device 305. According to one or more embodiments, the location module can implement location, geographic social network, space navigation, satellite orientation, measurement, distance, direction, and / or time software.

[0060] An augmented reality module can be configured to generate, construct, store, and provide algorithms and models for interactive experiences that provide a real-world environment, wherein objects residing in the real world are augmented by computer-generated perceptual information, sometimes spanning multiple sensory categories. A virtual reality module can be configured to generate, construct, store, and provide algorithms and models that simulate experiences similar to or completely different from the real world. According to one or more embodiments, virtual reality and / or augmented reality modules can provide enhanced, hybrid, immersive, and / or text-based virtual realities.

[0061] A blockchain module can be configured to generate, construct, store, and provide algorithms and models for supplying cryptographically linked records or blocks, such that each block contains at least one or more of the following: a cryptographic hash of a previous block (e.g., thereby forming a chain), a timestamp, and transaction data (e.g., social data, connection data, preference data, etc.). The timestamp identifies the transaction data present when a block is published to enter its hash.

[0062] An ML / AI algorithm module can be configured to generate, build, store, and provide algorithms and models that are automatically improved through experience, and to mimic the "natural" cognitive abilities of humans. In one instance, ML software uses training data to build and improve a specific model, while AI software perceives an environment (e.g., receives active data) and takes action (e.g., applies a model) to solve a problem and / or generate an output. The AI ​​software can use a model built by humans and / or ML software. The AI ​​software can further provide feedback to the ML software to improve any of its models. ML / AI can exist independently and / or coexist.

[0063] Relative to the adapter 325 in FIG3, the computing device 305 may specifically include an input / output (I / O) adapter, a device adapter, and / or a communication adapter. According to one or more embodiments, considering Frequency Division Multiple Access (FDMA), Single-Carrier FDMA (SC-FDMA), Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), Orthogonal Frequency Division Multiplexing (OFDM), and Orthogonal Frequency Division Multiple Access (OFDMA), the I / O adapter can be configured to support a Small Computer System Interface (SCSI), Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), cdma2000, or Broadband CDMA. (W-CDMA), High-Speed ​​Downlink Packet Access (HSDPA), High-Speed ​​Uplink Packet Access (HSUPA), High-Speed ​​Packet Access (HSPA), Long Term Evolution (LTE), LTE Advanced (LTE-A), 802.11x, Wi-Fi, Zigbee, Ultra Wideband (UWB), 802.16x, 802.15, Home Node-B (HnB), Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Near Field Communication (NFC), 5G, New Radio (NR), or any other wireless or wired device / transceiver used for communication. The device adapter interconnects input / output devices to system bus 315, such as a display 341, a sensor 342, a controller 343, or the like (e.g., a camera, a speaker, etc.).

[0064] A communication adapter interconnects system bus 315 with a network 350, which may be an external network, enabling computing device 305 to communicate data with other such devices (e.g., computing system 355 via network 350). In one embodiment, adapter 325 may be connected to one or more I / O buses connected to system bus 315 via an intermediate bus bridge. Suitable I / O buses for connecting peripheral devices (such as hard disk controllers, network adapters, and graphics adapters) typically include common protocols such as Peripheral Component Interconnect (PCI).

[0065] Display 341 is configured to provide one or more UIs or graphical user interfaces (GUIs) that can be captured and analyzed by software 330 when a user interacts with computing device 305. Examples of display 341 may include (but are not limited to) a plasma, a liquid crystal display (LCD), a light-emitting diode (LED), a field-emitting diode (FED), an organic light-emitting diode (OLED), a flexible OLED, a flexible substrate display, a projection display, a 4K display, a high-definition (HD) display, a retina© display, an in-switching in-line display (IPS), or the like. Display 341 may be configured to use one of the following: resistive, capacitive, surface acoustic wave (SAW) capacitor, infrared, optical imaging, dispersive signal technology, acoustic pulse identification, suppressed total internal reflection, or the like for touch, three-dimensional (3D) touch, multi-input touch, or multi-touch display, as understood by those skilled in the art for input / output (I / O).

[0066] Sensor 342 (such as any transponder configured to convert one or more environmental conditions into an electrical signal) may be further coupled to system bus 315 for input to computing device 305. For example, sensor 342 may generate parameter data based on detected voltage, temperature, and current. In addition, sensor 342 may generate parameter data based on detected solar radiation, wind and ambient temperature, as well as other environmental and electrical properties of the bracket frame (e.g., frame 110) or a separate module (e.g., frame 105).

[0067] Additionally, one or more inputs may be provided remotely to computing system 300 via another computing system (e.g., computing system 355) in communication with it, or computing device 305 may operate autonomously. For example, sensor 342 may include one or more of an electrode, a temperature sensor (e.g., a thermocouple), a current sensor, a light sensor, an accelerometer, a microphone, a radiation sensor, a proximity sensor, a position sensor, and a long-range (LoRa) sensor (e.g., any low-power wide area network modulation sensor).

[0068] According to one or more embodiments, sensors 342 may be installed at various levels and integrated into an environment (e.g., environment 100 in FIG. 1) to monitor operation therein, such as identifying when a particular solar module is not functioning correctly. For example, when the current of a panel falls below a defined threshold, sensor 342 (e.g., a current sensor) sends a signal to software 330 to identify the precise location of a faulty module. Each sensor 342 includes a serial number that can be matched with a corresponding level of the environment (e.g., environment 100 in FIG. 1) of each panel / casing / frame / truss, etc. (e.g., as identified on a scannable code).

[0069] Controller 343 (such as a computer mouse, a touchpad, a touch screen, a keyboard, a keypad, or the like) may be further coupled to system bus 315 for input to computing device 305. Additionally, one or more inputs may be provided remotely to computing system 300 via another computing system (e.g., computing system 355) with which it communicates, or computing device 305 may operate autonomously. Controller 343 may also represent one or more actuators or the like for moving, locking, or unlocking parts of an environment (e.g., environment 100 in FIG. 1).

[0070] According to one or more embodiments, the functionality of the computing device 305 relative to the software 330 can also be implemented on the computing system 355, as represented by a separate execution of the software 390. It should be noted that the software 390 can be stored in a common storage repository located in one of the computing devices 305 and / or the computing system 355 and can be downloaded (as needed) to and / or from each of the computing devices 305 and / or the computing system 355. Therefore, the functionality of the computing device 305 and the computing system 355 may include ML / AI, power production management, power prediction, power forecasting, power fault tolerance, etc.

[0071] According to one or more embodiments, the computing system 355 may be a server, a database, and / or a cloud device. The computing system 355 can collect and analyze operational and / or parameter data of any system (e.g., environment 100 in FIG1). For example, the computing system 355 is one of the distributed cloud-based implementations for big data processing of operational and / or parameter data of any system (e.g., environment 100 in FIG1). According to one or more technical effects, advantages, and benefits, because each sensor 342 may be pre-wired and / or include positioning (i.e., GPS) technology, the computing device 305 and / or the computing system 355 can use big data processing to determine the precise location of the problem (i.e., contrary to the conventional hub and spoke-type dummy transmitter design) and determine remedial measures. According to one or more embodiments, computing device 305 and / or computing system 355 can determine, for example, the degraded or suboptimal performance of any module (e.g., one or more modules 210, 230, and 240) by analyzing the operation and parameter data of the smart solar bracket system. According to one or more embodiments, computing device 305 and / or computing system 355 can determine the long-term trend of the smart solar bracket system by analyzing the operation and / or parameter data of any system (e.g., environment 100 in FIG1). According to one or more embodiments, computing device 305 and / or computing system 355 can optimize or reconfigure the electrical operation of any system (e.g., environment 100 in FIG1) by bypassing or isolating any module (e.g., one or more modules 210, 230, and 240) or by adjusting voltage or current levels to generate automatic feedback to the relative device / system 305 / 355. The computing system 355 generates feedback to a third-party intelligent system (such as a drone, cleaner, robot, scheduling system, maintenance personnel, etc.) to take actions that optimize the electrical output of any system (such as environment 100 in Figure 1) (such as module cleaning, module replacement).

[0072] According to one or more embodiments, software 330 utilizes the ML / AI algorithm module therein, as described with respect to Figures 4 and 5. As indicated herein, the description of Figures 4 and 5 is referenced to Figure 3 for ease of understanding where appropriate.

[0073] FIG4 illustrates a graphical depiction of an artificial intelligence system 400 according to one or more embodiments. The artificial intelligence system 400 includes data 410, a machine 420, a model 430, a result 440, and (underlying) hardware 450. For ease of understanding, FIG4 is described with reference to FIG3 where appropriate. For example, the machine 420, model 430, and hardware 450 may represent the state of the software 330 of FIG3 (e.g., the ML / AI algorithm module therein), and the hardware 450 may also represent the computing device 305 of FIG3. Generally, the machine learning and / or artificial intelligence algorithms of the artificial intelligence system 400 (e.g., implemented by the software 330 of FIG3) use data 410 to operate relative to the hardware 450 to train the machine 420, build the model 430, and predict the result 440.

[0074] For example, machine 420 acts as a controller or data collection operation associated with and / or associated with hardware 450. Data 410 may be ongoing data or output data associated with hardware 450. Data 410 may also include currently collected data, historical data, or other data from hardware 450 and may be related to hardware 450. Data 410 may be divided into one or more subsets by machine 420. For example, data 410 may be one or more of the following: temperature, current, light, motion, sound, radiation, proximity sensor and / or location data, as well as historical data, data from other environments, and data from other systems (e.g., weather data).

[0075] Furthermore, machine 420 is trained (e.g., relative to hardware 450). This training may also include analysis and correlation of one of the collected data 410. According to another embodiment, training machine 420 may include self-training performed by software 330 of FIG3 using one or more subsets. Accordingly, software 330 of FIG3 learns to detect and predict data from data 410 (e.g., predictive comparative weather operations).

[0076] Furthermore, model 430 is built upon data 410 associated with hardware 450. Building model 430 may include physical hardware or software modeling, algorithmic modeling, and / or seeking representations of the collected and trained data 410 (or a subset thereof). In some cases, the construction of model 430 is part of the self-training operation of machine 420. Model 430 may be configured to model the operation of hardware 450 and model the data 410 collected from hardware 450 to predict the result 440 achieved by hardware 450. The predicted result 440 (model 430 associated with hardware 450) may utilize a trained model 430. Therefore, using the predicted result 440, machine 420, model 430, and hardware 450 may be configured accordingly.

[0077] Therefore, for the operation of the artificial intelligence system 400 relative to the hardware 450, data 410 is used to train the machine 420, build a model 430, and predict results 440, wherein the machine learning and / or artificial intelligence algorithms may include neural networks. Generally speaking, a neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network (ANN) composed of artificial neurons, nodes, or cells.

[0078] For example, an ANN involves a network of processing elements (artificial neurons) that can exhibit complex global behavior determined by the connections between processing elements and element parameters. These connections of the neuron network or circuit are modeled as weights. A positive weight reflects a stimulatory connection, while a negative value signifies an inhibitory connection. The input is modified by a weight and summed using a linear combination. A start function controls the magnitude of the output. For example, an acceptable output range is typically between 0 and 1, or it can be -1 and 1. In most cases, an ANN is an adaptive system that changes its structure based on external or internal information flowing through the network.

[0079] In more practical applications, neural networks can be used to model complex relationships between inputs and outputs or to discover patterns in nonlinear statistical data models or decision-making tools. Therefore, ANNs can be used for predictive modeling and adaptive control applications, while being trained on a dataset. It should be noted that self-learning derived from experience can occur within ANNs that can derive conclusions from a complex and seemingly unrelated set of information. The utility of artificial neural network models lies in the fact that they can be used to infer a function from observation and also use that function. Unsupervised neural networks can also be used to learn input representations that capture salient characteristics of the input distribution, and recently, deep learning algorithms have been developed that can explicitly learn the distribution function of observed data. Neural network learning is particularly useful in applications where the complexity of the data or task makes manually designing such functions impractical.

[0080] Neural networks can be used in various fields. Therefore, for an artificial intelligence system 400, the machine learning and / or artificial intelligence algorithms may include neural networks typically categorized according to the tasks applied to them. These categorizations tend to fall into the following categories: regression analysis (e.g., function approximation), which includes time series prediction and modeling; classification, which includes pattern and sequence recognition; novelty detection and sequential decision-making; data processing, which includes filtering; clustering; and blind signal separation and compression. For example, application areas of ANNs include nonlinear system recognition and control (vehicle control, program control), games and decision-making (backgammon, chess, racing), pattern recognition (radar systems, face recognition, object recognition), sequence recognition (gesture, speech, handwritten text recognition), financial applications, data mining (or knowledge discovery in databases, "KDD"), visualization, and email spam filtering.

[0081] According to one or more embodiments, the neural network may implement a long short-term memory neural network architecture, a convolutional neural network (CNN) architecture, or other similar architectures. The neural network may be configured with respect to several layers, several connections (e.g., encoder / decoder connections), a regularization technique (e.g., exit); and an optimized feature.

[0082] Long Short-Term Memory (LSTM) neural network structures include feedback connections and can process single data points (e.g., images) along with entire data sequences (e.g., speech or video). One unit of an LTM neural network architecture may consist of a cell, an input gate, an output gate, and a forget gate, wherein the cell remembers values ​​at any time interval and the gate regulates the flow of information entering and leaving the cell.

[0083] The CNN architecture is a shared weight architecture with translation invariance, in which each neuron in one layer is connected to all neurons in the next layer. Regularization techniques in the CNN architecture can utilize the hierarchical patterns in the data and assemble more complex patterns using smaller and simpler patterns. If a neural network implements a CNN architecture, other configurable features of the architecture may include several filters in each stage, kernel size, and the number of kernels in each layer.

[0084] Turning now to Figure 5, the figure shows an example of a neural network 500 according to one or more embodiments and a block diagram of a method 501 performed in the neural network 500. The neural network 500 is used to support implementations of the machine learning and / or artificial intelligence algorithms described herein (e.g., implemented by the software 330 of Figure 3). The neural network 500 may be implemented in hardware, such as the machine 420 and / or hardware 450 of Figure 4.

[0085] In one instance operation, the software 330 of FIG3 includes data collection 410 from the hardware 450. In the neural network 500, an input layer 510 is represented by a plurality of inputs (e.g., inputs 512 and 514 of FIG5). The input layer 510 receives inputs 512 and 514 relative to block 520 of method 501.

[0086] At block 525 of method 501, neural network 500 encodes inputs 512 and 514 using any portion of data 410 (e.g., datasets and predictions generated by artificial intelligence system 400) to produce a latent representation or data encoding. The latent representation includes one or more intermediate data representations derived from a plurality of inputs. According to one or more embodiments, the latent representation is generated by one of the element-wise activation functions (e.g., a sigmoid function or a rectified linear unit) of software 330 of FIG3. As shown in FIG5, inputs 512 and 514 are provided to a hidden layer 530 depicted as including nodes 532, 534, 536, and 538. Neural network 500 performs processing via the hidden layer 530 of nodes 532, 534, 536, and 538 to exhibit complex global behavior determined by the connections between processing elements and element parameters. Therefore, the transition between layers 510 and 530 can be viewed as an encoder level that receives inputs 512 and 514 and transmits them to a deep neural network (within layer 530) to learn some smaller representations of the inputs (e.g., a derived latent representation).

[0087] The deep neural network may be a CNN, a long short-term memory neural network, a fully connected neural network, or a combination thereof. This encoding provides dimensionality reduction for inputs 512 and 514. Dimensionality reduction is a procedure that reduces the number of random variables (inputs 512 and 514) by obtaining a set of principal variables. For example, dimensionality reduction may be a feature extraction that transforms data (e.g., inputs 512 and 514) from a high-dimensional space (e.g., more than 10 dimensions) to a low-dimensional space (e.g., 2 to 3 dimensions). The technical effects and benefits of dimensionality reduction include reducing the time and storage space requirements of data 410, improving the visualization of data 410, and improving the parameter interpretation of machine learning. This data transformation may be linear or non-linear. The receiving (block 520) and encoding (block 525) operations can be considered as a data preparation part of a multi-step data operation by software 330.

[0088] At block 545 of method 501, neural network 500 decodes the latent representation. The decoding phase acquires the encoder output (e.g., the resulting latent representation) and attempts to reconstruct some form of the inputs 512 and 514 using another deep neural network. Accordingly, nodes 532, 534, 536, and 538 are combined to produce an output 552 in output layer 550, as shown in block 580 of method 510. That is, output layer 550 reconstructs inputs 512 and 514 in a reduced dimension without signal interference, signal artifacts, and signal noise.

[0089] Turning now to Figure 6, which illustrates a method 600 according to one or more exemplary embodiments (e.g., performed by the software 330 of Figure 3). Method 600 determines and monitors the operation of the smart solar bracket system, and provides a panel offline position sensor and a software alarm system.

[0090] Method 600 begins at block 620, wherein software 330 monitors environment 100. Accordingly, software 330 receives one or more inputs (e.g., output signals from sensors 136, 139, and 140). For example, one or more inputs may be parameter data provided by one or more sensors 342, computing system 355, and / or other sources provided in real time or otherwise. According to one or more embodiments, the parameter data may include one or more of the following: temperature, current, light, motion, sound, radiation, proximity sensor and / or location data, as well as historical data, data from other environments, and data from other systems (e.g., weather data, radiation data).

[0091] At block 630, software 330 records one or more inputs. Software 330 may record one or more inputs together with real-time timestamps at computing device 305 and / or computing system 355 as parameter data. The recorded inputs may accumulate over time and be aggregated with inputs from other systems.

[0092] At block 640, software 330 identifies one or more inputs. Identifying one or more inputs may relate to matching the serial number and / or location of sensor 342 with system parameter data. It should be noted that the operation of blocks 630 and 640 can occur in a loop, operate in parallel, or operate independently.

[0093] According to one or more embodiments, after installing a modular solar system, a maintenance service provider or a technician can scan (using a mobile device connected to a computing system 355) the codes 138 on each module 105, which provides (as data) a precise geographical location (e.g., input data) of each module 105 to a database or storage of the computing system 355. When a module 104 fails to generate an electrical design current, a sensor 342 corresponding to the precise geographical location sends a signal to the software 330 (e.g., input data).

[0094] According to one or more embodiments, the location (e.g., an x, y, z position) within the frame 110 and system 102 can be used to preset the sensor 139. Furthermore, during installation, the distance of the sensor 139 relative to a geographic location can be determined from the device 160. In some cases, the sensor 139 may incorporate GPS technology and work in conjunction with other components of the system 102 to determine the precise geographic location of each sensor. Therefore, upon insertion of any module 105 (whether smart or conventional), the sensor 139 is located and associated with each of the modules 105. Turning to Figure 7, a detection example 700 according to one or more embodiments is depicted.

[0095] Detection example 700 illustrates a frame 701 including a plurality of sensors 705, 706, 708, and 709. Each of the plurality of sensors 705, 706, 708, and 709 is positioned at a specific height of the frame 701 (i.e., along a Z1-Z2 axis). Therefore, each sensor 705, 706, 708, and 709 can detect the presence of at least one module as data parameters of module 720. Alternatively, device 160 can detect the connection of module 720 to a socket at one of the locations along a Z1-Z2 axis (e.g., the corresponding connector 985 in FIG. 9, a pre-wired socket, a pin connection, a pigtail connection, or the like).

[0096] Returning to Figure 6, at block 650, software 330 performs analysis of all input data (e.g., analyzing output signals to determine and monitor the operation of the smart solar bracket system). For example, by utilizing the output signals of sensors 705, 706, 708, and 709 to a specific module, the software inherently knows that the provided information is relative to a specific part of frame 701. Depending on one or more technical effects, advantages, and benefits, because each sensor 342 may be pre-wired and / or include positioning (i.e., GPS) technology, computing device 305 and / or computing system 355 can utilize big data processing to determine the precise location of the problem (i.e., contrary to conventional hub and spoke-type dummy transmitter designs) and determine remedial measures. In addition, software 330 can operate big data and ML / AI analysis, which can further make individual determinations regarding heat, energy conversion, energy use, cleaning needs, and the needs of mobile module 105. Therefore, the software 330 coordinates the operation of the smart solar bracket system by performing cross-data / module analysis.

[0097] At block 660, software 330 receives external data. External data may include (but is not limited to) weather data, weather forecasts, solar radiation data, solar flare data, solar radiation predictions, wind data, power grid data, real-time weather information, historical data, data from other environments, data from other systems, etc. Computing system 355 can collect and analyze the operation, parameter data, and / or external data of any system (e.g., environment 100 in Figure 1). According to one or more embodiments, computing system 355 is a distributed cloud-based implementation of big data processing of the operation, parameter data, and / or external data of any system (e.g., environment 100 in Figure 1). According to one or more embodiments, computing device 305 and / or computing system 355 can determine the degraded or suboptimal performance of any module (e.g., one or more modules 210, 230, and 240) by analyzing the operation, parameter data, and / or external data of the smart solar bracket system. According to one or more embodiments, the computing device 305 and / or computing system 355 can determine the long-term trend of the smart solar bracket system by analyzing the operation of any system (e.g., environment 100 in FIG1), parameter data, and / or external data. Additionally, for example, by detecting the electrical operation of a module and combining the light above and below the frame 110 of the light sensor, the software 330 can determine the expected efficiency to further determine whether a module 105 has malfunctioned due to the panel.

[0098] At block 670, software 330 initiates behavior. According to one or more embodiments, software 330 may optimize or reconfigure the electrical operation of any system (e.g., environment 100 of FIG. 1) by bypassing or isolating any module (e.g., one or more modules 210, 230 and 240 of FIG. 2) or by adjusting voltage or current levels to generate automatic feedback to computing system 355. As an example, software 330 implements one or more implementations of behaviors (such as automatic repair, power-on / power-off and / or cleaning).

[0099] According to one or more embodiments, software 330 may generate feedback to a third-party intelligent system (e.g., drone, cleaner, robot, scheduling system, maintenance personnel, etc.) to take actions that optimize the electrical output of any system (e.g., environment 100 in FIG1) (e.g., module cleaning, module replacement). For example, software 330 may send one or more messages to a technician to perform the repair determined at block 650 (e.g., software 330 may generate a notification and immediately provide the notification to the operation and maintenance service provider or its similar). Then, after completing one or more repairs, software 330 sends one or more messages confirming the repair and recording any information in a history report.

[0100] According to one or more embodiments, software 330 can perform real-time reconfiguration and optimize electrical output of a solar array based on parameter data, current weather, etc. For example, as shadows propagate across the array, software 330 can dynamically configure the solar array by taking modules offline (because the electrical output of an offline module is better than that of an inefficient module). Additionally, software 330 can schedule maintenance service providers on cloudy days based on current weather and / or weather forecasts.

[0101] Turning now to Figure 8, which illustrates an environment 800 according to one or more embodiments. It should be noted that other figures are combined by way of example, and the same element symbols in the figures indicate the same elements and will not be repeated for the sake of brevity.

[0102] As discussed herein, environment 800 may include one or more modular solar energy systems. More specifically, environment 800 may include one or more mechanically stacked solar transmission modules within one or more devices to receive light (e.g., from at least sun 201, although other sources are contemplated). In this manner, embodiments of environment 800 include any devices, systems, methods, and / or computer program products that may be integrated at the level of technical detail.

[0103] According to one or more embodiments, environment 800 may represent a modular solar energy system located within field 101 and including one or more support structures 802.n (where n is an integer). Field 101 may be any terrain or open or vacant land to support one or more support structures 802.n, as well as rooftops and / or other property areas. Each support structure 802 may be considered as a system, an architecture, and a stacked structure.

[0104] Each support structure 802 includes at least one combiner 1815, a switch 117, a series connector 120, and one or more frames 830 (where m is an integer). Each support structure 802 may include one or more modules 105 (e.g., modules 210, 230, and 240 in FIG2). After the solar module is inserted into the bracket frame 110, each frame 830 can be electrically connected to each of the modules 105.

[0105] One or more sensors 1845 may be positioned via support structure 802 and environment 800. Environment 800 and its components (e.g., any of the sensors 845) may be managed by a device 860 (e.g., computing device 305, ARM processor, or the like). Furthermore, environment 800 may be connected to a grid 170. Typically, environment 800 is a system that uses sensors 845 to hold frame 830 to support structure 802 in field 801. Environment 800 illustrates at a macroscopic level how components and things are connected within a larger network for alarm purposes and to supply power to grid 870. One or more embodiments of environment 800 may also include a uniform design in which modules are connected in series or parallel.

[0106] FIG9 depicts a figure 900 of a frame 905 according to one or more embodiments. Accordingly, FIG900 shows that the frame 905 includes an arm 910 and one or more supports for the arm 910. A module 930 is inserted into a top or Z1 position. Furthermore, since no module is located in the remaining position of the frame 910, the figure shows a corresponding connector 985 (e.g., via a pre-wired socket, a pin connection, a pigtail connection, or the like) electrically coupled to any module in that position within the frame 910.

[0107] According to one or more embodiments, connector 985 includes a wired electrical connection, such as providing a receptacle for receiving a module, a PCI connection, or a junction box connection like the like. FIG10 depicts electrical snap power sensors 1161 and 1162 according to one or more embodiments. Electrical snap power sensors 1161 and 1162 can be considered as examples of connector 985 of FIG9. Electrical snap power sensors 1161 and 1162 can be positioned on a flat member 1170 and include corresponding fasteners 1175. It should be noted that the wired electrical connection may contain a sensor (e.g., sensor 342 of FIG3), and the wired electrical connection can engage wiring from a separate module. For example, a wiring bundle of module 930 may protrude to connector 985 for easy installation, repair, and maintenance (e.g., in a plug-and-play manner). These wired electrical connections and sockets can be weatherproof, quick-connect hardware (e.g., for connecting wires to junction boxes or one or more boxes 270 in Figure 2 to simplify installation and reduce field quality errors). Each wired electrical connection may include a fuse with an indicator light to ensure the module is powered off during installation and maintenance.

[0108] FIG11 depicts prefabricated wiring examples 1101, 1102, and 1103 according to one or more embodiments. Wiring diagram 1101 illustrates a frame and module wiring (e.g., serial connection 120 of FIG1 and FIG8) that enables modules on a layer to be wired in series or in parallel. Wiring diagram 1102 illustrates a serial wiring, while wiring diagram 1103 illustrates a parallel wiring. As shown, wiring diagram 1101 includes a 4-pin connector (e.g., connector 985 of FIG9) for each module as a base wiring. According to one or more embodiments, by using a 4-pin connector, any suitable serial / parallel wiring can be placed into the module itself to effectively reconfigure from a parallel wiring to serial wiring (or vice versa). For example, if a layer is perovskite, the modules of that layer can be configured to be wired in parallel. Furthermore, if another layer is crystalline silicon (c-Si), that layer can be configured in series. Subsequently, proper wiring configuration can be completed in the modules of this layer during manufacturing to ensure that each layer is always correctly wired. Additionally, based on the wiring, hybrid modules (e.g., one serial, one parallel) can be provided, alternating between them in certain locations (e.g., thereby allowing one of the configurations to be mixed parallel / serial aggregation). Furthermore, a switch on the module can enable immediate reconfiguration from parallel to serial (or vice versa) and / or use of two different wiring configurations of the same module. According to one or more embodiments, a hybrid configuration can be provided in which the serial portion includes a module with parallel wiring while the serial portion itself is serially wired. This hybrid configuration can result in a moderate current of 11,600 volts.

[0109] Figure 12 depicts method 1200 according to one or more embodiments. Method 1200 can be executed by software 330 of Figure 3. Method 1200 determines and monitors the operation of the smart solar bracket system and provides a panel offline position sensor and software alarm system.

[0110] According to one or more embodiments, a smart solar bracket system is provided. The smart solar bracket system includes a bracket frame configured to receive and mechanically support one or more solar modules. The smart solar bracket system includes a plurality of sensors distributed throughout the bracket frame. Each of the plurality of sensors detects and reports parameter data by generating an output signal. The plurality of sensors includes one or more module sensors positioned to be associated with each of the one or more solar modules and detecting the presence of at least one module as parameter data for the one or more solar modules. The smart solar bracket system includes at least one computing device for receiving, storing, and analyzing the output signals to determine and monitor the operation of the smart solar bracket.

[0111] According to any of the embodiments herein or any of the embodiments of the smart solar bracket system, the plurality of sensors may include one or more frame sensors associated with the bracket frame and distributed throughout the bracket frame.

[0112] According to one or more embodiments herein or any of the embodiments of the smart solar bracket system, the bracket frame can be configured to electrically connect each of the one or more solar modules after the solar module is inserted into the bracket frame.

[0113] According to one or more embodiments of this document or any of the embodiments of the smart solar bracket system, the one or more solar modules may include at least one transmission solar module.

[0114] According to one or more embodiments herein or any of the embodiments of the smart solar bracket system, the at least one computing device may be configured to collect the output signals from one or more frame sensors of the plurality of sensors associated with and distributed throughout the second bracket of one of the smart solar bracket systems.

[0115] According to one or more embodiments of this document or any embodiment of the intelligent solar bracket system, the parameter data may include one or more of voltage, temperature, current, solar radiation, wind and ambient temperature.

[0116] According to any of the embodiments herein or any of the embodiments of the smart solar bracket system, the parameter data may include the environmental and electrical properties of the bracket frame or an individual module of the plurality of solar modules mounted in the bracket frame.

[0117] According to any one or more embodiments of this document or any embodiment of the intelligent solar bracket system, the at least one computing device can automatically determine the position of one or more solar modules within the bracket frame.

[0118] According to any of the embodiments herein or any of the embodiments of the smart solar bracket system, the one or more solar modules may include a smart module having one of the plurality of sensors or one of the plurality of internal sensors.

[0119] According to one or more embodiments herein or any of the embodiments of the smart solar bracket system, once the smart module is inserted into the smart solar bracket, the one or more internal sensors can communicate with the at least one computing device.

[0120] According to one or more embodiments of this document or any embodiment of the smart solar bracket system, at least one server may be provided and configured to collect and analyze the operation and parameter data of the smart solar bracket.

[0121] According to one or more embodiments of this document or any of the embodiments of the smart solar bracket system, the at least one server may include a distributed cloud-based implementation to realize the operation of the smart solar bracket and the big data processing of the parameter data.

[0122] According to any of the embodiments herein or any of the embodiments of the smart solar bracket system, the at least one server can determine the degraded or suboptimal performance of any of the one or more solar modules by analyzing the operation of the smart solar bracket system and the parameter data.

[0123] According to one or more embodiments of the present invention or any embodiment of the smart solar bracket system, the at least one server can determine the long-term trend of the smart solar bracket system by analyzing the operation of the smart solar bracket system and the parameter data.

[0124] According to any of the embodiments herein or any of the embodiments of the smart solar bracket system, the at least one server can generate automatic feedback to the at least one computing device by bypassing or isolating at least one of the one or more solar modules or by adjusting the voltage level or current level to optimize or reconfigure the electrical operation of the smart solar bracket system.

[0125] According to one or more embodiments herein or any of the embodiments of the smart solar bracket system, the at least one server may generate feedback to a third-party smart system to take measures to optimize the electrical output of one of the smart solar bracket systems.

[0126] According to one or more embodiments, a method is provided. The method includes monitoring output signals generated by a plurality of sensors distributed in a bracket frame of a smart solar bracket system by at least one computing device. Each of the plurality of sensors is configured to detect and report parameter data in the output signals. The bracket frame is configured to receive and mechanically support one or more solar modules. The plurality of sensors includes one or more module sensors positioned to be associated with each of the one or more solar modules. The method includes determining the operation of the smart solar bracket system by the at least one computing device based on the output signals.

[0127] According to any of one or more embodiments or method embodiments, the plurality of sensors may include one or more frame sensors associated with the bracket frame and distributed throughout the bracket frame. The one or more module sensors may also detect the presence of at least one module as parameter data of the one or more solar modules.

[0128] According to any of one or more embodiments or method embodiments, the bracket frame may be configured to electrically connect each of the one or more solar modules after the solar module is inserted into the bracket frame.

[0129] According to any of one or more embodiments or method embodiments, the at least one computing device may be configured to collect the output signals from one or more frame sensors associated with and distributed throughout the second bracket frame of one of the smart solar bracket systems.

[0130] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Accordingly, each block in the flowchart or block diagram may represent a portion of a module, segment, or instruction, which includes one or more executable instructions for implementing one or more specified logical functions. In some alternative implementations, the functions marked in the blocks may occur in the order indicated in the figures. For example, in fact, two blocks shown consecutively may be executed substantially simultaneously, or sometimes may be executed in reverse order depending on the functionality involved. It will also be noted that the blocks in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, may be implemented by a dedicated hardware-based system that performs the specified functions or behaviors or implements a combination of dedicated hardware and computer instructions.

[0131] Although features and elements are described above in specific combinations, those skilled in the art will understand that each feature or element can be used alone or in any combination with other features and elements. Furthermore, the methods described herein can be implemented in a computer program, software, or firmware incorporated into a computer-readable medium for execution by a computer or processor. As used herein, a computer-readable medium itself should not be construed as a transient signal, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted over a single wire.

[0132] Examples of computer-readable media include electrical signals (transmitted via wired or wireless connections) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, registers, cache memory, semiconductor storage devices, magnetic media (e.g., internal hard disks and removable disks), magneto-optical media, optical media (e.g., optical discs (CDs) and digital versatile discs (DVDs)), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), and memory sticks. A processor associated with software can be used to implement a radio frequency transceiver used in a terminal, base station, or any host.

[0133] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms "a" and "the" are intended to include the plural forms as well. It will be further understood that when the term "comprising" is used in this specification, it specifies the presence of the stated feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof.

[0134] This document has been presented with descriptions of various embodiments for illustrative purposes and not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein has been chosen to best explain the principles, practical applications, or technical improvements of the embodiments relative to those found in the market, or to enable those skilled in the art to understand the embodiments disclosed herein. [Simplified Explanation of the Diagram]

[0006] A more detailed understanding can be obtained from the following description, given by way of example in conjunction with the accompanying drawings, wherein similar element symbols in the drawings indicate similar elements, and wherein:

[0007] Figure 1 depicts an environment according to one or more embodiments;

[0008] Figure 2 depicts a system according to one or more embodiments;

[0009] Figure 3 depicts a system according to one or more embodiments;

[0010] Figure 4 depicts a system according to one or more embodiments;

[0011] Figure 5 depicts a neural network and a method according to one or more embodiments;

[0012] Figure 6 depicts a method according to one or more embodiments;

[0013] Figure 7 depicts a detection example according to one or more embodiments;

[0014] Figure 8 depicts an environment according to one or more embodiments;

[0015] Figure 9 depicts a frame diagram according to one or more embodiments;

[0016] Figure 10 depicts an electrical snap-on power sensor according to one or more embodiments; and

[0017] Figure 11 depicts a wiring diagram of a solar module bracket system for solar power generation according to one or more embodiments.

Claims

1. A solar panel bracket system, comprising: A bracket frame that receives and mechanically supports a plurality of solar modules in a vertical order; The system includes a plurality of sensors distributed throughout the solar bracket system. Each of the plurality of sensors generates parameter data and reports the parameter data as an output signal. The plurality of sensors includes: at least two frame sensors located on the bracket frame, positioned to be associated with each of the plurality of solar modules, and detecting the position and presence of at least one module within the bracket frame as the parameter data; and at least one computing device that receives and analyzes the output signals to determine and monitor the operation of the solar bracket system.

2. The solar bracket system of claim 1, wherein the bracket frame electrically connects each of the plurality of solar modules after the solar module is inserted into the bracket frame.

3. The solar bracket system of claim 1, wherein the plurality of solar modules includes at least one transmission solar module.

4. The solar bracket system as requested in item 1, wherein the parameter data includes one or more of voltage, temperature, current, solar radiation and wind.

5. The solar bracket system as claimed in claim 1, wherein the parameter data includes the environmental and electrical properties of the bracket frame or one of the plurality of solar modules mounted in the bracket frame.

6. The solar bracket system of claim 1, wherein the at least one computing device automatically determines the position of one of the plurality of solar modules within the bracket frame.

7. The solar bracket system of claim 1, wherein the plurality of solar modules includes a first solar module, the first solar module including one or more module sensors.

8. The solar bracket system of claim 7, wherein once the first module is inserted into the solar bracket system, the one or more module sensors communicate with the at least one computing device.

9. The solar bracket system of claim 1, further comprising: At least one server that collects and analyzes the operation and parameter data of the solar bracket system.

10. The solar bracket system of claim 9, wherein the at least one server includes a distributed cloud-based implementation to perform the operations of the solar bracket system and the big data processing of the parameter data.

11. The solar bracket system of claim 9, wherein the at least one server determines the degraded or suboptimal performance of any one of the plurality of solar modules by analyzing the operation and parameter data of the solar bracket system.

12. The solar bracket system of claim 9, wherein the at least one server determines the long-term trend of the solar bracket system by analyzing the operation and parameter data of the solar bracket system.

13. The solar bracket system of claim 9, wherein the at least one server generates automatic feedback to the at least one computing device to optimize or reconfigure the electrical operation of the solar bracket system by bypassing or isolating at least one of the plurality of solar modules or by adjusting the voltage or current level of the at least one of the plurality of solar modules.

14. The solar bracket system of claim 1, wherein each frame sensor detects the position and presence of a corresponding module within the bracket frame by detecting one of the module sensors of the plurality of solar modules.

15. A method for monitoring a solar panel system, comprising: The system monitors output signals generated by a plurality of sensors distributed throughout a solar bracket system using at least one computing device. Each of the plurality of sensors generates parameter data in the output signals. The solar bracket frame receives and mechanically supports a plurality of solar modules in a vertical order. The plurality of sensors includes at least two frame sensors located on the solar bracket frame, positioned to be associated with each of the plurality of solar modules, and detecting the position of at least one module within the solar bracket frame and the presence of at least two frame sensors serving as the parameter data. A first frame sensor detects a first solar module at a first height on the solar bracket frame, and a second frame sensor detects a second solar module at a second height on the solar bracket frame. The system determines the operation of the solar bracket system based on the output signals using the at least one computing device.

16. The method of claim 15, wherein the bracket frame electrically connects each of the plurality of solar modules after the solar module is inserted into the bracket frame.

17. The method of claim 15, wherein the at least one computing device collects output signals from two or more second sensors of the plurality of sensors associated with and distributed throughout the second bracket frame of the solar bracket system.

18. The method of claim 15, wherein the at least one computing device receives and analyzes the output signals to determine and monitor the operation of the solar bracket system.

19. The method of claim 15, wherein the plurality of solar modules includes at least one transmission solar module.

20. The method of claim 15, wherein the parameter data includes one or more of voltage, temperature, current, solar radiation and wind.

21. The method of claim 15, wherein the parameter data includes the environmental and electrical properties of the solar bracket frame or one of the plurality of solar modules mounted in the solar bracket frame.

22. The method of claim 15, wherein the at least one computing device automatically determines the position of one of the plurality of solar modules within the solar bracket frame.

23. The method of claim 15, wherein the plurality of solar modules includes a module that includes one or more module sensors.

24. The method of claim 23, wherein when the module is inserted into the solar bracket system, the one or more module sensors communicate with the at least one computing device.

25. The method of claim 15, further comprising: The operation and parameter data of the solar bracket system are collected and analyzed by at least one server.

26. The method of claim 25, wherein the at least one server includes a distributed cloud-based implementation to perform the operations of the solar bracket system and the big data processing of the parameter data.

27. The method of claim 25, wherein the at least one server determines the degraded or suboptimal performance of any one of the plurality of solar modules by analyzing the operation of the solar bracket system and the parameter data.

28. The method of claim 15, wherein the plurality of solar frame sensors detect the presence of at least the module and the geographical location of one of the solar bracket systems as the parameter data.

29. The method of claim 15, wherein the first frame sensor detects the first module at the first height in a vertical order and the second frame sensor detects the second module at the second height in the same vertical order.

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