Quantifying energy loss due to solar tracker malfunction of solar PV plants

Abstract

Disclosed is a system (100) for quantifying energy losses in a solar power plant due to malfunctioning solar trackers. The system (100) includes a processor (104) that filters tracker-day data (118) of the one or more solar trackers (116) based on predefined quality and quantity criteria to identify malfunctioning trackers. It determines whether energy losses are controllable or uncontrollable based on tracker mode information. Using a solar position and irradiance model, the system (100) estimates expected irradiance for each malfunctioning tracker. Energy loss is calculated by comparing the expected irradiance to the actual irradiance measured by a pyranometer on a properly functioning tracker, adjusted for the tracker's capacity. The system (100) categorizes the losses as controllable or uncontrollable and analyses these losses to identify patterns of the malfunctioning trackers, guiding appropriate maintenance actions.

Description

[0001] QUANTIFYING ENERGY LOSS DUE TO SOLAR TRACKER

[0002] MALFUNCTION OF SOLAR PV PLANTS

[0003] TECHNICAL FIELD

[0004] The present disclosure relates to the field of solar power plants and, more specifically, to a system for quantifying energy losses in a solar power plant due to malfunctioning solar trackers. Moreover, the present disclosure relates to a method for quantifying energy losses in a solar power plant due to malfunctioning solar trackers.

[0005] BACKGROUND

[0006] Advancement in the field of solar energy tracking systems have gained popularity over the years due to their plethora of applications, such as optimizing energy generation and minimizing power losses. Solar energy tracking systems are designed to orient solar panels or modules towards the sun, maximizing the absorption of solar radiation and improving overall energy conversion efficiency. These systems utilize various tracking algorithms and mechanisms to continuously adjust the position of the solar panels throughout the day, ensuring that they are always facing the sun at the optimal angle. By dynamically tracking the sun's position, solar energy tracking systems can significantly increase the amount of solar energy harvested, making them an essential component in large-scale solar power plants and other solar energy installations.

[0007] However, despite the numerous benefits offered by solar energy tracking systems, there are several challenges and problems that need to be addressed in this domain. One of the primary concerns is the accurate detection and identification of faulty solar trackers. Malfunctioning trackers can lead to suboptimal positioning of solar panels, resulting in reduced energy generation and potential power losses. Existing approaches in the field primarily focus on detecting tracker malfunctions based on deviations between the target and actual tracker angles. While this provides some insight into the performance of the trackers, it fails to quantify the actual amount of energy lost due to these malfunctions. Therefore, there is a need for advanced methodologies that can accurately assess the energy losses caused by tracker malfunctions and provide valuable insights for optimizing solar power plant operations.

[0008] Therefore, in the light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks.

[0009] SUMMARY

[0010] The present disclosure provides a system and method for quantifying energy losses in a solar power plant due to malfunctioning solar trackers. The present disclosure provides a solution to the technical problem of how to accurately estimate the expected solar irradiance that a malfunctioning tracker should have received and calculate the actual energy losses caused by the tracker malfunction. An aim of the present disclosure is to provide an improved method and system that overcomes at least partially the problems encountered in the prior art and provide an improved technique for identifying malfunctioning solar trackers, categorizing the energy losses as controllable or uncontrollable based on tracker mode information, and allowing solar plant operators to better understand loss patterns across 100s or 1000s of trackers installed at the solar plant and determine appropriate maintenance actions.

[0011] One or more objectives of the present disclosure is achieved by the solutions provided in the enclosed independent claims. Advantageous implementations of the present disclosure are further defined in the dependent claims.

[0012] In one aspect, the present disclosure provides a system for quantifying energy losses in a solar power plant due to malfunctioning solar trackers. The system comprising: at least one processor configured to: filter tracker-day data received from one or more solar trackers based on predefined data quality and data quantity criteria, wherein the filtered data corresponds to one or more malfunctioning trackers; determine whether energy losses associated with the one or more malfunctioning trackers are controllable or uncontrollable based on tracker mode information; estimate the expected irradiance received by each malfunctioning tracker using a solar position and irradiance model and tracker-specific parameters; calculate the energy loss for each malfunctioning tracker by comparing the estimated expected irradiance to an actual plant irradiance measured from a pyranometer mounted on an ideally working tracker, accounting for tracker's capacity relative to plant capacity; and categorize the calculated energy losses as controllable or uncontrollable based on the tracker mode determination, wherein the controllable and uncontrollable energy losses are analysed to identify patterns of malfunctioning solar trackers and determine appropriate maintenance actions.

[0013] By filtering tracker data based on predefined quality and quantity criteria, the system can accurately identify malfunctioning solar trackers that are causing energy losses. It then leverages a solar position and irradiance model along with trackerspecific parameters to estimate the expected irradiance that each malfunctioning tracker as received because it is not working properly. By comparing this expected irradiance to the actual measured plant irradiance from an ideally working tracker, the system can precisely calculate the energy losses attributable to each malfunctioning tracker while properly accounting for that tracker's capacity relative to the total plant capacity. Significantly, the system determines whether the energy losses are controllable or uncontrollable based on tracker mode information, allowing the losses to be categorized accordingly. This categorization, coupled with the ability to analyze patterns of controllable and uncontrollable losses over time, enables plant operators to pinpoint the root causes of losses, such as manual errors or weather conditions. With this insight, operators can implement appropriate, targeted maintenance procedures and actions to cost-effectively minimize future energy losses from tracker malfunctions, thereby improving plant performance and overall return on investment.

[0014] In another aspect, the present disclosure provides a method for quantifying energy losses in a solar power plant due to malfunctioning solar trackers. The method comprising: filtering, by at least one processor, tracker-day data received from one or more solar trackers based on predefined data quality and data quantity criteria, wherein the filtered data corresponds to one or more malfunctioning trackers; determining, by the at least one processor, whether energy losses associated with the one or more malfunctioning trackers are controllable or uncontrollable based on tracker mode information; estimating, by the at least one processor, an expected irradiance received by each malfunctioning tracker using a solar position and irradiance model and tracker- specific parameters; calculating, by the at least one processor, the energy loss for each malfunctioning tracker by comparing the estimated expected irradiance to an actual plant irradiance measured from a pyranometer mounted on an ideally working tracker, accounting for tracker's capacity relative to plant capacity; and categorizing, by the at least one processor, the calculated energy losses as controllable or uncontrollable based on the tracker mode determination, wherein the controllable and uncontrollable energy losses are analysed to identify patterns of malfunctioning solar trackers and determine appropriate maintenance actions.

[0015] The method achieves all the advantages and technical effects of the testing apparatus of the present disclosure.

[0016] It is to be appreciated that all the aforementioned implementation forms can be combined. All steps which are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims. Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative implementations construed in conjunction with the appended claims that follow.

[0017] BRIEF DESCRIPTION OF THE DRAWINGS

[0018] FIG. l is a block diagram of a system for quantifying energy losses in a solar power plant due to malfunctioning solar trackers, in accordance with an embodiment of the present disclosure;

[0019] FIG. 2 is a flowchart of a series of operations for identifying malfunctioning of solar trackers, in accordance with an embodiment of the present disclosure;

[0020] FIG. 3 is a flowchart of a series of operations for calculating plane of array irradiance of each solar tracker, in accordance with an embodiment of the present disclosure;

[0021] FIG. 4 is a flowchart of a series of operations for correction of the plane of array irradiance of each solar tracker, in accordance with an embodiment of the present disclosure;

[0022] FIG. 5 is a flowchart of a series of operations for quantification of energy loss in the solar power plant, in accordance with an embodiment of the present disclosure;

[0023] FIG. 6 is a flowchart of a series of operations for categorization of energy loss in the solar power plant as controllable or uncontrollable, in accordance with an embodiment of the present disclosure;

[0024] FIG. 7 is a graphical representation of controllable energy losses in the solar power plant, in accordance with an embodiment of the present disclosure;

[0025] FIG. 8 is a graphical representation of uncontrollable energy losses in the solar power plant, in accordance with an embodiment of the present disclosure; FIG. 9 is a graphical representation of energy losses of the solar tracker in a diffuse mode, in accordance with an embodiment of the present disclosure;

[0026] FIG. 10 is a graphical representation of energy losses of the solar tracker when an actual tracker angle of the solar tracker is not following an ideal tracker angle for a time interval, in accordance with an embodiment of the present disclosure;

[0027] FIG. 11 is a graphical representation of energy losses of the solar tracker when the actual tracker angle of the solar tracker is not following the ideal tracker angle for a whole day, in accordance with an embodiment of the present disclosure; and

[0028] FIG. 12 is a flowchart of a method of for quantifying energy losses in the solar power plant due to malfunctioning solar trackers, in accordance with an embodiment of the present disclosure.

[0029] DETAILED DESCRIPTION OF DRAWINGS

[0030] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practising the present disclosure are also possible.

[0031] As used throughout this disclosure, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to.

[0032] The phrases “at least one”, “one or more”, and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together. The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising”, “including”, and “having” can be used interchangeably.

[0033] The term “automatic” and variations thereof, as used herein, refers to any process or operation done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material”.

[0034] The present subject matter may have a variety of modifications and may be embodied in a variety of forms, and specific embodiments will be described in more detail with reference to the drawings. It should be understood, however, that the embodiments of the present subject matter are not intended to be limited to the specific forms, but include all modifications, equivalents, and alternatives falling within the spirit and scope of the present subject matter.

[0035] FIG. 1 is a block diagram of a system for quantifying energy losses in a solar power plant due to malfunctioning solar trackers, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a block diagram that includes a system 100. The system 100 includes a processor 104 and a memory 106. In an implementation, the system 100 further includes a first model 108 (i.e., a solar position model) and a second model 110 (i.e., an irradiance model). The processor 104 is communicatively coupled with the memory 106, the first model 108, and the second model 110. In an implementation, the processor 104, the memory 106, the first model 108, and the second 110 may be implemented on a same server, such as a server 102. In some implementations, the system 100 further includes a display 112. In some implementations, the display 112 is configured to display a visual presentation of information by the system 100 about energy losses in the solar power plant due to the malfunctioning solar trackers. In some implementations, the display 112 is communicatively coupled to the server 102, via a communication network 114. The server 102 may be communicatively coupled to one or more solar trackers 116, via the communication network 114. The one or more sola trackers 116 provides tracker day data.

[0036] The present disclosure provides the system 100 for quantifying energy losses in the solar power plant due to the malfunctioning solar trackers. This system utilizes the solar position and irradiance model, along with tracker-specific parameters, to estimate an expected irradiance received by each malfunctioning tracker. By comparing this estimated expected irradiance to an actual plant irradiance measured from a pyranometer mounted on an ideally working tracker, the system 100 calculates the energy loss for each malfunctioning tracker. The system 100 is subjected to categorize the calculated energy losses as controllable or uncontrollable based on tracker mode information. The advantages of this aspect are twofold. Firstly, it allows for the identification of patterns of malfunctioning solar trackers, enabling appropriate maintenance actions to be taken. Secondly, it provides insights into the controllable and uncontrollable energy losses, helping to determine the extent of the energy gap between the actual and target performance of the plant. The features of this aspect synergistically work together by filtering the tracker-day data, estimating the expected irradiance, calculating the energy loss, and categorizing the losses based on tracker mode information. This comprehensive approach enables a thorough analysis of energy losses and facilitates informed decision-making for optimizing solar power plant performance.

[0037] The server 102 includes suitable logic, circuitry, interfaces, and code that may be configured to communicate with the one or more solar trackers 116 via the communication network 114. In an implementation, the server 102 may be a master server or a master machine that is a part of a data center that controls an array of other cloud servers communicatively coupled to it for load balancing, running customized applications, and efficient data management. Examples of the server 102 may include, but are not limited to a cloud server, an application server, a data server, or an electronic data processing device.

[0038] Throughout the present disclosure, the term "processor" refers to a device or a combination of devices that perform various operations, such as executing instructions, processing data, and controlling the overall functioning of a system, typically through the use of electronic circuits and logical operations. The system 100 for quantifying energy losses in the solar power plant caused by malfunctioning solar trackers, comprising the at least one processor 104 that is configured to: The system 100 quantifies energy losses in the solar power plant by analyzing the performance of the one or more solar trackers 116. It is able to bifurcate the losses into controllable and uncontrollable categories based on the modes of each solar tracker. The system 100 ignores losses in Diffuse mode, as this mode is intentionally designed to avoid energy loss. The system 100 identifies malfunctioning trackers by applying a 5 -degree and 2-instance filter. The 5 -degree and 2-instance filter is used to ensure accurate identification of malfunctioning solar trackers. By setting a threshold of 5 degrees, the system 100 can distinguish between normal variations in tracker positioning and significant deviations that indicate a malfunction. The 2-instance filter helps to confirm the consistency of the deviation across multiple instances, reducing the likelihood of false positives. The use of the 5-degree and 2-instance filter improves the accuracy of identifying malfunctioning solar trackers, allowing for more precise quantification of energy losses in the solar power plant. This helps in identifying and addressing issues promptly, leading to improved overall plant performance and increased energy generation.

[0039] Throughout the present disclosure, the term "tracker-day data" refers to the data collected from one or more solar trackers over a specific period, typically a day, which includes information such as the position, orientation, and performance of the trackers. The term "predefined data quality" refers to the predetermined standards or criteria set for the accuracy, reliability, and completeness of the collected data. The term "data quantity criteria" refers to the predetermined thresholds or benchmarks established to determine the minimum amount of data required for analysis and evaluation. The term "filtered data" refers to the processed and refined data that has undergone various filtering techniques to remove noise, outliers, and other unwanted elements. The term "one or more solar trackers" refers to the individual devices or systems used to track the movement of the sun and optimize the positioning of solar panels for maximum energy generation. The term "one or more malfunctioning trackers" refers to the solar trackers that are not functioning properly or experiencing operational issues, resulting in suboptimal performance and energy losses. The system 100 is claimed for quantifying energy losses in the solar power plant caused by the malfunctioning solar trackers. The system 100 receives filter tracker-day data 118 from the one or more solar trackers 116, which is based on predefined criteria for data quality and quantity. The filtered data corresponds to the one or more malfunctioning trackers. The system 100 quantifies energy losses in the solar power plant by analyzing data from the one or more solar trackers 116. It filters the tracker-day data 118 based on predefined criteria for data quality and quantity. The filtered data corresponds to the malfunctioning trackers. To achieve this, an open source python library is used to determine the solar irradiance that a faulty tracker would have received. Based on this information, the system 100 calculates the amount of energy that the tracker would have lost. The purpose of this system 100 is to accurately measure and quantify the energy losses in the solar power plant caused by the malfunctioning solar trackers. By identifying and analyzing the data from the malfunctioning trackers, the system 100 provides valuable insights into the performance and efficiency of the solar power plant. This information can help in identifying and addressing issues with the one or more solar trackers, optimizing energy production, and minimizing losses. The system 100 enables the identification and quantification of energy losses specifically attributed to the malfunctioning solar trackers in the solar power plant. By filtering and analyzing the tracker-day data, the system 100 provides a clear understanding of the impact of the malfunctioning trackers on energy production. This allows for targeted maintenance and optimization efforts to improve the overall performance and efficiency of the solar power plant. Throughout the present disclosure, the term "energy losses" refers to the reduction in the amount of energy generated by the solar power plant due to malfunctioning solar trackers, resulting in a decrease in the overall power output. The term "tracker mode information" refers to the data and parameters associated with the operational status and performance of the solar trackers, including their position, orientation, and movement patterns. The system 100 further comprises the ability to determine whether the energy losses associated with the one or more malfunctioning trackers are controllable or uncontrollable, based on the tracker mode information. The system 100 quantifies energy losses in the solar power plant caused by the malfunctioning solar trackers. It achieves this by determining whether the energy losses associated with the one or more malfunctioning trackers are controllable or uncontrollable, based on the tracker mode information. If the tracker mode field is available, the losses are bifurcated into controllable and uncontrollable categories. However, if the tracker mode field is not available, all losses are accounted for as controllable loss. The system 100 also has the ability to ignore losses in Diffuse mode, as it is a deliberate logical decision to avoid energy loss. To calculate the amount of energy lost per day due to tracker malfunction, the system 100 utilizes an open-source Python library. This library helps identify the solar irradiance that a faulty tracker would have received, and based on that, the system 100 calculates the amount of energy that the tracker would have lost. The purpose of this system 100 is to provide a means of quantifying energy losses in a solar power plant caused by malfunctioning solar trackers. By determining whether the losses are controllable or uncontrollable, the system 100 enables plant operators to identify the extent of energy gap between the actual and target energy production. This information is crucial for exploring options to recover the energy losses and optimize plant performance. The technical effect of the system 100 is the ability to accurately quantify energy losses in the solar power plant due to the malfunctioning solar trackers. By categorizing the losses as controllable or uncontrollable, the system 100 provides valuable insights into the performance of individual trackers. This information can be used to identify malfunctioning trackers and take appropriate actions to recover the energy losses. Additionally, the system's 100 ability to ignore losses in Diffuse mode helps avoid unnecessary calculations and provides a more accurate assessment of energy losses caused by tracker malfunction.

[0040] Throughout the present disclosure, the term "tracker-specific parameters" refers to the specific operational characteristics and settings of a solar tracker, including but not limited to the tracking angle, target angle, and tracker mode. The term "expected irradiance" refers to the anticipated amount of solar radiation or sunlight that is expected to be incident on a solar panel or a specific location within a solar power plant, based on factors such as the time of day, day of year, and geographical location. The term "the solar position and irradiance model" refers to a computer- based model or operation that calculates the position of the sun in the sky and estimates the amount of solar radiation reaching a specific location on the Earth's surface. The system 100 further estimates the anticipated irradiance received by each malfunctioning tracker using the solar position and irradiance model, along with tracker- specific parameters. In some implementations, the system utilizes an open-source python library to quantify energy losses in the solar power plant caused by the malfunctioning solar trackers. By leveraging this library, the system 100 is able to determine the amount of solar irradiance that a faulty tracker would have received. This is achieved by inputting various parameters such as the surface tilt angle and surface azimuth angle of the faulty tracker, as well as the GHI (Global Horizontal Irradiance), DNI (Direct Normal Irradiance), DHI (Diffuse Horizontal Irradiance), solar zenith angle, and solar azimuth angle obtained from the PVLIB Clearsky Model and PVLIB Solar Position. The system 100 then calculates the amount of energy that the tracker would have lost based on the observed tracker irradiance. The purpose of this system is to accurately quantify the energy losses in a solar power plant resulting from malfunctioning solar trackers. By estimating the expected irradiance received by each malfunctioning tracker, plant operators can identify and address the specific trackers that are causing energy losses. This allows for targeted maintenance and repair efforts, ultimately improving the overall efficiency and performance of the solar power plant. The technical effect of the system 100 is the ability to precisely quantify the energy losses caused by malfunctioning solar trackers. By utilizing the solar position and irradiance model, along with the tracker- specific parameters, the system 100 provides accurate estimates of the irradiance that each malfunctioning tracker would have received. This information enables the plant operators to identify and address the specific trackers that are underperforming, leading to improved energy generation and overall plant efficiency.

[0041] Throughout the present disclosure, the term "actual plant irradiance" refers to the amount of solar radiation received by the solar power plant, which is measured using a pyranometer. The term "pyranometer" refers to a device that measures the solar irradiance incident on a surface. The term "ideally working tracker" refers to a solar tracker that is functioning optimally and accurately aligning the solar panels with the sun's position. The term "tracker's capacity" refers to the maximum power produced by the solar panels placed on that solar tracker. The term "plant capacity" refers to the maximum amount of power that a solar power plant can generate under ideal conditions. The system 100 further comprises the following: 1. Calculating the energy loss for each malfunctioning tracker by comparing the estimated expected irradiance with the actual plant irradiance, which is measured using a pyranometer mounted on an ideally functioning tracker. 2. Accounting for the capacity of each tracker relative to the overall capacity of the plant. In some implementations, the system 100 utilizes an open source Python library to identify the amount of solar irradiance that a faulty tracker would have received. This is achieved by comparing the estimated expected irradiance with the actual plant irradiance, which is measured using a pyranometer mounted on an ideally functioning tracker. By calculating the difference between these two values, the system 100 determines the amount of energy that the malfunctioning tracker has lost. The purpose of this system is to extend the current state of the art in tracker malfunction detection. While tracker manufacturers can already detect and report if a tracker is malfunctioning based on the difference between the target tracker angle and the actual tracker angle, the system 100 goes further by quantifying the amount of energy lost per day due to tracker malfunction. This information is valuable in understanding the energy gap between the actual and target performance of the plant, enabling the identification of potential loss recovery options. Furthermore, by quantifying the energy loss in terms of cost, the system 100 allows plant operators to perform cost-benefit analyses, enabling them to decide whether and how much to invest in recovering the lost energy or to accept the loss if recovery costs would exceed the value of the lost energy. The system's 100 technical effect is the ability to accurately quantify energy losses in the solar power plant caused by malfunctioning solar trackers. By utilizing the proprietary method to calculate the irradiance received by the faulty tracker, the system 100 provides timeseries irradiance data that reflects the actual energy loss. This enables plant operators to have a comprehensive understanding of the impact of tracker malfunction on energy production, facilitating informed decision-making for optimizing plant performance and exploring strategies for recovering lost energy.

[0042] In some implementations, the system 100 categorizes the calculated energy losses as either controllable or uncontrollable based on the determination of the tracker mode. The system 100 quantifies energy losses in the solar power plant caused by the malfunctioning solar trackers by categorizing the calculated energy losses as controllable or uncontrollable based on the determination of the tracker mode. It bifurcates the losses into these two categories. This is done to provide a clear understanding of the nature of the energy losses and to identify the potential for control and recovery. By categorizing the losses, it becomes easier to analyze and prioritize the necessary actions to mitigate the losses. The system 100 allows for the identification and separation of energy losses in a solar power plant. It provides insights into the controllable losses, which can be addressed through corrective measures, and the uncontrollable losses, which may be due to extreme weather conditions or other factors beyond control. This information helps in optimizing the performance of the solar power plant and improving overall energy generation efficiency.

[0043] Throughout the present disclosure, the term "uncontrollable energy losses" refers to the energy losses in a solar power plant that are beyond the control of the system and method disclosed herein. The term "maintenance actions" refers to the activities performed on the solar trackers to ensure their proper functioning and prevent malfunction. The term "controllable and uncontrollable energy losses" refers to the combined energy losses in a solar power plant that can be controlled through maintenance actions and those that are beyond control, respectively. The system 100 further includes an analysis of both controllable and uncontrollable energy losses to identify patterns of malfunctioning solar trackers and determine suitable maintenance actions. In some implementations, the system 100 further utilizes the open source python library to quantify energy losses in a solar power plant caused by the malfunctioning solar trackers. By analyzing the amount of solar irradiance a faulty tracker would have received, the system 100 calculates the corresponding energy loss. In some examples, this analysis is performed using as few as 5 parameters, specifically designed for Single Axis Trackers, which are structures on which solar panels lie and rotate from east to west throughout the day. The purpose of the system 100 is to extend the current state of the art in tracker malfunction detection. While tracker manufacturers can already identify malfunctions based on differences between the target and actual tracker angles, the system 100 goes further by calculating the daily energy losses resulting from tracker malfunctions. This information is crucial for understanding the energy gap between the actual and target performance of the plant, enabling the identification of potential loss recovery options. The system's 100 analysis of both controllable and uncontrollable energy losses allows for the identification of patterns in malfunctioning solar trackers. In some examples, by detecting vertical patterns in a controllable loss graph, which indicate individual fault reasons causing a tracker to remain faulty for multiple days, appropriate maintenance actions can be determined. Similarly, in some other examples, the system 100 identifies horizontal patterns in an uncontrollable loss graph, which represent weather -induced losses affecting all trackers in the plant simultaneously. This information enables targeted maintenance interventions and optimization strategies to minimize energy losses and improve overall plant performance.

[0044] In an implementation, the controllable energy losses are attributed to manually controllable or correctable events. Throughout the present disclosure, the term "manually controllable events" refers to events or actions that can be directly controlled or adjusted by human intervention. The term "manually controllable or correctable events" refers to events or actions that can be both directly controlled or adjusted by human intervention and can be rectified or fixed by such intervention. The system 100 is able to bifurcate energy losses into controllable and uncontrollable categories based on the tracker modes. It identifies and categorizes losses that can be manually controlled or corrected as controllable losses. Additionally, it ignores losses in the Diffuse mode, as this mode is intentionally designed to avoid energy loss. This categorization allows for a more accurate assessment of the sources of energy losses in the system. By distinguishing between controllable and uncontrollable losses, it enables targeted efforts to address and mitigate the controllable losses, leading to improved overall energy efficiency. The system 100 calculates the difference between the actual plant irradiance and the observed tracker irradiance, which represents the energy loss. By analyzing the minute-wise percentage difference, it determines the extent of the loss. The system 100 then utilizes the tracker mode information to attribute the losses to either controllable or uncontrollable events. This technical effect enables the system to provide valuable insights into the controllable energy losses, facilitating better decision-making and optimization of energy generation.

[0045] In an implementation, the uncontrollable energy losses are attributed to extreme weather conditions and conditions outside the control of human beings. Throughout the present disclosure, the term "extreme weather conditions" refers to severe and abnormal atmospheric phenomena, such as hurricanes, tornadoes, heavy rain, snowstorms, extreme temperatures, and high winds, which can significantly impact the operation and performance of solar trackers in a solar power plant. The term "conditions outside the control of human beings" refers to circumstances or situations that are beyond the influence or manipulation of human beings, including but not limited to natural disasters, acts of God, force majeure events, and other uncontrollable factors that can affect the functioning and efficiency of the one or more solar trackers 116 in the solar power plant. The system 100 utilizes Tracker Mode data to identify and categorize energy losses as either controllable or uncontrollable. By analyzing the Tracker Mode, the system 100 determines if the tracker is in a special mode due to extreme weather conditions or operating in normal conditions. This information allows for the segregation of energy losses based on their controllability. The purpose of attributing energy losses to extreme weather conditions and conditions outside human control is to provide a comprehensive understanding of the factors contributing to energy loss in the system 100. By differentiating between controllable and uncontrollable losses, it becomes easier to identify areas for improvement and develop strategies to mitigate energy loss. The system's 100 ability to accurately attribute energy losses to extreme weather conditions and uncontrollable factors provides valuable insights into the performance of the system. This information enables stakeholders to assess the impact of weather conditions on energy generation and make informed decisions regarding loss recovery options. Additionally, by distinguishing between controllable and uncontrollable losses, the system facilitates targeted interventions to optimize energy production and minimize losses within human control, while also providing a basis for fair performance evaluation by allowing plant owners to avoid penalizing operators for uncontrollable losses.

[0046] In an implementation, the solar position and irradiance model comprises: the first model 108 for calculating solar position and irradiance based on location and time. Throughout the present disclosure, the term "first model" refers to a mathematical representation or simulation that captures the behaviour and characteristics of a system or process, typically used for analysis, prediction, or optimization purposes. The solar position and irradiance model utilizes the PVLIB Clearsky Model, which takes inputs such as time, latitude, longitude, and time zone of the plant to calculate Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI). Additionally, the PVLIB Solar Position module uses the same inputs to determine the sun's position as Zenith and azimuth. These calculations are performed using the PVLIB which is an open-source Python library. The purpose of calculating solar position and irradiance based on location and time is to accurately determine the amount of solar energy available at a given location and time. This information is crucial for various applications, such as optimizing solar panel placement, tracking the sun's movement for solar trackers, and assessing the performance of solar energy systems. By accurately calculating solar position and irradiance, the solar position and irradiance model enables the identification of the amount of solar irradiance a faulty solar tracker would have received. This information is then used to calculate the amount of energy that the tracker would have lost. Additionally, the model is used for the generation of timeseries data indicating the angle at which each solar tracker should travel to follow the sun throughout the day.

[0047] In an implementation, the solar position and irradiance model further comprises the second model 110 for converting the global horizontal irradiance to plane of array irradiance using the tracker- specific parameters. Throughout the present disclosure, the term "global horizontal irradiance" refers to the total amount of solar radiation received on a horizontal surface, including both direct and diffuse radiation. The term "plane of array irradiance" refers to the amount of solar radiation received on the surface of a solar panel, taking into account the tilt and orientation of the panel. The term "second model" refers to a mathematical or computational model that is used to estimate the expected energy output of a solar tracker based on various inputs and parameters. The system 100 utilizes the second model 110 to convert the global horizontal irradiance to the plane of array irradiance. This conversion is achieved by incorporating specific parameters related to the faulty tracker, including the surface tilt angle and surface azimuth angle. Additionally, the system 100 takes into account various inputs from the PVLIB Clearsky Model, such as global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). Furthermore, the PVLIB Solar Position provides the solar zenith angle and solar azimuth angle. By combining these tracker- specific parameters and solar position data, the system 100 calculates the observed tracker irradiance, which represents the amount of irradiance received by the stuck or faulty tracker. The purpose of this conversion is to accurately determine the irradiance received by the faulty tracker. By considering the specific parameters of the tracker, such as its tilt angle and azimuth angle, as well as the solar position, the system 100 can account for the unique characteristics and orientation of the tracker. This enables a more precise estimation of the irradiance received by the tracker, which is crucial for diagnosing and addressing any issues or faults in its performance. The technical effect of utilizing the second model 110 and tracker- specific parameters is the ability to accurately convert the global horizontal irradiance to the plane of array irradiance for the faulty tracker. This conversion takes into account the specific orientation and characteristics of the tracker, as well as the solar position, resulting in a more precise estimation of the irradiance received by the tracker. This information is valuable for analyzing the performance of the tracker and identifying any potential faults or issues that may be affecting its efficiency.

[0048] In an implementation, the tracker- specific parameters further include a surface tilt angle and a surface azimuth angle adjustment factor. Throughout the present disclosure, the term "surface tilt angle" refers to the angle at which a solar panel is inclined with respect to the horizontal plane, determining the optimal orientation for capturing solar radiation. The term "surface azimuth angle adjustment factor" refers to a factor that quantifies the adjustment required in the azimuth angle of a solar panel, which represents the deviation of the solar tracker from the due south direction. The system adjusts the surface tilt angle and surface azimuth angle based on tracker- specific parameters. This adjustment helps to bring the faulty tracker calculated irradiance value close to ground reality by accounting to local cloud or hindrances as scene in POA irradiance measured through pyranometer in the solar power plant.

[0049] In an implementation, the at least one processor 104 is further configured to ignore energy losses during periods when the one or more malfunctioning trackers are intentionally in a diffuse mode, wherein the diffuse mode sets the target angle to horizontal to optimize performance during cloudy conditions. Throughout the present disclosure, the term "diffuse mode" refers to the condition in which sunlight is scattered or reflected by the atmosphere, resulting in a more uniform distribution of light. The term "target angle" refers to the desired position or orientation of a solar tracker that maximizes the capture of solar energy. The term "optimize performance" refers to the process of improving the efficiency and effectiveness of a system or device to achieve the best possible results. The term "cloudy conditions" refers to the state in which the sky is covered with clouds, reducing the amount of direct sunlight reaching the solar panels. The term "horizontal" refers to a position or orientation that is parallel to the ground or a reference plane. The system 100 utilizes an algorithm that bifurcates losses into controllable and uncontrollable based on the tracker modes. When the tracker is intentionally in a diffuse mode due to cloud cover, the algorithm identifies this mode and ignores energy losses. In the diffuse mode, the target angle is set to horizontal, optimizing performance during cloudy conditions. The purpose of ignoring energy losses during the diffuse mode is to avoid wasting tracking energy and to ensure that the solar panels can capture the maximum amount of diffused irradiance. By parallelizing the one or more solar trackers 116 with the land surface, the system 100 prevents unnecessary tracking and allows for better utilization of available energy. The ability to ignore energy losses during the diffuse mode improves the overall efficiency of the system during cloudy conditions. By optimizing the target angle and disregarding losses, the system 100 can maximize energy capture from diffused irradiance, resulting in improved performance and increased energy generation.

[0050] In an implementation, the at least one processor 104 is further configured to determine the tracker mode information based on a time-series data of tracker modes received from the one or more solar trackers 116. Throughout the present disclosure, the term "time-series data" refers to a collection of data points that are recorded and organized in chronological order, allowing for the analysis of patterns and trends over time. The term "tracker modes" refers to the different operational states or configurations in which a solar tracker can operate, such as the tracking mode, stow mode, maintenance mode, or any other predefined modes that control the movement and positioning of the solar panels. The system 100 utilizes an open source python library to analyze the time-series data of tracker modes received from the one or more solar trackers 116. This library enables the identification of the angle in which a solar tracker should travel to follow the sun as preprogrammed.

[0051] In an implementation, the tracker modes include at least one of idle mode, manual mode, wind stow mode, snow stow mode, clean stow mode, night stow mode, emergency stow mode, communication error stow mode, auto mode, cycle test mode, and hail stow mode. Throughout the present disclosure, the term "idle mode" refers to a state in which the solar tracker remains inactive and does not perform any tracking or movement operations. The term "manual mode" refers to a mode in which the solar tracker is manually controlled by an operator, allowing for precise positioning and adjustment. The term "wind stow mode" refers to a mode in which the solar tracker automatically moves to a stowed position to minimize wind loads and protect the system during high wind conditions. The term "snow stow mode" refers to a mode in which the solar tracker automatically moves to a stowed position to prevent snow accumulation and potential damage to the system. The term "clean stow mode" refers to a mode in which the solar tracker automatically moves to a stowed position to facilitate cleaning and maintenance operations. The term "night stow mode" refers to a mode in which the solar tracker automatically moves to a stowed position during nighttime to conserve energy and protect the system. The term "emergency stow mode" refers to a mode in which the solar tracker quickly moves to a stowed position in response to an emergency situation or a predefined event. The term "communication error stow mode" refers to a mode in which the solar tracker automatically moves to a stowed position when a communication error or loss of communication with the control system occurs. The term "auto mode" refers to a mode in which the solar tracker operates automatically based on predefined algorithms and inputs from sensors, optimizing the tracking performance. The term "cycle test mode" refers to a mode in which the solar tracker undergoes a series of predefined movements and tests to evaluate its performance and identify any potential issues. The term "hail stow mode" refers to a mode in which the solar tracker automatically moves to a stowed position to protect the system from hail or other adverse weather conditions. It is noted that the identifiers or names of the tracker modes may vary among different manufacturers. However, the functionality of these modes remains the same across different systems. The system 100 tracks the actual angle and target angle of the trackers, and if the Tracker mode field is available, it separates the loss into controllable loss and uncontrollable loss. If the Tracker mode field is not available, all losses are considered as controllable loss. The purpose of having tracker modes and categorizing losses into controllable and uncontrollable is to identify malfunctioning trackers and determine the cause of the losses. By analyzing the tracker modes and separating the losses, it becomes easier to pinpoint the issues and take appropriate actions for maintenance and optimization. The utilization of tracker modes and categorization of losses provide a systematic approach to identify malfunctioning trackers. The 5-degree and 2-instance filter facilitates distinguishing between normal variations in tracker angles and instances where the actual angle deviates significantly from the ideal angle. This filter ensures that only significant deviations are considered as losses and facilitates accurately assessing the controllable and uncontrollable losses. This technical effect enables efficient monitoring and management of the trackers, leading to improved performance and maintenance of the system 100.

[0052] In an implementation, the predetermined data quality and data quantity criteria include at least one of a configuration issue, a data unavailability issue, an angle out-of-bounds issue, and a sun angle stuck issue. Throughout the present disclosure, the term "configuration issue" refers to a problem arising from incorrect settings or parameters, leading to suboptimal performance. The term "data unavailability issue" refers to a situation where the necessary data for tracking the sun's position, such as weather conditions or sensor readings, is not accessible or missing. The term "angle out-of-bounds issue" refers to a situation where the received data is out of bound proving that the received data is not correct so analysis may not be run on such data. Lastly, the term "sun angle stuck issue" refers to a malfunction in the target angle data as the target angle can never go flat / constant. Such situation refers to data quality issue so analysis will not be run on such data too. The system 100 addresses the predetermined data quality and data quantity criteria by implementing various measures. Firstly, a data selection algorithm is employed to identify and remove tracker-days with bad data, which accounts for approximately 30% of the data. This helps to mitigate the impact of unreliable data on the analysis. Additionally, the system 100 utilizes available meta data but acknowledges the absence of certain mandatory timeseries data, such as the target angle or actual angle. The system 100 relies on the timeseries data of the angle in which a solar tracker should travel, as preprogrammed to follow the sun. This data is crucial for accurate analysis and decision-making. Furthermore, the system incorporates timeseries irradiance data, which quantifies the amount of irradiance received by stuck or faulty trackers. This data is calculated using a proprietary method specifically designed for this purpose. The system 100 implements these measures to ensure the accuracy and reliability of the analysis conducted on solar plant data. By addressing data quality and data quantity issues, such as configuration problems, data unavailability, angle out-of-bounds, and sun angle stuck issues, the system 100 aims to minimize uncertainty and improve the overall quality of the analysis results. This is crucial for making informed decisions and optimizing the performance of the solar plant. The implementation of these measures has several technical effects. Firstly, it helps to filter out unreliable data, reducing the impact of data quality issues on the analysis. Lastly, the incorporation of timeseries irradiance data allows for the identification and quantification of irradiance received by stuck or faulty trackers. This information is valuable for diagnosing and addressing issues related to sun angle stuck problems, ultimately improving the overall performance and efficiency of the solar plant.

[0053] In an implementation, the display 112 is configured to visualize the controllable and uncontrollable energy losses for identifying patterns of the malfunctioning solar trackers. Throughout the present disclosure, the term "display" refers to a visual output device that presents information or data in a visual format, typically through the use of a screen or panel, allowing users to view and interpret the displayed content. In some examples, the system 100 utilizes an open source python library to identify the solar irradiance that a faulty tracker would have received. This information is used to calculate the amount of energy lost by the solar tracker. The purpose of the system 100 is to identify patterns of the malfunctioning solar trackers by visualizing the controllable and uncontrollable energy losses. By analyzing these patterns, it becomes possible to detect and address individual fault reasons and extreme weather-induced losses. The system 100 provides a display that visualizes the controllable and uncontrollable energy losses. This enables the identification of malfunctioning solar trackers and helps in determining the amount of energy gap between the actual and target performance of the plant. This information can be used to explore options for recovering the energy losses.

[0054] FIG. 2 is a flowchart of a series of operations for identifying malfunctioning of solar trackers, in accordance with an embodiment of the present disclosure. FIG. 2 is described in conjunction with elements from FIG. 1. With reference to FIG. 2, there is shown a flowchart 200 that illustrates the process of for identifying malfunctioning of the one or more solar trackers 116. The flowchart 200 includes a series of operations 202 to 212. The processor 104 (of FIG. 1) is configured to execute the operations shown in the flowchart 200.

[0055] The flowchart 200 begins at operation 202 by checking if the diffuse mode is on for the solar trackers. As discussed above with reference to FIG. 1, the diffuse mode refers to a specific operating condition where the trackers are set to a tilt angle optimized for capturing diffuse solar radiation during cloudy conditions when direct normal irradiance is obscured.

[0056] If diffuse mode is on (at operation 202), the flowchart 200 proceeds to operation 204 where the target angle for the trackers is set equal to the optimal diffuse tilt angle, which is generally horizontal or zero degrees. This ensures the system 100 properly accounts for diffuse mode conditions.

[0057] If diffuse mode is not on (at operation 202), or after setting the target angle in operation 204, the flowchart 200 advances to operation 206 for data quality filtering. In this step, the system 100 removes or filters out tracker-day data where there are configuration issues, data quality issues (such as angle values out of reasonable range or stuck at a single value), and data availability issues (where more than a threshold percentage, e.g. 75%, of the data is missing or unavailable). This data filtering helps ensure only reliable, high-quality data is used for subsequent energy loss calculations.

[0058] At operator 208, the system 100 calculates the absolute difference between the ideal tracker angle for optimally tracking the sun's position (referred to as the target angle) and the actual measured tracker angle. The ideal tracker angle is a predefined value based on the solar position calculated for maximum energy capture at that date, time and location.

[0059] The flowchart 200 then moves to operation 210 to identify malfunctioning trackers based on the calculated angle difference. If the absolute difference between the ideal and actual tracker angles exceeds a predetermined threshold angle (e.g. 5 degrees, which can be configured at the plant level), the system identifies or flags that tracker as malfunctioning for that time period.

[0060] For the time periods or tracker-day data points identified as involving a malfunctioning tracker in operation 210, the flowchart 200 calculates the energy loss in operation 212 using the techniques described in other portions of this disclosure. Conversely, if the absolute angle difference is below the threshold (e.g. < 5 degrees) indicating the tracker is operating as expected, the system 100 does not calculate any energy loss for those tracker-day data points.

[0061] By systematically checking for diffuse mode, filtering low -quality data, calculating angle deviations from the ideal, and calculating energy losses only for identified malfunctioning periods, the flowchart 200 of FIG. 2 enables accurate identification and quantification of energy losses specifically due to malfunctioning solar trackers. This methodology can be applied across different solar tracker manufacturers and plant configurations.

[0062] FIG. 3 is a flowchart of a series of operations for calculating plane of array irradiance of each solar tracker, in accordance with an embodiment of the present disclosure. FIG. 3 is described in conjunction with elements from FIGs. 1 and 2. With reference to FIG. 3, there is shown a flowchart 300 that illustrates the process of calculating plane of array irradiance of each solar tracker of the one or more solar trackers 116. The flowchart 300 includes a series of operations 302 to 312. The processor 104 (of FIG. 1) is configured to execute the operations shown in the flowchart 300.

[0063] The flowchart 300 begins at operation 302 by obtaining input parameters including the latitude and longitude coordinates of the solar power plant site, and the day of the year for which the calculation is being performed. These parameters are used in subsequent steps.

[0064] At operation 304, the PVLIB Clear Sky model component uses the input latitude, longitude and day of the year to estimate values for clear sky global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) at the plant site. These are irradiance values that would be expected under clear sky conditions.

[0065] The flowchart 300 then moves to operation 306, where the first model 108 (e.g., PVLIB Solar Position model) calculates the solar zenith angle and the solar azimuth angle based on the latitude, longitude, day of the year and other solar position parameters for the site. These angles represent the position of the sun in the sky relative to the solar trackers at that date and time.

[0066] At operation 308, the system 100 obtains the tilt angle and azimuth angle parameters specific to the solar tracker being analysed. The tilt angle is the angle between the plane of the solar modules and the horizontal plane, which is adjusted by the tracker's orientation. The azimuth angle accounts for the compass direction the tracker is facing - it is set to 90 degrees (East) for negative tilt angles and 270 degrees (West) for positive tilt angles.

[0067] The flowchart 300 then proceeds to operation 310, where the second model 110 (e.g., PVLIB get_total_irradiance model) calculates the plane of array (POA) irradiance for the specific tracker being analysed. This model combines the inputs from the clear sky model (GHI, DNI, DHI), the solar position angles, and the tilt / azimuth angles for that tracker. The output is an estimate of the total solar irradiance that would be incident on the plane of the tracker's solar module array.

[0068] Finally, at operation 312 the system 100 outputs the calculated POA irradiance as estimated by PVLIB for the tracker in question. This estimated POA irradiance value is then compared against the actual measured irradiance to identify any deviations indicating a tracker malfunction, as described in FIG. 2. The irradiance calculation process of FIG. 3 is carried out for each solar tracker being analysed by the overall system.

[0069] FIG. 4 is a flowchart of a series of operations for correction of the plane of array irradiance of each solar tracker, in accordance with an embodiment of the present disclosure. FIG. 4 is described in conjunction with elements from FIGs. 1, 2 and 3. With reference to FIG. 4, there is shown a flowchart 400 that illustrates the process of correction of the plane of array irradiance of each solar tracker of the one or more solar trackers 116. The flowchart 400 includes a series of operations 402 to 414. The processor 104 (of FIG. 1) is configured to execute the operations shown in the flowchart 400.

[0070] The process begins at operation 402 by obtaining the ideal tracker angle, which represents the optimal tilt angle the tracker should be at to maximize energy capture from the sun's position. The flowchart 400 also obtains the actual measured tracker angle at operation 404, which is the real-time tilt angle of the tracker.

[0071] In operation 406, the system 100 uses the PVLIB module to calculate the POA irradiance for the ideal tracker angle obtained in step 400. This ideal tracker PVLIB POA represents the solar irradiance the tracker would receive if operating at the optimal angle.

[0072] The flowchart 400 then moves to operation 408, where the PVLIB module calculates the POA irradiance for the actual measured tracker angle from operation 404. This actual tracker PVLIB POA represents the estimated irradiance the tracker would receive while at its current angle, which may be non-optimal if malfunctioning.

[0073] In operation 410, the system 100 obtains the actual measured POA irradiance for the solar power plant. This plant POA irradiance value comes from a pyranometer sensor installed on an ideally functioning tracker within the plant, providing a reference for the irradiance the trackers should be receiving. The flowchart 400 then proceeds to operation 412 to calculate the corrected POA irradiance for the tracker being analysed. This uses the formula:

[0074] Corrected Tracker POA = (Actual Tracker PVLIB POA / Ideal Tracker PVLIB POA) x Plant POA

[0075] This formula adjusts the PVLIB -calculated actual tracker POA based on the ratio of the actual vs ideal PVLIB POAs, scaled by the actual measured plant POA irradiance. This corrected value accounts for any deviations between the PVLIB estimates and real-world conditions at the plant. The PVLIB estimates are based on clear sky conditions. This correction allows us to take the measured POA Irradiance’ deviations like local cloud cover, or obstructions come from buildings or mountains.

[0076] Finally, in operation 414, the system 100 outputs the corrected POA irradiance calculated in operation 412 for the tracker being analysed. This corrected tracker POA irradiance is then compared against the ideal expected irradiance as measured by plant pyranometer to identify and quantify any energy losses due to that tracker potentially malfunctioning, as per the process described in other portions of this disclosure.

[0077] The irradiance correction technique of FIG. 4 helps ensure accurate identification of malfunctioning trackers by properly accounting for both the PVLIB modelled estimates and the actual measured plant irradiance when calculating the POA irradiance each tracker should be receiving. This corrected POA is a key input for the overall system's energy loss calculations.

[0078] FIG. 5 is a flowchart of a series of operations for quantification of energy loss in the solar power plant, in accordance with an embodiment of the present disclosure. FIG. 5 is described in conjunction with elements from FIGs. 1, 2, 3, and 4. With reference to FIG. 5, there is shown a flowchart 500 that illustrates the process of quantification of energy loss in the solar power plant. The flowchart 500 includes a series of operations 502 to 516. The processor 104 (of FIG. 1) is configured to execute the operations shown in the flowchart 500. The flowchart 500 begins at operation 502 by obtaining the direct current (DC) capacity rating of the individual tracker being analysed for malfunction. In operation 504, the system 100 also obtains the total DC capacity rating of the entire solar power plant.

[0079] In operation 506, the flowchart 500 obtains the total energy generation of the plant on the specific day being analysed for tracker malfunctions and associated energy losses.

[0080] Using the tracker DC capacity, plant DC capacity, and plant generation values from operations 502-506, the system 100 calculates a loss ratio in operation 508. This loss ratio allows conversion of deviations in plane of array (POA) irradiance into corresponding energy loss values quantified in kilowatt-hours (kWh).

[0081] The flowchart 500 then advances to operation 510 to categorize the calculated loss ratio into two portions - a recoverable loss ratio and an irrecoverable loss ratio. The recoverable portion represents energy losses that could potentially be mitigated through corrective maintenance actions for the malfunctioning trackers. The irrecoverable portion represents losses that are uncontrollable and cannot be recovered due to inherent issues like weather conditions.

[0082] In operation 512, the system 100 calculates ratios to convert the corrected faulty tracker POA irradiance values (determined in FIG. 4) into actual energy loss amounts in kWh. This calculation incorporates the corrected faulty tracker POA irradiance, the reference plant POA irradiance from an ideally functioning tracker, and the tracker's operational mode which influences whether the loss should be considered recoverable or irrecoverable.

[0083] Based on the ratios calculated in operation 512, the flowchart 500 proceeds to operation 514 to quantify the total energy losses in kWh categorized into recoverable and irrecoverable losses. The recoverable loss quantification corresponds to losses that can potentially be addressed through maintenance, while irrecoverable captures uncontrollable losses. Finally, in operation 516, the system 100 outputs the detailed quantification of the total energy losses segregated into the recoverable and irrecoverable categories. This quantified loss data provides crucial insights to solar plant operators, allowing them to understand the precise extent and sources of energy losses caused by tracker malfunctions. With this information, they can evaluate the potential for recovered energy through maintenance actions and optimize the overall plant performance.

[0084] The methodology illustrated in FIG. 5, by accurately quantifying and categorizing losses based on corrected irradiance, tracker capacity, operational modes and real- world measurements, enables solar operators to implement effective data-driven maintenance strategies to mitigate the impacts of malfunctioning solar trackers on their plants.

[0085] FIG. 6 is a flowchart of a series of operations for categorization of energy loss in the solar power plant as controllable or uncontrollable, in accordance with an embodiment of the present disclosure. FIG. 6 is described in conjunction with elements from FIGs. 1, 2, 3, 4, and 5. With reference to FIG. 6, there is shown a flowchart 600 that illustrates the process of categorization of energy loss in the solar power plant as controllable or uncontrollable. The flowchart 600 includes a series of operations 602 to 624. The processor 104 (of FIG. 1) is configured to execute the operations shown in the flowchart 600.

[0086] At operation 602, receive faulty tracker plane of array (POA) irradiance data.

[0087] For filtering the faulty tracker POA irradiance data based on the tracker mode, at operation 604, if the tracker mode indicates a recoverable mode, categorize the corresponding POA irradiance data as recoverable mode minutes. At operation 606, if the tracker mode indicates an irrecoverable mode, categorize the corresponding POA irradiance data as irrecoverable mode minutes.

[0088] For the recoverable mode minutes, at operation 608, calculate the difference between the plant POA irradiance (measured by a pyranometer on an ideal tracker) and the faulty tracker's POA irradiance for each minute. At operation 610, sum the differences over 60 / At intervals to determine the total irradiance lost in recoverable mode. At operation 612, calculate the total energy lost in recoverable mode by summing the irradiance lost over all intervals. At operation 614, calculate the recoverable loss ratio by dividing the total energy lost in recoverable mode by the total plant POA irradiance.

[0089] For the irrecoverable mode minutes, at operation 616, calculate the difference between the plant POA irradiance and the faulty tracker's POA irradiance for each minute. At operation 618, sum the differences over 60 / At intervals to determine the total irradiance lost in irrecoverable mode. At operation 620, calculate the total energy lost in irrecoverable mode by summing the irradiance lost over all intervals. At operation 622, calculate the irrecoverable loss ratio by dividing the total energy lost in irrecoverable mode by the total plant POA irradiance.

[0090] Finally, at operation 624, output the recoverable loss ratio and the irrecoverable loss ratio, representing the categorized and quantified energy losses due to malfunctioning solar trackers.

[0091] This flowchart 600 enables the system 100 to distinguish between energy losses that can be recovered through maintenance actions (recoverable mode) and those that are unavoidable due to extreme weather conditions or other uncontrollable factors (irrecoverable mode). By calculating the loss ratios for each category, the system 100 can quantify the impact of malfunctioning trackers and identify potential areas for improvement.

[0092] FIG. 7 is a graphical representation of controllable energy losses in the solar power plant, in accordance with an embodiment of the present disclosure. FIG. 7 is described in conjunction with elements from FIGs. 1 to 6. With reference to FIG. 7, there is shown a graphical representation 700 that visualizes the controllable energy losses attributed to malfunctioning solar trackers in the solar power plant. The x-axis lists the individual tracker identifiers, with a total of 88 out of 884 trackers being identified as malfunctioning. The y-axis represents different days to know on which days tracker was malfunctioning. So, each cell represents 1 Tracker- day. Intensity of colour represents the amount of energy lost on each Tracker-Day. For each malfunctioning tracker shown on the x-axis, there is a corresponding bar extending vertically, indicating the quantified controllable energy loss associated with that specific tracker over the analysed period.

[0093] The graphical representation 700 allows for easy identification of trackers experiencing the highest controllable losses, which appears darker. This enables plant operators to quickly pinpoint the trackers contributing most significantly to overall energy losses, likely requiring prioritized maintenance or corrective actions. The flowchart 700 effectively visualizes the controllable portion of the total energy losses, specifically attributed to malfunctioning tracker issues that could potentially be addressed through maintenance activities or operational adjustments within the plant operator's control.

[0094] The graphical representation 700 of the controllable energy loss distribution across the malfunctioning trackers, allowing plant operators to analyze patterns, prioritize interventions, and take data-driven actions to mitigate these preventable losses and optimize plant performance. The vertical pattern of losses represents a particular tracker is under malfunction for multiple days.

[0095] FIG. 8 is a graphical representation of uncontrollable energy losses in the solar power plant, in accordance with an embodiment of the present disclosure. FIG. 8 is described in conjunction with elements from FIGs. 1 to 7. With reference to FIG. 8, there is shown a graphical representation 800 that visualizes the uncontrollable energy losses in the solar power plant caused by extreme weather conditions or environmental factors beyond the operator's control. The x-axis represents the individual dates or time periods analysed, while The y-axis represents different days to know on which days tracker was malfunctioning. So, each cell represents 1 Tracker-day. Intensity of colour represents the amount of energy lost on each Tracker-Day.

[0096] The graphical representation 800 displays a series of horizontal bars, with each cell’s colour intensity representing the quantified uncontrollable energy loss for a specific tracker day. Each cell’s colour intensity indicate the extent of the losses attributed to factors like severe weather events, cloud cover, or other environmental conditions that adversely impacted the performance of the solar trackers.

[0097] Notably, the graphical representation 800 exhibits a relatively horizontal pattern across the x-axis, with the bar heights being relatively consistent or uniform over multiple adjacent time periods. This horizontal pattern is characteristic of uncontrollable losses, as extreme weather conditions or environmental factors tend to affect the entire solar plant and all trackers simultaneously, leading to a consistent level of energy loss across the plant during those periods.

[0098] By visualizing the uncontrollable losses in this manner, FIG. 8 allows plant operators to identify and distinguish the periods when energy losses were primarily driven by factors outside their control, such as severe weather events or cloud cover. This information can be valuable for understanding the plant's performance under different environmental conditions and potentially guiding decision-making related to weather monitoring, forecasting, or other mitigation strategies for uncontrollable losses.

[0099] The graphical representation 800 of the uncontrollable energy losses in the solar power plant, enabling plant operators to analyze patterns, identify periods of significant environmental impact, and make informed decisions regarding plant operations and maintenance strategies in the face of uncontrollable factors affecting energy production.

[0100] FIG. 9 is a graphical representation of energy losses of the solar tracker in a diffuse mode, in accordance with an embodiment of the present disclosure. FIG. 9 is described in conjunction with elements from FIGs. 1 to 8. With reference to FIG. 9, there is shown a graphical representation 900 that shows the behaviour of a particular solar tracker on a particular day when operating in diffuse mode conditions. The x-axis represents the time of day, while the y-axis on the left shows the tracker angle in degrees, and the y-axis on the right shows the irradiance values (W / m2). The graphical representation 900 includes a curve 902 that indicates the diffuse mode status, where a value of 1 likely represents diffuse mode being active. The graphical representation 900 further includes a curve 904 that represents the ideal or optimal tracker angle for maximum energy capture based on the sun's position. The graphical representation 900 demonstrates how the system handles diffuse mode conditions, where an actual tracker angle is set to a horizontal or near- zero position to optimize capture of diffuse irradiance. During these periods, the system 100 recognizes the diffuse mode status and does not calculate or report energy losses.

[0101] FIG. 10 is a graphical representation of energy losses of the solar tracker when an actual tracker angle of the solar tracker is not following an ideal tracker angle for a time interval, in accordance with an embodiment of the present disclosure. FIG. 10 is described in conjunction with elements from FIGs. 1 to 9. With reference to FIG. 10, there is shown a graphical representation 1000 that illustrates the behaviour of a particular solar tracker on a particular day (not specified) when the actual tracker angle deviates from the ideal tracker angle for a specific time interval. The x-axis represents the time of day, while the y-axis on the left shows the tracker angle in degrees, and the y-axis on the right shows the irradiance values (W / m2).

[0102] The graphical representation 1000 includes a curve 1002 that indicates the diffuse mode status, where a value of 0 likely represents diffuse mode being inactive. The graphical representation 1000 further includes a shaded region 1004 that represents the calculated energy loss for the tracker during the time interval when the actual angle deviates from the ideal angle. The graphical representation 1000 further includes a curve 1006 that shows the actual measured irradiance at the solar plant, which serves as a reference for the expected irradiance levels. The graphical representation 1000 further includes a curve 1008 that displays the estimated irradiance received by the solar tracker when operating with a non-ideal angle, calculated using the system's 100 proprietary method. The graphical representation 1000 further includes a curve 1010 that plots the actual measured angle of the tracker throughout the day. The graphical representation 1000 further includes a curve 1012 that represents the ideal or optimal tracker angle for maximum energy capture based on the sun's position.

[0103] The graphical representation 1000 shows that from approximately 8:00 to 10:30, the actual tracker angle (shown by the curve 1010) deviates from the ideal tracker angle (shown by the curve 1012). During this time interval, the system 100 calculates and reports the energy losses, as indicated by the non-zero values in the Loss curve 1004.

[0104] FIG. 11 is a graphical representation of energy losses of the solar tracker when the actual tracker angle of the solar tracker is not following the ideal tracker angle for a whole day, in accordance with an embodiment of the present disclosure. FIG. 11 is described in conjunction with elements from FIGs. 1 to 10. With reference to FIG. 11, there is shown a graphical representation 1100 that illustrates the behaviour of a particular solar tracker on a particular day when the actual tracker angle deviates from the ideal tracker angle for the entire day, representing a stuck or malfunctioning tracker scenario. The x-axis represents the time of day, while the y-axis on the left shows the tracker angle in degrees, and the y-axis on the right shows the irradiance values (W / m2).

[0105] The graphical representation 1100 includes a curve 1102 that indicates the diffuse mode status, where a value of 0 likely represents diffuse mode being inactive throughout the day. The graphical representation 1100 further includes a curve 1104 that represents the calculated energy loss for the tracker due to the deviation between the actual and ideal tracker angles. The graphical representation 1100 further includes a curve 1106 that indicates the actual measured irradiance at the solar plant, which serves as a reference for the expected irradiance levels. The graphical representation 1100 further includes a curve 1108 that displays the estimated irradiance received by the malfunctioning tracker, calculated using the system's 100 proprietary method. The graphical representation 1100 further includes a curve 1110 that plots the actual measured angle of the tracker, which remains constant or stuck throughout the day, deviating from the ideal angle. The graphical representation 1100 further includes a curve 1112 that represents the ideal or optimal tracker angle for maximum energy capture based on the sun's position, varying throughout the day as expected. The graphical representation 1100 further includes a curve 1114 that represents the operational mode of the tracker, which may influence whether the energy losses are classified as controllable or uncontrollable.

[0106] The graphical representation 1100 shows that the actual tracker angle 1110 remains constant or stuck at a non-ideal angle throughout the day, deviating significantly from the varying ideal tracker angle 1112. This results in energy losses calculated and reported by the system 100, as illustrated by the non-zero values in the Loss curve 1104.

[0107] In an example, the losses for this particular tracker on this day are categorized as 57.7453 kWh of controllable loss and 6.4612 kWh of uncontrollable or extreme weather loss, likely based on the tracker mode (shown by the curve 1114) and other factors considered by the system 100.

[0108] FIG. 12 is a flowchart of a method for quantifying energy losses in the solar power plant due to malfunctioning solar trackers, in accordance with an embodiment of the present disclosure. FIG. 12 is described in conjunction with elements from FIGs. 1 to 11. With reference to FIG. 12, there is shown a flowchart that illustrates a method 1200 for quantifying energy losses in the solar power plant due to the malfunctioning solar trackers. The method 1200 includes a series of operations 1202 to 1210. The processor 104 (of FIG. 1) is configured to execute the operations shown in the method 1200.

[0109] At step 1202, the method 1200 includes filtering the tracker-day data 118 received from the one or more solar trackers 116 using predefined criteria for data quality and data quantity. The filtered data corresponds to the one or more malfunctioning trackers. The filtering is performed by the at least one processor 104. The method 1200 for quantifying energy losses in the solar power plant due to the malfunctioning solar trackers involves filtering the tracker-day data 118 received from the one or more solar trackers 116 based on predefined data quality and data quantity criteria. This is achieved by utilizing an open source python library that allows for the identification of solar irradiance received by faulty trackers. Using this information, the amount of energy lost by each malfunctioning tracker is calculated. The purpose of filtering the tracker-day data 118 is to ensure accurate and reliable analysis of energy losses caused by malfunctioning solar trackers. As solar plants are spread across multiple acres, data quality and availability issues are common, with approximately 30% of the data being deemed as bad. By removing tracker-days with poor data quality, the analysis can avoid introducing uncertainty and maintain the integrity of the results. The technical effect of filtering the tracker- day data 118 is the ability to accurately quantify energy losses in the solar power plant. By utilizing the predefined data quality and data quantity criteria, the method 1200 ensures that only reliable and relevant data corresponding to malfunctioning trackers is considered. This allows for a more precise assessment of the impact of malfunctioning solar trackers on energy generation, enabling effective troubleshooting and optimization of the solar power plant's performance.

[0110] At step 1204, the method 1200 includes determining, by at least one processor 104, whether the energy losses associated with the one or more malfunctioning trackers are controllable or uncontrollable based on the tracker mode information. The method 1200 involves analyzing the target angle and actual angle of the one or more solar trackers 116, along with the tracker mode information. If the tracker mode field is available, the energy losses are separated into controllable and uncontrollable losses. If the tracker mode field is not available, all losses are considered as controllable. Additionally, losses in Diffuse mode are ignored as it is a deliberate decision to avoid energy loss. The current state of the art allows tracker manufacturers to detect and report malfunctioning trackers based on a significant difference between the target and actual tracker angles. We extend this by calculating the daily energy loss caused by tracker malfunction, which helps identify the energy gap between the actual and target output of the plant for potential loss recovery options. This calculation is performed using an open-source Python library, which determines the solar irradiance the faulty tracker would have received and calculates the corresponding energy loss. The purpose of this method is to accurately quantify the energy losses in the solar power plant resulting from the malfunctioning solar trackers. By determining whether the losses are controllable or uncontrollable based on the tracker mode information, it provides valuable insights for optimizing the plant's performance and identifying potential areas for improvement. This information is crucial for decision -making regarding loss recovery options and overall plant efficiency. The technical effect of the method 1200 is the ability to precisely quantify the energy losses caused by the malfunctioning solar trackers. By categorizing the losses into controllable and uncontrollable, it allows for a more targeted approach in addressing the issues. Furthermore, the ability to ignore losses in Diffuse mode prevents unnecessary consideration of intentional energy loss. Overall, the method 1200 enhances the understanding of energy gaps in the plant's output and facilitates informed decisionmaking for optimizing solar power generation.

[0111] At step 1206, the method 1200 includes estimating, by the at least one processor 104, the expected irradiance received by each malfunctioning tracker using the solar position and irradiance model and the tracker- specific parameters. The method 1200 begins by utilizing an open-source python library to estimate the amount of solar irradiance that a faulty tracker would have received. This estimation is based on various parameters, including the surface tilt angle and surface azimuth angle of the malfunctioning tracker, as well as the GHI (Global Horizontal Irradiance), DNI (Direct Normal Irradiance), DHI (Diffuse Horizontal Irradiance), solar zenith angle, and solar azimuth angle obtained from the PVLIB Clearsky Model and PVLIB Solar Position. By feeding these parameters into the method 1200, the observed tracker irradiance is calculated, which represents the amount of irradiance received by the stuck or faulty tracker. This observed tracker irradiance is obtained as timeseries irradiance data. The purpose of estimating the expected irradiance received by each malfunctioning tracker is to quantify the energy losses in the solar power plant caused by these faulty solar trackers. By determining the amount of irradiance that the one or more solar trackers 116 may have received if they were functioning properly, we can calculate the energy that is lost due to their malfunction. This information is crucial for identifying and addressing the performance issues of the solar power plant. The technical effect of the method 1200 is the ability to accurately assess the energy losses in a solar power plant resulting from the malfunctioning solar trackers. By utilizing the solar position and irradiance model, along with the tracker- specific parameters, the method 1200 provides a reliable estimation of the expected irradiance received by each malfunctioning tracker. This enables the identification of specific trackers that are underperforming and allows for targeted maintenance or repair actions to optimize the overall energy generation of the solar power plant.

[0112] At step 1208, the method 1200 includes utilizing at least one processor 104 to calculate the energy loss for each malfunctioning tracker. Then comparing the estimated expected irradiance with the actual plant irradiance, which is measured from the pyranometer mounted on an ideally functioning tracker. After that, taking into account the capacity of the malfunctioning tracker relative to the overall capacity of the plant. The method 1200 utilizes an open source python library to identify the amount of solar irradiance that a faulty tracker would have received. This is achieved by comparing the estimated expected irradiance to the actual plant irradiance, which is measured from the pyranometer mounted on an ideally functioning tracker. The difference between these two values represents the energy loss for each malfunctioning tracker. Additionally, the capacity of the malfunctioning tracker is taken into account relative to the overall capacity of the plant. The purpose of this method is to quantify the energy losses in the solar power plant caused by the malfunctioning solar trackers. By calculating the energy loss for each malfunctioning tracker, it becomes possible to assess the impact of tracker malfunction on the overall energy generation of the plant. This information is valuable for identifying and addressing energy gaps in the plant's performance, allowing for potential loss recovery options. The technical effect of the method 1200 is the ability to accurately measure and quantify the energy losses resulting from the malfunctioning solar trackers. By comparing the estimated expected irradiance to the actual plant irradiance, the method 1200 provides a precise assessment of the energy loss for each malfunctioning tracker. Additionally, accounting for the capacity of the malfunctioning tracker relative to the plant capacity allows for a comprehensive understanding of the overall impact on energy generation. This information can be used to optimize plant performance and identify areas for improvement.

[0113] At step 1210, the method 1200 includes categorizing the calculated energy losses as controllable or uncontrollable based on the determination of the tracker mode by the at least one processor 104. The method 1200 involves quantifying energy losses in the solar power plant caused by the malfunctioning solar trackers. The calculated energy losses are categorized as controllable or uncontrollable based on the determination of the tracker mode. If the tracker mode is available, the losses are bifurcated into controllable and uncontrollable. If not, all losses are considered controllable. The tracker mode is determined by comparing the target tracker angle with the actual tracker angle. If the difference is more than 5 degrees, indicating a malfunction, the tracker is unable to track the sun perfectly. The losses are then categorized based on the tracker mode, such as stow mode or auto mode. The categorization of energy losses as controllable or uncontrollable helps in understanding the nature of the losses and identifying potential areas for loss recovery. By differentiating between controllable and uncontrollable losses, appropriate actions can be taken to mitigate the controllable losses and improve overall energy generation efficiency. Ignoring losses in the Diffuse mode, which is an intentional logical call to avoid energy loss, ensures accurate categorization of losses. The method 1200 extends the current state of the art by not only detecting and reporting malfunctioning trackers but also calculating the amount of energy lost per day due to tracker malfunction. This provides valuable information about the energy gap between the actual and target performance of the solar power plant. The ability to quantify and categorize energy losses based on the tracker mode determination enables better analysis and decision-making for optimizing energy generation and implementing loss recovery strategies.

[0114] The method 1200 further involves analyzing both controllable and uncontrollable energy losses to identify patterns of malfunctioning solar trackers and determine appropriate maintenance actions. The method 1200 involves utilizing an open source python library to quantify energy losses in the solar power plant caused by the malfunctioning solar trackers. By leveraging this library, the solar irradiance may be determined that a faulty tracker may have received. Using this information, the amount of energy is calculated that the solar tracker, may have lost. The purpose of the method 1200 is to analyze both controllable and uncontrollable energy losses in order to identify patterns of the malfunctioning solar trackers. By quantifying the energy losses, insights on the performance of the trackers may be gained and appropriate maintenance actions may be determined. The method 1200 focuses on the specific scope of Single Axis Trackers, which are structures on which solar panels lie and rotate from east to west throughout the day. By considering as few as 5 parameters, the losses in any make of the solar tracker is determined. This extends the current state of the art, which primarily detects tracker malfunction based on the difference between the target and actual tracker angles. The method 1200 further calculates the actual amount of energy lost per day due to tracker malfunction, providing valuable information for assessing the energy gap between the actual and target performance of the plant and exploring options for loss recovery. The method 1200 also allows for the analysis of both controllable and uncontrollable energy losses, enabling the identification of vertical patterns indicating individual fault reasons and prolonged tracker malfunction, as well as horizontal patterns indicating weather-induced losses affecting multiple trackers in the plant.

[0115] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and / or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure.

Claims

CLAIMSWe claim:

1. A system (100) for quantifying energy losses in a solar power plant due to malfunctioning solar trackers, the system (100) comprising: at least one processor (104) configured to: filter tracker-day data (118) received from one or more solar trackers (116) based on predefined data quality and data quantity criteria, wherein the filtered data (116) corresponds to one or more malfunctioning trackers; determine whether energy losses associated with the one or more malfunctioning trackers are controllable or uncontrollable based on tracker mode information; estimate the expected irradiance received by each malfunctioning tracker using a solar position and irradiance model and tracker- specific parameters; calculate the energy loss for each malfunctioning tracker by comparing the estimated expected irradiance to an actual plant irradiance measured from a pyranometer mounted on an ideally working tracker, accounting for tracker's capacity relative to plant capacity; and categorize the calculated energy losses as controllable or uncontrollable based on the tracker mode determination, wherein the controllable and uncontrollable energy losses are analysed to identify patterns of malfunctioning solar trackers and determine appropriate maintenance actions.

2. The system (100) as claimed in claim 1, wherein the controllable energy losses are attributed to manually controllable or correctable events, and the uncontrollable energy losses are attributed to extreme weather conditions and conditions outside the control of human beings.

3. The system (100) as claimed in claim 1, wherein the solar position and irradiance model comprises: a first model (108) for calculating solar position and irradiance based on location and time; and a second model (110) for converting global horizontal irradiance to plane of array irradiance using the tracker-specific parameters.

4. The system (100) as claimed in claim 3, wherein the tracker- specific parameters further include a surface tilt angle and a surface azimuth angle adjustment factor.

5. The system (100) as claimed in claim 1, wherein the at least one processor (104) is further configured to ignore energy losses during periods when the one or more malfunctioning trackers are intentionally in a diffuse mode, wherein the diffuse mode sets the target angle to horizontal to optimize performance during cloudy conditions.

6. The system (100) as claimed in claim 1, wherein the at least one processor (104) is further configured to determine the tracker mode information based on a time- series data of tracker modes received from the one or more solar trackers (116).

7. The system (100) as claimed in claim 6, wherein the tracker modes include at least one of idle mode, manual mode, wind stow mode, snow stow mode, clean stow mode, night stow mode, emergency stow mode, communication error stow mode, auto mode, cycle test mode, and hail stow mode.

8. The system (100) as claimed in claim 1, wherein the predetermined data quality and data quantity criteria include at least one of a configuration issue, a data unavailability issue, an angle out-of-bounds issue, and a sun angle stuck issue.

9. The system (100) as claimed in claim 1, further comprising a display (112) configured to visualize the controllable and uncontrollable energy losses for identifying patterns of the malfunctioning solar trackers.

10. A method (1200) for quantifying energy losses in a solar power plant due to malfunctioning solar trackers, the method (1200) comprising: filtering, by at least one processor (104), tracker-day data (118) received from one or more solar trackers (116) based on predefined data quality and data quantity criteria, wherein the filtered data (118) corresponds to one or more malfunctioning trackers; determining, by the at least one processor (104), whether energy losses associated with the one or more malfunctioning trackers are controllable or uncontrollable based on tracker mode information; estimating, by the at least one processor (104), an expected irradiance received by each malfunctioning tracker using a solar position and irradiance model and tracker- specific parameters; calculating, by the at least one processor (104), the energy loss for each malfunctioning tracker by comparing the estimated expected irradiance to an actual plant irradiance measured from a pyranometer mounted on an ideally working tracker, accounting for tracker's capacity relative to plant capacity; and categorizing, by the at least one processor (104), the calculated energy losses as controllable or uncontrollable based on the tracker mode determination, wherein the controllable and uncontrollable energy losses are analysed to identify patterns of malfunctioning solar trackers and determine appropriate maintenance actions.

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