Temperature-aware protection system and method based on low voltage photovoltaic intelligent switch

Abstract

The application discloses a temperature sensing protection system and method based on a low-voltage photovoltaic intelligent switch, belongs to the technical field of low-voltage photovoltaic power distribution systems, and aims to solve the problems of easy missed or missed triggering of temperature protection of the associated parts of the intelligent switch, insufficient temperature fitting precision and low efficiency of early warning and disposal in the prior art. The application comprises the following modules: high-incidence sensitive point identification, staged temperature modeling and fitting, abnormality evaluation and early warning and disposal, a matrix is constructed through temperature and system operation parameters, and high-incidence sensitive points are screened in combination with double exponential; special temperature models are adapted to the starting, stable power generation and power mutation stages and are verified and adjusted; multi-dimensional abnormality features are extracted, early warning risk values are calculated, and operation and maintenance sequences are formed in combination with part importance ranking; a protection method corresponds to the core steps of the system; the application improves the temperature sensing precision and protection reliability of the intelligent switch, optimizes fault disposal efficiency, effectively reduces the risk of equipment damage or shutdown of the low-voltage photovoltaic system caused by temperature abnormalities, and adapts to the temperature protection requirements of the low-voltage photovoltaic intelligent switch in all scenarios.

Description

Technical Field

[0001] This invention belongs to the technical field of low-voltage photovoltaic power distribution systems, specifically relating to a temperature sensing protection system and protection method based on low-voltage photovoltaic intelligent switches. Background Technology

[0002] Low-voltage photovoltaic intelligent switches are core control components of low-voltage photovoltaic power distribution systems. The temperature status of their associated electrical connections directly determines the safety and stability of system operation. However, existing temperature protection schemes for this type of switch have the following shortcomings:

[0003] In the electrical connection parts associated with low-voltage photovoltaic smart switches, it is difficult to accurately distinguish between real heat-generating faults and false temperature rises caused by environmental interference, and it is also impossible to efficiently locate key heat-generating parts with high fault frequency and high risk level, which can easily lead to false triggering or missed triggering of switch temperature protection.

[0004] The temperature change patterns of low-voltage photovoltaic smart switches vary significantly during different operating stages such as startup, stable power generation, and power surge. Existing single temperature models are difficult to adapt to the characteristics of each stage, resulting in insufficient temperature fitting accuracy and failing to provide reliable data support for judging switch temperature anomalies.

[0005] The current temperature anomaly assessment indicators for low-voltage photovoltaic smart switches are too simplistic and fail to fully reflect the severity of the anomalies. Furthermore, the lack of a scientific maintenance prioritization mechanism that combines the urgency of the fault with the importance of the location leads to insufficient accuracy in temperature warnings and low efficiency in fault handling. To address this, we propose a temperature sensing protection system and method based on low-voltage photovoltaic smart switches. Summary of the Invention

[0006] The purpose of this invention is to provide a temperature sensing protection system and method based on a low-voltage photovoltaic smart switch to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch, comprising:

[0008] High-risk sensitive point identification module: Collects the temperature of electrical connection parts and core operating parameters of photovoltaic system, constructs a matrix, and screens candidate heat-sensitive points according to temperature fluctuation value and peak value; calculates the thermal sensitivity evaluation index by combining the temperature of sensitive points with operating parameters, calculates the historical fault over-temperature index based on historical fault data, and screens high-risk temperature sensitive points accordingly.

[0009] The phased temperature modeling and fitting module calculates the photovoltaic module's operating load index, first derivative, second derivative, and adjacent fluctuation amplitude, determines the startup, stable power generation, and power surge stages, integrates continuous time periods, constructs a dedicated model for temperature-sensitive points according to the corresponding stage, verifies the parameters through the coefficient of determination and root mean square error, and adjusts the model if it is not qualified until it meets the standard.

[0010] Anomaly assessment, early warning, and handling module: Extracts the temperature rise rate, temperature fluctuation residual, synergy of multiple sensitive points, and trend deviation during qualified periods of high-temperature sensitive points, integrates them to form comprehensive features and normalizes them, calculates early warning risk values, and marks temperature fault points; matches the importance values ​​of key parts, calculates priority handling evaluation values, sorts them according to these values ​​to form a fault handling sequence, and pushes it to the operation and maintenance end.

[0011] Preferably, the specific process for screening candidate heat-sensitive points is as follows:

[0012] Distributed sensors are used to collect temperature data from all electrical connection points of the entire low-voltage photovoltaic power distribution chain during the acquisition period, and a temperature time series matrix is ​​constructed. At the same time, the core operating parameters of the photovoltaic system at each moment are collected, and an operating parameter time series matrix is ​​constructed.

[0013] For each electrical connection, the variance of the temperature value within the acquisition period is calculated to obtain the temperature fluctuation value, and the temperature peak value is obtained.

[0014] If the temperature fluctuation exceeds the preset temperature fluctuation baseline variance, or the temperature peak exceeds the preset temperature threshold, the location is marked as a candidate heat-sensitive point; all candidate heat-sensitive points are compiled to form a set of candidate heat-sensitive points.

[0015] The preferred process for screening temperature-sensitive points is as follows:

[0016] For each candidate heat-sensitive point, its temperature time series and the time series of each operating parameter are extracted from the temperature time series matrix and the operating parameter time series matrix, respectively.

[0017] The Pearson correlation coefficient is calculated with temperature as the dependent variable and operating parameters as independent variables, and the absolute value is taken to obtain the thermal parameter correlation coefficient. Combined with the preset weighting coefficient of operating parameters, the thermal sensitivity evaluation index is obtained. If the index reaches the preset threshold, it is marked as a heat-sensitive point.

[0018] Retrieve historical fault data related to temperature, calculate the percentage of historical over-temperature occurrences, and obtain the historical fault over-temperature index by combining the severity weight of the fault; if the index reaches a preset threshold, mark it as a high-temperature sensitive point; integrate all high-temperature sensitive points to form a set of high-temperature sensitive points.

[0019] Preferably, the specific process for calculating the photovoltaic module operating load index and its first derivative, second derivative, and adjacent fluctuation amplitude is as follows:

[0020] The power generation and loop current of the photovoltaic module at each moment within the current acquisition period are obtained. Combined with the rated power, rated current and preset weighting coefficient of the photovoltaic module, the operating load index corresponding to each moment is obtained.

[0021] Based on the numerical differentiation method, the first derivative, second derivative, and adjacent fluctuation amplitude of the operating load index are calculated for each data acquisition time.

[0022] Among them, the first derivative represents the rate of change at the corresponding acquisition time, the second derivative represents the acceleration of change at the corresponding acquisition time, the adjacent fluctuation amplitude represents the stability of the operating state at the corresponding acquisition time and the previous time, and the adjacent fluctuation amplitude at the first acquisition time is zero.

[0023] Preferably, the specific process for determining the start-up, stable power generation, and power surge phases and integrating the continuous time periods is as follows:

[0024] Preset thresholds for startup load, first derivative of operating load index, second derivative of operating load index, and adjacent fluctuation range of operating load index.

[0025] The startup phase must simultaneously meet four conditions: load range, rapid load increase, stable rate of increase, and no significant fluctuations between adjacent time periods.

[0026] During the stable power generation phase, three conditions must be met simultaneously: extremely slow load change rate, no significant acceleration change, and minimal fluctuation between adjacent time periods.

[0027] The power surge phase meets either the condition of a sudden load change or an excessively large adjacent fluctuation amplitude. In case of a conflict, the start-up phase is prioritized.

[0028] Obtain the operating load index and its first derivative, second derivative, and adjacent fluctuation amplitude at each time point, match the stage standard and mark the operating stage label, and merge consecutive time periods with the same label to form an initial set;

[0029] If the fragmented time period is preceded and followed by non-fragmented time periods of the same type, it is merged into that time period; if it exists only on one side, it is merged into that side; if it exists independently, it is merged into the adjacent time period of the same type, thus obtaining the final set of time periods.

[0030] For each temperature-sensitive point, the temperature sequence is divided according to the start and end times of each consecutive time period in the final time period set, resulting in a stage-temperature subsequence that corresponds one-to-one with each time period.

[0031] The preferred method for constructing a dedicated model for temperature-sensitive areas at the corresponding stage is as follows:

[0032] For the stage-temperature subsequence, if its operation stage label is the start-up stage, a first-order linear model is adopted, and the model includes the temperature intercept parameter and temperature-time coefficient specific to this stage.

[0033] If the label is for the stable power generation stage, a third-order polynomial model is used. The model includes the basic temperature intercept parameter, and the coefficients of the first, second, and third terms of temperature change over time.

[0034] If the label is the power change phase, a piecewise linear model is adopted with the load change time as the boundary, and corresponding dedicated model parameters are configured before and after the change time.

[0035] Preferably, after solving for the parameters, the model is verified using the coefficient of determination and root mean square error. If the model fails to meet the requirements, adjustments are made until it meets the standards. The specific process is as follows:

[0036] For each temperature-sensitive point, with the goal of minimizing the sum of squared deviations between the fitted temperature and the actual temperature for the corresponding time period, the least squares method is used to solve for the specific parameters of the model for the startup, stable power generation, and power surge stages.

[0037] The model is quantitatively verified using two indicators: the coefficient of determination and the root mean square error. Both thresholds are preset. If both are met, the model is considered qualified and the corresponding time period is marked as a qualified time period. If they are not met, the general model order is adjusted, and the data is substituted again to repeat the solution and verification until the model meets the dual indicator standard.

[0038] Preferably, the specific process for calculating the early warning risk value and marking the temperature fault point is as follows:

[0039] For each qualified time period of high-temperature sensitive points, four core abnormal features are extracted: temperature rise rate, temperature fluctuation residual, synergy of multiple sensitive points, and trend deviation.

[0040] By integrating the characteristics of each qualified time period, we obtain the comprehensive temperature rise rate, comprehensive fluctuation residual, comprehensive synergy, and comprehensive trend deviation.

[0041] The four comprehensive characteristics are normalized and dimensionless, and the warning risk value is calculated by combining the preset weight coefficients.

[0042] If the warning risk value reaches the preset threshold, a temperature fault warning will be triggered, and the high-temperature sensitive point will be marked as a temperature fault point.

[0043] Preferably, the specific process of forming a fault handling sequence and pushing it to the operation and maintenance end is as follows:

[0044] Construct a value library for assigning importance to temperature-sensitive points, in which each temperature-sensitive point is configured with a preset importance value for key components; match temperature fault points with this value library to obtain the corresponding importance values ​​for key components;

[0045] For each temperature fault point, the priority evaluation value is calculated based on the pre-set weighting coefficient, combining its early warning risk value and the importance value of key parts.

[0046] All temperature fault points are sorted from largest to smallest according to the priority evaluation value to form a priority handling sequence for temperature faults. This sequence is sent to the fault operation and maintenance terminal, which then carries out operation and maintenance processing in sequence.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] (1) The temperature sensing protection system and protection method based on low-voltage photovoltaic smart switch screens potential hot spots in electrical connection parts of smart switch by temperature fluctuation value and peak value. Combined with the thermal evaluation index, it distinguishes between real electrical fault temperature rise and environmental distortion false temperature rise. The index is calculated based on the Pearson correlation coefficient and preset weight coefficient of temperature and system operating parameters. Then, it focuses on high fault risk parts based on historical fault over-temperature index, realizes accurate identification of temperature-sensitive points, avoids false triggering or missed triggering of smart switch temperature protection, and provides clear targets for subsequent protection actions.

[0049] (2) The temperature sensing protection system and protection method based on low-voltage photovoltaic smart switch, by accurately dividing the time period according to the start-up, stable power generation and power change stages of the smart switch, by quantifying the operating load index and its derivative characteristics, adapting the exclusive temperature model and solving the parameters respectively, and with the dual index verification and model adjustment mechanism, achieves high-precision fitting of temperature changes in each stage, making the temperature sensing of the smart switch more in line with the actual operating state, ensuring that the protection action is based on the actual temperature anomaly trigger, and improving the reliability of temperature protection.

[0050] (3) The temperature sensing protection system and protection method based on low-voltage photovoltaic smart switch extracts four core temperature anomaly features of the high-temperature sensitive points associated with the smart switch, integrates them and normalizes them to calculate the early warning risk value, and then combines the importance value of the parts to form a disposal sequence. This not only achieves accurate sensing and early warning of temperature anomalies in the relevant parts of the smart switch, but also provides scientific priority guidance for operation and maintenance, making temperature protection disposal more timely and efficient, and effectively reducing the risk of equipment damage or shutdown caused by temperature anomalies in the relevant parts of the smart switch in the low-voltage photovoltaic system. Attached Figure Description

[0051] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0053] Please see Figure 1 The present invention provides a temperature sensing and protection system based on a low-voltage photovoltaic smart switch, comprising: a high-sensitivity point identification module, a phased temperature modeling and fitting module, and an anomaly assessment, early warning and handling module;

[0054] High-risk sensitive point identification module: Collects temperature data from electrical connection points and core operating parameters of the photovoltaic system, constructs a matrix and preprocesses it, and filters candidate heat-generating sensitive points according to temperature fluctuation and peak values; calculates the thermal sensitivity evaluation index by combining the temperature of the sensitive points with the operating parameters, calculates the historical fault over-temperature index based on historical fault data, and filters high-risk temperature sensitive points accordingly, compiling them into a set. The specific process is as follows:

[0055] Distributed sensors are used to collect temperature data from all electrical connection points in the entire low-voltage photovoltaic power distribution chain during the acquisition period. These electrical connection points include: photovoltaic module output terminals, inverter terminals, smart switch in / out terminals, busbar connectors, cable connection points, fuse contacts, load access terminals, etc.

[0056] Based on the temperature data of each electrical connection at each acquisition time, a temperature time series matrix is ​​constructed. Where x is the label of the electrical connection part, M represents the total number of electrical connections in the entire low-voltage photovoltaic power distribution chain, and y represents the index of the data acquisition time. N is the total number of acquisition moments in the acquisition period. Let y be the temperature value corresponding to the xth electrical connection point at the yth sampling time.

[0057] Preprocess the data in the temperature time series matrix;

[0058] The core operating parameters of the photovoltaic system at each acquisition time are collected. These core operating parameters include: power generation, loop current, bus voltage, etc., monitored by the intelligent switch, and a time-series matrix of operating parameters is constructed. ; m is the label of the core operating parameter. G represents the total number of operating parameters;

[0059] Perform preprocessing operations on the data in the time series matrix of runtime parameters;

[0060] For each electrical connection, the variance of the temperature value at each acquisition time within the acquisition period is calculated to obtain the temperature fluctuation value; at the same time, the peak temperature of the electrical connection within the acquisition period is obtained.

[0061] Preset the baseline variance of temperature fluctuation and the temperature threshold;

[0062] If the temperature fluctuation value of the electrical connection part is greater than the corresponding preset temperature fluctuation benchmark variance or the temperature peak value is greater than the corresponding preset temperature threshold, then the electrical connection part is marked as a candidate heat-sensitive point.

[0063] All candidate heat-sensitive points within the collection period are organized to form a candidate heat-sensitive point set;

[0064] For each candidate heat-sensitive point, its temperature time series is extracted from the temperature time series matrix, and the time series corresponding to each operating parameter is extracted from the operating parameter time series matrix. Using the temperature of the sensitive point as the dependent variable and the corresponding operating parameters as independent variables, the Pearson correlation coefficient is calculated and its absolute value is taken to obtain the thermal correlation coefficient of the candidate heat-sensitive point relative to each operating parameter. And using the formula: The thermal sensitivity index RP was obtained, among which... , which is the preset weighting coefficient corresponding to the m-th operating parameter; where, the larger the thermal sensitivity evaluation index, the higher the degree of consistency between the candidate heat-sensitive point and the synchronous fluctuation of each core operating parameter; if it causes temperature abnormality, it is more likely to be a temperature rise driven by a real electrical fault, rather than a false temperature rise caused by environmental distortions such as outdoor exposure to the sun or electromagnetic interference.

[0065] A preset thermal sensitivity evaluation index threshold is set. If the thermal sensitivity evaluation index corresponding to a candidate thermal sensitivity point is greater than or equal to the corresponding preset threshold, the candidate thermal sensitivity point is marked as a thermal sensitivity point.

[0066] For each heat-sensitive point, retrieve the temperature-related historical fault data of the low-voltage photovoltaic smart switch; within the collection period corresponding to each historical temperature-related fault condition, count the number of times the current heat-sensitive point temperature value exceeds the preset temperature threshold, and divide by the total number of collection times in the corresponding collection period to obtain the historical over-temperature frequency ratio under the corresponding historical temperature-related fault condition.

[0067] Assign a corresponding fault severity weight to each historical temperature-related fault condition; the higher the fault severity, the greater the weight, and the fault severity weights of all temperature-related fault conditions are added together to equal one.

[0068] The historical fault over-temperature index of the heat-sensitive point is obtained by multiplying the proportion of historical over-temperature frequency of each historical temperature-related fault condition by the corresponding fault severity weight and summing them up.

[0069] A preset historical fault over-temperature index threshold is set. If the historical fault over-temperature index of a heat-sensitive point is greater than or equal to the corresponding preset threshold, the heat-sensitive point is marked as a high-temperature sensitive point.

[0070] Integrate all temperature-sensitive points to form a set of temperature-sensitive points.

[0071] It should be noted that the initial screening stage is supported by temperature time series matrix and operating parameter time series matrix. The temperature fluctuation value is obtained by calculating the variance of temperature values ​​within the acquisition period of each electrical connection part, and the temperature peak value is extracted. Parts that exceed the preset benchmark variance or temperature threshold are marked as candidate heat-sensitive points, which quickly narrows down the monitoring range and ensures the efficiency of screening.

[0072] The screening stage achieves accurate identification through dual index calculations: First, the thermal sensitivity evaluation index extracts the temperature time series and the time series of each operating parameter of the candidate points from two matrices. The Pearson correlation coefficient is calculated with temperature as the dependent variable and operating parameters as independent variables, and the absolute value is taken to obtain the thermal parameter correlation coefficient. This is then combined with the preset weight coefficient of the operating parameters to obtain the index. This index can effectively quantify the degree of synchronization between the candidate point temperature and the system operating parameters, accurately eliminate false temperature rises caused by environmental distortions such as outdoor exposure and electromagnetic interference, and lock the heat-sensitive points driven by real electrical faults. Second, the historical fault over-temperature index retrieves temperature-related historical fault data, counts the proportion of historical over-temperature frequency of candidate points under each fault condition, and calculates the index by accumulating the fault severity weight. This can specifically focus on key parts with high fault frequency and high severity, improving the targeting of identification.

[0073] The final integrated set of temperature-sensitive points provides a clear target for the phased temperature modeling and fitting module's operational stage division and dedicated model construction. This eliminates the need for modeling to cover all electrical components, significantly improving modeling efficiency and fitting accuracy. Simultaneously, it lays a precise data foundation for feature extraction and risk value calculation in the anomaly assessment, early warning, and handling module. This ensures that subsequent early warning and handling can directly target high-risk areas, significantly reducing the probability of misjudgment and missed judgment in the entire system, and improving the overall efficiency of fault prediction and maintenance.

[0074] The phased temperature modeling and fitting module calculates the photovoltaic module's operating load index, first derivative, second derivative, and adjacent fluctuation amplitudes. It identifies the startup, stable power generation, and power surge stages and integrates continuous time periods. For each corresponding stage, it constructs a dedicated model for temperature-sensitive high-incidence points. After solving the parameters, it verifies the model using the coefficient of determination and root mean square error. If the model is unqualified, it is adjusted until it meets the standards. The specific process is as follows:

[0075] Obtain the power generation of the photovoltaic modules at each acquisition time within the current acquisition period. With loop current And using the formula: The operating load index corresponding to each data collection time is obtained. ;

[0076] in, and These are the rated power and rated current of the photovoltaic module, respectively, with a1 and a2 being preset weighting coefficients;

[0077] For each data acquisition moment, the derivative characteristics are calculated using numerical differentiation methods. Specifically, the first and second derivatives of the operating load index at each acquisition moment, as well as the adjacent fluctuation amplitudes, are calculated.

[0078] First derivative Characterization The rate of change at the y-th acquisition time (reflecting how fast the operating state changes):

[0079] Using the formula: ,

[0080] in, The time interval between adjacent data collection moments;

[0081] Second derivative Characterization The change in acceleration at the y-th acquisition time (reflecting the degree of drastic change in operating state);

[0082] Using the formula: ;

[0083] Adjacent fluctuation amplitude : Represents the stability of the operating state at the y-th acquisition time compared to the previous time;

[0084] Using the formula: When y=1, Zero;

[0085] The criteria for determining the startup phase, stable power generation phase, and power surge phase of a low-voltage photovoltaic power distribution system are established as follows:

[0086] The criteria for determining the start-up phase must be met simultaneously:

[0087] ( (This refers to the startup load threshold, corresponding to the component startup power ratio).

[0088] (Indicates a rapid increase in load);

[0089] (This indicates a steady rate of ascent without drastic acceleration or deceleration.)

[0090] (Indicates that there are no significant fluctuations between adjacent time points);

[0091] The criteria for determining a stable power generation phase must simultaneously meet the following:

[0092] (This indicates that the rate of load change is extremely slow);

[0093] (Indicates no significant change in acceleration);

[0094] (This indicates that the fluctuations between adjacent time points are minimal);

[0095] During the power surge phase, any of the following conditions must be met:

[0096] (This indicates a sudden and drastic increase / decrease in load).

[0097] (This indicates significant load fluctuations between adjacent time points);

[0098] When the startup phase and the mutation phase conflict, the startup phase takes precedence (because the startup phase is a specific process of the initial operation of the system).

[0099] in, Preset threshold for startup load, A threshold is preset for the first derivative of the operating load index. Preset threshold for the second derivative of the operating load index. Preset thresholds for adjacent fluctuations in the operating load index;

[0100] The system acquires the operating load index, its first and second derivatives, and adjacent fluctuation amplitudes at each acquisition time, and matches them with the criteria for determining the start-up phase, stable power generation phase, and power burst phase. It then outputs the corresponding operating phase (start-up phase, stable power generation phase, and power burst phase) and labels it.

[0101] Starting from the first collection moment of the collection cycle, sequentially determine whether the labels of the current moment and the previous moment's running stage are consistent;

[0102] If multiple consecutive time points meet the criteria, have the same and adjacent labels, they are merged into a single continuous time period, and the start and end times of that time period are recorded.

[0103] After the traversal is complete, all consecutive time periods will be integrated according to the time order to obtain the initial time period set;

[0104] Traverse the initial time period set, preset a minimum continuous duration threshold. If the continuous duration of the initial time period set is less than the minimum continuous duration threshold, it is determined to be a fragmented time period.

[0105] If the fragmented period is preceded and followed by two non-fragmented periods of the same type, then the fragmented period is merged into the preceding and following periods.

[0106] If a fragmented time period exists only on one side and is not fragmented, it is directly merged into the non-fragmented time period on that side.

[0107] If a fragmented time period exists independently, it is merged into the nearest adjacent time period of the same type based on its label type (determined by a combination of label similarity and time distance).

[0108] After integration, a complete and continuous set of final time periods for the operational phases is obtained;

[0109] For each high-temperature sensitive point Based on the complete temperature time series data within its collection period, the temperature sequence is divided into stages according to the start and end times of each continuous time period in the final time period set of the operation stage, to obtain a stage-temperature subsequence that corresponds one-to-one with each time period in the set; where i is the label of the high temperature point, i=1,2,...,n, and n is the total number of high temperature sensitive points.

[0110] If the stage label of the continuous time period corresponding to the stage-temperature subsequence is the start-up stage, then substituting it into the first-order linear model, the expression is: ,in, , and This refers to the start and end times of the continuous period. Sensitive points for high temperature The fitted temperature at time y during the startup phase. and Sensitive points for high temperature The specific startup phase model parameters are: the temperature intercept parameter in the startup phase and the temperature-time coefficient in the restart phase.

[0111] If the operating stage label of the stage-temperature subsequence corresponding to the continuous time period is the stable power generation stage, then substituting it into the third-order polynomial model, the expression is:

[0112] ,in, Sensitive points for high temperature Let y be the fitted temperature at time y during the stable power generation phase; , , , Sensitive points for high temperature The exclusive model parameters are: the basic temperature intercept parameter of the high temperature sensitivity point in the stable power generation stage, the coefficient of the first term of temperature changing with time, the coefficient of the second term of temperature changing with time, and the coefficient of the third term of temperature changing with time.

[0113] If the stage label of the stage-temperature subsequence corresponding to the continuous time period is the power mutation stage, then substituting it into the piecewise linear model with the mutation time as the boundary, the expression is: ,in, Sensitive points for high temperature The fitted temperature at time y during the power mutation phase. The moment of load change within this continuous period (determination method: when the power fluctuation amplitude at a certain moment exceeds the preset threshold, that moment is the moment of load change). , Sensitive points for high temperature Dedicated model parameters prior to the moment of load mutation; , Sensitive points for high temperature Dedicated model parameters after the moment of load mutation;

[0114] For each high-temperature sensitive point All models aim to minimize the sum of squared deviations between the fitted temperature and the actual temperature within the corresponding time period. The least squares method is used to solve for the specific parameters of the general model at each stage. The specific solution process is as follows:

[0115] Solving for specific parameters during the startup phase, and constructing the objective function as follows:

[0116] ,in, Sensitive points for high temperature The objective function value corresponding to the startup phase represents the minimum sum of squared deviations between the actual temperature and the fitted temperature during this phase. Sensitive points for high temperature The actual temperature value at time y during the startup phase;

[0117] and Taking the partial derivative with respect to 0 and setting it equal to 0, we obtain the parameter estimates. and ( , for , (Estimation results)

[0118] Solving for specific parameters during the stable power generation phase, the objective function is constructed as follows:

[0119] ,

[0120] in, Sensitive points for high temperature The objective function value corresponding to the stable power generation stage represents the minimum value of the sum of squares of the deviations between the actual temperature and the fitted temperature during this stage.

[0121] right , , , Find the partial derivative and set it equal to 0 to obtain the parameter estimate. , , , ;

[0122] Power mutation phase: Construct objective functions for the data before and after the mutation time respectively:

[0123] The objective function before the mutation was: ;

[0124] The objective function after mutation is: ;

[0125] For the parameters in the objective function before mutation , and the parameters in the objective function after mutation , Find the partial derivatives and set them equal to 0. Then, solve the system of equations to obtain the parameter estimates. , , , ;

[0126] For each high-temperature sensitive point In each runtime phase, the model is quantitatively verified using both the coefficient of determination and the root mean square error to ensure that the model fitting accuracy meets the subsequent early warning requirements.

[0127] Coefficient of determination The expression is:

[0128] ,in, Sensitive points for high temperature The temperature value at the y-th data collection time. Sensitive points for high temperature The fitted temperature at the y-th acquisition time, Sensitive points for high temperature During the period The average temperature inside;

[0129] The coefficient of determination characterizes the model's ability to explain the temperature change trend at temperature-sensitive points with high incidence rates; the closer the value is to 1, the better the fit.

[0130] 2. Root mean square error The expression is:

[0131]

[0132] in, For time period Number of samples within;

[0133] The average deviation between the fitted temperature value and the actual value at the temperature sensitive point where high temperature incidence occurs; the smaller the value, the higher the fitting accuracy.

[0134] Preset determination coefficient threshold With root mean square error threshold If the runtime model simultaneously meets the following two conditions, then the runtime model is deemed qualified, and the runtime segment is marked as a qualified time period:

[0135] ;

[0136] ;

[0137] If any of the above conditions are not met, the model is deemed unqualified. The order of the general model is adjusted, and the corresponding time period data of the high-temperature sensitivity point is re-substituted after adjustment. The above-mentioned process of solving the specific parameters and quantification verification is repeated until the model meets the dual-index verification standard, ensuring that the model of each high-temperature sensitivity point has a reliable fitting effect in each operating period.

[0138] Furthermore, the order of the general model can be adjusted, specifically including:

[0139] During the stable power generation phase: The core type of the general model is a polynomial model. If the third-order polynomial model fails to accurately adapt to the nonlinear temperature fluctuation characteristics, the model order can be reduced to a second-order polynomial model to optimize the fitting accuracy by reducing the number of polynomial terms.

[0140] Start-up phase: The core type of the general model is a first-order linear model. If the fitting deviation is due to insufficient adaptation between the linear constraints and the heating trend, the constraints of the linear model can be optimized (specifically, the fitting weights of the temperature-time coefficients can be adjusted) to enhance the model's fit to the linear heating law.

[0141] During the power mutation phase: The core type of the general model is a piecewise linear model. If the linear fitting of the two segments before and after the mutation fails to accurately capture the temperature rate jump characteristics, the accuracy of segment node determination can be optimized (i.e., the identification threshold of the load mutation moment can be refined to ensure that the mutation moment is completely matched with the actual power mutation moment), or the fitting interval boundary of the two linear models can be adjusted (so that the interval completely corresponds to the stable temperature change interval before and after the mutation) to improve the adaptability of the piecewise fitting.

[0142] It should be noted that the operating load index is calculated based on the power generation and loop current of the photovoltaic module, combined with the rated power, rated current and preset weighting coefficients. Then, the first derivative, second derivative and adjacent fluctuation amplitude are obtained by numerical differentiation method. These three factors quantify the speed, intensity and stability of the change in the operating state, providing an objective and detailed quantitative basis for subsequent stage judgment, and avoiding the subjectivity of dividing stages based solely on experience.

[0143] The startup phase requires meeting four conditions, including load range and rapid rise; the stable power generation phase requires meeting three conditions, including extremely slow rate of change; and the power surge phase requires meeting any one of the conditions, namely, sudden load change or excessive fluctuation. In case of conflict, the startup phase is prioritized. At the same time, by merging continuous time periods and processing fragmented time periods, a complete and continuous final time period set is formed, ensuring the consistency of temperature data characteristics in each phase and eliminating data interference for targeted modeling.

[0144] A first-order linear model is used to address the linear temperature rise during the start-up phase, a third-order polynomial model is used to address the nonlinear fluctuations during the steady phase, and a piecewise linear model is used to address the rate jumps during the abrupt change phase. The least squares method is used to solve for the specific parameters of each model (with the goal of minimizing the sum of squares of the deviations between the fitted temperature and the actual temperature), so that the model can accurately adapt to the temperature change patterns of different phases.

[0145] The model is quantitatively verified using two indicators: the coefficient of determination (characterizing the explanatory power of the trend) and the root mean square error (characterizing the fitting deviation). When the threshold is not reached, the model is adjusted in a targeted manner: the polynomial order is reduced in the stable phase, the linear constraints are optimized in the startup phase, and the segment nodes are refined or the fitting interval is adjusted in the mutation phase, so as to ensure that the model has high fitting accuracy for each temperature high-incidence sensitive point in each time period.

[0146] Ultimately, the output qualified stage model and qualified time period provide an accurate temperature fitting data foundation for the anomaly assessment and early warning handling module to extract features such as temperature rise rate and temperature fluctuation residuals, effectively reducing the error of subsequent feature extraction and risk calculation, and significantly improving the early warning accuracy and reliability of the entire protection system.

[0147] The anomaly assessment, early warning, and handling module extracts the temperature rise rate, temperature fluctuation residual, multi-sensitive point synergy, and trend deviation during qualified periods of high-temperature sensitive points. These data are then integrated to form a comprehensive feature set, normalized, and used to calculate early warning risk values ​​and mark temperature fault points. The module matches the importance values ​​of key components, calculates priority handling evaluation values, and sorts these values ​​to form a fault handling sequence, which is then pushed to the operations and maintenance end. The specific process is as follows:

[0148] Targeting high-temperature sensitive points For each qualified time period q, q=1,2,...,Q, where Q is the total number of qualified time periods, the following core abnormal feature values ​​are extracted, including:

[0149] Temperature rise rate Characterization The rate of temperature increase / decrease in the q-th time period reflects the drasticness of temperature change, and is expressed as:

[0150] Start-up phase: ;

[0151] Stable power generation phase:

[0152] ;

[0153] Power mutation phase: ;

[0154] Temperature fluctuation residual Characterization The degree of deviation between the actual temperature and the fitted temperature during the q-th time period reflects the temperature stability, and is expressed as:

[0155] ,

[0156] The larger the temperature fluctuation residual, the more significant the deviation of the actual temperature from the normal trend, and the more likely there are abnormal risks such as poor local contact or overload.

[0157] Multi-sensory point synergy Characterization Synchronization with temperature changes at other temperature-sensitive points is used to distinguish between single-point, occasional anomalies and system-level fault-driven anomalies. The expression is:

[0158] ,in: Sensitive points for high temperature Within the acceptable time interval q, the fitted temperature at time y is... For the kth temperature-sensitive point The fitted temperature at time y within the acceptable time period q. The function for calculating the Pearson correlation coefficient;

[0159] The closer the synergy of multiple sensitive points is to 1, the better. The more synchronized the temperature changes are with other temperature-sensitive points, the more likely it is caused by global factors or cascading failures such as abnormal system load or power grid fluctuations, rather than single-point environmental interference.

[0160] Trend deviation Characterization The degree of deviation between the current temperature trend and the historical normal operating condition trend, excluding abnormal fluctuations, is expressed as:

[0161] ,in The core parameters of the model for the current time period (taken during the startup phase) During the stable phase, take Mutation stage ), It is the statistical average value of core parameters under the same period of normal operating conditions in history (calculated based on more than 3 years of fault-free operation data).

[0162] When the trend deviation exceeds the preset benchmark value, it indicates that the current temperature change pattern has deviated from the normal range and requires close attention.

[0163] Sensitive points for high temperature The temperature rise rate, temperature fluctuation residual, multi-sensor synergy, and trend deviation of all qualified time periods are fused to obtain comprehensive characteristics of the entire acquisition cycle, enabling the analysis of... A comprehensive assessment of the temperature conditions is as follows:

[0164] Overall temperature rise rate: Focusing on the most dramatic rate of temperature change throughout the entire cycle, highlighting key anomaly signals;

[0165] Overall fluctuation residual: It captures the largest temperature deviation within the entire cycle, reflecting the most severe stability anomalies;

[0166] Comprehensive synergy: By averaging, the accidental synchronicity of single-point periods is weakened, while the systematic synchronicity characteristics over the entire cycle are strengthened.

[0167] Overall trend deviation: A comprehensive assessment of the overall deviation between the temperature trend throughout the entire cycle and historical normal operating conditions is conducted.

[0168] After normalizing and dimensionlessly processing the comprehensive temperature rise rate, comprehensive fluctuation residual, comprehensive synergy, and comprehensive trend deviation, the formula is used: High-temperature sensitive points were obtained. Corresponding warning risk value ;

[0169] Where r1, r2, r3, and r4 are preset weight coefficients;

[0170] A preset risk threshold for high-incidence sensitive points is set. If the risk value of a high-incidence sensitive point is greater than or equal to the corresponding preset threshold, a temperature fault warning for the high-incidence sensitive point is triggered, and the high-incidence sensitive point is marked as a temperature fault point.

[0171] Construct a library of importance values ​​for high-temperature-incidence sensitive points, in which each high-temperature-incidence sensitive point corresponds to a preset importance value for a key component;

[0172] By substituting temperature fault points into the importance assignment library of temperature-sensitive high-incidence points, the corresponding importance values ​​of key components are matched.

[0173] For each temperature fault point, obtain its corresponding early warning risk value GF and critical component importance value ZY, and use the formula: The review priority processing evaluation value GY is obtained, where h1 and h2 are preset weight coefficients;

[0174] All temperature fault points to be processed are obtained and sorted from largest to smallest based on the corresponding temperature fault priority processing evaluation value to obtain a temperature fault priority processing sequence, which is then sent to the fault operation and maintenance terminal. The fault operation and maintenance terminal performs operation and maintenance processing on the temperature fault points in the sequence in turn based on the temperature fault priority processing sequence.

[0175] It should be noted that for each qualified time period of high temperature sensitivity points, four core abnormal features are accurately extracted: the temperature rise rate directly reflects the severity of temperature change; the temperature fluctuation residual quantifies the deviation between the actual temperature and the fitted temperature (which can promptly detect hidden dangers such as poor local contact and overload); the synergy of multiple sensitive points is calculated through Pearson correlation coefficient, which can effectively distinguish between single-point accidental anomalies and anomalies driven by system-level faults; and the trend deviation is based on the statistical average of core parameters of more than 3 years of historical fault-free data, which can eliminate the interference of normal fluctuations. The four features comprehensively cover different dimensions of anomalies and avoid misjudgment by a single indicator.

[0176] The comprehensive temperature rise rate focuses on the most dramatic changes throughout the entire cycle, highlighting key abnormal signals; the comprehensive fluctuation residual captures the maximum deviation, locking in the most serious stability problems; the comprehensive coordination takes the average value to weaken accidental synchronization and strengthen the characteristics of systematic anomalies; the comprehensive trend deviation comprehensively assesses the deviation from historical normal operating conditions, ensuring that no anomaly is missed and making the temperature status assessment more three-dimensional.

[0177] By eliminating unit differences of different features through normalization and dimensionless processing, and combining the pre-set weighting coefficients to calculate the early warning risk value, the risk level of each sensitive point can be objectively measured, ensuring that high-risk areas are accurately marked as temperature fault points, and avoiding the subjectivity of risk assessment.

[0178] By combining the early warning risk value with the importance of the location to calculate the priority evaluation value, and sorting them according to the value to form a processing sequence, the urgency of the fault is taken into account, as well as the impact weight of the location on the system. This allows maintenance work to focus on priorities without blindly investigating, which greatly improves maintenance efficiency and shortens the fault handling cycle.

[0179] By closely linking the qualified time period and fitted temperature data of the phased temperature modeling and fitting module, the system achieves accurate extraction of abnormal features based on precise fitting. At the same time, through a closed-loop early warning-sorting-push mechanism, the abstract temperature data is transformed into actionable operation and maintenance instructions, which significantly improves the accuracy of abnormal early warning and the targeted nature of fault handling of the entire protection system, and effectively reduces the risk of equipment damage or shutdown caused by abnormal temperature in low-voltage photovoltaic systems.

[0180] Temperature sensing protection methods based on low-voltage photovoltaic smart switches include:

[0181] Step 1: Collect the temperature of electrical connection parts and the core operating parameters of the photovoltaic system, construct a matrix, and screen candidate heat-sensitive points according to temperature fluctuation values ​​and peak values; calculate the thermal sensitivity evaluation index by combining the temperature of the sensitive points with the operating parameters, calculate the historical fault over-temperature index based on historical fault data, and screen high-temperature sensitive points accordingly.

[0182] Step 2: Calculate the photovoltaic module operating load index and its first derivative, second derivative, and adjacent fluctuation amplitude. Determine the startup, stable power generation, and power surge stages and integrate them into continuous time periods. Construct a dedicated model for the corresponding stage as the temperature high-incidence sensitive point. After solving the parameters, verify them through the coefficient of determination and root mean square error. If the model is not qualified, adjust it until it meets the standard.

[0183] Step 3: Extract the temperature rise rate, temperature fluctuation residual, synergy of multiple sensitive points and trend deviation during qualified periods of high temperature sensitivity points, integrate them to form comprehensive features and normalize them, calculate the early warning risk value, mark the temperature fault point; match the importance value of key parts, calculate the priority processing evaluation value, sort them according to the value to form a fault processing sequence and push it to the operation and maintenance end.

[0184] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch, characterized in that, include: High-incidence sensitive point identification module: Collects the temperature of electrical connection parts and core operating parameters of photovoltaic system, constructs a matrix, and screens candidate heat-generating sensitive points according to temperature fluctuation value and peak value; calculates the thermal sensitivity evaluation index by combining the temperature of sensitive points with operating parameters, calculates the historical fault over-temperature index based on historical fault data, and screens candidate heat-generating sensitive points whose thermal sensitivity evaluation index reaches a preset threshold and whose historical fault over-temperature index reaches a preset threshold as high-incidence temperature sensitive points. The phased temperature modeling and fitting module obtains the power generation and loop current of the photovoltaic module at each moment in the current acquisition period. It calculates the photovoltaic module operating load index by combining the rated power, rated current and preset weight coefficient of the photovoltaic module. It calculates the photovoltaic module operating load index and its first derivative, second derivative and adjacent fluctuation amplitude, determines the start-up, stable power generation and power change stage and integrates the continuous time period. It builds a special model for the temperature high-incidence sensitive point according to the corresponding stage. In the dedicated model, the first-order linear model for the startup phase uses time as the independent variable and fitted temperature as the dependent variable to solve for the temperature intercept parameter and temperature-time coefficient; the third-order polynomial model for the stable power generation phase uses time as the independent variable and fitted temperature as the dependent variable to solve for the basic temperature intercept parameter and the coefficients of the first, second, and third terms of temperature change over time; and the piecewise linear model for the power surge phase uses the time before and after the load surge as the independent variable and fitted temperature as the dependent variable to solve for the temperature intercept parameter and temperature-time coefficient before and after the surge. After solving the parameters, the model is verified by the coefficient of determination and the root mean square error. When the model verification meets the preset thresholds for both the coefficient of determination and the root mean square error, the continuous time period corresponding to the operation phase is marked as a qualified time period. If the model is unqualified, it is adjusted until it meets the standard. Anomaly assessment and early warning module: Extracts the temperature rise rate, temperature fluctuation residual, multi-sensitive point synergy, and trend deviation degree of the current temperature trend and historical normal operating condition trend from qualified time periods of high temperature sensitivity points. These are integrated to form a comprehensive feature and normalized to calculate the early warning risk value and mark temperature fault points. Match the importance values ​​of key components, calculate the priority evaluation values, sort them according to these values ​​to form a fault handling sequence, and push it to the operation and maintenance end.

2. The temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch according to claim 1, characterized in that: The specific process for screening candidate heat-sensitive points is as follows: Distributed sensors are used to collect temperature data from all electrical connection points in the entire low-voltage photovoltaic power distribution chain during the acquisition period, and a temperature time series matrix is ​​constructed. At the same time, the core operating parameters of the photovoltaic system at each moment are collected, and an operating parameter time series matrix is ​​constructed. For each electrical connection, the variance of the temperature value within the acquisition period is calculated to obtain the temperature fluctuation value, and the temperature peak value is obtained. If the temperature fluctuation exceeds the preset temperature fluctuation baseline variance, or the temperature peak exceeds the preset temperature threshold, the location is marked as a candidate heat-sensitive point; all candidate heat-sensitive points are compiled to form a set of candidate heat-sensitive points.

3. The temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch according to claim 2, characterized in that: The specific process for screening high-temperature sensitive points is as follows: For each candidate heat-sensitive point, its temperature time series and the time series of each operating parameter are extracted from the temperature time series matrix and the operating parameter time series matrix, respectively. The Pearson correlation coefficient is calculated with temperature as the dependent variable and operating parameters as independent variables, and the absolute value is taken to obtain the thermal parameter correlation coefficient. Combined with the preset weighting coefficient of operating parameters, the thermal sensitivity evaluation index is obtained. If the index reaches the preset threshold, it is marked as a heat-sensitive point. Retrieve historical fault data related to temperature, calculate the percentage of historical over-temperature frequency, and combine the fault severity weight to obtain the historical fault over-temperature index. If the index reaches a preset threshold, it is marked as a high-temperature sensitive point; all high-temperature sensitive points are integrated to form a set of high-temperature sensitive points.

4. The temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch according to claim 3, characterized in that: The specific process for calculating the photovoltaic module operating load index, its first derivative, second derivative, and adjacent fluctuation amplitudes is as follows: The power generation and loop current of the photovoltaic module at each moment within the current acquisition period are obtained. Combined with the rated power, rated current and preset weighting coefficient of the photovoltaic module, the operating load index corresponding to each moment is obtained. Based on the numerical differentiation method, the first derivative, second derivative, and adjacent fluctuation amplitude of the operating load index are calculated for each data acquisition time. Among them, the first derivative represents the rate of change at the corresponding acquisition time, the second derivative represents the acceleration of change at the corresponding acquisition time, the adjacent fluctuation amplitude represents the stability of the operating state at the corresponding acquisition time and the previous time, and the adjacent fluctuation amplitude at the first acquisition time is zero.

5. The temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch according to claim 4, characterized in that: The specific process for determining the startup, stable power generation, and power surge phases, and integrating the continuous time periods, is as follows: Preset thresholds for startup load, first derivative of operating load index, second derivative of operating load index, and adjacent fluctuation range of operating load index. The startup phase must simultaneously meet four conditions: load range, rapid load increase, stable rate of increase, and no significant fluctuations between adjacent time periods. During the stable power generation phase, three conditions must be met simultaneously: extremely slow load change rate, no significant acceleration change, and minimal fluctuation between adjacent time periods. The power surge phase meets either the condition of a sudden load change or an excessively large adjacent fluctuation amplitude. In case of a conflict, the start-up phase is prioritized. Obtain the operating load index and its first derivative, second derivative, and adjacent fluctuation amplitude at each time point, match the stage standard and mark the operating stage label, and merge consecutive time periods with the same label to form an initial set; If the fragmented time period is preceded and followed by non-fragmented time periods of the same type, it is merged into that time period; if it exists only on one side, it is merged into that side; if it exists independently, it is merged into the adjacent time period of the same type, thus obtaining the final set of time periods. For each temperature-sensitive point, the temperature sequence is divided according to the start and end times of each consecutive time period in the final time period set, resulting in a stage-temperature subsequence that corresponds one-to-one with each time period.

6. The temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch according to claim 5, characterized in that: The specific process for constructing a dedicated model for temperature-sensitive areas according to the corresponding stage is as follows: For the stage-temperature subsequence, if its stage label is the start-up stage, a first-order linear model with time as the input variable and temperature as the output variable is adopted. The model includes the temperature intercept parameter and temperature-time coefficient specific to this stage. If the label is the stable power generation stage, a third-order polynomial model with time as the input variable and fitted temperature as the output variable is adopted. The model includes the basic temperature intercept parameter and the coefficients of the first, second, and third terms of temperature changing with time. If the label is the power change phase, a piecewise linear model is adopted with the time before and after the load change moment as the input variable and the fitted temperature at the corresponding moment as the output variable, with the load change moment as the boundary. The corresponding exclusive model parameters are configured before and after the change moment.

7. The temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch according to claim 6, characterized in that: After solving for the parameters, the model is verified using the coefficient of determination and root mean square error. If the model fails to meet the requirements, adjustments are made until it meets the standards. The specific process is as follows: For each temperature-sensitive point, with the goal of minimizing the sum of squared deviations between the fitted temperature and the actual temperature for the corresponding time period, the least squares method is used to solve for the specific parameters of the model for the startup, stable power generation, and power surge stages. The model is quantitatively verified using two indicators: the coefficient of determination and the root mean square error. Both thresholds are preset. If both are met, the model is considered qualified and the corresponding time period is marked as qualified. If not, the general model order is adjusted, and the data is substituted again to repeat the solution and verification until the model meets the dual indicator standard.

8. The temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch according to claim 7, characterized in that: The specific process for calculating the early warning risk value and marking the temperature fault point is as follows: For each qualified time period of high-temperature sensitive points, four core abnormal features are extracted: temperature rise rate, temperature fluctuation residual, synergy of multiple sensitive points, and trend deviation. By integrating the characteristics of each qualified time period, we obtain the comprehensive temperature rise rate, comprehensive fluctuation residual, comprehensive synergy, and comprehensive trend deviation. The four comprehensive characteristics are normalized and dimensionless, and the warning risk value is calculated by combining the preset weight coefficients. If the warning risk value reaches the preset threshold, a temperature fault warning will be triggered, and the high-temperature sensitive point will be marked as a temperature fault point.

9. The temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch according to claim 8, characterized in that: The specific process of forming a fault handling sequence and pushing it to the operation and maintenance end is as follows: Construct a library of importance assignment values ​​for high-temperature-incidence sensitive points, in which each high-temperature-incidence sensitive point is assigned a preset importance value for key components; Match the temperature fault point with the assignment library to obtain the corresponding critical component importance value; For each temperature fault point, the priority evaluation value is calculated based on the pre-set weighting coefficient, combining its early warning risk value and the importance value of key parts. All temperature fault points are sorted from largest to smallest according to the priority evaluation value to form a priority handling sequence for temperature faults. This sequence is sent to the fault operation and maintenance terminal, which then carries out operation and maintenance processing in sequence.

10. A temperature sensing and protection method based on a low-voltage photovoltaic intelligent switch, applied to the temperature sensing and protection system based on a low-voltage photovoltaic intelligent switch proposed in any one of claims 1-9, characterized in that, include: Step 1: Collect the temperature of electrical connection parts and the core operating parameters of the photovoltaic system, construct a matrix, and screen candidate heat-sensitive points according to temperature fluctuation value and peak value; calculate the thermal sensitivity evaluation index by combining the temperature of the sensitive point with the operating parameters, calculate the historical fault over-temperature index based on historical fault data, and screen the candidate heat-sensitive points whose thermal sensitivity evaluation index reaches the preset threshold and whose historical fault over-temperature index reaches the preset threshold as high-temperature sensitive points. Step 2: Obtain the power generation and loop current of the photovoltaic module at each moment within the current acquisition period. Combine the rated power, rated current and preset weighting coefficient of the photovoltaic module to calculate the photovoltaic module operating load index. Calculate the photovoltaic module operating load index and its first derivative, second derivative and adjacent fluctuation amplitude. Determine the start-up, stable power generation and power change stages and integrate continuous time periods. Construct a dedicated model for the corresponding stage as the temperature high-sensitivity point. In the dedicated model, the first-order linear model for the startup phase uses time as the independent variable and fitted temperature as the dependent variable to solve for the temperature intercept parameter and temperature-time coefficient; the third-order polynomial model for the stable power generation phase uses time as the independent variable and fitted temperature as the dependent variable to solve for the basic temperature intercept parameter and the coefficients of the first, second, and third terms of temperature change over time; and the piecewise linear model for the power surge phase uses the time before and after the load surge as the independent variable and fitted temperature as the dependent variable to solve for the temperature intercept parameter and temperature-time coefficient before and after the surge. After solving the parameters, the model is verified by the coefficient of determination and the root mean square error. When the model verification meets the preset thresholds for both the coefficient of determination and the root mean square error, the continuous time period corresponding to the operation phase is marked as a qualified time period. If the model is unqualified, it is adjusted until it meets the standard. Step 3: Extract the following features from the qualified time period of high-temperature sensitive points: the temperature rise rate, the temperature fluctuation residual, the synergy of multiple sensitive points, the trend deviation, and the trend deviation of the current temperature trend from the historical normal operating condition trend. These features are then integrated and normalized to calculate the early warning risk value and mark the temperature fault points. Match the importance values ​​of key components, calculate the priority evaluation values, sort them according to these values ​​to form a fault handling sequence, and push it to the operation and maintenance end.

Images