Environmental hazard detection and mitigation systems and methods for a solar photovoltaic array tracker

Abstract

A control system for controlling a solar array row includes a processor and a memory in communication with the processor, wherein the control system is programmed to receive real-time fire detection data, identify a fire hazard to the solar array row based on the real-time fire detection data, and execute a fire stow protocol in response to identifying the fire hazard. The control system is further programmed to identify a fire stow position for the solar array row and automatically control the solar array row, according to the fire stow protocol, to rotate to the fire stow position.

Description

1ENVIRONMENTAL HAZARD DETECTION AND MITIGATION SYSTEMS AND METHODS FOR A SOLAR PHOTOVOLTAICARRAY TRACKERCROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. provisional application no. 63 / 706,411, filed October 11, 2024, the entire contents of which are incorporated herein by reference.FIELD

[0002] The field relates generally to systems and methods for detecting and mitigating environmental hazards for solar photovoltaic (PV) arrays.BACKGROUND

[0003] Solar arrays are devices that convert light energy into other forms of useful energy (e.g., electricity or thermal energy). One example of a solar array is a photovoltaic (PV) array that converts sunlight into electricity. Some photovoltaic arrays are configured to follow or track the path of the sun to minimize the angle of incidence between incoming sunlight and the photovoltaic array.

[0004] Solar tracker systems may include multiple photovoltaic arrays arranged in rows at a tracker site. Each of the arrays include a photovoltaic panels that are attached to a rotatable tube. A control system controls drives for the arrays to rotate the tubes of the rows about an axis. During normal tracking operations, the drive is controlled to rotate the panels to follow a path of the sun, such that each of the panels are oriented to face the position of the sun in the sky.

[0005] Known tracker systems are generally exposed to environmental elements, thereby rendering the tracker systems vulnerable to environmental wear and / or damage. For example, at least some known tracker systems are located in areas that have dense ground vegetation and / or are otherwise relatively prone to wildfires. However, current systems for mitigating damage to solar trackers from fire damage are often inefficient and costly, due to a lack of standardization and / or improper assessment of the fire risk for a given tracker location.2Accordingly, a need exists for modeling systems capable of efficiently evaluating the potential fire risks of a proposed site for a solar tracker and providing design recommendations for solar tracker systems that are based on the potential fire risks. Additionally, a need exists for control systems capable of identifying when a fire hazard to the tracker system is present and controlling the tracker system to mitigate potential damage from the fire hazard.

[0006] This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the disclosure, which are described and / or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.SUMMARY

[0007] In one aspect, a control system for controlling a solar array row includes a processor and a memory in communication with the processor, wherein the control system is programmed to receive real-time fire detection data, identify a fire hazard to the solar array row based on the real-time fire detection data, and execute a fire stow protocol in response to identifying the fire hazard. The control system is further programmed to identify a fire stow position for the solar array row and automatically control the solar array row, according to the fire stow protocol, to rotate to the fire stow position.

[0008] In another aspect, a computer system for determining one or more design parameters for a solar tracker system includes a modeling system including a processor and a memory in communication with the processor. The modeling system is programmed to receive fire risk data for a proposed site for the solar tracker system, generate a fire risk profile for the proposed site based on the fire risk data, and generate a design recommendation for the solar tracker system based on the fire risk profile.

[0009] Various refinements exist of the features noted in relation to the above- mentioned aspects of the present disclosure. Further features may also be incorporated in the above-mentioned aspects of the present disclosure as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to any of the illustrated embodiments of the present disclosure may be3 incorporated into any of the above-described aspects of the present disclosure, alone or in any combination.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a perspective view of an array row of an embodiment of a solar tracker system of this disclosure;

[0011] Figure 2 is an enlarged perspective view of the solar tracker system of Figure 1;

[0012] Figure 3 is a schematic of a control system showing a first array zone of the solar tracker system of Figure 1 ;

[0013] Figure 4 is a schematic end view of the array row of Figure 1 in a first position;

[0014] Figure 5 is a flow diagram showing a process for controlling a solar tracker;

[0015] Figure 6 is a schematic of a modeling system for determining design parameters of the solar tracker system of Figure 1; and

[0016] Figure 7 is a flow diagram showing a process for determining the design parameters of the solar tracker system of Figure 1.

[0017] Corresponding reference characters indicate corresponding parts throughout the drawings.DETAILED DESCRIPTION

[0018] The fire hazard mitigation systems and methods described herein are designed to provide a comprehensive wildfire mitigation solution for solar installations by integrating fire risk management into both the project design phase and operational phase of solar projects. The system combines advanced fire detection technologies, a fire risk assessment process, and a modular solar tracker configuration to proactively reduce fire risk and dynamically respond in real time when fires are detected. The fire hazard mitigation systems4 and methods described herein include a holistic protection system, addressing fire hazards at multiple levels and ensuring that solar installations are resilient in wildfire-prone areas.

[0019] A fire hazard mitigation method starts with a fire risk data collection and assessment process during the initial project design phase. This allows solar developers to understand the unique fire risks associated with each site and make informed decisions about the layout and design of the solar farm. The system integrates data from satellite imagery, drone reconnaissance, on-site sensors, and other sources to monitor ground fuel levels, vegetation type and / or density, soil conditions, and local weather patterns. By leveraging this data, the fire hazard mitigation method helps determine the optimal solar tracker configuration, including tracker height, foundation type, and cabling system placement, based on the fire risk profile of each site.

[0020] The fire hazard mitigation method includes use of an algorithm that automatically processes the collected fire risk data and recommends site-specific design parameters. The algorithm enables the solar installations to be proactively configured to withstand fire threats by adjusting critical elements such as the height of solar modules from the ground, fire-resistant material selection, and foundation types based on the fire risk profile. The algorithm also accounts for dynamic fire risk levels, enabling modular designs that are adaptive to different fire risk zones within the same project. For example, higher fire risk areas might require elevated module heights or fire-tolerant foundations, while lower-risk areas can use more standard configurations.

[0021] The fire hazard mitigation systems and methods include a preventative zero-degree stow position, also referred to herein as a “fire stow”. A control system dynamically adjusts the orientation of the rows to minimize fire exposure when a fire threat is detected. The flat, parallel zero-degree stow positioning elevates the solar panels away from the ground, protecting the panels from heat exposure and wind-blown embers. Raising module height dynamically during fire events adds an additional layer of protection, ensuring that solar panels, electrical systems, and critical components are moved to the furthest position away from ground- based fire hazards.

[0022] The fire detection system may utilize satellite imagery, drones imaging, and on-site sensors to detect emerging fire threats. The fire detection system continuously monitors the site and surrounding areas, enabling early detection of fire risks. When a fire is5 detected, the control system triggers an automated response: adjusting the tracker stow position to the preventative angle and sending real-time alerts to a remote computing device and / or server, which may be monitored Operations & Maintenance (O&M) personnel. The alert notifications enable rapid responses to fire threats. The control system can also be configured to notify emergency services to enable timely intervention.

[0023] One of the technical benefits of the fire hazard mitigation systems and methods described herein is the modular design of the methods (e.g., tracker design, tracker model algorithms, etc.), allowing use for projects of all sizes, from small, distributed generation (DG) projects to large utility-scale solar farms. The modular system enables flexible configuration, meaning developers can customize fire mitigation solutions based on their specific project’s fire risk profile. As part of the scalable architecture, the tracker system components — such as tracker configurations, materials selection, and fire-resistant foundations — can be tailored to match the budget and risk tolerance of each project, reducing costs for such systems without compromising fire hazard mitigation.

[0024] The fire hazard mitigation systems and methods described herein are designed to evolve alongside new technologies and emerging regulatory standards. As fire detection technologies improve or new materials become available, the modular structure of the fire hazard mitigation systems and methods described herein allow for easy upgrades, ensuring that solar installations remain protected and compliant with changing regulations over time. The system can also integrate future innovations in artificial intelligence (Al) and machine learning, enhancing ability to assess and mitigate fire risks with even greater accuracy and precision.

[0025] The fire hazard mitigation systems and methods described herein offer a combination of proactive design features and real-time response capabilities specifically tailored for wildfire protection. By embedding fire risk mitigation into both the design and operational phases, the fire hazard mitigation systems and methods provide an ability to dynamically adapt to changing fire risks. This system not only protects the solar assets but also reduces the financial risks associated with fires, including potential insurance premium reductions, increased project viability, and improved asset lifespan. The flexibility and scalability of the systems and methods enable deployment of solar tracker systems in a wide range of project environments.

[0026] Referring now to Figure 1, an example embodiment of a solar tracker system 100 including a PV solar array row 102 is shown. The solar array row 102 may be used in6 a solar power generation system. The solar array row 102 is used to generate power, typically in combination with an array of similarly arranged solar array rows 102 (not all rows shown). The solar array row 102 includes an array of solar panel assemblies 104. Each solar panel assembly 104 extends between a back side 106 and a panel side 108 (Figure 4). The solar panel assemblies 104 are rectangular shaped. In other embodiments, the solar panel assemblies 104 may have any shape that allows the solar array row 102 to function as described herein.

[0027] In the example embodiment, the solar array 102 is configured as a single row of portrait solar panel assemblies (also referred to herein as “one-in-portrait” or “IP”) connected to the torque tube 112. In other embodiments, the solar array 102 may include any suitable configuration of the panel assemblies 104, such as, but not limited to, a double row of portrait solar panel assemblies (also referred to herein as “two-in-portrait” or “2P”).

[0028] The solar array row 102 includes a mounting assembly 110 that supports the solar panel assemblies 104. The mounting assembly 110 includes a torque tube 112 to which the solar panel assemblies 104 are connected. The solar panel assemblies 104 may be connected to the torque tube 112 by any suitable method including, for example, fasteners such as bolts and clips or by a clamping device. The solar panel assemblies 104 pivot about a rotational axis that extends through the torque tube 1 12.

[0029] The torque tube 112 of this embodiment is pivotably connected to a plurality of support columns 116. In the illustrated embodiment, the support columns 116 are I- beam posts. Other support columns 1 16 may be used in other embodiments (e.g., a tubular support column 116). The support columns 116 may be connected to a base, which may include any structure that anchors the row, for example a frame member (e.g., a horizontal rail that the solar panel assemblies 104 with one or more posts securing the rail to the ground), stanchion, ram, pier, ballast, post or the like. The base may also include a foundation which encases a portion of the support columns 116 or may include brackets, fasteners or the like that connect to the support columns 116. In other embodiments, the row 102 may be connected to another structure which supports the solar panels 104 (e.g., roof-top applications).

[0030] The solar panel assemblies 104 are a photovoltaic array. In other embodiments, the solar panel assemblies 104 include a thermal collector that heats a fluid such as water. In such embodiments, the panel assemblies may include tubes of fluid which are heated by solar radiation. While the present disclosure may describe and show a photovoltaic array, the7 principles disclosed herein are also applicable to a solar array used as a thermal collector unless stated otherwise.

[0031] Referring to Figure 2, the mounting assembly 1 10 also includes a drive 120 that adjusts the position of the solar panel assemblies 104. The drive 120 engages the torque tube 112 such that operation of the drive 120 causes the torque tube 112 to pivot relative to the support columns 116 (Figure 1). The drive 120 is positioned between the torque tube 1 12 and the base to which the support columns 1 16 are connected. The drive 120 in the embodiment of Figure 2 is a slew drive, though the mounting assembly 110 may include any drive that enables the mounting assembly 110 to function as described herein.

[0032] The solar array row 102 includes a row controller housing 122. The row controller housing 122 contains a row controller 312 (Figure 3) therein. The row controller 312 is electronically connected to the drive 120 and programmed to control operation of the drive 120. During operation, the row controller 312 controls the drive 120 to rotate the torque tube 112 such that the panel assemblies 104 follow the path of the sun, such as during movement of the sun over a course of a day. In some embodiments, the row controller 312 positions the panel assemblies 104 based on seasonal variations in the position of the sun. The solar array row 102 may be a single axis tracker or a dual axis tracker with the torque tube 112 defining at least one axis of rotation of the array. The other axis of rotation may be a vertical axis with rotation being achieved by a rotatable coupling and, optionally, a second drive (not shown).

[0033] Figure 3 shows a schematic view of a control system 300 showing a first array zone 302 of the solar tracker system 100 (shown in Figure 1). Although only a first zone 302 is shown, the solar tracker system 100 may include multiple zones that are the same or substantially the same as the first zone 302 at a given tracker site. The first zone 302 includes multiple solar array rows that are the same or substantially the same as the row 102, shown in Figures 1 and 2. Specifically, the first zone 302 includes the first solar array row 102 to an “nth” solar array row 304. In some embodiments, the first zone 302 may include around 100 solar array rows.

[0034] The first zone 302 includes a first zone controller 306. The first zone controller 306 includes a processor and a memory storing instructions for execution by the processor. The first zone controller 306 is electronically connected to a camera 308, an environmental sensor 310, and each of the row controllers 312 for each of the respective rows.8The row controllers 312 are each connected in electronic, wireless or wired communication with the first zone controller 306.

[0035] Each of the row controllers 312 is also in communication with the drive 120 of the row and an inclinometer 314. The inclinometer 314 is operable to detect an orientation of the panels and / or torque tube of the solar array row. The inclinometer 314 may include a sensor operable to detect an orientation (e.g., a rotational angle) of the row, such as gyroscope and / or accelerometer. The inclinometer is provided within the row controller 312 housing, shown in Figure 2. In the example embodiment, each of the inclinometers 314 are accelerometers operable to detect positions and orientations about six degrees of freedom, three of which are used to determine the orientation of the panels. The row controllers 312 are operable to determine, based on the detected orientation from the inclinometer 314, whether the inclinometer 314 has reached a target angle (e.g., when in a stowed position) and control the drives 120 based on the detected orientation.

[0036] The environmental sensor 310 is for example, a sensor that is adapted to, or is suitable to manually or automatically detect one or more environmental conditions of the first zone 302, such as temperature, humidity, wind speed, and air quality. In some embodiments, the environmental sensor includes a network of on-site sensors that continuously monitor environmental conditions such as temperature, humidity, wind speed, and air quality. These sensors enable detecting, at least in part, the local conditions that contribute to fire risk, such as prolonged dry spells or high winds that could spread a fire rapidly. Heat and smoke detection sensors provide immediate alerts when a fire is detected, ensuring a rapid response from the control system 300 and enabling early-stage fire mitigation. For example, the environmental sensor 310 may include at least one of a wind speed sensor, a wind direction sensor, an air quality sensor, a smoke sensor, a snow sensor, a soiling sensor and a hail sensor. The hail sensor is an impact hail sensor that detects an impact force of hailstones contacting the sensor. Additionally, where the environmental sensor 310 is a snow sensor, the sensor is an ultrasonic snow depth sensor. Moreover, in embodiments where the camera 308 is connected to a drone, the environmental sensor 310 may also be connected to the drone. In some embodiments, the solar tracker system does not include the environmental sensor 310.

[0037] The first zone controller 306 is in electronic communication with the environmental sensor 310 and with each row controller 312 of each of the solar array rows of the first zone 302. The first zone controller 306 controls operations of the rows of the solar tracker9 based on real-time fire detection data that is provided by external sources, such as the camera 308 and / or the environmental sensor 310.

[0038] The camera 308 is positioned at or near the first zone 302 and is programmed or adapted to capture an image in the field of view of the camera 308 of at least some of the panel surfaces of the solar tracker system. The camera 308 may be either physically mounted to a stationary support structure or may be attached to a remote vehicle, such as an aerial drone. The image captured by the camera 308 is used to determine environmental conditions, such as fire and / or smoke near or within the vicinity of the solar tracker system, snow cover of the panels, dust cover of the panels, or the presence of hail within the first zone 302. Based on the images, one or more corrective actions are performed, such as instructing the row controllers 312 of the respective rows to perform one or more control operations on the solar array rows, such as rotating the rows to a particular stow position, and / or the generation of one or more alerts. The camera 308 may be programmed or controlled to capture photos intermittently (e.g., capturing a photo each time after a predetermined time period elapses) and / or may capture a video feed. The camera 308 may be connected in wired or wireless communication with the first zone controller 306.

[0039] The camera 308 is connected in communication with an image analysis unit 316, which is connected to the first zone controller 306. The image analysis unit 316 performs an image analysis of the images captured by the camera 308 to determine an environmental state of the panels and / or rows. In the example the image analysis unit 316 is attached to or positioned adjacent to the first zone controller 306. In other embodiments, the first zone controller 306 and the image analysis unit 316 may be provided as a single control unit. Additionally, the camera 308 may be connected in direct or indirect communication (i.e., such as through one or more memory drives of the camera 308) with the first zone controller 306 and / or the image analysis unit 316.

[0040] In some embodiments, the image analysis unit 316 includes a single board microcontroller, such as a RASPBERRY PI® or ARDUINO® microcontroller, and includes a processor and a memory storing a machine learning (ML) image algorithm 318. The ML algorithm 318 performs an image analysis of the images captured by the camera 308 to detect environmental conditions, such as environmental hazards, of the first zone 302, in combination with or alternatively to the environmental sensor 310. Based on the detections, the zone controller 306 may generate one or more alerts or control the row controllers 312 to move10 the panels 104 to predetermined rotational position based on the detected environmental condition. For example, the alerted information can be used by solar plant operators to take appropriate actions, such as scheduling maintenance to clean the panels or adjusting the output of the plant to account for reduced efficiency due to snow or dust buildup. In the example, the machine learning image algorithm 318 includes a Convolutional Neural Network (“CNN”) such as the AlexNet or VGG16 algorithms, though other suitable machine learning algorithms may be used.

[0041] To train the machine learning image algorithm 318, a dataset of images is suitably used, and the dataset contains both images of a solar field in a non- fire hazard state and images of a solar field in a fire hazard state, such as images containing sufficient quantities of smoke and / or air pollution indicative of a fire hazard. As an example, the dataset may include tens of thousands of images of such panels / solar fields. During the training process, the machine learning image algorithm 318 is provided with the images and their corresponding labels (e.g., no fire hazard, fog cover, or smoke present). The machine learning image algorithm 318 learns to identify patterns and features in the images that identify solar fields in which a fire hazard is present. Once the training is complete, the machine learning image algorithm 318 can be used to predict whether a fire hazard is present at or near a solar field by inputting a new image, e.g., from on-site cameras and / or satellite imagery and generating an output determination from the machine learning image algorithm 318. In some embodiments, the machine learning image algorithm 318 is further trained to determine additional environmental conditions at the solar field, such as dust cover, hail, or snow.

[0042] The image analysis unit 316 updates the machine learning image algorithm 318 based on determinations made by the machine learning image algorithm 318 during operational use in the solar field to improve the accuracy of the determinations made by the machine learning image algorithm 318. The process of updating the machine learning image algorithm 318 is also referred to as “reinforcement learning” and may be automated or manual (i.e., user review of the images). To implement the reinforcement learning, the data including the differences calculated between the predictions of the algorithm and the actual conditions is collected. The data collection includes capturing video, converting it into images, making predictions, calculating metrics, and saving the data to a database. Later, the images are annotated with their correct conditions and compared with the predictions made by the current11 machine learning image algorithm 318. Both automatic reinforcement and manual training may be used to train and update the ML models.

[0043] Figure 4 shows a schematic view of the solar array row in a first or “flat” position, also referred to herein as a “zero-degree” position or a “fire-stow” position.

[0044] Referring to Figure 4, in the fire stow position the panels 104 of the row 102 are oriented in a position in which the potential for damage to components of the solar array row is most reduced. The fire stow position may be based on an initial predetermined stow position. In the example of Figure 4, the panels 104 are oriented parallel to the ground 402, in the fire stow position. When oriented parallel to the ground 402, the panels 104 are raised on the tube 112 to provide a maximum clearance from the ground to protect the panels 104 from ground-based fire hazards. In some embodiments, in the fire stow position, the panels 104 of the solar array row are oriented relative to the ground level at an angle between -15 degrees and 15 degrees, between -10 degrees and 10 degrees, and between -5 degrees and 5 degrees. In the example of Figure 4, the panels 104 are oriented at a zero-degree angle (i.e., parallel) relative to ground level when the solar array row is in the fire stow position.

[0045] Referring to Figure 4, a top surface 404 of the panel defines an array plane Ap of the solar array row 102. The solar array row 102 defines a rotational plane Rpthat is coplanar with the longitudinal axis Li of the array 102. In the example, when the panels 104 are in the fire stow position, the panels 104 are oriented such that the array plane Ap is parallel to (i.e., oriented at a zero-degree angle relative to) the rotational plane Rp of the row. During tracking operations, the row may be oriented in the flat position when the sun is generally directly overhead of the row (e.g., around mid-day). Additionally, or alternatively, the row may be moved to the flat stow position in response to high wind speeds or other environmental hazards, such as in response to a fire-hazard.

[0046] Figure 5 shows a process 500 for controlling a solar tracker (e.g., the solar tracker system of Figures 1-4).

[0047] The process 500 includes a first step 502 of receiving real-time fire detection data. The real-time fire detection data may include at least one of image data including at least one of a photographic image captured by an on-site camera (e.g., camera 308), a satellite image 324 (Figure 3), and a detected reading from the environmental sensor 310.12

[0048] The control system 300 integrates a variety of detection methods to enable early detection of potential fire risks. Satellite-based monitoring provides large-scale coverage of surrounding regions, offering early detection of wildfires even before they approach the site. In high-risk regions, remote operated vehicles such as aerial drones or land-based drones may be used for detailed, on-the-ground monitoring of the surrounding environment. These drones provide real-time data on ground fuel conditions, allowing for live fire risk assessment and detection of emerging threats. The control system 300 integrates with on-site fire detection sensors, such as heat and smoke detectors, as well as camera-based image recognition systems to provide localized detection of fire hazards. These sensors and cameras can continuously monitor the site and its surroundings, providing data-driven insights into fire risk.

[0049] The process 500 includes a second step 504 of identifying a fire hazard to the solar array row based on the real-time fire detection data.

[0050] In some embodiments, the control system identifies the fire hazard by determining a current risk level (e.g., on a numeric scale) based on the real-time fire detection data and comparing the risk level to a threshold risk level stored in the memory. The current risk level may be updated based on the real-time fire detection data, such as readings from the environmental sensor 310 indicating a current environmental condition (e.g., temperature, wind speed, and humidity level).

[0051] The control system 300 is adaptable and learns from the site’s historical data, making it more effective over time. By continually analyzing new data, the system refines its fire risk models, ensuring that both stow positions and alert systems are fine-tuned to the specific needs of the site. The control system 300 uses one or more machine learning algorithms to analyze past fire events and adjust its real-time response system accordingly. This adaptive learning allows the control system 300 to improve its fire risk predictions and response strategies over time, ensuring that the system remains highly effective in mitigating wildfire threats. By incorporating historical fire data and site-specific fire risk trends, the control system 300 can make more accurate assessments of future fire threats and proactively adjust the system’s stow positions and alert protocols. The control system 300 continuously monitors and updates its fire risk assessment using real-time data from satellites, drones, and sensors, allowing for dynamically adapting responses to changing conditions and ensuring that the solar installation remains protected even as environmental risks evolve. As new data is collected, the control13 system 300 can make live adjustments to the tracker stow positions or other fire mitigation measures to enable improved asset protection at all times.

[0052] The process 500 includes a third step 506 of executing a fire stow protocol in response to identifying the fire hazard. The fire stow protocol is executed automatically in response to identifying the fire hazard. The process 500 further includes a fourth step 508 of identifying a fire stow position for the solar array row.

[0053] The fire mitigation systems and methods described utilize a preventative stow position to protect solar panels by automatically repositioning them when a fire threat is detected. Upon the detection of a fire or heightened fire risk in the vicinity of a solar installation, the control system 300 automatically identifies the fire stow position as the zero-degree flat stow position. In the flat position, the solar panels are aligned parallel to the ground, which offers several advantages in reducing fire exposure.

[0054] By lowering the tilt of the panels, the control system 300 raises the effective clearance between the modules and the ground, moving more valuable components (the solar panels) farther away from heat and flames. The flat position also minimizes or reduces exposure to embers carried by the wind, reducing the risk of flying sparks landing on the panels or electrical systems. This flat positioning helps reduce heat accumulation under the panels, especially when compared to high-tilt or angular stow positions, which can trap heat and exacerbate fire damage. Moreover, the flat stow position is particularly well suited for large tracker configurations, such as two-in-portrait (2P) configurations, which naturally elevates the panels further from the ground relative to one-in-portrait (“IP”) designs. As an example, when positioned in the flat stow configuration, the panel are positioned at a ground level clearance of between one meter and 3.5 meters, thereby reducing exposure to radiant heat from fires.

[0055] In some embodiments, the stow position is dynamically configured based on site-specific fire risk data collected during the design phase. For example, in regions where fire risks are particularly high or where ground conditions may increase fire spread (e.g., dry vegetation or flammable soils), the stow angle or module height may be further adjusted to create additional clearance. This adaptability allows the control system 300 to customize the stow position to the specific environmental conditions of the site, enabling improved protection of the tracker system. As an example, some tracker sites may have an inclined ground level14 surface and the fire stow position may be set based on factors such as the incline of the ground level terrain, the direction and or speed of wind.

[0056] The control system’s 300 dynamic height adjustment feature allows the tracker system to raise the entire row of modules, providing even greater separation between the fire hazard and the critical solar assets during high-risk fire events.

[0057] The process 500 includes a fifth step 510 of automatically controlling the solar array row, according to the fire stow protocol, to rotate to the fire stow position.

[0058] Once a fire threat is detected, the control system 300 triggers an automated response that initiates the appropriate actions to protect the solar installation. In response to the detection, the control system 300 will adjust the trackers to the zero-degree preventative stow position, protecting the panels from further fire exposure. Simultaneously, the control system 300 sends out automated alerts to Operations & Maintenance (O&M) personnel, who are responsible for managing the site. These notifications are issued via multiple channels (e.g., text, email, and dedicated platform alerts) to provide an increased likelihood that site managers are made aware of the fire risk and can take necessary action.

[0059] The entire tracker system is continuously monitored for any changes in the fire risk level. If conditions worsen, additional measures (such as triggering more aggressive stow actions or shutting down parts of the system) may be automatically initiated.

[0060] The control system 300 can be configured to directly notify emergency services when a fire is detected, reducing the time it takes for firefighting teams to respond to the site. To prevent false alarms, the system is designed to first notify O&M teams, who can verify the fire threat before triggering an emergency response. Verification of the fire threat helps to enable efficient use of resources and timely addressing of genuine fire threats.

[0061] When a fire is detected, the control system 300 provides real-time event notifications that may include: a location of the fire threat in relation to the site, severity of the risk, based on the analysis of the fire's size, proximity, and speed, and recommended actions for site personnel to take, based on the fire risk profile. These notifications inform all relevant parties — O&M teams, site operators, and emergency services — that a fire has been identified and enable effective response coordination.15

[0062] The control system 300 is designed to integrate with existing fire mitigation systems at the site, such as firebreaks, sprinkler systems, and physical barriers, to create a multi-layered defense against fire threats. This integration enhances the overall fire resilience of the solar installation. The system can be configured to automatically initiate local fire mitigation measures, such as deploying fire suppression systems or engaging pre-installed sprinklers in high-risk zones.

[0063] At least one technical benefit of the control system 300 is the modular design of the control system 300, which allows for flexibility, adaptability, and scalability across solar projects of varying sizes and risk levels. The system’s modular protection system is engineered to provide customized fire mitigation solutions that are tailored to the unique fire risk profiles of individual project sites. This adaptability enables the control system 300 to be deployed on both small, distributed generation (DG) projects and large utility-scale solar farms, optimizing fire protection without incurring unnecessary costs. The control system 300 utilizes a modular design that enables solar developers to selectively implement fire-resistant materials and components based on the specific fire risk of each site. By focusing resources on areas where they are most needed, the control system 300 enables a cost-effective approach to fire protection.

[0064] Beyond the proactive stow position, the control system’s 300 real-time detection and response systems are designed to instantly react to fire threats as they emerge, ensuring a comprehensive fire protection strategy. Direct notification of emergency services in response to identifying the fire hazard is particularly beneficial for remote locations, where fires may otherwise go unnoticed for longer periods.

[0065] The control system 300 can be integrated with the site’s centralized fire control systems, allowing it to trigger automated responses such as activating fire suppression systems or closing firebreaks when a wildfire is detected. The system is designed to work seamlessly with on-site water-based fire suppression systems (e.g., sprinklers) and physical firebreaks, coordinating with these systems to minimize the spread of fire and protect key areas of the solar installation. The control system 300 can automatically alert emergency response teams when a fire threat is detected, ensuring rapid response times and potentially reducing fire damage. This feature is particularly valuable in remote locations, where early detection and notification can make a significant difference in limiting the spread of fire. The control system 300 can be configured to comply with local emergency protocols and can provide real-time updates to firefighting teams as the situation evolves, helping them respond more effectively.16

[0066] The control system 300 is able to be integrated with existing Supervisory Control and Data Acquisition (SCAD A) systems and other monitoring platforms, allowing site operators to manage fire mitigation alongside other operational activities. This integration provides real-time visibility into the status of fire risk and fire response actions, ensuring that solar operators can make informed decisions in real time. The integration with SCADA systems allows for centralized monitoring of fire risk levels, tracker stow positions, and fire response actions. Site operators can view and control tracker functions through the same interface they use to manage other operational aspects of the solar installation, streamlining the process and reducing complexity. By incorporating fire risk data into the broader operational management system, the control system 300 provides site operators with a holistic view of the project’s status, allowing for more coordinated and effective responses to fire threats. The control system 300 supports cloud-based monitoring, allowing project stakeholders to access fire risk data and system performance information remotely. This capability is particularly valuable for large, geographically distributed solar installations, where site managers may not be physically present at each location. The system’s real-time data sharing also enables insurance providers, regulators, and emergency responders to have access to up-to-date fire risk information, streamlining communications and improving coordination during fire events.

[0067] Figures 6 shows a modeling system 600 for determining one or more design parameters for a solar tracker system, such as the system 100 shown in Figures 1-4. The modeling system may be a part of the control system 300 (shown in Figure 3) and / or may be a separate controller used for designing and generating models of the solar tracker system. In embodiments, the modeling system includes a memory storing instructions (e.g., algorithms) that cause one or more controllers and / or processors to perform the operations described herein.

[0068] Referring to Figure 6, the modeling system 600 includes a site model 602. The site model 602 receives fire risk data 604 as an input. Based on the received fire risk data 604, the site model 602 develops a risk profile 606 for the proposed tracker site. The site model 602 outputs, based on the generated risk profile 606, one or more design recommendations 608 based on the executing the model 602 according to the received fire risk data 604.

[0069] As shown in Figure 6, example fire risk data 604 may include, but is not limited to, satellite imagery 610, infrared data 612 (e.g., collected from one or more infrared sensors) at the site, a terrain map 614 of the site, on-site imaging 616 (e.g., from a stationary17 camera or drone mounted camera), an environmental data 618 (e.g., collected by environmental sensor 310), and historical environmental data 620.

[0070] The design recommendations 608 output by the site model 602 are determined based on the fire risk profile 606. Example design recommendations include, but are not limited to, a height 622 of the tracker row (e.g., a height at which the tube 112 of the tracker row is positioned from the ground), a recommendation of foundation material 624 used for the foundation of one or more areas of the solar tracker 100, electrical component location 626, and fireproofing locations 628.

[0071] The modeling system 600 uses a machine learning algorithm 630 to predict future fire risks based on historical fire data and environmental patterns. By understanding how fires have behaved in similar conditions in the past, the system can make forward-looking recommendations for potential design adjustments, ensuring the project is future proofed against emerging fire threats. This predictive capability gives developers and asset owners an edge in fire risk management, enabling them to plan and preemptively adjust their systems for changing environmental conditions. The modeling system 600 protects solar projects from fire risks and provides cost optimization for the design of solar projects through the targeted application of fire mitigation strategies. By analyzing the specific fire risk of each area, the modeling system 600 provides targeted recommendations for the use of fire-resistant materials and elevated trackers, helping to balance performance and cost-effectiveness. Rather than applying a one-size-fits-all approach, the modeling system 600 enables concentrating fire resistance resources in high fire risk areas. This approach minimizes unnecessary material costs and construction expenses while still providing the increased levels of protection against fire risks in areas that are most vulnerable to fire.

[0072] The modeling system 600 provides flexibility in choosing fire-resistant materials for the solar tracker system, especially in high-risk fire zones. For example, structural components, including torque tubes, slew drives, and dampers, can be coated with fireproof or heat-resistant materials to protect against fire damage. The design recommendations 608 can also include recommendations for the use of fire-resistant components in targeted areas of the solar tracker system 100. For example, the design recommendations 608 may include fire- resistant electrical enclosures, which are designed to protect wiring, inverters, and other key components from heat exposure and prevent electrical malfunctions during a fire.18

[0073] The modeling system 600 allows for tracker height adjustments based on the fire risk profile 606 of each project. In high fire-risk areas, the system can recommend raising the height of the solar trackers, increasing the clearance between the ground and the solar modules to mitigate heat exposure from wildfires. This dynamic adjustability in tracker configuration allows for tailoring the tracker system 100 to unique environmental conditions of each site.

[0074] The modeling system 600 is designed to allow for future upgrades as new fire-resistant materials and technologies become available. Solar developers can replace or add fire-resistant components over time, ensuring that the system remains compliant with evolving standards and technologies without requiring a full redesign.

[0075] The modeling system 600 is customizable, allowing solar developers to adjust the system’s configuration based on the specific fire risk zones within a project. This localized configuration allows for the fire protection to be concentrated in areas where it is most needed, while standard configurations can be applied in areas with lower risk. The modeling system 600 ’s ability to assess fire risk on a zone-by-zone basis within a solar installation allows for targeted deployment of fire-resistant materials and systems. High-risk zones, such as those near dense vegetation or within fire -prone regions, can be configured with elevated trackers, additional fire-resistant coatings, or fireproof electrical systems.

[0076] Conversely, in areas where fire risks are minimal, the system can be configured with more standard tracker materials and configurations, reducing costs while still providing comprehensive protection. The modeling system 600 ’s modular design allows it to be deployed on solar projects of any size. For small DG projects, the modeling system 600 can provide a scaled-down version of its fire protection system, focusing on critical components such as tracker height adjustments and fire-resistant coatings. For large utility-scale projects, the system can be expanded to provide fire protection across vast areas, using multiple layers of fire risk assessment and dynamically adjusted tracker configurations. This scalability allows for the system to deliver cost-effective fire protection regardless of project size, making the system suitable for a wide range of solar installations.

[0077] Referring to Figure 7, a process 700 for assessing fire risk at proposed sites using the modeling system 600 of Figure 6 is shown.19

[0078] A first step 702 of the process 700 includes collecting fire risk data 604 for a proposed site of a solar tracker. The fire risk data 604 may include at least one of a satellite image of the proposed site, a photographic image from a camera at the proposed site, a signal from an infrared sensor at the proposed site, a signal from an environmental sensor monitoring one or more environmental conditions at the proposed site, historical environmental data 620 for the proposed site, and a terrain map 614 of the proposed site.

[0079] By integrating advanced technologies such as satellite imagery 610, drone reconnaissance, and on-site sensors, a detailed fire risk profile 606 may be generated that informs both the design of the project and ongoing operational strategies. The collection of data is performed dynamically, enabling solar installations to be built with fire risks in mind and allowing for proactive risk mitigation at every stage of the project’s lifecycle.

[0080] Satellite-based imaging may be used to monitor the site and its surrounding areas. Satellite images provide information about the density of vegetation, the presence of flammable materials, and any significant ground fuel accumulations that could increase fire risk. The satellite data also helps map terrain features that may affect fire spread, such as hills, valleys, and open spaces. By regularly capturing imagery of the project site, a time-lapse view of how ground fuel conditions change over time may be generated, allowing for continuous monitoring and real-time fire risk updates. In addition to satellite imagery 610, in some embodiments drones are equipped with high-resolution cameras and infrared sensors to conduct detailed aerial surveys of the project site. These drones provide real-time, ground-level data on vegetation type, fuel dryness, and other fire risk indicators that are not visible from space. Drone-based surveys can also detect heat signatures that might indicate the early stages of a fire, allowing for preemptive action to be taken before the fire becomes a significant threat. Various data sources may be used to perform ground fuel assessments and environmental risk profiling. These data sources include both remote sensing technologies and on-site equipment, creating a robust, multi-layered fire risk assessment process that captures risk factors for potential fire hazards.

[0081] Once the data is collected from satellite imagery 610, drone reconnaissance, and / or on-site sensors at step 702, at step 704, the modeling system 600 generates a comprehensive fire risk profile 606 for the solar project site based on the collected fire risk data 604.20

[0082] The fire risk profile 606 may include current conditions and / or a longterm fire risk based on the collected fire risk data 604. Using image recognition analysis (e.g., as described with respect to image analysis unit 316), the modeling system 600 analyzes the satellite and drone imagery to estimate the type and density of vegetation in and around the project site. The modeling system 600 can categorize different areas of the proposed site based on the amount of flammable material present and the likelihood of ignition under varying environmental conditions. The modeling system 600 also takes into account the surrounding areas within a certain radius, ensuring that external fire threats are considered as part of the project’s overall fire risk profile 606.

[0083] To generate the fire risk profile 606, the modeling system 600 integrates site-specific environmental data such as longitude and latitude, seasonal temperature variations, humidity levels, wind patterns, and historical fire data, thereby accounting for the immediate fire risks and long-term trends that may affect the site over time. In some embodiments, the modeling system 600 can generate a fire risk index for each site based on this data, helping developers and asset owners to understand the specific risks they are facing and allowing them to make informed decisions about how to mitigate those risks through design choices and operational strategies.

[0084] The data collection processes do not stop once the project is operational. The modeling system 600 continuously updates its fire risk profile 606 based on new data from satellites, drones, and sensors. This allows the modeling system 600 to adapt to changing conditions, ensuring that the fire mitigation strategies remain relevant as the environmental conditions evolve. The modeling system 600 provides real-time updates on fire risk levels, notifying site operators of any changes in the fire risk profile 606 as they occur. The continuous updating of the fire risk profile 606 enables proactive adjustments to the tracker stow position, system configuration, or operational protocols, ensuring that the solar installation remains protected as new fire risks emerge. The modeling system 600’s continuous monitoring capabilities mean that early signs of fire risk may be identified, such as increasing ground fuel accumulation or a shift in weather patterns, and generate design adjustment recommendations to the solar tracker system configuration and / or notify O&M teams before a fire becomes a threat.

[0085] The modeling system 600 uses one or more machine learning algorithms 630 to analyze historical fire data from the project site and surrounding areas. This adaptive learning process allows the system to improve its fire risk assessments over time,21 becoming more accurate and better able to predict future fire threats. As the modeling system 600 collects more data from multiple projects, the modeling system 600 updates a centralized database of fire risk patterns, thereby improving the modeling system 600’ s ability to make predictive assessments and optimize fire mitigation strategies.

[0086] At step 706 of the process 700, the modeling system 600 generates one or more design recommendations 608 for the solar tracker system based on the fire risk profile 606.

[0087] For example, based on the fire risk profile 606, the modeling system 600 recommends specific design parameters for the solar tracker system, including module height 622, foundation selection, and electrical system placement. In areas with elevated fire risk, the modeling system 600 may recommend raising the tracker height 622 to increase the distance between the solar modules and potential fire sources on the ground. Such recommendations may reduce the exposure of critical components of the solar tracker system to heat and flames, protecting the most valuable assets. The modeling system 600 may recommend the use of fire-resistant foundations, such as concrete piles or ground screws, in areas where ground conditions increase the risk of fire spreading. These materials are more resistant to the heat and stress of wildfires compared to traditional driven piles, ensuring that the foundation remains intact even in the event of a fire.

[0088] The modeling system 600 also assesses the placement of electrical cabling and inverters. In higher-risk areas, the modeling system 600 may recommend trenching cabling or using fire-resistant enclosures to protect the electrical systems from fire damage, ensuring that power generation is not disrupted. The fire risk data 604 feeds into an algorithm 630 that interprets this information to recommend customized design and configuration parameters for each solar project. This algorithm- driven approach allows for the solar tracker system to be optimally configured to minimize fire risks while remaining cost-effective. By using a combination of dynamic environmental data, fire risk profiling, and customizable tracker configurations, the modeling system 600 enables solar developers to make informed design decisions that proactively mitigate fire hazards.

[0089] The modeling system 600 is able to take the extensive fire risk data 604 collected from satellite imagery 610, drone reconnaissance, and on-site sensors, and turn it into actionable design recommendations 608. The algorithm 630 evaluates fire risks based on site-22 specific data and outputs tailored configurations that help protect the solar project from wildfires.

[0090] One of the outputs of the modeling system 600 is the recommendation for tracker height 622 based on the assessed fire risk of the site. In areas where ground fuel and fire hazards are high, the modeling system 600 suggests raising the height 622 of the solar modules to increase their clearance from potential ground fires. This height adjustment allows for critical tracker components, such as solar modules and electrical wiring, to be positioned further away from heat sources, reducing the risk of fire damage. The modeling system 600 can also optimize the placement of cabling systems and electrical components. In areas where the fire risk is higher, the modeling system 600 may recommend trenching electrical cables underground or using fire-resistant protective coverings for above-ground cables to shield them from exposure to flames or heat. Inverter placement and other electrical system components can be configured in such a way that minimizes their exposure to fire -prone areas, ensuring the continued operation of the power system even in high-risk environments.

[0091] The modeling system 600 also suggests foundation materials that are best suited to the fire risk profile 606 of the project site. For instance, in high fire-risk zones, concrete foundations or ground screws may be recommended over traditional driven piles due to their fire-resistant properties. This selection helps protect the structural integrity of the solar tracker system during and after a fire, preventing significant damage to the support structures.

[0092] One technical benefit of the modeling system 600 is an ability to customize the design configuration for each section of a solar project site based on localized fire risk data 604. This modular approach allows developers to adjust fire mitigation strategies based on the specific needs of different areas within the same project, optimizing both safety and costefficiency. Using the modeling system 600’s fire risk algorithm 630, each part of the solar farm can be individually assessed for its specific fire risks. In areas where vegetation is denser or the risk of fire spread is higher, the system may recommend higher clearance tracker systems, fire- resistant materials, or more robust electrical protections. Conversely, in lower-risk areas of the site, standard tracker heights and materials may be sufficient, reducing unnecessary costs while maintaining safety.

[0093] The modeling system 600 ’s modularity allows developers to incorporate fire-resistant materials only where necessary. For example, fireproof coatings may23 be recommended for key components such as slew drives, dampers, and structural supports in high-risk areas, while standard materials may be used in other zones. By utilizing fire-tolerant modular components, the modeling system 600 enables reduces costs for the tracker system while delivering targeted fire protection where it is most needed. The modeling system 600 enables allows the solar tracker system and / or design recommendations 608 for the solar tracker system to be dynamically adapt based on real-time fire risk updates and changes in environmental conditions. As fire risks evolve — whether due to seasonal changes, vegetation growth, or unexpected dry conditions — the modeling system 600 can automatically reconfigure its recommendations to enable optimized protection. The algorithm 630 is continuously updated with real-time data from satellite imagery 610, drones, and on-site sensors. As the fire risk changes, the modeling system 600 can prompt design reconfigurations — such as adjusting the stow position or raising tracker heights — based on the latest risk assessment. This dynamic adaptability protects the solar trackers even as the fire risk evolves over time, maintaining resilience of the solar tracker throughout the lifecycle of the project.

[0094] By implementing site-specific fire mitigation strategies and dynamic fire risk responses, solar developers can potentially reduce insurance premiums by demonstrating to insurers that the site has been designed to minimize fire risks. This targeted protection also reduces the overall risk of asset loss or downtime due to fire, further improving the project’s financial viability.

[0095] The systems’ modular design not only enhances fire protection but also provides opportunities for cost optimization. By focusing fire mitigation efforts on high-risk zones and using standard configurations where appropriate, developers can achieve the highest return on investment (RO I) for their fire protection strategies. The systems are designed to optimize the use of fire-resistant materials and other resources. By assessing fire risk at a granular level and deploying targeted fire protection measures, developers can avoid the unnecessary cost of applying fire-resistant solutions across the entire project site. This efficient use of resources enables solar projects to remain cost-effective while still benefiting from enhanced fire protection in critical areas. By deploying a modular fire protection system, solar developers may be able to negotiate lower insurance premiums. Insurers may view the system’s proactive fire risk mitigation strategies — such as advanced fire detection, dynamic stow positioning, and fire-resistant components — as a way to reduce overall project risk, resulting in potential cost savings on insurance. This makes the systems not only a valuable tool for fire24 protection but also a smart financial investment for solar developers. The systems are designed to integrate seamlessly with existing fire mitigation systems, site management platforms, and regulatory frameworks, ensuring a comprehensive approach to fire protection.

[0096] The systems enable compliance with both local fire safety regulations and international standards for solar projects. As fire safety regulations continue to evolve, particularly in fire -prone regions, the ability to meet and exceed these requirements gives solar developers a competitive advantage in both the permitting process and ongoing operations. The systems are designed to comply with fire safety standards established by organizations such as the National Fire Protection Association (NFPA), International Code Council (ICC), and other regulatory bodies. This allows for solar installations that incorporate the systems and methods described herein to be built and operated in accordance with best practices for fire safety. The system can also be configured to meet region-specific regulations, such as California’s Wildfire Mitigation Plans (WMPs) or Australia’s Bushfire Protection Standards, providing compliance of solar installations with regional fire safety mandates.

[0097] Referring collectively to Figures 1 -7, example fire hazard mitigation systems and methods are described herein. The mitigation systems may include proactive fire hazard mitigation and / or reactive / preventative fire hazard mitigation. The fire hazard mitigation systems and methods may be implemented with the solar tracker system 100, the control system 300, and the modeling system 600 shown in Figures 1-7 or may be implemented using any other suitable solar tracker system, control system, and / or modeling system 600.

[0098] The control system 300 and modeling system 600, collectively, are able to both prevent and react to wildfire threats, enabling the protection of solar assets through dynamic, data-driven responses. This two-pronged approach — proactive stow positioning and real-time operational responses — is designed to mitigate the impact of wildfires while maximizing the resilience of solar installations.

[0099] The modeling system and control system 300 are upgradable, enabling them to remain effective over the long term. As new fire detection technologies become available — such as more advanced satellite monitoring, Al-driven fire risk assessments, or improved fire-resistant materials — the systems’ modular architecture allows these innovations to be seamlessly integrated into existing systems. This adaptability enables the solar installation to remain compliant with the latest standards and maximally protected from emerging fire threats.25The systems are designed to enable compliance with both current and future fire safety regulations. As regulatory bodies introduce new standards for fire mitigation in solar installations, The systems can be easily modified or upgraded to meet these new requirements, allowing for solar farms to remain compliant without costly overhauls. The system’s ability to scale and adapt to new regulations allows solar developers to future -proof their installations, minimizing the risk of needing to retrofit or upgrade systems as regulations evolve.

[0100] By working in tandem with external systems and adhering to industry standards, the systems provide solar developers with a complete solution that not only enhances fire safety but also streamlines compliance and operations. This integration allows for the systems to be compatible with regional regulations, insurance requirements, and site-wide fire safety protocols, making it a highly adaptable and versatile system. The systems are designed to work alongside traditional fire prevention and mitigation systems, including sprinkler systems, firebreaks, and other fire suppression infrastructure. This allows solar developers to build a multi-layered fire protection system by combining the advanced tracker stow positioning and fire detection capabilities with more conventional fire mitigation measures.

[0101] The ability of the fire hazard mitigation systems and methods to provide detailed fire risk assessments and customized fire mitigation strategies can ease the permitting process for solar developers. In regions where fire risk assessments are required as part of the project approval process, the systems’ data-driven approach provides the documentation and insights necessary to satisfy regulatory authorities. By proactively addressing fire risks, developers can also reduce the likelihood of permitting delays, such that projects move forward on schedule. A comprehensive approach to fire mitigation also aligns with the requirements of insurance providers, which may demand evidence of fire protection measures before offering coverage. The modeling system’s detailed fire risk assessments and the control system’s 300 ongoing fire monitoring data can be shared with insurance providers as part of the underwriting process. By demonstrating that the solar installation has a robust fire mitigation system in place, developers may be able to secure more favorable insurance terms, including lower premiums and broader coverage. The improved ability to provide real-time fire monitoring and automated response actions can also serve as an additional layer of risk reduction, further improving the project’s insurability.

[0102] In some embodiments, the above systems and methods are electronically or computer controlled. The embodiments described are not limited to any particular system26 controller or processor for performing the processing tasks described herein. The term “controller” or “processor”, as used herein, is intended to denote any machine capable of performing the calculations, or computations, necessary to perform the tasks described herein. The terms “controller” and “processor” also are intended to denote any machine capable of accepting a structured input and of processing the input in accordance with prescribed rules to produce an output. It should also be noted that the phrase "configured to" as used herein means that the controller / processor is equipped with a combination of hardware and software for performing the tasks of embodiments of the disclosure, as will be understood by those skilled in the art. The terms “controller” and “processor”, as used herein, refers to central processing units, microprocessor, microcontrollers, reduced instruction set circuits (RISC), application specific integrated circuits (ASIC), logic circuits, and any other circuit or processor capable of executing the functions described herein.

[0103] The computer implemented embodiments described embrace one or more computer readable media, including non-transitory computer readable storage media, wherein each medium may be configured to include or includes thereon data or computer executable instructions for manipulating data. The computer executable instructions include data structures, objects, programs, routines, or other program modules that may be accessed by a processing system, such as one associated with a general-purpose computer capable of performing various different functions or one associated with a special-purpose computer capable of performing a limited number of functions. Aspects of the disclosure transform a general-purpose computer into a special-purpose computing device when configured to execute the instructions described herein. Computer executable instructions cause the processing system to perform a particular function or group of functions and are examples of program code means for implementing steps for methods disclosed herein. Furthermore, a particular sequence of the executable instructions provides an example of corresponding acts that may be used to implement such steps. Examples of computer readable media include random-access memory ("RAM"), read-only memory ("ROM"), programmable read-only memory ("PROM"), erasable programmable read-only memory ("EPROM"), electrically erasable programmable read-only memory ("EEPROM"), compact disk read-only memory ("CD-ROM"), or any other device or component that is capable of providing data or executable instructions that may be accessed by a processing system.27

[0104] A computer or computing device such as described has one or more processors or processing units, system memory, and some form of computer readable media. By way of example and not limitation, computer readable media comprise computer storage media and communication media. Computer storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically embody computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and include any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.

[0105] The terms “about,” “substantially,” “essentially” and “approximately” when used in conjunction with ranges of dimensions, concentrations, temperatures or other physical or chemical properties or characteristics is meant to cover variations that may exist in the upper and / or lower limits of the ranges of the properties or characteristics, including, for example, variations resulting from rounding, measurement methodology or other statistical variation.

[0106] When introducing elements of the present disclosure or the embodiment(s) thereof, the articles "a", "an", "the" and "said" are intended to mean that there are one or more of the elements. The terms "comprising," "including," “containing” and "having" are intended to be inclusive and mean that there may be additional elements other than the listed elements. The use of terms indicating a particular orientation (e.g., "top", "bottom", "side", etc.) is for convenience of description and does not require any particular orientation of the item described.

[0107] As various changes could be made in the above constructions and methods without departing from the scope of the disclosure, it is intended that all matter contained in the above description and shown in the accompanying drawing[s] shall be interpreted as illustrative and not in a limiting sense.

Claims

28WHAT IS CLAIMED IS:

1. A control system for controlling a solar array row, the control system comprising a processor and a memory in communication with the processor, wherein the control system is programmed to: receive real-time fire detection data; identify a fire hazard to the solar array row based on the real-time fire detection data; execute a fire stow protocol in response to identifying the fire hazard; identify a fire stow position for the solar array row; and automatically control the solar array row, according to the fire stow protocol, to rotate to the fire stow position.

2. The control system of claim 1, wherein panels of the solar array row are oriented at an angle between -10 degrees and 10 degrees relative to ground level when the solar array row is in the fire stow position.

3. The control system of claim 2, wherein panels are oriented parallel to ground level when the solar array row is in the fire stow position.

4. The control system of claim 1, wherein the real-time fire detection data includes image data including at least one of a photographic image captured by an on-site camera and a satellite image, and wherein identifying the fire hazard is performed by: performing an image analysis of the image data; and identifying, based on the image analysis, a presence of fire in a vicinity of the solar array row.

5. The control system of claim 1, wherein the control system is programmed to: generate an alert indicating the detection of the fire hazard.

296. The control system of claim 1, wherein the control system is programmed to: store, in the memory, a current fire hazard risk level; and update the current fire hazard risk level based on the received real-time fire detection data, wherein identifying the fire hazard is performed by comparing the updated fire hazard risk level to a threshold fire hazard risk level stored in the memory.

7. The control system of claim 6, wherein the control system is programmed to: receive a signal from an environmental sensor indicating an environmental state, wherein the current fire risk level is updated based on the environmental state.

8. The control system of claim 7, wherein the environmental state includes at least one of a detected temperature, wind speed, and humidity level.

9. The control system of claim 1, wherein the fire stow position is a predetermined stow position stored in the memory and is set based on a fire risk profile generated for a site at which the solar array row is located, the fire risk profile being based on fire risk data collected for the site, and wherein the fire risk data includes at least one of a satellite image of the site, a photographic image from a camera at the site, a signal from an infrared sensor at the site, a signal from an environmental sensor monitoring one or more environmental conditions at the site, historical environmental data for the site, and a terrain map of the site.

10. The control system of claim 9, wherein the processor is programmed to: update the fire risk profile based on receiving additional fire risk data for the site; and update, automatically, the predetermined stow position based on the updated fire risk profile for the site.

11. A computer system for determining one or more design parameters for a solar tracker system comprising:30 a modeling system including a processor and a memory in communication with the processor, wherein the modeling system is programmed to: receive fire risk data for a proposed site for the solar tracker system; generate a fire risk profile for the proposed site based on the fire risk data; and generate a design recommendation for the solar tracker system based on the fire risk profile.

12. The computer system of claim 11, wherein the fire risk data includes at least one of a satellite image of the proposed site, a photographic image from a camera at the proposed site, a signal from an infrared sensor at the proposed site, and a signal from an environmental sensor monitoring one or more environmental conditions at the proposed site.

13. The computer system of claim 11, wherein the fire risk profile is generated based on historical environmental data for the proposed site and a terrain map of the proposed site.

14. The computer system of claim 11, wherein the processor is programmed to generate an estimate of at least one of a type and density of vegetation at the proposed site based on the fire risk data, wherein the fire risk profile is generated based on the estimate.

15. The computer system of claim 11, wherein the design recommendation for the solar tracker system includes at least one of a row height of the solar tracker, a material selection for a foundation of the solar tracker, and location for positioning of one or more electrical components of the solar tracker.

16. The computer system of claim 11, wherein the processor is programmed to: categorize divisions of the proposed site based on the collected fire risk data, wherein the design recommendation includes a first recommendation for a first division of the proposed site and a second recommendation for a second division of the proposed site.

17. The computer system of claim 16, wherein the modeling system is programmed to:31 determine, based on the fire risk data, a first vegetation parameter for the first division of the proposed site and a second vegetation parameter for the second division of the proposed site; output a first recommended tracker clearance height for the first division; and output a second recommended tracker clearance height for the second division that is greater than the first recommended tracker clearance height based on the second vegetation parameter indicating an increased risk of fire damage relative to the first vegetation parameter.

18. The computer system of claim 17, wherein the first vegetation parameter includes an estimate of vegetation density for the first division and the second vegetation parameter includes an estimate of vegetation density for the second division.

19. The computer system of claim 11, wherein the processor is programmed to: receive, after generating the design recommendation, additional fire risk data for the proposed site of the solar tracker system; update the fire risk profile based on the additional fire risk data; and generate an updated design recommendation for the solar tracker system based on the updated fire risk profile.

20. The computer system of claim 11, wherein the fire risk data includes at least one of historical fire data and historical environmental patterns, wherein the modeling system includes a machine learning algorithm, and wherein the modeling system is programmed to: generate an initial fire risk profile based on the at least one of historical fire data and historical environmental patterns, wherein the fire risk profile is generated by updating the initial fire risk profile based on the received fire risk data and the machine learning algorithm.

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