FAULT LOCATION DETERMINATION IN SOLAR CELL

Abstract

The present disclosure provides a fault identification system (100) for solar cell strings, designed to determine fault locations. The system comprises a current sensing unit (102) that detects real-time current values along the solar cell string and a voltage sensing module (104) aligned with the current sensing unit to acquire voltage values from the same solar cell string. A self-learning processor (106) is operatively coupled to both the current sensing unit and the voltage sensing module, analyzing historical data and using the current and voltage values to predict potential fault locations within the solar cell string, improving overall system reliability and efficiency.

Specification

Description:Field of the Invention


The present disclosure relates to solar energy systems. Particularly, the present disclosure relates to fault identification and location determination in solar cell strings using real-time current and voltage sensing, coupled with a self-learning processor.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Fault identification in solar cell strings is crucial for ensuring optimal performance of photovoltaic systems. Solar energy generation has seen rapid advancements in recent years, and large-scale installations of photovoltaic arrays are now common. Solar cell strings, consisting of multiple interconnected solar cells, generate electricity in such systems. Various techniques are known for monitoring the health and performance of solar cells. Real-time monitoring of current and voltage values in solar cells is essential for the detection of potential faults in solar cell strings. However, the effectiveness of traditional systems used for monitoring solar cell performance is often compromised due to the lack of intelligent fault prediction capabilities and limitations in data analysis, leading to undetected faults that reduce overall system efficiency.
One well-known conventional system for monitoring solar cell strings involves the use of current sensors to detect the electrical output of individual solar cells or strings. The system is configured to compare the output current values to predetermined thresholds and raise an alert if the current drops below the set threshold. Although such a system detects deviations in real-time, it only identifies a fault after the current has already deviated, resulting in delayed identification. Further, such systems are typically reactive and do not have the ability to predict faults in advance, leading to potential energy losses in the system.
Another conventional method for fault detection in solar cell strings includes the use of voltage sensors to monitor voltage levels in solar cells. Such systems are configured to continuously track the voltage generated by the solar cells and compare the readings to the expected range of voltage outputs. If any abnormal voltage patterns are detected, the system triggers an alarm indicating that a fault has occurred. However, this approach suffers from a major drawback in that it merely identifies the existence of a fault but lacks the ability to locate the specific fault or predict its occurrence in advance, thus necessitating manual intervention and detailed inspection of the entire string to determine the fault location.
Both of the aforementioned systems exhibit shortcomings in terms of early fault detection and accurate fault location identification. Further, the manual effort required to investigate and resolve faults identified by these systems results in time delays and inefficiencies, particularly in large-scale solar installations. Additionally, other state-of-the-art techniques also fail to incorporate predictive analysis capabilities that use historical data to forecast potential faults. The absence of self-learning capabilities in such systems leads to an overall reduction in the performance and longevity of the solar cell strings.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for fault identification in solar cell strings.
All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
The present disclosure relates to solar energy systems. Particularly, the present disclosure relates to fault identification and location determination in solar cell strings using real-time current and voltage sensing, coupled with a self-learning processor.
An objective of the present disclosure aims to provide a fault identification system for solar cell strings that enables real-time detection of current and voltage values and predicts potential fault locations based on historical data. The system of the present disclosure aims to enhance fault detection accuracy and efficiency in solar cell strings by integrating a self-learning processor with sensing units.
In an aspect, the present disclosure provides a fault identification system for solar cell strings comprising a current sensing unit disposed along the solar cell string to detect real-time current values, a voltage sensing unit longitudinally aligned with the current sensing unit to acquire voltage values from the solar cell string, and a self-learning processor operatively coupled to both sensing units to analyze historical data and predict potential fault locations based on the acquired current and voltage values.
The fault identification system improves predictive fault detection by analyzing historical data and continuously monitoring current and voltage values, enhancing system reliability. Furthermore, the system dynamically adjusts fault prediction parameters based on environmental conditions, ensuring optimal performance under varying sunlight intensities and weather conditions.
Moreover, the fault identification system further comprises an intersecting arrangement of the current and voltage sensing units, enabling real-time synchronized data input to the self-learning processor, thereby improving the accuracy of fault prediction along the solar cell string. The intersection facilitates the acquisition of synchronized data, which enables the self-learning processor to predict faults more accurately.
Further, the self-learning processor is longitudinally aligned with the current sensing unit to allow continuous monitoring and prediction of faults by comparing deviations in current and voltage values from predefined operational thresholds. The alignment improves the efficiency of fault prediction over time.
Moreover, the voltage sensing unit further comprises a filtering unit that removes noise from the voltage data, which enhances the accuracy of the voltage readings. The filtering unit improves the data quality, leading to more reliable fault detection by the self-learning processor.
Furthermore, the current sensing unit includes a temperature compensation unit to provide temperature-adjusted current readings to the self-learning processor. This adjustment improves the accuracy of the readings and the overall predictive performance of the system under varying environmental conditions.
Moreover, the self-learning processor includes a data storage unit that stores historical current and voltage data to analyze patterns over time. The storage of such data allows the system to improve its predictive capabilities, thereby increasing the reliability of the fault identification system.
Furthermore, the self-learning processor generates an alert signal when a fault is predicted. The alert signal is transmitted to a remote monitoring station, enabling timely intervention when a fault is detected.
Moreover, the current sensing unit includes an adjustable sensitivity control, which allows for the detection of minute fluctuations in current, improving the sensitivity of the system in detecting small-scale faults.
Furthermore, the voltage sensing unit includes a redundant voltage detection circuit that ensures consistent voltage readings even in the event of a partial circuit failure. Such redundancy enables the system to provide reliable data to the self-learning processor, thereby maintaining system integrity.
Finally, the self-learning processor dynamically adjusts fault prediction parameters based on environmental conditions, which improves the system's fault detection performance under different sunlight intensities and weather conditions.

Brief Description of the Drawings


The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a fault identification system (100) for solar cell strings, in accordance with the embodiments of the pressent disclosure.
FIG. 2 illustrates sequential diagram of a fault identification system (100) for solar cell strings, in accordance with the embodiments of the pressent disclosure.
Detailed Description
The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The present disclosure relates to solar energy systems. Particularly, the present disclosure relates to fault identification and location determination in solar cell strings using real-time current and voltage sensing, coupled with a self-learning processor.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
As used herein, the term "current sensing unit" refers to a device disposed along a solar cell string that detects real-time current values flowing through the string. Such a current sensing unit continuously monitors the electrical current, offering insights into the performance and operational status of the solar cells. The current sensing unit may be designed to capture both standard and anomalous current values, enabling the identification of performance variations. Such a current sensing unit typically includes sensors that detect current flow in a non-intrusive manner, without disrupting the energy transmission within the solar cell string. Furthermore, the current sensing unit is aligned with other components in the system, such as the voltage sensing unit, to ensure that the current data is synchronized for further analysis. The current sensing unit may also operate in conjunction with additional components such as temperature sensors to provide adjusted current readings under varying environmental conditions. Such real-time detection of current values facilitates accurate system monitoring.
As used herein, the term "voltage sensing unit" refers to a device aligned with the current sensing unit to acquire voltage values from the solar cell string. Such a voltage sensing unit measures the potential difference across the cells in the string, allowing for real-time monitoring of the voltage output generated by the solar cells. The voltage sensing unit captures deviations in voltage that may indicate faults or inefficiencies within the solar cells. Such a voltage sensing unit typically includes mechanisms for filtering out noise from the acquired data to ensure accurate readings. The alignment of the voltage sensing unit with the current sensing unit enables synchronized acquisition of both current and voltage values. Additionally, such a voltage sensing unit may include features for redundancy, ensuring consistent voltage detection even if part of the circuit fails. Such a voltage sensing unit provides crucial data for further processing and analysis by other components in the system.
As used herein, the term "self-learning processor" refers to a processor operatively coupled to both the current sensing unit and the voltage sensing unit. Such a self-learning processor is responsible for analyzing data acquired by the sensing units, including historical data on current and voltage values. Based on this analysis, the self-learning processor identifies potential fault locations within the solar cell string. Such a processor is capable of improving its predictions over time by learning from the data patterns it processes, thus adapting its fault identification methods according to historical performance. The self-learning processor is further equipped with storage capability to retain historical data and may utilize such data to refine its analysis. Additionally, the processor may dynamically adjust its parameters based on environmental conditions to enhance the accuracy of fault predictions. Such a self-learning processor contributes to the efficient monitoring and fault detection of the solar cell string system.
FIG. 1 illustrates a fault identification system (100) for solar cell strings, in accordance with the embodiments of the pressent disclosure. In an embodiment, a current sensing unit 102 is disposed along a solar cell string for detecting real-time current values within the solar cell string. The current sensing unit 102 continuously monitors the current flowing through the string, enabling the identification of variations in current that may indicate operational issues or potential faults in the solar cell string. The current sensing unit 102 may include a plurality of current sensors strategically placed to detect current at different points along the string. Said current sensing unit 102 may operate in conjunction with a temperature sensor to adjust current readings based on environmental conditions such as ambient temperature, ensuring that the values reflect accurate current measurements across varying conditions. Additionally, the current sensing unit 102 may incorporate an adjustable sensitivity control, enabling the detection of small fluctuations in current values, which could signify early-stage faults. The data acquired by the current sensing unit 102 is transmitted in real-time to a processing component for further analysis, contributing to fault detection in the solar cell string.
In an embodiment, a voltage sensing unit 104 is longitudinally aligned with the current sensing unit 102 for acquiring voltage values from the solar cell string. The voltage sensing unit 104 is positioned to measure the potential difference across individual solar cells or groups of cells within the string, allowing for real-time tracking of the voltage output. The voltage sensing unit 104 may include a noise-filtering component to remove signal interference, ensuring that the voltage values acquired are accurate and free from external distortions. Additionally, the voltage sensing unit 104 may incorporate a redundant voltage detection circuit that maintains consistent voltage readings in the event of partial circuit failures. The voltage sensing unit 104 operates in synchronization with the current sensing unit 102, and the data collected is transmitted to a central processing component for further analysis. The alignment of the voltage sensing unit 104 with the current sensing unit 102 enables the system to acquire real-time, synchronized current and voltage data from the solar cell string.
In an embodiment, a self-learning processor 106 is operatively coupled to the current sensing unit 102 and the voltage sensing unit 104 to analyze the real-time data provided by both units. The self-learning processor 106 is capable of processing historical data related to the current and voltage values, enabling predictive analysis to identify potential fault locations along the solar cell string. The self-learning processor 106 stores historical data in a data storage component, allowing for pattern recognition and analysis of trends over time. Said processor 106 may adjust its fault prediction parameters dynamically based on changes in environmental factors such as sunlight intensity or temperature. Furthermore, the self-learning processor 106 may generate alerts when potential faults are detected, with such alerts being transmitted to a remote monitoring system for further action. The coupling of the self-learning processor 106 with the current and voltage sensing units 102 and 104 facilitates continuous fault detection and monitoring in the solar cell string.
In an embodiment, the fault identification system includes a current sensing unit 102 intersecting a voltage sensing unit 104, allowing for the simultaneous and synchronized acquisition of both current and voltage data from the solar cell string. The intersection of the current sensing unit 102 and the voltage sensing unit 104 facilitates real-time monitoring of electrical parameters without causing delays or discrepancies in data collection. Such an arrangement allows the system to capture current and voltage values at the same point along the string, which provides highly accurate and reliable data for processing. The synchronized data acquisition minimizes the possibility of mismatched or unsynchronized values, thereby allowing the system to predict faults more accurately. The intersection is configured in such a way that both current and voltage data streams are fed directly to the self-learning processor 106, enabling real-time analysis. This real-time synchronized data helps the self-learning processor 106 make informed predictions regarding the location and severity of any faults detected along the solar cell string.
In an embodiment, the fault identification system is arranged such that the self-learning processor 106 is longitudinally aligned with the current sensing unit 102. This alignment enables continuous monitoring of current and voltage values along the solar cell string. The self-learning processor 106 receives real-time data from the current sensing unit 102 and the voltage sensing unit 104, facilitating uninterrupted analysis of electrical parameters. The longitudinal alignment allows the self-learning processor 106 to detect any deviations in current or voltage values from established operational thresholds. The processor 106 can then compare these deviations with historical data to predict potential fault locations within the solar cell string. This alignment improves the ability of the system to monitor the performance of the solar cell string consistently and without interruption. By being aligned with the current sensing unit 102, the self-learning processor 106 maintains close proximity to the real-time data sources, ensuring timely and accurate fault detection.
In an embodiment, the voltage sensing unit 104 comprises a filtering unit, which is configured to remove noise from the voltage data acquired from the solar cell string. The filtering unit is responsible for eliminating any unwanted electrical noise or signal interference that could distort the voltage readings. Such interference can arise from various environmental factors, including electromagnetic fields or fluctuations in the external electrical environment. The filtering unit is designed to isolate the relevant voltage signals and discard any extraneous noise, ensuring that the voltage readings provided to the self-learning processor 106 are as accurate as possible. The filtering unit works in conjunction with the rest of the voltage sensing unit 104 to deliver clean, reliable data, which is essential for the proper functioning of the fault identification system. By eliminating noise, the filtering unit helps prevent false readings or misinterpretations of the solar cell string's performance, thus improving the overall accuracy of the system's fault detection capabilities.
In an embodiment, the current sensing unit 102 includes a temperature compensation unit, which provides temperature-adjusted current readings to the self-learning processor 106. The temperature compensation unit accounts for the effect of varying temperatures on the current flowing through the solar cell string. Changes in temperature can influence the electrical characteristics of the solar cells, leading to variations in current that may not necessarily indicate a fault. The temperature compensation unit adjusts the current readings to reflect the actual performance of the solar cells under different temperature conditions, ensuring that the data being analyzed by the self-learning processor 106 is accurate and reflective of the true operational state of the solar cell string. By compensating for temperature fluctuations, the current sensing unit 102 helps the system avoid false fault predictions caused by temperature-induced variations in current. The temperature compensation unit operates in real-time, providing continuous temperature-adjusted current data to the self-learning processor 106 for analysis.
In an embodiment, the self-learning processor 106 comprises a data storage unit configured to store historical current and voltage data collected from the solar cell string. The data storage unit allows the self-learning processor 106 to analyze patterns and trends in the electrical performance of the solar cells over time. By storing and analyzing historical data, the self-learning processor 106 can improve its ability to predict potential faults based on deviations from normal operating patterns. The data storage unit enables the self-learning processor 106 to compare real-time current and voltage values with previously recorded values, allowing the system to detect anomalies that may indicate the presence of a fault. Additionally, the stored data can be used to refine the system's fault prediction algorithms, improving its accuracy and reliability in identifying potential faults in the solar cell string. The data storage unit provides the self-learning processor 106 with the ability to learn from past performance and enhance its predictive capabilities over time.
In an embodiment, the self-learning processor 106 is configured to generate an alert signal when a fault is predicted along the solar cell string. The alert signal is transmitted to a remote monitoring station, allowing operators to take timely action in response to potential faults. The self-learning processor 106 continuously monitors the data provided by the current sensing unit 102 and the voltage sensing unit 104, analyzing the data in real-time to identify any deviations from normal operating parameters. When such deviations are detected and determined to represent a potential fault, the processor 106 generates the alert signal, which includes details about the location and nature of the predicted fault. The alert signal may be transmitted via a wired or wireless communication channel, ensuring that the monitoring station receives the information promptly. By generating an alert signal, the self-learning processor 106 enables the system to react quickly to potential faults, reducing the likelihood of prolonged downtime or damage to the solar cell string.
In an embodiment, the current sensing unit 102 further comprises an adjustable sensitivity control, which allows the unit to detect minute fluctuations in current flowing through the solar cell string. The adjustable sensitivity control enables the current sensing unit 102 to be fine-tuned to detect smaller variations in current, which may be indicative of early-stage faults or minor operational inefficiencies within the solar cell string. The sensitivity control can be manually or automatically adjusted depending on the operational conditions and the specific requirements of the fault identification system. By increasing or decreasing the sensitivity, the system can be optimized for different environments or performance expectations. The adjustable sensitivity control ensures that the current sensing unit 102 is capable of detecting even the most subtle changes in current, providing more detailed and accurate data for analysis by the self-learning processor 106. This fine-tuning capability enhances the overall performance of the fault identification system.
In an embodiment, the voltage sensing unit 104 is equipped with a redundant voltage detection circuit, which facilitates the acquisition of consistent voltage readings even in the event of a partial circuit failure within the solar cell string. The redundant voltage detection circuit operates as a backup to the primary voltage detection components, ensuring that voltage data continues to be accurately collected and transmitted to the self-learning processor 106, even if part of the circuit becomes compromised. The inclusion of a redundant voltage detection circuit enhances the reliability of the fault identification system, as it minimizes the impact of failures or disruptions in the voltage sensing process. The circuit provides continuous voltage readings to the self-learning processor 106, allowing the system to maintain its fault detection capabilities without interruption. The redundant voltage detection circuit operates in parallel with the primary voltage detection components to provide a failsafe mechanism for uninterrupted data acquisition.
In an embodiment, the self-learning processor 106 is configured to dynamically adjust its fault prediction parameters based on environmental conditions, such as variations in sunlight intensity and weather conditions. The dynamic adjustment allows the processor 106 to modify its analysis of current and voltage data to account for the effects of environmental factors on the solar cell string's performance. For example, fluctuations in sunlight intensity can lead to changes in the electrical output of the solar cells, which the self-learning processor 106 takes into consideration when predicting faults. The ability to adjust its fault prediction parameters ensures that the processor 106 maintains a high level of accuracy in identifying potential faults, regardless of external conditions. This dynamic adjustment process occurs in real-time, allowing the self-learning processor 106 to continuously adapt its analysis as environmental conditions change. The system thus remains effective in predicting faults under a wide range of operating conditions, ensuring reliable performance over time.
The disclosed fault identification system (100) focuses on detecting and locating faults within solar cell strings, enhancing the operational reliability of solar power systems. The system employs a current sensing unit (102) that continuously monitors real-time current values from the solar cell string. This unit ensures that any anomalies in current flow are detected immediately, which can signal potential malfunctions or performance inefficiencies within the string. Aligned longitudinally with the current sensing unit is a voltage sensing module (104), which acquires voltage readings along the same solar cell string. The combination of both current and voltage data allows for a comprehensive analysis of the electrical performance of the solar cells.
Central to the system is the self-learning processor (106), which is responsible for analyzing the acquired data from the current sensing unit and voltage sensing module. By leveraging historical performance data, the self-learning processor identifies patterns or discrepancies that may indicate the presence of faults. Over time, the processor improves its fault detection accuracy by learning from past events, enabling it to predict potential fault locations within the solar cell string more effectively. This predictive capability is particularly valuable for proactive maintenance, allowing operators to address faults before they lead to significant power loss or system failure.
In practice, this fault identification system helps solar energy operators to maintain optimal performance of solar arrays by reducing downtime associated with fault diagnosis and repair. The real-time monitoring capabilities of the current and voltage sensors, combined with the advanced analytical functions of the self-learning processor, ensure that any faults in the solar cell strings can be quickly identified and located, leading to more efficient repairs and higher overall energy yield from the solar installation. This makes the system ideal for large-scale solar power applications where timely fault detection and resolution are critical to maintaining system efficiency.
FIG. 2 illustrates sequential diagram of a fault identification system (100) for solar cell strings, in accordance with the embodiments of the pressent disclosure. It shows the interactions between four key components: the solar cell string, the current sensing unit (102), the voltage sensing unit (104), and the self-learning processor (106). The solar cell string first sends real-time current values to the current sensing unit and real-time voltage values to the voltage sensing unit. Both sensing units then transmit the acquired current and voltage data to the self-learning processor. The self-learning processor analyzes the real-time and historical data to predict potential fault locations along the solar cell string. If additional data is required, the self-learning processor requests more current data from the current sensing unit and more voltage data from the voltage sensing unit. After receiving the requested data, the self-learning processor refines the fault prediction to ensure accurate detection and analysis of potential issues in the solar cell string. This loop continues for continuous monitoring and fault detection.
In an embodiment, the current sensing unit 102 is disposed along the solar cell string and is configured to detect real-time current values. The placement of said current sensing unit 102 along the solar cell string provides continuous monitoring of the current flowing through the cells, enabling early detection of irregularities that could indicate potential faults or inefficiencies in the system. The real-time detection of current values allows for timely responses to any deviations from expected performance. By providing immediate feedback on the current conditions within the solar cell string, the current sensing unit 102 aids in maintaining optimal system operation by identifying performance issues as they occur, thereby preventing long-term damage to the solar cells.
In an embodiment, the voltage sensing unit 104 is longitudinally aligned with the current sensing unit 102 and is configured to acquire voltage values from the solar cell string. This longitudinal alignment facilitates the simultaneous acquisition of both current and voltage data from the same section of the solar cell string, allowing for a more accurate analysis of the system's electrical characteristics. The real-time voltage data collected by said voltage sensing unit 104 can be correlated with the current data to detect discrepancies or patterns that may suggest a potential fault in the system. The ability to acquire voltage values in conjunction with current values improves the overall diagnostic capabilities of the system, allowing for a more comprehensive understanding of the solar cell string's performance.
In an embodiment, the self-learning processor 106 is operatively coupled to the current sensing unit 102 and the voltage sensing unit 104. Said self-learning processor 106 analyzes the current and voltage values in conjunction with historical data to predict potential fault locations along the solar cell string. By leveraging historical performance data, the self-learning processor 106 can identify patterns and deviations that may indicate a developing fault. This predictive capability enables more proactive maintenance, as potential issues can be identified and addressed before they lead to significant performance degradation. The ability of the self-learning processor 106 to continuously learn and refine its fault prediction improves the system's overall reliability and operational longevity.
In an embodiment, the current sensing unit 102 is intersecting the voltage sensing unit 104, providing synchronized current and voltage data to the self-learning processor 106 for accurate fault prediction. The intersection of the sensing units allows for simultaneous data acquisition at a single point along the solar cell string, reducing the risk of data misalignment between the current and voltage measurements. This real-time synchronization of electrical data ensures that the self-learning processor 106 receives consistent and accurate input for its analysis. By minimizing temporal discrepancies between the current and voltage data, the system is better equipped to accurately identify potential faults and operational irregularities.
In an embodiment, the self-learning processor 106 is longitudinally aligned with the current sensing unit 102. This alignment allows for continuous monitoring of the solar cell string's electrical performance, as the self-learning processor 106 can directly receive and process real-time current data. The alignment further facilitates the processor's ability to detect deviations from predefined operational thresholds, allowing for rapid fault detection and response. By maintaining a direct and uninterrupted data flow between the sensing unit and the processor, the system enhances its fault prediction capabilities, providing a more seamless and integrated approach to solar cell monitoring and diagnostics.
In an embodiment, the voltage sensing unit 104 further comprises a filtering unit designed to remove noise from the acquired voltage data. External noise or interference can distort the voltage readings and lead to inaccurate conclusions regarding the solar cell string's performance. By incorporating said filtering unit, the voltage sensing unit 104 ensures that only relevant and accurate voltage data is transmitted to the self-learning processor 106. This noise reduction allows for more precise fault detection and analysis, as the processor receives clean data free from external electrical distortions. The filtering unit enhances the reliability of the voltage measurements, improving the overall diagnostic performance of the system.
In an embodiment, the current sensing unit 102 includes a temperature compensation unit that adjusts the current readings based on ambient temperature variations. Changes in temperature can significantly affect the electrical characteristics of solar cells, potentially leading to erroneous current readings if left uncompensated. The temperature compensation unit ensures that the current data accurately reflects the true operational status of the solar cell string, regardless of environmental temperature fluctuations. By providing temperature-adjusted current data to the self-learning processor 106, the system can avoid false fault detection and better assess the actual performance conditions of the solar cells.
In an embodiment, the self-learning processor 106 comprises a data storage unit configured to store historical current and voltage data. This stored data allows the processor to analyze long-term trends and patterns in the solar cell string's performance. By comparing real-time data with historical records, the self-learning processor 106 can identify deviations that may signal an emerging fault. The ability to store and reference past performance data enhances the predictive capabilities of the system, enabling more accurate and timely fault detection. Additionally, the data storage unit allows for continuous improvement of the processor's analytical models as more historical data is accumulated.
In an embodiment, the self-learning processor 106 is configured to generate an alert signal when a fault is predicted based on the analysis of current and voltage data. This alert signal is transmitted to a remote monitoring station, enabling operators to take immediate action to address the potential fault. By providing real-time alerts, the system ensures that faults are addressed promptly, minimizing downtime and preventing further damage to the solar cells. The ability to generate alert signals based on predictive analysis enhances the system's overall responsiveness, allowing for more efficient and effective maintenance of the solar cell string.
In an embodiment, the current sensing unit 102 further comprises an adjustable sensitivity control. Said sensitivity control allows the current sensing unit 102 to detect minute fluctuations in current, which may indicate early-stage faults or other operational irregularities. The adjustable nature of the sensitivity control enables the system to be fine-tuned for different operational environments or fault detection requirements. By detecting even small deviations in current flow, the system can identify potential issues at an earlier stage, allowing for more proactive intervention and reducing the likelihood of severe performance degradation over time.
In an embodiment, the voltage sensing unit 104 is equipped with a redundant voltage detection circuit that maintains consistent voltage readings even in the event of a partial circuit failure. This redundancy ensures that the voltage data remains reliable, allowing the self-learning processor 106 to continue its analysis without interruption. The presence of the redundant circuit improves the system's resilience to component failures, ensuring that accurate voltage data is consistently available for fault detection. By maintaining data integrity even during partial failures, the system enhances its ability to monitor and diagnose the solar cell string effectively.
In an embodiment, the self-learning processor 106 is configured to dynamically adjust its fault prediction parameters based on environmental conditions such as sunlight intensity and weather variations. Environmental factors can have a significant impact on the electrical output of solar cells, potentially affecting current and voltage measurements. By adjusting its analysis to account for these factors, the self-learning processor 106 ensures that its fault predictions remain accurate under varying environmental conditions. This dynamic adjustment capability allows the system to optimize its performance in different climates, ensuring that potential faults are detected with greater precision, regardless of external influences.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
The term "memory," as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage su












I/We Claims


1. A fault identification system (100) for solar cell strings, comprising:
a current sensing unit (102) disposed along said solar cell string, said current sensing unit (102) configured to detect real-time current values;
a voltage sensing module (104) longitudinally aligned with said current sensing unit (102), said voltage sensing module (104) configured to acquire voltage values from said solar cell string; and
a self-learning processor (106) operatively coupled to said current sensing unit (102) and said voltage sensing module (104), said self-learning processor (106) configured to analyze historical data and predict potential fault locations based on said current and voltage values.
2. The fault identification system (100) of claim 1, wherein said current sensing unit (102) is intersecting said voltage sensing module (104), said intersection providing real-time synchronized current and voltage data to said self-learning processor (106) for accurate fault prediction along said solar cell string.
3. The fault identification system (100) of claim 1, wherein said self-learning processor (106) is longitudinally aligned with said current sensing unit (102), said alignment for continuous monitoring and prediction of faults based on deviations in current and voltage values from predefined operational thresholds.
4. The fault identification system (100) of claim 1, wherein said voltage sensing module (104) further comprises a filtering unit, said filtering unit configured to remove noise from voltage data.
5. The fault identification system (100) of claim 1, wherein said current sensing unit (102) comprises a temperature compensation unit, said temperature compensation unit providing temperature-adjusted current readings to said self-learning processor (106).
6. The fault identification system (100) of claim 1, wherein said self-learning processor (106) comprises a data storage unit configured to store historical current and voltage data, said data storage unit allowing said self-learning processor (106) to analyze patterns over time and improve predictive fault identification.
7. The fault identification system (100) of claim 1, wherein said self-learning processor (106) is configured to generate an alert signal when a fault is predicted, said alert signal being transmitted to a remote monitoring station.
8. The fault identification system (100) of claim 1, wherein said current sensing unit (102) further comprises an adjustable sensitivity control, said sensitivity control allowing said current sensing unit (102) to detect minute fluctuations in current.
9. The fault identification system (100) of claim 1, wherein said voltage sensing module (104) is equipped with a redundant voltage detection circuit, said redundant voltage detection circuit facilitating that voltage readings remain consistent even in the event of a partial circuit failure, providing reliable data to said self-learning processor (106).
10. The fault identification system (100) of claim 1, wherein said self-learning processor (106) is configured to dynamically adjust fault prediction parameters based on environmental conditions, said adjustment for optimal fault detection performance under different sunlight intensities and weather conditions.




The present disclosure provides a fault identification system (100) for solar cell strings, designed to determine fault locations. The system comprises a current sensing unit (102) that detects real-time current values along the solar cell string and a voltage sensing module (104) aligned with the current sensing unit to acquire voltage values from the same solar cell string. A self-learning processor (106) is operatively coupled to both the current sensing unit and the voltage sensing module, analyzing historical data and using the current and voltage values to predict potential fault locations within the solar cell string, improving overall system reliability and efficiency.
, Claims:I/We Claims


1. A fault identification system (100) for solar cell strings, comprising:
a current sensing unit (102) disposed along said solar cell string, said current sensing unit (102) configured to detect real-time current values;
a voltage sensing module (104) longitudinally aligned with said current sensing unit (102), said voltage sensing module (104) configured to acquire voltage values from said solar cell string; and
a self-learning processor (106) operatively coupled to said current sensing unit (102) and said voltage sensing module (104), said self-learning processor (106) configured to analyze historical data and predict potential fault locations based on said current and voltage values.
2. The fault identification system (100) of claim 1, wherein said current sensing unit (102) is intersecting said voltage sensing module (104), said intersection providing real-time synchronized current and voltage data to said self-learning processor (106) for accurate fault prediction along said solar cell string.
3. The fault identification system (100) of claim 1, wherein said self-learning processor (106) is longitudinally aligned with said current sensing unit (102), said alignment for continuous monitoring and prediction of faults based on deviations in current and voltage values from predefined operational thresholds.
4. The fault identification system (100) of claim 1, wherein said voltage sensing module (104) further comprises a filtering unit, said filtering unit configured to remove noise from voltage data.
5. The fault identification system (100) of claim 1, wherein said current sensing unit (102) comprises a temperature compensation unit, said temperature compensation unit providing temperature-adjusted current readings to said self-learning processor (106).
6. The fault identification system (100) of claim 1, wherein said self-learning processor (106) comprises a data storage unit configured to store historical current and voltage data, said data storage unit allowing said self-learning processor (106) to analyze patterns over time and improve predictive fault identification.
7. The fault identification system (100) of claim 1, wherein said self-learning processor (106) is configured to generate an alert signal when a fault is predicted, said alert signal being transmitted to a remote monitoring station.
8. The fault identification system (100) of claim 1, wherein said current sensing unit (102) further comprises an adjustable sensitivity control, said sensitivity control allowing said current sensing unit (102) to detect minute fluctuations in current.
9. The fault identification system (100) of claim 1, wherein said voltage sensing module (104) is equipped with a redundant voltage detection circuit, said redundant voltage detection circuit facilitating that voltage readings remain consistent even in the event of a partial circuit failure, providing reliable data to said self-learning processor (106).
10. The fault identification system (100) of claim 1, wherein said self-learning processor (106) is configured to dynamically adjust fault prediction parameters based on environmental conditions, said adjustment for optimal fault detection performance under different sunlight intensities and weather conditions.

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