"SMART FAULT DETECTION AND MITIGATION IN SOLAR PHOTOVOLTAIC (PV) SYSTEMS"

Abstract

ABSTRACT The invention provides a fault detection and mitigation system for solar photovoltaic (PV) systems, enhancing efficiency and uptime. The system includes a plurality of Internet of Things 5 (loT) sensors (001) across the PV system, gathering real-time data on operational, electrical, and environmental parameters from PV panels and components. A digital twin simulation model (002) receives sensor data, creating a virtual model to represent expected performance under real-time and simulated conditions. A machine learning-based fault detection model (003) is operably linked to the digital twin; analyzing data patterns to detect anomalies, classify faults, 10 · and predict failures based on insights from the digital twin and loT sensors. An augmented reality (AR) interface (004), connected to the fault detection model (003), aids maintenance personnel by displaying real-time diagnostics, overlays, and guidance on physical components. A fault mitigation model (005) associated with the AR interface autonomously recommends or performs corrective actions, enabling continuous, real-time fault detection and mitigation.

Specification

FIELD OF INVENTION
5 [0001] The embodiments disclosed herein generally relates to renewable energy technologies,
and more specifically for enhancing solar photovoltaic (PV) systems' reliability and
performance by using a combination of loT, digital twin simulations, machine learning,
and augmented reality (AR) to detect, predict, and mitigate faults within solar PV
systems. The inclusion of AR enables real-time, interactive diagnostics and maintenance
guidance, further optimizing system uptime and efficiency.
BACKGROUND
[0002] Optimizing fault monitoring and mitigation in solar photovoltaic (PV) systems is crucial
for ensuring sustained efficiency, reliability, and safety. With PV systems operating
continuously in diverse and often harsh environmental conditions, faults can arise from
electrical issues, component degradation, environmental factors, or installation errors, all
of which can reduce energy output, increase maintenance costs, and, in some cases, pose
safety risks. An optimized monitoring system enables real-time detection and predictive
analytics to identify potential issues before they become critical, thereby reducing costly
downtime and preventing more severe damage. Moreover, effective mitigation strategies
that include automated or guided corrective actions streamline maintenance processes,
helping technicians address faults quickly and accurately. This proactive approach is
essential for maximizing the lifespan of PV systems, improving return on investment, and
ensuring consistent power generation, which is particularly important as solar energy
becomes a more prominent part of the global energy mix.
(0003] The US patent application US12063011B2 "Solar Farm Fault Detection and Diagnosis"
discloses a method for detecting faults in a solar photovoltaic (PV) generation system that
·consists ofinultiple PV panel strings connected in parallel. It employs· a series of current·
sensors to monitor the electrical currents of these strings. The system processes this data
to identify any string currents that deviate significantly from the average (representative)
current. If any strings are found to be outliers beyond a set threshold, the system issues an
. alarm, highlighting which strings may have faults for prompt maintenance.
5 (0004) The US patent application US20110137475AI "Adaptive control of a concentrated solar
power-enabled power plant" describes a system for controlling a solar-fossil-fuel hybrid
power plant by utilizing data on the thermal inertia of its components and forecasts of
atmospheric conditions. This information helps to adjust the plant's operations to ensure it
meets specific performance requirements, especially when facing anticipated weather
changes. By proactively managing operations based on these factors, the plant can
optimize its efficiency and reliability.
(0005] The US patent application US I 08264288 I "Monitoring and fault detection method and
system for photovoltaic plants" discloses a system for monitoring photovoltaic (PV)
panels to ensure they operate efficiently by using intelligent performance evaluation and
fault detection. It classifies data into three categories-ideal, transition, and downturn
periods-and builds predictive models to compare real-time power output with expected
performance. This allows engineers to quickly identify issues and suggest maintenance,
ensuring optimal operation of the PV panels.
[0006] The Solar PV systems are prone to various faults due to environmental factors, aging
components, and operational stressors. Traditional fault detection and maintenance
approaches often require manual inspections, leading to delayed repairs and increased
costs. Intelligent systems that leverage loT, digital twins, machine learning, and AR can
overcome these challenges by enabling real-time fault detection, predictive maintenance,
and interactive, guided repairs, maximizing energy production and system lifespan.
OBJECTIVES
[0_007) The principal objective of the invention is to deploy a plurality oflntef!!et ()fThings (loT)
:!:: sensors positioned across the solar photovoltaic (PV) system to collect real-time data on operational, electrical, and environmental parameters, thereby facilitating comprehensive
monitoring and performance assessment of the PV panels and related components.
[0008] The principal objective of the invention is to create a digital twin simulation model that
receives data ftom the JoT sensors, enabling the development of a virtual model of the
PV system which represents expected performance under both real-time and simulated
conditions, thus providing insights into system behavior and performance optimization.
[0009] The principal objective of the invention is to incorporate a machine learning-based fault
detection model that is operably linked to the digital twin simulation model, allowing it to
analyze data patterns, detect anomalies, classify faults, and predict potential failures
within the PV system based on the information obtained from the digital twin and loT
sensors.
[0010) The principal objective of the invention is to utilize an augmented reality (AR) interface
connected to the machine learning-based fault detection model to assist maintenance
personnel by displaying real-time diagnostics, interactive overlays, and visual guidance
directly on the physical components of the PV system, thereby enhancing the efficiency
and accuracy of maintenance operations.
[0011) In addition, the principal objective of the invention is to design a fault mitigation model
associated with the AR interface, which is capable of recommending or autonomously
executing corrective actions based on the type and severity of faults detected by the
machine learning-based fault detection model, thus ensuring timely interventions to
minimize system downtime and maintain optimal performance. ·
[0012] In addition, the principal objective of the invention also involves establishing a system
that continuously monitors, detects, predicts, and mitigates faults in real-time, thereby
maximizing the efficiency and uptime of the solar photovoltaic (PV) system and
enhancing its overall_reliability
SUMMARY
[0013) The following presents a simplified summary of one or more _examples in order to
provide a basic understanding of the disclosure. This summary is not an extensive
overview of all contemplated examples, and is not intended to either identify key or
critical elements of all examples or delineate the scope of any or all examples. Its purpose
is to present some concepts of one or more examples in a simplified form as a prelude to
the more detailed description that is presented below.
10 (0014) The invention provides a comprehensive fault detection and mitigation system for solar
photovoltaic (PV) installations, enhancing system efficiency, reliability, and uptime
through integrated loT, digital twin, machine learning, and augmented reality (AR)
technologies. The loT sensors collect real-time operational, electrical, and environmental
data from the PV panels, which is sent to a digital twin simulation model that creates a
virtual model of the PV system to benchmark expected performance. A machine
learning-based fault detection model analyzes this data to detect anomalies, classify
faults, and predict potential failures, while an AR interface visually guides maintenance
personnel with diagnostics, overlays, and repair instructions on physical PV components.
Finally, a fault mitigation model linked to the AR interface recommends or autonomously
executes corrective actions based on fault type and severity, allowing for continuous,
proactive monitoring and mitigation to maximize PV system uptime and performance.
[0015) The embodiments of the present invention a method describes a process for detecting and
mitigating faults in solar photovoltaic (PV) systems using a combination of loT sensors,
digital twin simulation, machine learning, and augmented reality (AR) for efficient realtime
diagnostics and response. The method begins by positioning a plurality of loT
sensors across the PV system to collect real-time data on operational, electrical, and
environmental parameters. This data is transmitted to a digital twin simulation model ,
which generates a virtual model of the PV system to establish expected performance
under actual and simulated conditions. Ne~t,. a machine le!ll'ning-~as~d fault detection
model analyzes the data received from the digital twin and loT sensors, identifying data
patterns, detecting anomalies, classifying faults, and predicting potential system failures.
An AR interface then assists maintenance personnel by displaying real-time diagnostics,
interactive overlays, and visual guidance on PV components for targeted troubleshooting.
If a fault is detected, the fault mitigation model provides actionable recommendations or
autonomously executes corrective measures based on the fault type and severity. This
method continuously monitors, detects, predicts, and mitigates faults, maximizing system
efficiency and minimizing downtime through an integrated, proactive approach.
[0016[ To the accomplishment of the foregoing and related ends, the following description and
annexed drawings set forth certain illustrative aspects and implementations. These are
indicative of but a few of the various ways in which one or more ,.,;p~cts may be
employed. Other aspects, advantages, and novel features of the disclosure will become
apparent from the following detailed description when considered in conjunction with the
annexed drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017[ FIG. I illustrates a comprehensive fault detection and mitigation system for solar
photovoltaic (PV) systems.
DETAILED DESCRIPTION
[0018[ The present invention will now be described more fully hereinafter with reference to the
accompanying drawings, in which preferred embodiments of the invention are shown.
This invention may, however, be embodied in many different forrns and should not be
construed as limited to the embodiments set forth herein. Rather, these embodiments are
provided so that this disclosure will be thorough and complete, and will fully convey the
scope of the invention to those skilled in the art. Those of ordinary skill in the art realize
that the following descriptions of the embodiments of the present invention are
illustrative and are not intended to be limiting in any way: Other embodiments of the·
present invention will readily suggest themselves to such skilled persons having the
benefit of this disclosure. Like numbers refer to like elements throughout.
[0019) Although the following detailed description contains many specifics for the purposes of
illustration, anyone of ordinary skill in the art will appreciate that many variations and
alterations to the following details are within the scope of the invention. Accordingly, the
following embodiments of the invention are set forth without any loss of generality to,
and without imposing limitations upon, the claimed invention.
10 [0020) In this detailed description of the present invention, a person skilled in the art should note
that directional terms, such as "above," "below," "uppt:r," "lower," and other like terms
are used for the convenience of the reader in reference to the drawings. Also, a person
skilled in the art should notice this description may contain other terminology to convey
position, orientation, and direction without departing from the principles of the present
invention.
[0021) Furthermore, in this detailed description, a person skilled in the art should note that
quantitative qualifying terms such as "generally," "substantially," "mostly," and other
terms are used, in general, to mean that the referred to object, characteristic, or quality
constitutes a majority of the subject of the reference. The meaning of any of these terms
is dependent upon the context within which it is used, and the meaning may be expressly
modified.
[0022) Solar photovoltaic (PV) systems are vulnerable to various faults that can affect
performance, efficiency, and safety. These faults typically stem from electrical issues,
environmental factors, component wear, and errors in installation or maintenance.
Electrical· faults are common and include open circuits from broken connections, short
circuits due to insulation failure, ground faults from conductor contact with grounded
parts, and reverse polarity when components are connected incorrectly. Environmental
issues _l\lso contribute significantly, with_ shading_ fro_m objects like trees or buildings
reducing sunlight, soiling from dust or dirt diminishing panel efficiency, and hotspots
causing parts of a panel to overheat. Weather-related factors like hail or lightning can
further damage panels, wiring, and structures, emphasizing the need for preventive
monitoring.
5 [0023] Component failures, design flaws, and monitoring errors can also compromise PV system
performance. Over time, PV panels degrade, inverters can malfunction, batteries lose
capacity, and connectors may corrode. Improper installation, such as incorrect wiring or
misalignment, can cause power losses and accelerate wear on components. Faults in
monitoring and control systems, such as malfunctioning sensors or communication errors,
. .
may lead to inaccurate data and delayed maintenance. Thermal issues also affect PV
systems, as excessive heat in panels, inverters, or batteries reduces efficiency and
increases the risk of damage or fire hazards. Monitoring these faults and addressing them
promptly helps maximize PV system uptime and efficiency.
[0024] FIG. I illustrates a comprehensive fault detection and mitigation system for solar
photovoltaic (PV) systems, the system comprises a plurality of Internet of Things (loT)
sensors (001) positioned across the PV system, configured to collect real-time data
including operational, electrical, and environmental parameters of PV panels and related
components; a digital twin simulation model (002) that receives data from the loT
sensors (001) and creates a virtual model of the PV system, representing expected
performance under real-time and simulated conditions; a machine learning-based fault
detection model (003) operably linked to the digital twin simulation model (002), trained
to analyze data patterns, detect anomalies, classify faults, and predict potential failures
within the PV system based on information from the digital twin and loT sensors; an
augmented reality (AR) interface (004) connected to the machine learning-based fault
detection model (003), configured to assist maintenance personnel by displaying realtime
diagnostics, interactive overlays, and visual guidance directly on physical
components of the PV system; a fault mitigation model (005) associated with the
augmented reality (AR) interface (004), designed to recommend or autonomously
execute corrective actions based on the type and severity of faults detected by the
machine learning-based fault detection model
0025) According to the embodiments of the present invention the plurality of loT sensors (001)
strategically positioned across a solar .photovoltaic (PV) system enables comprehensive,
real-time monitoring by collecting data from various points within the system. These loT
sensors (001) are designed to measure multiple parameters that affect PV performance,
including operational, electrical, and environmental conditions. By gathering such diverse
data, these sensors provide an in-depth understanding of the system's functioning and
facilitate prompt detection of any anomalies or faults, enhancing both the system's
efficiency and its longevity.
(0026) For -example, temperature sensors embedded on PV panels can monitor the surface
temperature, which is critical for efficiency. High temperatures may reduce a panel's
output and can even cause physical degradation over time. By tracking temperature
variations across different panels, these sensors can detect abnormal heating patterns,
known as hotspots, which might result from shading, soiling, or even panel damage.
Another example is irradiance sensors, which measure the amount of sunlight reaching
each panel. Variations in irradiance data help in identifying shading issues from nearby
objects or indicate soiling on specific panels, enabling timely cleaning or adjustments to
maintain optimal performance.
(00271 Electrical sensors, such as current and voltage sensors, monitor the output of individual
PV panels or groups of panels. Voltage sensors detect drops in output that could indicate
issues like open circuit faults or short circuits, while current sensors can identify
mismatches in output between panels. For example, if one string of panels is producing
25 less current than expected, it could indicate a fault or a connection issue that requires
maintenance. Additionally, sensors measuring power output of inverters are useful in
monitoring the AC conversion process, allowing for early detection of inverter faults,
which can disrupt the entire power conversion in the system.
30 [0(),28) In addition to operational and electrical data, envircmmentl!L s~nsors can be used t<?
monitor external factors like temperature, humidity, and wind speed, which impact PV
system performance. For example, high humidity and dust conditions can increase the
likelihood of soiling on the panels. Wind sensors may indicate conditions that could
affect the structural stability ofPV panels or inform cleaning schedules by tracking dust
patterns. By integrating data from these environmental sensors with operational
parameters, PV system operators can make data-driven maintenance and operational
decisions, ensuring that the system consistently operates at peak efficiency.
[0029) According to the embodiments of the present invention the digital twin simulation model
(002) for a solar photovoltaic (PV) system is a sophisticated virtual replica that mirrors
. .
the physical PV system in real time. By receiving continuous, real-time data from loT
sensors (001) positioned across the PV installation, the digital twin simulates the
operational and environmental conditions the PV panels experience. This virtual model
takes into account various parameters such as temperature, irradiance, humidity, and
electrical output data like voltage and current. Through this real-time data exchange, the
digital twin provides a comprehensive representation of the PV sysl"m's "xpecled
performance and enables a more accurate analysis of any deviation from normal
operations.
[0030) For example, if loT sensors (00 I) detect that a specific PV panel's temperature has risen
above optimal levels, the digital twin can simulate the impact of this increased
temperature on the panel's efficiency and expected power output. The digital twin's
simulated model (002) would recognize the heat's effect, showing a corresponding
reduction in power production in the virtual model. If the difference between the digital
twin's expected performance and the actual performance data from the loT sensors (00 I)
is significant, this could indicate an anomaly, such as a developing hotspot or shading
issue on the panel. Thus, the digital twin not only represents the real-time state of the
system but also allows operators to predict and identify potential issues proactively.
[0031) Beyond real-time operations, the digital twin simulation model (002) can also operate
under simulated conditions to forecast how the PV sy~tem will perform in various
scenarios. For example, the model can simulate performance under different seasonal
sunlight conditions or predict how energy production might change with gradual panel
degradation over time. By incorporating historical data, it can also estimate the effects of
common faults, such as soiling or shading from nearby structures, on power output over
the years. If loT data indicates an increase in soiling levels, for example, the digital twin
could predict the impact on the overall system efficiency and reconunend maintenance
like panel cleaning to restore performance.
(0032] The digital twin's ability to simulate potential issues before they occur makes it a valuable
tool for maintenance planning and fault prevention. For example, by simulating
conditions such as high wind speeds or extreme temperatures, the model can predict how
tbe~e factors might affect structural components or electrical outputs. This predictive
insight allows operators to take preventive actions, like reinforcing mounting structures
or adjusting electrical loads to safeguard the system. The digital twin's capacity to run
real-time and simulated scenarios transforms it into a proactive diagnostic tool that
enhances operational efficiency, reduces downtime, and extends the lifespan of the PV
system
[0033] According to the embodiments of the present invention the machine learning-based fault
detection model (003) in a solar photovoltaic (PV) system uses advanced algorithms to
analyze and interpret data collected from loT sensors (001). This machine learning-based
fault detection model (003) is designed to identify patterns in the data that indicate the
PV system's normal operational state, and it can detect deviations or anomalies from this
baseline. By comparing real-time data with the expected performance data generated by
the digital twin, the machine learning-based fault detection model (003) can pinpoint
when certain parameters, such as temperature, current, or power output, fall outside of
normal ranges. These deviations often signal the presence of faults or suboptimal
conditions, allowing the machine learning-based fault detection model (003) to act as an
early warning system.
[0034) For example, the machine learning-based_ f~ltdetection model (003) might detect_a
pattern where one PV panel consistently produces less power than other, similar panels in
identical conditions. This anomaly might indicate shading, soiling, or even a developing
fault in the PV panel. By classifying this anomaly based on previously seen data, the
. machine leaming-base_d fault detection model (003) can determine whether the fault is
likely due to temporary shading, requiring no immediate intervention, or a more critical
fault like an open circuit, which would need prompt maintenance. This type of
classification helps operators prioritize maintenance tasks, focusing first on critical issues
that could lead to significant losses or damage.
[0035] The machine learning model (003) also continuously learns from new data, refming its
understanding of the PV system's normal operating patterns under various conditions.
For example, as it accumulates data on seasonal variations in irradiance, temperatun::, or
humidity, it becomes better at distinguishing between normal and abnormal deviations. In
a scenario where one panel's temperature rises above expected levels during midday, the
machine learning-based fault detection model (003) may initially flag it as a potential
hotspot. However, by factoring in digital twin data and correlating it with seasonal trends,
the machine learning-based fault detection model (003) could correctly reclassify this as a
temporary and harmless temperature rise, avoiding unnecessary maintenance.
[0036] Moreover, the machine learning-based fault detection model (003) can predict potential
failures by recognizing patterns that typically precede certain faults. If the machine
learning-based fault detection model (003) observes an increase in resistance within panel
connections or a gradual drop in power output, it might signal potential degradation. By
classifying this trend as an early warning for panel failure, the machine learning-based
fault detection model (003) provides maintenance teams with a lead time to replace or
repair the affected component before it causes larger disruptions. This predictive
capability not only minimizes downtime but also extends the lifespan of the PV system
by preventing faults from escalating. Thus, through anomaly detection, fault
classification, and predictive maintenance, the machine learning-based fault
According to the embodiments of the present invention the augmented reality (AR)
interface (004) connected to a machine learning-based fault detection model (003)
transforms the way maintenance personnel interact with a solar photovoltaic (PV) system
by providing real-time, on-site diagnostics and guidance. This AR interface (004) is
equipped to overlay critical system information directly onto the physical components of
the PV system when viewed through an AR-enabled device, such as a tablet or smart
glasses. By linking to the fault detection model (003), the AR interface (004) provides
immediate access to system diagnostics and visually highlights issues, allowing
maintenance personnel to quickly identify, assess, and resolve faults without needing to
interpret complex data separately.
)0038] For example, if the machine learning-based fault detection model (003) identifies an
underperforrning PV panel, the AR interface (004) can guide maintenance personnel to
the specific panel by highlighting it visually within their field of view. Once in proximity,
the AR interface (004) displays data such as current output, temperature, and fault
classification directly overlaid on the panel's image. If the machine learning-based fault
detection model (003) has been flagged for an anomaly, such as a potential hotspot or
electrical fault, the AR interface (004) can show specific troubleshooting steps to follow,
customized for the detected issue. This capability reduces the time needed to diagnose
problems and minimizes the risk of errors during troubleshooting, especially in complex
arrays with many identical panels .
)0039] Additionally, the AR interface (004) can provide interactive overlays that assist personnel
with repair procedures. For example, if the system requires a component replacement, the
AR interface (004) might display step-by-step guidance on safely disconnecting and
replacing the component, complete with visuals indicating each tool and part needed. In a
scenario where a faulty inverter requires repair, the AR system can highlight the
inverter's exact location and overlay technical diagrams, helping personnel understand
how to access and replace specific subcomponents. This guidance, combined with realtime
diagnostic updates from the .m~chine learning-base~ fault detection model
(0040) The AR interface (004) also enhances predictive maintenance by alerting personnel to
potential issues before they escalate. For example, if the machine learning-based fault
detection model (003) anticipates that a particular PV panel is at risk for degradation
based on performance trends, the AR system can mark it for a proactive inspection during
routine maintenance rounds. Maintenance staff can then inspect the panel in situ, viewing
historical performance data and predicted degradation patterns overlaid on the panel
itself, helping them assess whether a repair or replacement is warranted. By providing
immediate, context-specific information through intuitive visual overlays, the AR
interface (004) improves the efficiency and accuracy of maintenance operations,
ultimately helping to reduce downtime and extend th" life of the PV system.
)0041) According to the embodiments of the present invention the fault mitigation model (005)
in a solar photovoltaic (PV) system, associated with an augmented reality (AR) interface
(004), is responsible for recommending ur autonomously executing corrective actions
based on the type and severity of faults detected by the machine learning-based fault
detection model (003). When a fault is detected, this fault mitigation model (005)
evaluates the fault's characteristics, such as, its impact on power output, potential for
equipment damage, and risk to system safety and determines the most appropriate
response. Through the AR interface (004), maintenance personnel can receive real-time
recommendations or instructions, while in some cases, the fault mitigation model (005)
can initiate automated corrective actions to promptly address the issue.
)0042) For example, if the fault mitigation model (005) identifies a mild issue, such as minor
soiling on a PV panel causing a slight reduction in efficiency, the fault mitigation model
(005) may display a cleaning recommendation to maintenance staff via the AR interface
(004). In this scenario, the AR system can guide personnel to the affected PV panel and
suggest appropriate cleaning techniques based on the type of soiling detected, such as
dust or bird droppings. By targeting specific maintenance actions only where needed, the
system helps reduce _ unnecessary cleaning _ routin_es, optimizi11g both labor and
maintenance costs.
[0043) In more critical scenanos, the fault mitigation model (005) may take immediate,
autonomous action to protect the system from potential damage. For example, if a severe
fault, such as a short circuit, is detected in one of the PV strings, the fault mitigation
model (005) could autonomously isolate the affected section to prevent further damage or
risk of frre. Simultaneously, it could send a notification to the AR interface (004), alerting
maintenance staff to inspect the disconnected section and guiding them through the steps
to safely assess and repair the issue. This quick isolation response protects other
components from collateral damage and minimizes the risk of a system-wide shutdown.
[0044) Additionally, the fault mitigation model's (005) recommendations vary based on
historical data and previous fault patterns stored within the machine learning model
(003 ). If it detects recurring issues with specific components, such as frequent inverter
faults, it might recommend a preventive replacement instead of repeated repairs. Through
the AR interface (004), it can display the recommended replacement procedure, iocluding
parts required and any safety precautions. For example, if the fault mitigation model
(005) anticipates that an inverter's cooling fan is close to failing due to frequent
overheating, it could suggest a proactive fan replacement and show the technician where
to access and replace the fan within the inverter. This preventive approach helps avoid
unexpected failures and contributes to improved system uptime and efficiency.
[0045 ) By integrating the fuult mitigation model's (005) with an AR interface (004), the system
combines automated responses with real-time guidance, allowing maintenance teams to
address faults promptly and effectively. The AR-driven visualizations of recommended
corrective actions make troubleshooting and repairs more straightforward, even for
personnel with limited experience, ensuring that complex PV systems can be managed
with greater precision and reduced downtime.
CLAIMS
1/We Claim that,
5 Claim 1: A fault detection and mitigation system for solar photovoltaic (PV) systems,
10
15
comprising:
a plurality of Internet of Things (loT) sensors (00 I) positioned across the PV
system, configured to coll.ect real-time data inclu.ding operational, electrical, and
environmental parameters of PV panels and related components;
a digital twin simulation model (002) that receives data from the loT sensors (00 I)
and creates a virtual model of the PV system, representing expected performance
under real-time and simulated conditions;
a machine learning-based fault detection model (003) operably linked to the digital
twin simulation model (002), trained to analyze data patterns, detect anomalies,
classify faults, and predict potential failures within the PV system based on
information from the digital twin and loT sensors;
an augmented reality (AR) interface (004) connected to the machine learningbased
fault detection model (003), configured to assist maintenance personnel by
displaying real-time diagnostics, interactive overlays, and visual guidance directly
on physical components of the PV system; and
a fault mitigation model (005) associated with the augmented reality (AR)
interface (004), designed to recommend or autonomously execute corrective
actions based on the type and severity of faults detected by the machine learningbased
fault detection model (003),
wherein the system continuously monitors, detects, predicts, and mitigates faults in
real-time, maximizing the efficiency and uptime of the PV system.
Claim 2:
Claim 3:
Complete Specification
The said fault detection and mitigation system of claim I, wherein the digital twin
simulation model (002) is further configured to model component degradation
over time based on historical and environmental data, enabling predictive
maintenance by simulating the expected lifespan and performance degradation of
individual PV panels and system components.
The said fault detection and mitigation system of claim I, wherein the machine
learning-based fault detection model (003) includes anomaly detection,
classification, and predictive modeling algorithms, wherein: anomaly detection
identifies deviations from expected performance patterns by comparing real-time
data with the digital twin model (002); classification categorizes faults into
specific types including shading, soiling, disconnection, or inverter malfunction;
and predictive modeling forecasts future faults or degradation patterns, enabling
proactive scheduling of maintenance activities.
Claim 4 : The said fault detection and mitigation system of claim I, wherein the augmented
reality (AR) interface (004) is configured to display live loT sensor (001) data
along with digital twin simulation overlaid onto the physical PV components
through a smart device or AR glasses, further providing interactive, step-by-step
repair guidance and real-time safety alerts to maintenance personnel.
Claim 5: The said fault detection and mitigation system of claim I, wherein the fault
mitigation model (005) is configured to autonomously recalibrate PV system
parameters or isolate malfunctioning components upon detecting minor faults,
while alerting operators of critical faults that require manual intervention through
the · AR interface, thus minimizing downtime and ensuring optimal system
performance.
Claim 6: A method for fault detection and mitigation in a solar photovoltaic (PV) system,
compnsmg:
Collecting real-time operational, electrical, and environmental data rrom PV
panels and related components using a plurality of Internet of Things (loT)
sensors (001) positioned across the PV system;
Creating a virtual model of the PV system using a digital twin simulation model
(002) that receives data ftom the loT sensors (001) to represent expected
performance under real-time and simulated conditions;
Analyzing data patterns using a machine learning-based fault detection model
(003) operably linked to the digital twin simulation model (002), trained to detect
anomalies, classify faults, and predict potential failures within the PV system
based on the information from the digital twin and loT sensors;
Displaying real-time diagnostics and visual guidance on physical components of
the PV system through an augmented reality (AR) interface (004), connected to
the machine learning-based fault detection rnuut:l (003), to assist maintenance
personnel;
Recommending or autonomously executing corrective actions through a fault
mitigation model (005) associated with the augmented reality (AR) interface
(004), based on the type and severity of faults detected by the machine learningbased
fault detection model (003); and
Continuously monitoring, detecting, predicting, and mitigating faults in real-time
to maximize the efficiency and uptime of the PV system.
Claim 7 : The said method of claim 6, wherein creating the digital twin simulation further
comprises modeling component degradation over time using historical data and
environmental factors, thereby allowing the system to forecast the expected
lifespan and maintenance needs of individual PV panels and system components.
Claim 8: The said method of claim 6, wherein analyzing data patterns using the machine
learning model includes: performing anomaly detection by identifying deviations
from expected performance patterns compared to the digital twin; classifying
faults into categories such as shading, soiling, disconnection, or inverter
malfunction based on real-time data anomalies; using predictive modeling to
forecast future faults or performance degradation, allowing for proactive
scheduling of maintenance tasks.
The said method of claim 6, wherein presenting real-time diagnostics and
maintenance guidance through the augmented reality (AR) interface includes
displaying live loT sensor data directly overlaid on physical PV components via
an AR-cnabled device, providing interactive repair instructions, safety alerts, and
visual markers to facilitate efficient troubleshooting by maintenance personnel.
The said method of claim 6, wherein initiating corrective actions includes
autonomously recalibrating system parameters or isolating malfunctioning
components in response to minor faults, while issuing alerts for critical faults
that require manual intervention through the AR interface, thereby reducing
downtime and maintaining optimal system performance.

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