A method and system for monitoring the state of connection of a photovoltaic array module

Abstract

The application discloses a photovoltaic array module connection state monitoring method and system, relates to the technical field of photovoltaic systems, and is based on connection disturbance event extraction connection response data, and connection offset data, thermal hysteresis data and adjacent difference data are combined to establish a connection baseline, so that hierarchical identification and positioning of photovoltaic array module connection points are realized, compared with the prior art, the application can discover a connection weakening state earlier, improve connection abnormal early warning capability and positioning precision, and can adaptively adjust a monitoring strategy according to a connection state, and is suitable for online operation and maintenance monitoring of large-scale photovoltaic power stations and industrial and commercial photovoltaic systems.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic system technology, and in particular to a method and system for monitoring the connection status of photovoltaic array modules. Background Technology

[0002] Photovoltaic arrays typically consist of multiple photovoltaic modules connected in series to form a power generation unit. During long-term outdoor operation, the connection points, connectors, lead crimping areas, and corresponding conductive interfaces between modules are continuously affected by diurnal temperature variations, wind load vibration, moisture erosion, dust accumulation, salt spray corrosion, and periodic current variations caused by irradiance fluctuations. Under these conditions, the connection interfaces are prone to problems such as increased contact resistance, loose connections, localized oxidation, and intermittent unstable conduction. These problems usually do not manifest as significant failures in the early stages, but rather as gradual anomalies such as discontinuous connection response, slower localized temperature recovery, and decreased consistency of dynamic response with adjacent modules.

[0003] Existing photovoltaic array monitoring technologies typically rely on branch-level electrical parameter monitoring, periodic infrared inspections, or fault-based alarms. While branch-level monitoring can reflect overall output changes, it struggles to pinpoint specific module connection points, and in the early stages of connection degradation, branch-level power changes are often insignificant. Infrared inspections, while capable of detecting some heat points, are usually offline or periodic, failing to continuously reflect the evolution of connection status or identify early connection weakening before significant heating has occurred. Furthermore, methods based solely on static voltage, current, or temperature thresholds can only identify clearly abnormal connection points, lacking effective utilization of the dynamic offset characteristics, thermal hysteresis characteristics, and adjacent difference characteristics exhibited by connection degradation under disturbance events.

[0004] Especially in large-scale ground-mounted photovoltaic power plants, mountainous terrain power plants, and commercial and industrial rooftop photovoltaic systems, due to the large number of components, dispersed installations, significant environmental differences, and frequent changes in operating conditions, connection point anomalies often do not appear as sudden failures, but rather gradually accumulate and amplify during repeated operational disturbances. Therefore, if only existing steady-state threshold monitoring methods are used, it is often difficult to identify the evolution of connection points from a healthy state to a weakened state, and then to an abnormal state in a timely manner, which can easily lead to delayed anomaly detection, resulting in local overheating, power generation loss, increased downtime for maintenance, and even safety risks. Summary of the Invention

[0005] The technical problem solved by this invention is that existing technologies are unable to continuously identify, provide early warnings, and accurately locate the gradual degradation process of module connection points under actual operating disturbance conditions of photovoltaic arrays.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A method for monitoring the connection status of a photovoltaic array module includes the following steps: Step S1: Collect connection topology data, module operation data, and connection point thermal response data of each module in the photovoltaic array, establish a connection mapping table, and output the basic connection data; Step S2: Identify connection disturbance events based on module operation data and connection point thermal response data, determine the corresponding target module, adjacent module and connection point location based on connection topology data, extract data for the corresponding time period, and output connection response data; Step S3: Based on the module voltage change, module current change, and connection point temperature change process in the connection response data, calculate the connection offset data, thermal hysteresis data, and adjacent difference data, and establish the connection baseline. Step S4: Compare the connection offset data, thermal hysteresis data, and adjacency difference data with the connection baseline to generate connection degradation data, and output connection status data based on the connection degradation data. Step S5: Determine the monitoring strategy based on the connection status data and input the monitoring strategy to the control terminal.

[0007] Preferably, step S1 includes the following sub-steps: Step S101: Collect the module number, serial position, adjacent module number and connection point position of each module, and output the connection topology data; Step S102: Collect the module voltage, module current and branch power of each module, and output the module operation data; Step S103: Collect the temperature and temperature rise changes of each connection point and output the thermal response data of the connection points; Step S104: Establish a connection mapping table based on the connection topology data, module operation data, and connection point thermal response data, and output the connection mapping table and corresponding data as connection basic data.

[0008] Preferably, step S2 includes the following sub-steps: Step S201: Analyze the connection basic data, identify the time period in which the change in module current or the change in branch power exceeds the preset event threshold, and mark it as a connection disturbance event. Step S202: Using the start and end times of the connection disturbance event as boundaries, extract the module voltage, module current, branch power, and connection point temperature within the corresponding time period to establish an event segment; Step S203: Associate the data of each module in the event segment with the corresponding connection point position according to the connection mapping table, and output the connection response data.

[0009] Preferably, step S3 includes the following sub-steps: Step S301: Calculate the module voltage change amplitude, module current change amplitude, and branch power change amplitude of each module in the event segment based on the connection response data; Step S302: Calculate the response deviation of each module relative to adjacent modules in the same series based on the module voltage change amplitude, module current change amplitude, and branch power change amplitude, and output the connection offset data. Step S303: Calculate thermal hysteresis data based on the temperature drop time of the connection point after the connection disturbance event, and calculate adjacency difference data based on the difference in response deviation of each module. Step S304: Calculate the mean and standard deviation based on the connection offset data, thermal hysteresis data, and adjacency difference data, and establish the connection baseline using the mean and standard deviation.

[0010] Preferably, the logic of step S302 is as follows: For a target module within the same string, find the adjacent module corresponding to its adjacent module number; Calculate the difference in module voltage variation, module current variation, and branch power variation between the target module and adjacent modules in the same event segment; The response deviation is calculated based on the difference in module voltage variation, the difference in module current variation, and the difference in branch power variation. Establish a correspondence between each response deviation and the location of the connection point, and output the connection offset data.

[0011] Preferably, step S4 includes the following sub-steps: Step S401: Input the connection response data into the connection baseline at the corresponding connection point location, and calculate the degree of deviation of the connection offset data, thermal hysteresis data and adjacency difference data relative to the connection baseline. Step S402: Calculate the connection degradation value based on the degree of deviation, establish the correspondence between the connection degradation value and the connection point position, and output the connection degradation data; Step S403: Analyze the connection degradation data and output the connection status data.

[0012] Preferably, the logic of step S402 is as follows: If any one of the connectivity offset data, thermal hysteresis data, and adjacency difference data exceeds the corresponding connectivity baseline, the connectivity degradation value is increased by one level. If two of the connection offset data, thermal hysteresis data, and adjacency difference data exceed the corresponding connection baseline, the connection degradation value is increased by two levels. If the connection offset data, thermal hysteresis data, and adjacency difference data all exceed the corresponding connection baseline, the connection degradation value is increased by three levels. Output the connection degradation values ​​as connection degradation data.

[0013] Preferably, step S403 includes the following sub-steps: If the connection degradation value is lower than the preset first state threshold, it is marked as a stable connection state; If the connection degradation value is at or above the first state threshold and below the preset second state threshold, it is marked as a connection weakening state. If the connection degradation value is at or above the second state threshold, it is marked as a connection abnormal state; Output stable connection state, weakened connection state, and abnormal connection state as connection state data.

[0014] Preferably, step S5 includes the following sub-steps: Step S501: Select a monitoring strategy based on the connection status data. The monitoring strategy includes maintaining the existing sampling frequency, increasing the sampling frequency, and triggering on-site re-inspection. Step S502: If the connection status data indicates a stable connection, then maintain the existing sampling frequency. If the connection status data is in a weakened connection state, then increase the sampling frequency. If the connection status data indicates an abnormal connection status, then trigger on-site re-inspection and increase the sampling frequency. Step S503: Input the monitoring strategy and the corresponding sampling frequency to the control terminal.

[0015] A photovoltaic array module connection status monitoring system includes a connection mapping module, a disturbance identification module, a baseline generation module, a status determination module, and a monitoring and control module; The connection mapping module is used to collect connection topology data, module operation data and connection point thermal response data of each module in the photovoltaic array, establish a connection mapping table and output basic connection data. The disturbance identification module is used to identify connection disturbance events based on module operation data and connection point thermal response data, and to determine the corresponding target module, adjacent module and connection point location based on connection topology data, extract data for the corresponding time period, and output connection response data. The baseline generation module is used to calculate connection offset data, thermal hysteresis data, and adjacent difference data based on the module voltage change, module current change, and connection point temperature change process in the connection response data, and to establish a connection baseline. The state determination module is used to compare the connection offset data, thermal hysteresis data, and adjacency difference data with the connection baseline respectively, generate connection degradation data, and output connection status data based on the connection degradation data. The monitoring and control module is used to determine the monitoring strategy based on the connection status data and input the monitoring strategy to the control terminal.

[0016] The beneficial effects of this invention are as follows: This invention extracts connection response data based on connection disturbance events and establishes a connection baseline by combining connection offset data, thermal hysteresis data, and adjacency difference data, thereby realizing hierarchical identification and positioning of connection points of photovoltaic array modules. Compared with the prior art, this invention can detect connection weakening states earlier, improve connection anomaly early warning capabilities and positioning accuracy, and can adaptively adjust monitoring strategies according to connection status. It is suitable for online operation and maintenance monitoring of large-scale photovoltaic power plants and industrial and commercial photovoltaic systems. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the steps of a photovoltaic array module connection status monitoring method according to an embodiment of the present invention; Figure 2 This is a system composition diagram of a photovoltaic array module connection status monitoring system provided in one embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Example 1, referring to Figure 1 A method for monitoring the connection status of a photovoltaic array module is provided, comprising the following steps: Step S1: Collect connection topology data, module operation data, and connection point thermal response data of each module in the photovoltaic array, establish a connection mapping table, and output the basic connection data.

[0020] Step S2: Identify connection disturbance events based on module operation data and connection point thermal response data, determine the corresponding target module, adjacent module and connection point location based on connection topology data, extract data for the corresponding time period, and output connection response data.

[0021] Step S3: Based on the module voltage change, module current change, and connection point temperature change process in the connection response data, calculate the connection offset data, thermal hysteresis data, and adjacent difference data, and establish the connection baseline.

[0022] Step S4: Compare the connection offset data, thermal hysteresis data, and adjacency difference data with the connection baseline to generate connection degradation data, and output connection status data based on the connection degradation data.

[0023] Step S5: Determine the monitoring strategy based on the connection status data and input the monitoring strategy to the control terminal.

[0024] This embodiment provides a method for monitoring the connection status of photovoltaic array modules. The method is applied to a series photovoltaic array in a ground-mounted centralized photovoltaic power station. Each series contains several photovoltaic modules, each of which has a module number. Its series position, adjacent module number, and connection point position are recorded in the connection mapping table. The connection point position is used to characterize the physical position of the photovoltaic module's output connection point in the series. This method does not only compare thresholds for static voltage or static temperature, but also establishes a connection baseline around the dynamic response process under connection disturbance events, and predicts the trend of connection status changes based on connection response data, so as to achieve early identification of connection weakening and connection abnormal states.

[0025] Step S1 includes the following sub-steps: Step S101: Collect the module number, serial position, adjacent module number and connection point position of each module, and output the connection topology data.

[0026] Step S102: Collect the module voltage, module current and branch power of each module, and output the module operation data.

[0027] Step S103: Collect the temperature and temperature rise changes of each connection point and output the thermal response data of the connection points; Step S104: Establish a connection mapping table based on the connection topology data, module operation data, and connection point thermal response data, and output the connection mapping table and corresponding data as connection basic data.

[0028] First, connection topology data is collected on the array side. The connection topology data includes module numbers. Serial position Adjacency module and connection point location .in, This indicates the module's number within its respective series. The connection topology data is generated from installation documentation and field numbering information and written to the connection mapping table. Next, module operating data is collected. Module operating data includes module voltage. Module current and branch power .in, At the sampling time, the branch power is calculated from the branch voltage and branch current of its respective branch. Next, thermal response data at the connection points is collected. This thermal response data includes the connection point temperature. and the temperature rise change between adjacent sampling times. ,in: ; In the formula, For the previous sampling time, This represents the current sampling time. Finally, the connection topology data, module operation data, and connection point thermal response data are sorted by module number. With connection point location Binding is performed to form the basic connection data. Each record in the basic connection data includes the module, serial position, adjacent module number, connection point position, module voltage, module current, branch power, and connection point temperature. The result of this step is the establishment of a connection mapping table, which provides a unified data entry point for subsequent event extraction and baseline construction.

[0029] Step S2 includes the following sub-steps: Step S201: Analyze the connection basic data, identify the time period in which the change in module current or branch power exceeds the preset event threshold, and mark it as a connection disturbance event.

[0030] Step S202: Using the start and end times of the connection disturbance event as boundaries, extract the module voltage, module current, branch power, and connection point temperature within the corresponding time period to establish an event segment.

[0031] Step S203: Associate the data of each module in the event segment with the corresponding connection point position according to the connection mapping table, and output the connection response data.

[0032] The control unit continuously reads basic connection data and identifies connection disturbance events based on changes in module current and branch power. Let two adjacent sampling times be... and The change in module current is defined as follows: ; The change in branch power is defined as: ; When the same sampling time satisfies: ; Or satisfy: ; When the same sampling time satisfies or When this time occurs, the time period in which it occurs is marked as a connection disturbance event. Among these, To preset the current event threshold, A preset power event threshold is set. In actual deployment, if the sampling period is set to ten seconds, six consecutive sampling points can be taken to form an event segment, that is, from several sampling points before the start time of the event to several sampling points after the end time of the event, and the module voltage within this interval can be extracted. Module current Branch power and connection point temperature This forms the event fragment data for that module. Then, according to the connection mapping table, the module numbers within the same event fragment are... Adjacent module number Location of connection point This corresponds to the output of connectivity response data. The connectivity response data is not isolated raw sampled values, but structured dynamic data with topological relationships and event boundary information, providing input for subsequent construction of connectivity offset data, thermal hysteresis data, and adjacency difference data.

[0033] Step S3 includes the following sub-steps: Step S301: Calculate the module voltage change amplitude, module current change amplitude, and branch power change amplitude of each module in the event segment based on the connection response data.

[0034] Step S302: Calculate the response deviation of each module relative to adjacent modules in the same series based on the module voltage change amplitude, module current change amplitude, and branch power change amplitude, and output the connection offset data.

[0035] Step S303: Calculate thermal hysteresis data based on the temperature drop time of the connection point after the connection disturbance event, and calculate adjacency difference data based on the difference in response deviation of each module.

[0036] Step S304: Calculate the mean and standard deviation based on the connection offset data, thermal hysteresis data, and adjacency difference data, and establish the connection baseline using the mean and standard deviation.

[0037] The logic of step S302 is as follows: For a target module within the same string, find the adjacent module corresponding to its adjacent module number.

[0038] Calculate the difference in module voltage variation, module current variation, and branch power variation between the target module and its adjacent modules in the same event segment.

[0039] The response deviation is calculated based on the difference in module voltage variation, the difference in module current variation, and the difference in branch power variation.

[0040] Establish a correspondence between each response deviation and the location of the connection point, and output the connection offset data.

[0041] First, the response magnitude of each module in the connection disturbance event is calculated. Let the start time of a certain event segment be... The end time is The voltage change amplitude of the module is defined as: ; The module current change amplitude is defined as: ; The branch power variation range is defined as: ; Then, based on the adjacent module number Locate the adjacent modules of the target module and calculate the differences in module voltage variation, module current variation, and branch power variation between the target module and its adjacent modules in the same event segment. Let the target module be... Its adjacent module is Then we have: ; ; ; The three differences mentioned above are further grouped together as the response deviation, i.e., the connection offset data. To maintain dimensional consistency, we first normalize the data using the mean of historical event segments within this sequence. Let the historical means be... , and Then the connection offset data is defined as: ; The above formula characterizes the degree of deviation of the target module's response relative to adjacent modules under event disturbance. The greater the deviation, the more unstable the conduction state at the connection point. Next, the thermal hysteresis data is calculated. Let the end time of the connection disturbance event be... The upper bound of the stable temperature range after the event ends is Then find the earliest satisfaction The moment The thermal hysteresis data are as follows: ; in, This characterizes the time required for the temperature at the connection point to return to the baseline range after the event ends. A larger thermal hysteresis value indicates more significant heat accumulation and heat dissipation lag at that connection point. Furthermore, adjacent module difference data is calculated. To reflect the dynamic differences between the target module and adjacent modules throughout the entire event segment, the voltage and temperature differences at each sampling time are averaged. Assume there are a total of... For each sampling point, the adjacent difference data for: ; In the formula, and These are the average module voltage and average connection point temperature of the target module during its historical healthy operating phase, respectively. Connection offset data is now complete. Thermal hysteresis data and adjacent difference data After calculation, for each connection point position Calculate the mean and standard deviation for each data point corresponding to the historical health phase. Taking the connected offset data as an example, its mean is denoted as... The standard deviation is denoted as ,but: ; ; in, This represents the number of historical event fragments during the healthy phase. Similarly, thermal hysteresis data and adjacency difference data are obtained respectively. , , and Based on this, a connection baseline is established, where the connection baseline interval corresponding to the connection offset data is: ; The connection baseline intervals corresponding to thermal hysteresis data and adjacent difference data are respectively denoted as: and The reason for using the upper limit loan method is that connection degradation often manifests first as an abnormally high level, rather than an abnormally low level.

[0042] Step S4 includes the following sub-steps: Step S401: Input the connection response data into the connection baseline at the corresponding connection point location, and calculate the degree of deviation of the connection offset data, thermal hysteresis data and adjacency difference data relative to the connection baseline.

[0043] Step S402: Calculate the connection degradation value based on the degree of deviation, establish the correspondence between the connection degradation value and the location of the connection point, and output the connection degradation data.

[0044] Step S403: Analyze the connection degradation data and output the connection status data.

[0045] The logic of step S402 is as follows: If any one of the connection offset data, thermal hysteresis data, and adjacency difference data exceeds the corresponding connection baseline, the connection degradation value is increased by one level.

[0046] If two of the connection offset data, thermal hysteresis data, and adjacency difference data exceed the corresponding connection baseline, the connection degradation value is increased by two levels.

[0047] If the connection offset data, thermal hysteresis data, and adjacency difference data all exceed the corresponding connection baseline, the connection degradation value is increased by three levels.

[0048] Output the connection degradation values ​​as connection degradation data.

[0049] Step S403 includes the following sub-steps: If the connection degradation value is lower than the preset first state threshold, it is marked as a stable connection state.

[0050] If the connection degradation value is at or above the first state threshold and below the preset second state threshold, it is marked as a connection weakening state.

[0051] If the connection degradation value is at or above the second state threshold, it is marked as a connection abnormal state.

[0052] Output stable connection state, weakened connection state, and abnormal connection state as connection state data.

[0053] First, input the connection response data corresponding to the current connection disturbance event into the connection baseline at the corresponding connection point location, and calculate the deviation of the connection offset data, thermal hysteresis data, and adjacency difference data from the connection baseline. Let the current value of the connection offset data be... Then its degree of deviation Defined as: ; The degree of deviation corresponding to thermal hysteresis data and adjacent difference data is denoted as follows: and The calculation method is the same, and then a connection degradation value is established based on the degree of deviation. This embodiment preferentially employs hierarchical counting logic, and introduces continuous quantity correction on this basis. If , and If only one of the terms is greater than 0, the basic connection degradation value is recorded as 1; if two terms are greater than 0, the basic connection degradation value is recorded as 2; if all three terms are greater than 0, the basic connection degradation value is recorded as 3. Furthermore, to improve prediction sensitivity, the mean of the deviations of the three terms is used as a correction term to obtain the final connection degradation value: ; In the formula, This is the base connection degradation value obtained from the above hierarchical counting logic. After obtaining the connection degradation value, a relationship is established between the connection degradation value and the connection point position. The corresponding relationship is established, and connection degradation data is output. Next, connection state identification is performed based on the connection degradation data. Let the first state threshold be... The second state threshold is ,and .like: ; Then output the stable connection state; if: ; Then output the weakened connection state; if: ; The system then outputs a connection anomaly status, thus obtaining connection status data. The prediction logic here is that the connection degradation value is not a confirmation of an existing high-temperature fault, but rather a prediction of the connection status's future direction based on the degree of deviation in response to event disturbances. Especially when... It did not cause sustained high temperatures, but and When the connection has been synchronously increased, a weakened connection status will be output first to alert maintenance personnel that the connection point is likely to evolve into an abnormal connection status.

[0054] Step S5 includes the following sub-steps: Step S501: Select a monitoring strategy based on the connection status data. The monitoring strategies include maintaining the existing sampling frequency, increasing the sampling frequency, and triggering on-site re-inspection.

[0055] Step S502: If the connection status data is in a stable connection state, then select to maintain the existing sampling frequency.

[0056] If the connection status data is in a weakened state, then increase the sampling frequency.

[0057] If the connection status data indicates an abnormal connection, then trigger an on-site re-inspection and increase the sampling frequency.

[0058] Step S503: Input the monitoring strategy and the corresponding sampling frequency to the control terminal.

[0059] The control unit reads the connection status data and selects a monitoring strategy based on the data. If the connection status data indicates a stable connection, the existing sampling frequency is maintained. If the connection status data indicates a weakened connection, increase the sampling frequency to [a higher level]. If the connection status data indicates a connection error, increase the sampling frequency to [value missing]. This also triggers on-site re-inspection. To further align the monitoring strategy with the degree of connectivity degradation, it can also be based on the connectivity degradation value. The increased sampling frequency is continuously corrected. For example, in a weakened connectivity state, the following definition is made: ; In a connection failure state, the following definition applies: ; in, Location of connection point The current corresponding actual sampling frequency. Subsequently, the monitoring strategy and the corresponding sampling frequency are input to the control terminal, which then schedules the sampling equipment to further collect and transmit module voltage, module current, branch power, and connection point temperature.

[0060] Example 2, refer to Figure 2 A photovoltaic array module connection status monitoring system is provided, including a connection mapping module, a disturbance identification module, a baseline generation module, a status determination module, and a monitoring and control module.

[0061] The connection mapping module is used to collect connection topology data, module operation data and connection point thermal response data of each module in the photovoltaic array, establish a connection mapping table and output basic connection data.

[0062] The disturbance identification module is used to identify connection disturbance events based on module operation data and connection point thermal response data, and to determine the corresponding target module, adjacent module and connection point location based on connection topology data, extract data for the corresponding time period, and output connection response data.

[0063] The baseline generation module is used to calculate connection offset data, thermal hysteresis data, and adjacent difference data based on the module voltage change, module current change, and connection point temperature change process in the connection response data, and to establish the connection baseline.

[0064] The status determination module compares the connection offset data, thermal hysteresis data, and adjacency difference data with the connection baseline to generate connection degradation data, and outputs connection status data based on the connection degradation data.

[0065] The monitoring and control module is used to determine the monitoring strategy based on the connection status data and input the monitoring strategy to the control terminal.

[0066] The present invention provides a photovoltaic array module connection status monitoring method. First, a connection mapping table is established based on connection topology data, module operation data, and connection point thermal response data. This establishes a clear data correspondence between each module, adjacent modules, and corresponding connection point locations. Therefore, this method does not remain at the coarse-grained monitoring level of the entire branch, but can accurately map the subsequently calculated connection offset data, thermal hysteresis data, and adjacent difference data to specific connection point locations, thereby improving the positioning accuracy of connection status identification. This method is suitable for large-scale photovoltaic array scenarios with a large number of modules and complex series structures.

[0067] This invention extracts connection disturbance events by identifying time periods when changes in module current or branch power exceed a preset event threshold, and then generates connection response data around these events. Instead of relying solely on single-point measurements under static operating conditions, it establishes connection status monitoring based on dynamic disturbances that have objectively occurred during actual operation. Since early degradation of the connection interface is often more easily exposed under dynamic processes such as current changes, power fluctuations, start-stop switching, or shadow recovery, this method can amplify connection degradation characteristics by utilizing disturbance processes in real-world operating conditions, thereby improving early identification capabilities.

[0068] This invention further calculates connection offset data, thermal hysteresis data, and adjacency difference data. Connection offset data reflects the degree of response deviation between the target module and adjacent modules under the same disturbance event; thermal hysteresis data reflects the change in the recovery rate of the connection point temperature after the disturbance ends; and adjacency difference data reflects the degree of continuous difference between the target module and adjacent modules throughout the entire event segment. These three types of data characterize the connection status from three perspectives: electrical response consistency, thermal recovery process, and adjacency coupling relationship. This avoids the misjudgment and omission problems caused by relying solely on a single temperature exceedance or voltage anomaly in existing technologies, thus making connection status identification more consistent with the actual mechanism of photovoltaic module connection degradation.

[0069] This invention establishes a connection baseline and compares the current connection response data with the historical connection baseline during healthy phases to obtain connection degradation data. Based on this degradation data, it outputs connection stability, connection weakening, and connection anomaly states. This state classification method ensures that monitoring results are no longer simply binary outputs of normal and fault, but reflect the evolution of connection status from healthy to degradation and then to anomaly. It is particularly beneficial for identifying early connection weakening states that have not yet shown significant sustained high temperatures but already exhibit delayed thermal recovery and widening adjacency differences. Therefore, it allows for proactive maintenance actions, reducing power generation losses and on-site risks caused by further deterioration of connection anomalies.

[0070] This invention also adaptively adjusts the monitoring strategy based on connection status data. When the connection status data indicates a stable connection, the existing sampling frequency is maintained; when the connection status data indicates a weakened connection, the sampling frequency is increased; and when the connection status data indicates an abnormal connection, both the sampling frequency and on-site re-inspection are triggered. Therefore, this invention does not merely output monitoring conclusions, but can continue to use these conclusions for subsequent sampling control and maintenance, ensuring that all collected data enters the subsequent processing and control chain, realizing processing from data acquisition and status identification to monitoring strategy output. This approach is more suitable for hierarchical monitoring and prioritized maintenance of key connection locations in large power plants, helping to reduce the frequency of ineffective inspections and improve the utilization rate of maintenance resources.

[0071] For large-scale ground-mounted photovoltaic power stations, mountain photovoltaic power stations, and industrial and commercial rooftop photovoltaic systems, this invention can perform online monitoring of connection points that are affected by long-term temperature differences, humidity, wind vibration, and load fluctuations without changing the original array connection structure. It can also identify connection degradation trends by the response differences under disturbance events. Therefore, compared with existing periodic infrared inspection and simple steady-state threshold alarm methods, it is more suitable for continuous and refined monitoring of photovoltaic arrays that are distributed, have complex environments, and frequently change operating conditions, and has strong engineering application value.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A method for monitoring the connection status of a photovoltaic array module, characterized in that, Includes the following steps: Step S1: Collect connection topology data, module operation data, and connection point thermal response data of each module in the photovoltaic array, establish a connection mapping table, and output the basic connection data; Step S2: Identify connection disturbance events based on module operation data and connection point thermal response data, determine the corresponding target module, adjacent module and connection point location based on connection topology data, extract data for the corresponding time period, and output connection response data; Step S3: Based on the module voltage change, module current change, and connection point temperature change process in the connection response data, calculate the connection offset data, thermal hysteresis data, and adjacent difference data, and establish the connection baseline. Step S4: Compare the connection offset data, thermal hysteresis data, and adjacency difference data with the connection baseline to generate connection degradation data, and output connection status data based on the connection degradation data. Step S5: Determine the monitoring strategy based on the connection status data and input the monitoring strategy to the control terminal.

2. The photovoltaic array module connection status monitoring method as described in claim 1, characterized in that, Step S1 includes the following sub-steps: Step S101: Collect the module number, serial position, adjacent module number and connection point position of each module, and output the connection topology data; Step S102: Collect the module voltage, module current and branch power of each module, and output the module operation data; Step S103: Collect the temperature and temperature rise changes of each connection point and output the thermal response data of the connection points; Step S104: Establish a connection mapping table based on the connection topology data, module operation data, and connection point thermal response data, and output the connection mapping table and corresponding data as connection basic data.

3. The photovoltaic array module connection status monitoring method as described in claim 2, characterized in that, Step S2 includes the following sub-steps: Step S201: Analyze the connection basic data, identify the time period in which the change in module current or the change in branch power exceeds the preset event threshold, and mark it as a connection disturbance event. Step S202: Using the start and end times of the connection disturbance event as boundaries, extract the module voltage, module current, branch power, and connection point temperature within the corresponding time period to establish an event segment; Step S203: Associate the data of each module in the event segment with the corresponding connection point position according to the connection mapping table, and output the connection response data.

4. The photovoltaic array module connection status monitoring method as described in claim 3, characterized in that, Step S3 includes the following sub-steps: Step S301: Calculate the module voltage change amplitude, module current change amplitude, and branch power change amplitude of each module in the event segment based on the connection response data; Step S302: Calculate the response deviation of each module relative to adjacent modules in the same series based on the module voltage change amplitude, module current change amplitude, and branch power change amplitude, and output the connection offset data. Step S303: Calculate thermal hysteresis data based on the temperature drop time of the connection point after the connection disturbance event, and calculate adjacency difference data based on the difference in response deviation of each module. Step S304: Calculate the mean and standard deviation based on the connection offset data, thermal hysteresis data, and adjacency difference data, and establish the connection baseline using the mean and standard deviation.

5. The photovoltaic array module connection status monitoring method as described in claim 4, characterized in that, The logic of step S302 is as follows: For a target module within the same string, find the adjacent module corresponding to its adjacent module number; Calculate the difference in module voltage variation, module current variation, and branch power variation between the target module and adjacent modules in the same event segment; The response deviation is calculated based on the difference in module voltage variation, the difference in module current variation, and the difference in branch power variation. Establish a correspondence between each response deviation and the location of the connection point, and output the connection offset data.

6. The photovoltaic array module connection status monitoring method as described in claim 5, characterized in that, Step S4 includes the following sub-steps: Step S401: Input the connection response data into the connection baseline at the corresponding connection point location, and calculate the degree of deviation of the connection offset data, thermal hysteresis data and adjacency difference data relative to the connection baseline. Step S402: Calculate the connection degradation value based on the degree of deviation, establish the correspondence between the connection degradation value and the connection point position, and output the connection degradation data; Step S403: Analyze the connection degradation data and output the connection status data.

7. The photovoltaic array module connection status monitoring method as described in claim 6, characterized in that, The logic of step S402 is as follows: If any one of the connectivity offset data, thermal hysteresis data, and adjacency difference data exceeds the corresponding connectivity baseline, the connectivity degradation value is increased by one level. If two of the connection offset data, thermal hysteresis data, and adjacency difference data exceed the corresponding connection baseline, the connection degradation value is increased by two levels. If the connection offset data, thermal hysteresis data, and adjacency difference data all exceed the corresponding connection baseline, the connection degradation value is increased by three levels. Output the connection degradation values ​​as connection degradation data.

8. The photovoltaic array module connection status monitoring method as described in claim 7, characterized in that, Step S403 includes the following sub-steps: If the connection degradation value is lower than the preset first state threshold, it is marked as a stable connection state; If the connection degradation value is at or above the first state threshold and below the preset second state threshold, it is marked as a connection weakening state. If the connection degradation value is at or above the second state threshold, it is marked as a connection abnormal state; Output stable connection state, weakened connection state, and abnormal connection state as connection state data.

9. The photovoltaic array module connection status monitoring method as described in claim 8, characterized in that, Step S5 includes the following sub-steps: Step S501: Select a monitoring strategy based on the connection status data. The monitoring strategy includes maintaining the existing sampling frequency, increasing the sampling frequency, and triggering on-site re-inspection. Step S502: If the connection status data indicates a stable connection, then maintain the existing sampling frequency. If the connection status data is in a weakened connection state, then increase the sampling frequency. If the connection status data indicates an abnormal connection status, then trigger on-site re-inspection and increase the sampling frequency. Step S503: Input the monitoring strategy and the corresponding sampling frequency to the control terminal.

10. A photovoltaic array module connection status monitoring system, which is applied in the photovoltaic array module connection status monitoring method as described in any one of claims 1-9, characterized in that, It includes a connection mapping module, a disturbance identification module, a baseline generation module, a status determination module, and a monitoring and control module; The connection mapping module is used to collect connection topology data, module operation data and connection point thermal response data of each module in the photovoltaic array, establish a connection mapping table and output basic connection data. The disturbance identification module is used to identify connection disturbance events based on module operation data and connection point thermal response data, and to determine the corresponding target module, adjacent module and connection point location based on connection topology data, extract data for the corresponding time period, and output connection response data. The baseline generation module is used to calculate connection offset data, thermal hysteresis data, and adjacent difference data based on the module voltage change, module current change, and connection point temperature change process in the connection response data, and to establish a connection baseline. The state determination module is used to compare the connection offset data, thermal hysteresis data, and adjacency difference data with the connection baseline respectively, generate connection degradation data, and output connection status data based on the connection degradation data. The monitoring and control module is used to determine the monitoring strategy based on the connection status data and input the monitoring strategy to the control terminal.

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