Photovoltaic anti-islanding monitoring method, device and equipment

Abstract

The invention provides a photovoltaic anti-islanding monitoring method, a photovoltaic anti-islanding monitoring device and photovoltaic anti-islanding monitoring equipment. The method comprises the following steps: S1, monitoring an alternating current side of a photovoltaic system in real time to obtain monitoring data; step S2, processing and analyzing the monitoring data to obtain processed data; s3, performing island state judgment based on the processed data to obtain an island judgment result; s4, executing data reporting according to the island judgment result to obtain reporting confirmation information; s5, executing an anti-islanding control strategy based on the report confirmation information to obtain a control execution result; s6, performing system recovery and self-diagnosis according to the control execution result to obtain a recovery diagnosis report; according to the invention, the accuracy and reliability of island detection can be improved, and comprehensive monitoring and intelligent analysis of the running state of the photovoltaic system can be realized.

Description

Technical Field

[0001] The invention relates to the technical field of photovoltaic anti-islanding, in particular to a photovoltaic anti-islanding monitoring method, device and equipment. Background Art

[0002] With the rapid development and widespread application of photovoltaic power generation technology, the safety issues of photovoltaic system grid-connected operation have become increasingly prominent. Among them, anti-islanding protection is one of the key technologies to ensure the safe and reliable operation of photovoltaic systems. Traditional anti-islanding methods often have problems such as low detection accuracy, slow response speed, and high misjudgment rate, which are difficult to meet the safety requirements of large-scale photovoltaic grid-connected systems. Especially in complex power grid environments, traditional methods are difficult to accurately identify the islanding state, which may cause the photovoltaic system to continue to operate in the islanding state, posing a safety hazard to the power grid and maintenance personnel.

[0003] In addition, the existing anti-islanding monitoring systems generally lack the characteristics of intelligence and integration, making it difficult to achieve comprehensive monitoring and analysis of the operating status of the photovoltaic system. This not only affects the effectiveness of anti-islanding protection, but also limits the improvement of system operation and maintenance efficiency. At the same time, due to the lack of effective data processing and analysis methods, it is difficult to conduct in-depth cause analysis and evaluation of islanding events, which is not conducive to the continuous optimization and improvement of anti-islanding technology. Summary of the invention

[0004] The present invention provides a photovoltaic anti-islanding monitoring method, device and equipment, which can be used to improve the accuracy and reliability of islanding detection, and at the same time realize comprehensive monitoring and intelligent analysis of the operating status of the photovoltaic system.

[0005] The present invention adopts the following technical solutions.

[0006] The photovoltaic anti-islanding monitoring method is characterized in that: the method comprises the following steps;

[0007] Step S1, real-time monitoring of the AC side of the photovoltaic system to obtain monitoring data;

[0008] Step S2: Processing and analyzing the monitoring data to obtain processed data;

[0009] Step S3, performing island state judgment based on the processed data to obtain an island judgment result;

[0010] Step S4: reporting data according to the island determination result to obtain reporting confirmation information;

[0011] Step S5, executing the anti-islanding control strategy based on the reported confirmation information to obtain a control execution result;

[0012] Step S6: Perform system recovery and self-diagnosis according to the control execution result to obtain a recovery diagnosis report.

[0013] Step S1 specifically includes the following steps:

[0014] Step A1: Use an intelligent low-voltage shunt monitoring terminal to connect the AC output end of the photovoltaic system to obtain a connection configuration;

[0015] Step A2: Based on the connection configuration, measure electrical parameters through an AC voltage loop and an AC current loop to obtain original electrical data; sample the original electrical data to obtain sampled data;

[0016] Step A3, using a hardware clock circuit with a temperature compensation function to time-stamp the sampled data to obtain time-stamped data;

[0017] Step A4: storing the time stamp data in the internal memory of the terminal to obtain storage confirmation information;

[0018] Step A5: Generate monitoring data according to the storage confirmation information, wherein the monitoring data includes three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power.

[0019] Step S2 specifically includes the following steps:

[0020] Step B1, filtering and calibrating the monitoring data using a data processing module built into the terminal to obtain calibrated data;

[0021] Step B2, calculating the total power, power factor and apparent power based on the calibrated data to obtain derived parameters;

[0022] Step B3, performing electric energy measurement on the calibrated data to obtain electric energy data, wherein the electric energy data includes combined-phase forward active energy, reverse active energy, combined active electric energy, and four-quadrant reactive electric energy;

[0023] Step B4, calculating the phase-by-phase forward and reverse active harmonic electric energy based on the calibrated data to obtain harmonic content data;

[0024] Step B5, using the protection current fundamental component as the fault discrimination input to execute the fault detection algorithm to obtain fault indication information;

[0025] Step B6: Freeze the calibrated data, the derived parameters, the electric energy data, the harmonic content data and the fault indication information according to a preset time interval to obtain processed data.

[0026] Step S3 specifically includes the following steps:

[0027] Step C1, setting parameter thresholds under normal operating conditions to obtain a threshold setting set, wherein the threshold setting set includes a voltage range, a frequency range, and a power factor range;

[0028] Step C1, comparing the processed data with the threshold setting set to obtain parameter deviation data;

[0029] Step C2, determining whether the parameter deviation data exceeds a threshold range in a plurality of consecutive sampling periods, and obtaining a first determination result;

[0030] Step C3, analyzing the power change trend in the processed data to obtain the power change rate;

[0031] Step C4, determining whether the power change rate exceeds a preset threshold, and obtaining a second determination result;

[0032] Step C5, using the phase voltage / no voltage detection function of the terminal to detect the voltage state and obtain the voltage detection result;

[0033] Step C6: comprehensively determine the islanding state based on the first determination result, the second determination result and the voltage detection result to obtain an islanding determination result.

[0034] Step S4 comprises the following steps:

[0035] Step D1, start the dual-mode communication module of the intelligent low-voltage branch monitoring terminal to obtain a communication module ready signal;

[0036] Step D2, based on the communication module ready signal, sending the island event and related data through the main communication method to obtain a first sending result;

[0037] Step D3, determining whether the first sending result has been confirmed within a preset time, and obtaining a first communication state;

[0038] Step D4: If the first communication state is that no confirmation is received, switch to the backup communication mode to resend the data to obtain a second sending result;

[0039] Step D5, integrating the occurrence time, duration, key parameter values ​​triggering islanding judgment and detailed electrical parameter records of the islanding event to obtain a complete event data packet;

[0040] Step D6, uploading all frozen data related to the island event to the upper-level system, and obtaining data upload confirmation;

[0041] Step D7: Record the abnormal conditions in the entire communication process, form a communication log, and obtain reporting confirmation information.

[0042] Step S5 comprises the following steps:

[0043] Step E1, sending an emergency shutdown command to the photovoltaic inverter, and cutting off the power supply to the grid within a preset time, and obtaining inverter response information;

[0044] Step E2, activating the fault indication function inside the terminal, lighting up the red fault indicator light, and obtaining a visual alarm status;

[0045] Step E3, sending an alarm signal to the connected external device through the first RS-485 interface, and obtaining a response confirmation from the external device;

[0046] Step E4: Communicate with the power station monitoring system using the second RS-485 interface to trigger the power station-level emergency response plan and obtain the power station response result;

[0047] Step E5, executing the auxiliary topology analysis function, identifying other devices and lines within the impact range of the island, and obtaining impact range data;

[0048] Step E6: sending control instructions to relevant equipment according to the impact range data, including cutting off non-critical loads and adjusting reactive power compensation devices, to obtain equipment control feedback;

[0049] Step E7: Continuously monitor changes in system parameters, record all executed control operations, form a control strategy execution log, and obtain control execution results.

[0050] Step S6 comprises the following steps:

[0051] Step F1, continuously monitor the grid parameters, determine whether the grid voltage and frequency return to normal and remain stable for a preset time, and obtain a grid status assessment result;

[0052] Step F2: based on the grid status assessment result, start the system recovery program and obtain a recovery program activation signal;

[0053] Step F3: Send a restart instruction to the photovoltaic inverter to limit its output power to not exceed a preset percentage of the rated power, and obtain the first stage recovery data;

[0054] Step F4: monitor the system stability in the low-power operation state. If no abnormality is detected after the preset stable operation time, gradually increase the allowed output power to obtain a power recovery curve;

[0055] Step F5, execute the terminal self-diagnosis function, check the working status of the internal circuit, communication module, and measurement loop, and obtain the self-diagnosis result;

[0056] Step F6: Compare and analyze the data before and after the islanding event, evaluate the effectiveness of the anti-islanding measures, and obtain a measures evaluation report;

[0057] Step F7: based on the self-diagnosis result and the measure evaluation report, automatically adjust some operating parameters, including fault judgment threshold and sampling strategy, to obtain a parameter optimization solution;

[0058] Step F8: Generate a complete event report, including islanding cause analysis, system response assessment, recovery process record and improvement suggestions, to form a recovery diagnosis report.

[0059] When the photovoltaic system works in the anti-islanding condition, the AC side of the photovoltaic system is monitored in real time through the intelligent low-voltage shunt monitoring terminal, specifically:

[0060] The intelligent low-voltage shunt monitoring terminal adopts an integrated mutual inductor, which is connected to the AC output end of the photovoltaic system. Through the AC voltage loop and the AC current loop, it measures and monitors electrical parameters such as three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power; wherein, the voltage measurement range is 176V to 264V, and the accuracy is not greater than ±0.5%; the current measurement range is 1A to 600A, and the accuracy is not greater than ±0.5%; the frequency measurement range is 45Hz to 55Hz, and the accuracy is not greater than 0.01Hz; the monitoring data is sampled at intervals of 0.5s and stored in the internal memory of the monitoring terminal;

[0061] When the intelligent low-voltage branch monitoring terminal processes and analyzes the collected monitoring data, the following method is adopted: the data processing module built into the terminal first filters and calibrates the original data to eliminate possible interference and errors; wherein the specific implementation method of filtering and calibrating the original data includes digital filtering and data calibration;

[0062] The digital filtering is specifically as follows: a Butterworth low-pass filter is used for digital filtering; the Butterworth filter has a large flat passband response, almost no ripple in the passband, and can effectively suppress high-frequency noise;

[0063] The specific implementation steps of digital filtering are as follows:

[0064] a) Design filter: Set a 4th-order Butterworth low-pass filter with a cutoff frequency of 100 Hz to retain the fundamental frequency 50 Hz and low-order harmonic information in the power system and filter out high-frequency noise; use a 4th-order filter to provide sufficient attenuation slope to effectively suppress stop-band signals while ensuring passband flatness;

[0065] b) Filter implementation: Use the bilinear transformation method to convert the analog filter into a digital filter. The differential equation obtained after the conversion is:

[0066] y(n)=b0x(n)+b1x(n-1)+b2x(n-2)+b3x(n-3)+b4x(n-4)-a1y(n-1)-a2y(n-2)-a3y(n-3)-a4*y(n-4),

[0067] Among them, x(n) is the input signal, y(n) is the output signal after filtering, bi and ai are the filter coefficients;

[0068] c) Apply filter: Apply the above differential equation to the data of each sampling point in turn to obtain the filtered data sequence; in order to avoid transient response when the filter starts, fill the filter's historical data buffer with the first valid data point before the actual processing starts;

[0069] The data calibration is specifically as follows: after filtering, the data is calibrated using a linear calibration method to compensate for system errors and improve measurement accuracy;

[0070] The specific implementation steps of data calibration are as follows:

[0071] a) Establishing a calibration model: Establishing a linear calibration model for each measurement channel: y=kx+b, the measurement channel includes a voltage measurement channel and a current measurement channel; wherein y is the calibrated value, x is the filtered measurement value, k is the proportionality coefficient, and b is the offset;

[0072] b) Determine calibration parameters: Determine the values ​​of k and b by multi-point measurement of the standard source;

[0073] The specific steps to determine the values ​​of k and b are as follows: select at least 5 standard value points evenly distributed within the measurement range; perform multiple measurements on each standard value point and take the average value as the measurement value of the point. Use the least squares method to fit the values ​​of k and b;

[0074] c) Apply calibration: for each filtered data point x, apply the calibration formula y=kx+b to obtain the calibrated data y;

[0075] d) Calibration verification: Regularly verify the calibration results using a standard source. If the error is found to exceed the preset threshold, the calibration parameters need to be re-determined;

[0076] Through the above filtering and calibration steps, the quality and reliability of the original data can be effectively improved; that is: the Butterworth low-pass filter can effectively remove high-frequency noise while retaining the main characteristics of the signal, and the linear calibration can compensate for the inherent error of the system and improve the measurement accuracy. The derived parameters such as total power, power factor, and apparent power are calculated; at the same time, electric energy measurement is performed, including the calculation of the forward active power, reverse active power, and combined active power energy of the combined phase, as well as the calculation of the reactive power energy of the four quadrants; for the harmonic content, the forward and reverse active harmonic power energy of the phases are calculated separately; the processed data is frozen at the preset time interval to form a variety of frozen data including instantaneous freezing, timed freezing, daily freezing, and monthly freezing.

[0077] Based on the processed data, the islanding state is judged; first, the parameter thresholds under normal operating conditions are set, including the voltage range (such as ±10% of the nominal voltage), frequency range (such as 50Hz±0.5Hz), power factor range (such as 0.95 lagging to 0.95 leading), etc.; the real-time monitoring data is compared with the preset thresholds. If any parameter exceeds the threshold range within multiple consecutive sampling cycles (such as 5 cycles), an islanding warning is triggered. At the same time, the power change trend is analyzed. If a sudden power change is detected (such as a power change of more than 20% within 5 seconds), an early warning will also be triggered. If in high-frequency sampling mode, the duration of the abnormal state exceeds the preset value (such as 2s), it is determined to be an islanding state;

[0078] When an island state is detected, the terminal immediately starts the communication program and reports the island event and related data to the superior system. The terminal uses a dual-mode communication module of micro-power wireless RF and power line carrier HPLC to ensure the reliability of communication. The reported data includes the time of occurrence and duration of the island, the key parameter values ​​that trigger the island judgment, and the detailed electrical parameter records for a certain period of time before and after the island.

[0079] After confirming the islanding status and completing the data reporting, the system immediately executes the preset anti-islanding control strategy. The terminal sends an emergency shutdown command to the photovoltaic inverter, requiring it to cut off the power supply to the grid within 100ms. At the same time, the terminal activates its internal fault indication function, lights up the fault indicator light, and sends an alarm signal to the connected external device through the RS-485 interface;

[0080] The system enters the recovery and self-diagnosis phase; the terminal continuously monitors the grid parameters, and starts the system recovery procedure when it detects that the grid voltage and frequency have returned to normal and remain stable for a preset time (such as 5 minutes). The recovery procedure is carried out in stages, gradually increasing the allowed output power until full power operation is restored. During the recovery process, the terminal uses its self-diagnosis function to continuously check the working status of key components such as internal circuits, communication modules, and measurement loops. The terminal generates a complete event report, including analysis of the cause of the islanding, system response evaluation, recovery process records, and improvement suggestions, and uploads the report to the management platform.

[0081] A photovoltaic anti-islanding monitoring device is used to execute the photovoltaic anti-islanding monitoring method described above, characterized in that the device comprises:

[0082] A monitoring module is used to monitor the AC side of the photovoltaic system in real time and obtain monitoring data;

[0083] A processing module, used to process and analyze the monitoring data to obtain processed data;

[0084] A judgment module, used for judging the island state based on the processed data to obtain an island judgment result;

[0085] A reporting module, used to perform data reporting according to the island judgment result and obtain reporting confirmation information;

[0086] An execution module, used for executing the anti-islanding control strategy based on the reported confirmation information to obtain a control execution result;

[0087] The diagnosis module is used to perform system recovery and self-diagnosis according to the control execution result to obtain a recovery diagnosis report.

[0088] Photovoltaic anti-islanding monitoring equipment, the photovoltaic anti-islanding monitoring equipment comprising: a memory and at least one processor, the memory storing instructions;

[0089] The at least one processor calls the instructions in the memory to enable the photovoltaic anti-islanding monitoring device to execute the photovoltaic anti-islanding monitoring method described above.

[0090] The present invention realizes high-precision real-time monitoring of the electrical parameters of the AC side of the photovoltaic system by adopting an intelligent low-voltage shunt monitoring terminal and high-precision sampling technology, and provides a reliable data basis for island state judgment. The monitoring data is filtered, calibrated and analyzed by using the built-in data processing module of the terminal, which improves the accuracy and availability of the data and provides a more accurate input for island state judgment. By setting multiple parameter thresholds and combining power change trend analysis, multi-dimensional island state judgment is realized, and the accuracy and reliability of island detection are improved. The dual-mode communication module and multiple communication guarantee mechanism are adopted to ensure that island events and related data can be reported to the upper system in a timely and reliable manner. Based on the reported confirmation information, the system can quickly execute anti-islanding control strategies, including emergency shutdown, fault indication and equipment control, effectively reducing the risk of continuous island state. Through multi-stage system recovery procedures and self-diagnosis functions, the safe and controllable recovery of the photovoltaic system is achieved, and the adaptive ability of the system is improved. Through comprehensive analysis and evaluation of island events, the system can automatically adjust the operating parameters, continuously optimize the anti-islanding strategy, and improve the long-term reliability and efficiency of the system.

[0091] In the technical solution provided by the present invention, by adopting an intelligent low-voltage shunt monitoring terminal and high-precision sampling technology, high-precision real-time monitoring of the electrical parameters on the AC side of the photovoltaic system is achieved, providing a reliable data basis for island state judgment. The monitoring data is filtered, calibrated and analyzed by using the built-in data processing module of the terminal, which improves the accuracy and availability of the data and provides a more accurate input for island state judgment. By setting multiple parameter thresholds and combining power change trend analysis, multi-dimensional island state judgment is achieved, and the accuracy and reliability of island detection are improved. The dual-mode communication module and multiple communication guarantee mechanism are adopted to ensure that island events and related data can be reported to the upper system in a timely and reliable manner. Based on the reported confirmation information, the system can quickly execute anti-islanding control strategies, including emergency shutdown, fault indication and equipment control, effectively reducing the risk of continuous island state. Through multi-stage system recovery procedures and self-diagnosis functions, the safe and controllable recovery of the photovoltaic system is achieved, and the adaptive ability of the system is improved. Through comprehensive analysis and evaluation of island events, the system can automatically adjust the operating parameters, continuously optimize the anti-islanding strategy, and improve the long-term reliability and efficiency of the system.

[0092] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0093] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0095] Attached Figure 1 A schematic diagram of an embodiment of a photovoltaic anti-islanding monitoring method in an embodiment of the present invention;

[0096] Attached Figure 2 A schematic diagram of an embodiment of a photovoltaic anti-islanding monitoring device in an embodiment of the present invention;

[0097] Attached Figure 3 Schematic diagram of an embodiment of a photovoltaic anti-islanding monitoring device in an embodiment of the present invention. DETAILED DESCRIPTION

[0098] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0099] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0100] To facilitate understanding of this embodiment, a photovoltaic anti-islanding monitoring method disclosed in an embodiment of the present invention is first introduced in detail.

[0101] As shown in the figure, the photovoltaic anti-islanding monitoring method is characterized in that: the method comprises the following steps;

[0102] Step S1, real-time monitoring of the AC side of the photovoltaic system to obtain monitoring data;

[0103] Step S2: Processing and analyzing the monitoring data to obtain processed data;

[0104] Step S3, performing island state judgment based on the processed data to obtain an island judgment result;

[0105] Step S4: reporting data according to the island determination result to obtain reporting confirmation information;

[0106] Step S5, executing the anti-islanding control strategy based on the reported confirmation information to obtain a control execution result;

[0107] Step S6: Perform system recovery and self-diagnosis according to the control execution result to obtain a recovery diagnosis report.

[0108] Step S1 specifically includes the following steps:

[0109] Step A1: Use an intelligent low-voltage shunt monitoring terminal to connect the AC output end of the photovoltaic system to obtain a connection configuration;

[0110] Step A2: Based on the connection configuration, measure electrical parameters through an AC voltage loop and an AC current loop to obtain original electrical data; sample the original electrical data to obtain sampled data;

[0111] Step A3, using a hardware clock circuit with a temperature compensation function to time-stamp the sampled data to obtain time-stamped data;

[0112] Step A4: storing the time stamp data in the internal memory of the terminal to obtain storage confirmation information;

[0113] Step A5: Generate monitoring data according to the storage confirmation information, wherein the monitoring data includes three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power.

[0114] Step S2 specifically includes the following steps:

[0115] Step B1, filtering and calibrating the monitoring data using a data processing module built into the terminal to obtain calibrated data;

[0116] Step B2, calculating the total power, power factor and apparent power based on the calibrated data to obtain derived parameters;

[0117] Step B3, performing electric energy measurement on the calibrated data to obtain electric energy data, wherein the electric energy data includes combined-phase forward active energy, reverse active energy, combined active electric energy, and four-quadrant reactive electric energy;

[0118] Step B4, calculating the phase-by-phase forward and reverse active harmonic electric energy based on the calibrated data to obtain harmonic content data;

[0119] Step B5, using the protection current fundamental component as the fault discrimination input to execute the fault detection algorithm to obtain fault indication information;

[0120] Step B6: Freeze the calibrated data, the derived parameters, the electric energy data, the harmonic content data and the fault indication information according to a preset time interval to obtain processed data.

[0121] Step S3 specifically includes the following steps:

[0122] Step C1, setting parameter thresholds under normal operating conditions to obtain a threshold setting set, wherein the threshold setting set includes a voltage range, a frequency range, and a power factor range;

[0123] Step C1, comparing the processed data with the threshold setting set to obtain parameter deviation data;

[0124] Step C2, determining whether the parameter deviation data exceeds a threshold range in a plurality of consecutive sampling periods, and obtaining a first determination result;

[0125] Step C3, analyzing the power change trend in the processed data to obtain the power change rate;

[0126] Step C4, determining whether the power change rate exceeds a preset threshold, and obtaining a second determination result;

[0127] Step C5, using the phase voltage / no voltage detection function of the terminal to detect the voltage state and obtain the voltage detection result;

[0128] Step C6: comprehensively determine the islanding state based on the first determination result, the second determination result and the voltage detection result to obtain an islanding determination result.

[0129] Step S4 comprises the following steps:

[0130] Step D1, start the dual-mode communication module of the intelligent low-voltage branch monitoring terminal to obtain a communication module ready signal;

[0131] Step D2, based on the communication module ready signal, sending the island event and related data through the main communication method to obtain a first sending result;

[0132] Step D3, determining whether the first sending result has been confirmed within a preset time, and obtaining a first communication state;

[0133] Step D4: If the first communication state is that no confirmation is received, switch to the backup communication mode to resend the data to obtain a second sending result;

[0134] Step D5, integrating the occurrence time, duration, key parameter values ​​triggering islanding judgment and detailed electrical parameter records of the islanding event to obtain a complete event data packet;

[0135] Step D6, uploading all frozen data related to the island event to the upper-level system, and obtaining data upload confirmation;

[0136] Step D7: Record the abnormal conditions in the entire communication process, form a communication log, and obtain reporting confirmation information.

[0137] Step S5 comprises the following steps:

[0138] Step E1, sending an emergency shutdown command to the photovoltaic inverter, and cutting off the power supply to the grid within a preset time, and obtaining inverter response information;

[0139] Step E2, activating the fault indication function inside the terminal, lighting up the red fault indicator light, and obtaining a visual alarm status;

[0140] Step E3, sending an alarm signal to the connected external device through the first RS-485 interface, and obtaining a response confirmation from the external device;

[0141] Step E4: Communicate with the power station monitoring system using the second RS-485 interface to trigger the power station-level emergency response plan and obtain the power station response result;

[0142] Step E5, executing the auxiliary topology analysis function, identifying other devices and lines within the impact range of the island, and obtaining impact range data;

[0143] Step E6: sending control instructions to relevant equipment according to the impact range data, including cutting off non-critical loads and adjusting reactive power compensation devices, to obtain equipment control feedback;

[0144] Step E7: Continuously monitor changes in system parameters, record all executed control operations, form a control strategy execution log, and obtain control execution results.

[0145] Step S6 comprises the following steps:

[0146] Step F1, continuously monitor the grid parameters, determine whether the grid voltage and frequency return to normal and remain stable for a preset time, and obtain a grid status assessment result;

[0147] Step F2: based on the grid status assessment result, start the system recovery program and obtain a recovery program activation signal;

[0148] Step F3: Send a restart instruction to the photovoltaic inverter to limit its output power to not exceed a preset percentage of the rated power, and obtain the first stage recovery data;

[0149] Step F4: monitor the system stability in the low-power operation state. If no abnormality is detected after the preset stable operation time, gradually increase the allowed output power to obtain a power recovery curve;

[0150] Step F5, execute the terminal self-diagnosis function, check the working status of the internal circuit, communication module, and measurement loop, and obtain the self-diagnosis result;

[0151] Step F6: Compare and analyze the data before and after the islanding event, evaluate the effectiveness of the anti-islanding measures, and obtain a measures evaluation report;

[0152] Step F7: based on the self-diagnosis result and the measure evaluation report, automatically adjust some operating parameters, including fault judgment threshold and sampling strategy, to obtain a parameter optimization solution;

[0153] Step F8: Generate a complete event report, including islanding cause analysis, system response assessment, recovery process record and improvement suggestions, to form a recovery diagnosis report.

[0154] When the photovoltaic system works in the anti-islanding condition, the AC side of the photovoltaic system is monitored in real time through the intelligent low-voltage shunt monitoring terminal, specifically:

[0155] The intelligent low-voltage shunt monitoring terminal adopts an integrated mutual inductor, which is connected to the AC output end of the photovoltaic system. Through the AC voltage loop and the AC current loop, it measures and monitors electrical parameters such as three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power; wherein, the voltage measurement range is 176V to 264V, and the accuracy is not greater than ±0.5%; the current measurement range is 1A to 600A, and the accuracy is not greater than ±0.5%; the frequency measurement range is 45Hz to 55Hz, and the accuracy is not greater than 0.01Hz; the monitoring data is sampled at intervals of 0.5s and stored in the internal memory of the monitoring terminal;

[0156] When the intelligent low-voltage branch monitoring terminal processes and analyzes the collected monitoring data, the following method is adopted: the data processing module built into the terminal first filters and calibrates the original data to eliminate possible interference and errors; wherein the specific implementation method of filtering and calibrating the original data includes digital filtering and data calibration;

[0157] The digital filtering is specifically as follows: a Butterworth low-pass filter is used for digital filtering; the Butterworth filter has a large flat passband response, almost no ripple in the passband, and can effectively suppress high-frequency noise;

[0158] The specific implementation steps of digital filtering are as follows:

[0159] a) Design filter: Set a 4th-order Butterworth low-pass filter with a cutoff frequency of 100 Hz to retain the fundamental frequency 50 Hz and low-order harmonic information in the power system and filter out high-frequency noise; use a 4th-order filter to provide sufficient attenuation slope to effectively suppress stop-band signals while ensuring passband flatness;

[0160] b) Filter implementation: Use the bilinear transformation method to convert the analog filter into a digital filter. The differential equation obtained after the conversion is:

[0161] y(n)=b0x(n)+b1x(n-1)+b2x(n-2)+b3x(n-3)+b4x(n-4)-a1y(n-1)-a2y(n-2)-a3y(n-3)-a4*y(n-4),

[0162] Among them, x(n) is the input signal, y(n) is the output signal after filtering, bi and ai are the filter coefficients;

[0163] c) Apply filter: Apply the above differential equation to the data of each sampling point in turn to obtain the filtered data sequence; in order to avoid transient response when the filter starts, fill the filter's historical data buffer with the first valid data point before the actual processing starts;

[0164] The data calibration is specifically as follows: after filtering, the data is calibrated using a linear calibration method to compensate for system errors and improve measurement accuracy;

[0165] The specific implementation steps of data calibration are as follows:

[0166] a) Establishing a calibration model: Establishing a linear calibration model for each measurement channel: y=kx+b, the measurement channel includes a voltage measurement channel and a current measurement channel; wherein y is the calibrated value, x is the filtered measurement value, k is the proportionality coefficient, and b is the offset;

[0167] b) Determine calibration parameters: Determine the values ​​of k and b by multi-point measurement of the standard source;

[0168] The specific steps to determine the values ​​of k and b are as follows: select at least 5 standard value points evenly distributed within the measurement range; perform multiple measurements on each standard value point and take the average value as the measurement value of the point. Use the least squares method to fit the values ​​of k and b;

[0169] c) Apply calibration: for each filtered data point x, apply the calibration formula y=kx+b to obtain the calibrated data y;

[0170] d) Calibration verification: Regularly verify the calibration results using a standard source. If the error is found to exceed the preset threshold, the calibration parameters need to be re-determined;

[0171] Through the above filtering and calibration steps, the quality and reliability of the original data can be effectively improved; that is: the Butterworth low-pass filter can effectively remove high-frequency noise while retaining the main characteristics of the signal, and the linear calibration can compensate for the inherent error of the system and improve the measurement accuracy. The derived parameters such as total power, power factor, and apparent power are calculated; at the same time, electric energy measurement is performed, including the calculation of the forward active power, reverse active power, and combined active power energy of the combined phase, as well as the calculation of the reactive power energy of the four quadrants; for the harmonic content, the forward and reverse active harmonic power energy of the phases are calculated separately; the processed data is frozen at the preset time interval to form a variety of frozen data including instantaneous freezing, timed freezing, daily freezing, and monthly freezing.

[0172] Based on the processed data, the islanding state is judged; first, the parameter thresholds under normal operating conditions are set, including the voltage range (such as ±10% of the nominal voltage), frequency range (such as 50Hz±0.5Hz), power factor range (such as 0.95 lagging to 0.95 leading), etc.; the real-time monitoring data is compared with the preset thresholds. If any parameter exceeds the threshold range within multiple consecutive sampling cycles (such as 5 cycles), an islanding warning is triggered. At the same time, the power change trend is analyzed. If a sudden power change is detected (such as a power change of more than 20% within 5 seconds), an early warning will also be triggered. If in high-frequency sampling mode, the duration of the abnormal state exceeds the preset value (such as 2s), it is determined to be an islanding state;

[0173] When an island state is detected, the terminal immediately starts the communication program and reports the island event and related data to the superior system. The terminal uses a dual-mode communication module of micro-power wireless RF and power line carrier HPLC to ensure the reliability of communication. The reported data includes the time of occurrence and duration of the island, the key parameter values ​​that trigger the island judgment, and the detailed electrical parameter records for a certain period of time before and after the island.

[0174] After confirming the islanding status and completing the data reporting, the system immediately executes the preset anti-islanding control strategy. The terminal sends an emergency shutdown command to the photovoltaic inverter, requiring it to cut off the power supply to the grid within 100ms. At the same time, the terminal activates its internal fault indication function, lights up the fault indicator light, and sends an alarm signal to the connected external device through the RS-485 interface;

[0175] The system enters the recovery and self-diagnosis phase; the terminal continuously monitors the grid parameters, and starts the system recovery procedure when it detects that the grid voltage and frequency have returned to normal and remain stable for a preset time (such as 5 minutes). The recovery procedure is carried out in stages, gradually increasing the allowed output power until full power operation is restored. During the recovery process, the terminal uses its self-diagnosis function to continuously check the working status of key components such as internal circuits, communication modules, and measurement loops. The terminal generates a complete event report, including analysis of the cause of the islanding, system response evaluation, recovery process records, and improvement suggestions, and uploads the report to the management platform.

[0176] A photovoltaic anti-islanding monitoring device is used to execute the photovoltaic anti-islanding monitoring method described above, characterized in that the device comprises:

[0177] A monitoring module is used to monitor the AC side of the photovoltaic system in real time and obtain monitoring data;

[0178] A processing module, used to process and analyze the monitoring data to obtain processed data;

[0179] A judgment module, used for judging the island state based on the processed data to obtain an island judgment result;

[0180] A reporting module, used to perform data reporting according to the island judgment result and obtain reporting confirmation information;

[0181] An execution module, used for executing the anti-islanding control strategy based on the reported confirmation information to obtain a control execution result;

[0182] The diagnosis module is used to perform system recovery and self-diagnosis according to the control execution result to obtain a recovery diagnosis report.

[0183] Photovoltaic anti-islanding monitoring equipment, the photovoltaic anti-islanding monitoring equipment comprising: a memory and at least one processor, the memory storing instructions;

[0184] The at least one processor calls the instructions in the memory to enable the photovoltaic anti-islanding monitoring device to execute the photovoltaic anti-islanding monitoring method described above.

[0185] Example:

[0186] like Figure 1 As shown, the method comprises the following steps:

[0187] 101. Monitor the AC side of the photovoltaic system in real time to obtain monitoring data;

[0188] It is understandable that the execution subject of the present invention may be a photovoltaic anti-islanding monitoring device, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0189] 102. Process and analyze the monitoring data to obtain processed data;

[0190] 103. Perform island state judgment based on the processed data to obtain an island judgment result;

[0191] 104. Execute data reporting according to the island judgment result and obtain reporting confirmation information;

[0192] 105. Execute the anti-islanding control strategy based on the reported confirmation information and obtain the control execution result;

[0193] 106. Perform system recovery and self-diagnosis based on the control execution results to obtain a recovery diagnosis report.

[0194] Specifically, the AC side of the photovoltaic system is first monitored in real time through the intelligent low-voltage shunt monitoring terminal FC-5321. The terminal uses an integrated transformer, which is connected to the AC output end of the photovoltaic system. Through the AC voltage loop and the AC current loop, it measures electrical parameters such as three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power. Among them, the voltage measurement range is 176V to 264V, with an accuracy of no more than ±0.5%; the current measurement range is 1A to 600A, with an accuracy of no more than ±0.5%; the frequency measurement range is 45Hz to 55Hz, with an accuracy of no more than 0.01Hz. The monitoring data is sampled at intervals of 0.5s and stored in the terminal's internal memory.

[0195] The collected monitoring data is processed and analyzed. The built-in data processing module of the terminal first filters and calibrates the raw data to eliminate possible interference and errors. Among them, the specific implementation method of filtering and calibrating the raw data mainly includes two steps: digital filtering and data calibration. Digital filtering: Butterworth low-pass filter is used for digital filtering. The reason for choosing Butterworth filter is that it has the most flat passband response, almost no ripple in the passband, and can effectively suppress high-frequency noise. The specific implementation steps are as follows: a) Design filter: Set a 4th-order Butterworth low-pass filter with a cutoff frequency of 100Hz. 100Hz is selected as the cutoff frequency to retain the fundamental frequency (50Hz) and low-order harmonic information in the power system, while filtering out high-frequency noise. The 4th-order filter can provide sufficient attenuation slope to effectively suppress the stopband signal while ensuring the flatness of the passband. b) Implement the filter: Use the bilinear transformation method to convert the analog filter into a digital filter. The differential equation obtained after conversion is: y(n) = b0x(n) + b1x(n-1) + b2x(n-2) + b3x(n-3) + b4x(n-4) - a1y(n-1) - a2y(n-2) - a3y(n-3) - a4*y(n-4), where x(n) is the input signal, y(n) is the output signal after filtering, and bi and ai are filter coefficients. c) Apply the filter: Apply the above differential equation to the data of each sampling point in turn to obtain the filtered data sequence. In order to avoid transient response when the filter starts, the historical data buffer of the filter is filled with the first valid data point before the actual processing begins. Data calibration: After filtering, the data is calibrated using a linear calibration method to compensate for system errors and improve measurement accuracy. The specific implementation steps are as follows: a) Establish a calibration model: For each measurement channel (such as voltage, current, etc.), establish a linear calibration model: y = kx + b, where y is the calibrated value, x is the filtered measurement value, k is the proportional coefficient, and b is the offset. b) Determine calibration parameters: Determine the values ​​of k and b by multi-point measurement of the standard source. The specific steps are as follows: Select at least 5 standard value points evenly distributed within the measurement range. Perform multiple measurements (such as 10 times) on each standard value point and take the average value as the measurement value of the point. Use the least squares method to fit the values ​​of k and b. c) Apply calibration: For each filtered data point x, apply the calibration formula y = kx + b to obtain the calibrated data y. d) Calibration verification: Use the standard source to verify the calibration results regularly (such as monthly). If it is found that the error exceeds the preset threshold (such as 0.2%), it is necessary to re-determine the calibration parameters. Through the above filtering and calibration steps, the quality and reliability of the original data can be effectively improved. The Butterworth low-pass filter can effectively remove high-frequency noise while retaining the main characteristics of the signal. Linear calibration can compensate for the inherent errors of the system and improve measurement accuracy. Derivative parameters such as total power, power factor, and apparent power can be calculated.At the same time, electric energy is measured, including the calculation of the total phase forward active power, reverse active power and combined active power, as well as the calculation of the four-quadrant reactive power. For the harmonic content, the phase-by-phase forward and reverse active harmonic power are calculated separately. The processed data is frozen at a preset time interval (such as 15 minutes), forming a variety of frozen data such as instantaneous freezing, timed freezing, daily freezing and monthly freezing.

[0196] Based on the processed data, the island state is judged. First, the parameter thresholds under normal operating conditions are set, including the voltage range (such as ±10% of the nominal voltage), frequency range (such as 50Hz±0.5Hz), power factor range (such as 0.95 lagging to 0.95 leading), etc. Compare the real-time monitoring data with the preset thresholds. If any parameter exceeds the threshold range within multiple consecutive sampling cycles (such as 5 cycles), an island warning is triggered. At the same time, the power change trend is analyzed. If a sudden power change is detected (such as a power change of more than 20% within 5 seconds), an early warning will also be triggered. If the abnormal state lasts for more than the preset value (such as 2s) in the high-frequency sampling mode, it is determined to be an island state.

[0197] When an island state is detected, the terminal immediately starts the communication program and reports the island event and related data to the upper system. The terminal uses a dual-mode communication module of micro-power wireless RF and power line carrier HPLC to ensure the reliability of communication. The reported data includes the time of island occurrence, duration, key parameter values ​​that trigger island judgment, and detailed electrical parameter records for a certain period of time before and after the island.

[0198] After confirming the islanding status and completing the data reporting, the system immediately executes the preset anti-islanding control strategy. The terminal sends an emergency shutdown command to the photovoltaic inverter, requiring it to cut off the power supply to the grid within 100ms. At the same time, the terminal activates its internal fault indication function, lights up the red fault indicator light, and sends an alarm signal to the connected external device through the RS-485 interface.

[0199] The system enters the recovery and self-diagnosis stage. The terminal continuously monitors the grid parameters, and starts the system recovery procedure when it detects that the grid voltage and frequency have returned to normal and remain stable for a preset time (such as 5 minutes). The recovery procedure is carried out in stages, gradually increasing the allowed output power until full power operation is restored. During the recovery process, the terminal uses its self-diagnosis function to continuously check the working status of key components such as internal circuits, communication modules, and measurement loops. The terminal generates a complete event report, including analysis of the cause of the islanding, system response evaluation, recovery process records, and improvement suggestions, and uploads the report to the management platform.

[0200] In the embodiment of the present invention, by adopting an intelligent low-voltage shunt monitoring terminal and high-precision sampling technology, high-precision real-time monitoring of the electrical parameters on the AC side of the photovoltaic system is achieved, providing a reliable data basis for island state judgment. The monitoring data is filtered, calibrated and analyzed by using the built-in data processing module of the terminal, which improves the accuracy and availability of the data and provides a more accurate input for island state judgment. By setting multiple parameter thresholds and combining power change trend analysis, multi-dimensional island state judgment is achieved, and the accuracy and reliability of island detection are improved. The dual-mode communication module and multiple communication guarantee mechanism are adopted to ensure that island events and related data can be reported to the upper system in a timely and reliable manner. Based on the reported confirmation information, the system can quickly execute anti-islanding control strategies, including emergency shutdown, fault indication and equipment control, effectively reducing the risk of continuous island state. Through multi-stage system recovery procedures and self-diagnosis functions, the safe and controllable recovery of the photovoltaic system is achieved, and the adaptive ability of the system is improved. Through comprehensive analysis and evaluation of island events, the system can automatically adjust the operating parameters, continuously optimize the anti-islanding strategy, and improve the long-term reliability and efficiency of the system.

[0201] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0202] An intelligent low-voltage shunt monitoring terminal is used to connect the AC output end of the photovoltaic system to obtain a connection configuration;

[0203] Based on the connection configuration, electrical parameters are measured through the AC voltage loop and the AC current loop to obtain raw electrical data;

[0204] The original electrical data is sampled to obtain sampled data, and the sampling interval of the sampled data is 0.5 seconds;

[0205] Using a hardware clock circuit with a temperature compensation function to time-mark the sampled data, obtaining time-marked data;

[0206] The time stamp data is stored in the internal memory of the terminal to obtain storage confirmation information;

[0207] Monitoring data is generated according to the stored confirmation information, and the monitoring data includes three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power.

[0208] Specifically, the intelligent low-voltage shunt monitoring terminal FC-5321 is connected to the AC output of the photovoltaic system. The terminal uses an integrated transformer to ensure the consistency and accuracy of the measurement. After the connection is completed, the system automatically detects the connection status and generates connection configuration information to prepare for subsequent measurements.

[0209] Based on the established connection configuration, the terminal starts measuring electrical parameters through its AC voltage loop and AC current loop. The voltage measurement range is set to 176V to 264V, with an accuracy of no more than ±0.5%; the current measurement range is 1A to 600A, with an accuracy of no more than ±0.5%; the frequency measurement range is 45Hz to 55Hz, with an accuracy of no more than 0.01Hz. These high-precision measurements ensure the reliability of the original electrical data.

[0210] The acquired raw electrical data is sampled with a sampling interval of 0.5 seconds. This sampling frequency can effectively capture the rapid changes in power grid parameters without generating excessive data burden. During the sampling process, the system will conduct a preliminary validity check on the data and remove data points that are obviously abnormal.

[0211] While sampling, the terminal uses a built-in hardware clock circuit with temperature compensation function to accurately time-stamp each data point. The error of this clock circuit does not exceed 0.5 seconds per day, ensuring the timing accuracy of the data. The accuracy of time stamping is crucial for subsequent analysis of power grid parameter change trends and judgment of island status.

[0212] The sampled data with time stamp is then stored in the terminal's internal memory. The storage process uses a circular buffer mechanism to ensure that the latest data is always retained during long-term operation. After each set of data is stored, the system generates a storage confirmation message, which contains key information such as the stored timestamp and data volume.

[0213] Based on the storage confirmation information, the system generates a complete monitoring data set. This data set includes key parameters such as three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power. The data set adopts a standardized format to facilitate subsequent processing and analysis. At the same time, the system will perform integrity checks on the data set to ensure that no data is lost or damaged.

[0214] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0215] The monitoring data is filtered and calibrated using the built-in data processing module of the terminal to obtain calibrated data;

[0216] Calculate total power, power factor and apparent power based on the calibrated data to obtain derived parameters;

[0217] The calibrated data is measured for electric energy to obtain electric energy data, which includes the combined phase forward active energy, reverse active energy, combined active energy and four-quadrant reactive energy;

[0218] Based on the calibrated data, the phase-by-phase forward and reverse active harmonic electric energy is calculated to obtain the harmonic content data;

[0219] The fundamental component of the protection current is used as the input quantity for fault discrimination to execute the fault detection algorithm and obtain the fault indication information;

[0220] The calibrated data, derived parameters, electric energy data, harmonic content data and fault indication information are frozen according to a preset time interval to obtain processed data.

[0221] Specifically, the monitoring data is filtered and calibrated using the built-in data processing module of the intelligent low-voltage branch monitoring terminal FC-5321. The filtering process uses a digital filtering algorithm, such as a Butterworth filter, to eliminate high-frequency noise and interference. The calibration process performs linear or nonlinear compensation on the measured values ​​based on pre-stored calibration parameters to improve data accuracy. This step can effectively reduce the impact of external environmental factors on the measurement results and ensure the accuracy of subsequent analysis.

[0222] Based on the calibrated data, derived parameters such as total power, power factor and apparent power are calculated. The total power calculation adopts the three-phase four-wire power calculation formula, that is, P = Ua*Ia*cosφa+Ub*Ib*cosφb+Uc*Ic*cosφc. The power factor is obtained by the ratio of active power to apparent power, that is, PF = P / S. Apparent power is calculated by the vector sum of active power and reactive power, that is, S = √(P^2+Q^2).

[0223] In terms of electric energy measurement, the terminal uses the cumulative integration method to calculate electric energy. For active electric energy, the forward and reverse electric energy are accumulated separately to obtain the electric energy data of four quadrants. Reactive electric energy uses the four-quadrant measurement method, which divides the reactive power into inductive and capacitive types, and accumulates them in four quadrants respectively.

[0224] The calculation of harmonic content uses the Fast Fourier Transform (FFT) algorithm. First, the voltage and current signals are decomposed by FFT, and then the amplitude and phase of each harmonic are calculated. Based on this data, the total harmonic distortion (THD) and the content of each harmonic can be calculated.

[0225] The fault detection algorithm is mainly based on the fundamental component of the protection current. The fundamental component is extracted by discrete Fourier transform (DFT), and then judged by the set threshold. If the detected fundamental component exceeds the preset threshold and the duration exceeds the set value (such as 100ms), the fault indication is triggered.

[0226] The system freezes all processed data at preset time intervals (e.g., 15 minutes). During the freezing process, the system records the timestamp of the freezing moment and saves all data at that moment (including original measurements, calculated derivative parameters, electric energy data, harmonic content, and fault indication status, etc.) as a data set. These frozen data sets will be used for subsequent island status judgment and long-term trend analysis.

[0227] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0228] Setting parameter thresholds under normal operating conditions to obtain a threshold setting set, the threshold setting set including a voltage range, a frequency range, and a power factor range;

[0229] Compare the processed data with the threshold setting set to obtain parameter deviation data;

[0230] Determine whether the parameter deviation data exceeds a threshold range in a plurality of consecutive sampling periods to obtain a first determination result;

[0231] Analyze the power change trend in the processed data to obtain the power change rate;

[0232] Determine whether the power change rate exceeds a preset threshold value, and obtain a second determination result;

[0233] The voltage state is detected by using the phase voltage / no voltage detection function of the terminal to obtain the voltage detection result;

[0234] The islanding state is comprehensively determined based on the first determination result, the second determination result and the voltage detection result to obtain an islanding determination result.

[0235] Specifically, the system sets the parameter threshold under normal operation to obtain a threshold setting set. This threshold setting set includes a voltage range, a frequency range, and a power factor range. Specifically, the voltage range is set to ±10% of the nominal voltage, for example, in a 220V system, the voltage range is 198V to 242V; the frequency range is set to 50Hz±0.5Hz, i.e., 49.5Hz to 50.5Hz; and the power factor range is set to 0.95 lagging to 0.95 leading.

[0236] The system compares the processed data with the threshold setting set to obtain parameter deviation data. This process is completed through the built-in comparator module of the intelligent low-voltage branch monitoring terminal FC-5321. The comparator module compares the processed data with the preset threshold in real time to generate parameter deviation data.

[0237] The system determines whether the parameter deviation data exceeds the threshold range in multiple consecutive sampling periods to obtain the first judgment result. Specifically, the system checks whether any parameter exceeds the threshold range in 5 consecutive sampling periods (i.e. 2.5 seconds, because the sampling interval is 0.5 seconds). If this happens, the first judgment result is "abnormal", otherwise it is "normal".

[0238] At the same time, the system analyzes the power change trend in the processed data and obtains the power change rate. This is achieved by calculating the power difference between two adjacent sampling points and dividing it by the sampling time interval. The system determines whether the power change rate exceeds the preset threshold and obtains the second judgment result. For example, if the power change exceeds 20% within 5 seconds, the second judgment result is "abnormal", otherwise it is "normal".

[0239] In addition, the system uses the terminal's phase voltage / no voltage detection function to detect the voltage status and obtain the voltage detection result. This function can quickly detect single-phase or multi-phase voltage loss, providing additional basis for island judgment.

[0240] The system comprehensively determines the islanding state based on the first judgment result, the second judgment result and the voltage detection result to obtain the islanding judgment result. The judgment logic is as follows: If the first judgment result is "abnormal", or the second judgment result is "abnormal", or the voltage detection result shows that there is a loss of voltage, the system will enter the high-frequency sampling mode and shorten the sampling interval to 0.1 seconds. If the abnormal state lasts for more than 2 seconds in the high-frequency sampling mode, the final islanding judgment result is "islanding exists"; otherwise, the judgment result is "no islanding exists".

[0241] In a specific embodiment, the process of executing step 104 may specifically include the following steps:

[0242] Start the dual-mode communication module of the intelligent low-voltage branch monitoring terminal and obtain a communication module ready signal;

[0243] Based on the communication module ready signal, sending the island event and related data through the main communication method to obtain a first sending result;

[0244] Determine whether the first sending result is confirmed within a preset time to obtain a first communication state;

[0245] If the first communication state is that no confirmation is received, switching to the backup communication mode to resend the data to obtain a second sending result;

[0246] Integrate the occurrence time, duration, key parameter values ​​that trigger islanding judgment, and detailed electrical parameter records of the islanding event to obtain a complete event data packet;

[0247] Upload all frozen data related to the island event to the superior system and obtain data upload confirmation;

[0248] Record abnormal situations in the entire communication process, form a communication log, and obtain reporting confirmation information.

[0249] Specifically, when executing the data reporting step, the system first starts the dual-mode communication module of the intelligent low-voltage branch monitoring terminal FC-5321. The module integrates two communication modes, micro-power wireless RF and power line carrier HPLC, to ensure the reliability of communication. The startup process includes self-checking and initializing the communication module, and a communication module ready signal will be generated after completion.

[0250] After receiving the ready signal, the system selects micro-power wireless RF as the primary communication method by default. This is because RF communication usually has lower latency and higher bandwidth. The system packages the islanding event and related data and sends it through the RF channel. The data sent includes: the precise timestamp of the islanding event (accurate to milliseconds), the duration of the islanding event, the key parameter values ​​that trigger the islanding judgment (such as voltage deviation, frequency deviation, power change rate, etc.), and detailed electrical parameter records for 60 seconds before and after the islanding event.

[0251] The system will start a timer after sending, and the preset waiting time is 5 seconds. If a reception confirmation is received from the upper system within these 5 seconds, the communication is considered successful and the first communication status is marked as "confirmed".

[0252] If no confirmation is received within 5 seconds, the system will mark the first communication status as "unconfirmed" and automatically switch to power line carrier HPLC as a backup communication method. During the switching process, the system will repack the data and add additional error checking information to improve the reliability of the transmission. Then resend the entire data packet through the HPLC channel.

[0253] Regardless of the communication method used, the system will integrate more comprehensive event data. This includes high-frequency sampling data (sampling frequency can reach 10Hz) of all electrical parameters 60 seconds before and 30 seconds after the islanding event, as well as all types of frozen data related to the islanding event (including instantaneous freeze, timed freeze, daily freeze and monthly freeze). These data are packaged into a complete event data package and uploaded to the superior system.

[0254] During the entire communication process, the system will record all communication activities in real time, including communication mode switching, data packet sending time, number of retransmissions, signal strength and other information. If any abnormal situation occurs, such as signal interference, data packet loss, etc., it will be recorded in detail in the communication log. These log information, together with the final data upload confirmation status, constitute the reporting confirmation information.

[0255] In a specific embodiment, the process of executing step 105 may specifically include the following steps:

[0256] Send an emergency shutdown command to the photovoltaic inverter, cut off the power supply to the grid within a preset time, and obtain the inverter response information;

[0257] Activate the fault indication function inside the terminal, light up the red fault indicator light, and get a visual alarm status;

[0258] Send an alarm signal to the connected external device through the first RS-485 interface, and obtain a response confirmation from the external device;

[0259] The second RS-485 interface is used to communicate with the power station monitoring system, trigger the power station-level emergency response plan, and obtain the power station response result;

[0260] Perform auxiliary topology analysis to identify other devices and lines within the impact range of the island and obtain impact range data;

[0261] According to the impact range data, control instructions are sent to relevant equipment, including cutting off non-critical loads and adjusting reactive power compensation devices, to obtain equipment control feedback;

[0262] Continuously monitor changes in system parameters, record all executed control operations, form a control strategy execution log, and obtain control execution results.

[0263] Specifically, when executing the anti-islanding control strategy, the system first confirms the existence of the islanding state based on the reported confirmation information. The intelligent low-voltage branch monitoring terminal FC-5321 immediately sends an emergency shutdown command to the photovoltaic inverter. The command requires the inverter to cut off the power supply to the grid within 100 milliseconds to prevent a continuous islanding state. The terminal then receives and records the response information of the inverter, including the downtime and current status.

[0264] At the same time, the terminal activates its internal fault indication function. This includes lighting up the red fault indicator light on the device, providing an intuitive visual alarm. The system records the triggering time and duration of the alarm and forms a visual alarm status report.

[0265] The terminal sends an alarm signal to the connected external device (such as an audible and visual alarm) through its first RS-485 interface. This step expands the alarm range and ensures that on-site personnel can detect abnormal conditions in a timely manner. The system records the response confirmation of the external device, including the reception time and activation status.

[0266] For large PV plants, the terminal also uses a second RS-485 interface to communicate with the plant monitoring system. This communication triggers the plant-level emergency response plan, which may include starting the backup power supply or adjusting the load distribution. The system records the detailed results of the plant response, including the execution status and timestamp of each emergency measure.

[0267] In addition, the terminal performs its assisted topology analysis function to quickly identify other devices and lines within the island's impact range. This process generates detailed impact range data, including a list of affected devices and their current status.

[0268] Based on this impact range data, the terminal sends customized control instructions to relevant devices. This may include cutting off non-critical loads to reduce system burden, or adjusting reactive compensation devices to stabilize grid parameters. The system records control feedback from each device, including instruction execution status and execution time.

[0269] Throughout the process, the terminal continuously monitors changes in system parameters, including key indicators such as voltage, frequency, and power factor. All executed control operations are recorded in detail to form a comprehensive control strategy execution log. This log contains the timestamp, specific action, system response, and result evaluation of each operation.

[0270] In a specific embodiment, the process of executing step 106 may specifically include the following steps:

[0271] Continuously monitor grid parameters to determine whether grid voltage and frequency have returned to normal and remain stable for a preset time, and obtain grid status assessment results;

[0272] Based on the grid status assessment result, a system recovery program is started and a recovery program activation signal is obtained;

[0273] Send a restart command to the photovoltaic inverter to limit its output power to a preset percentage of the rated power, and obtain the first stage recovery data;

[0274] Monitor system stability in low-power operation state. If no abnormality is detected after a preset period of stable operation, gradually increase the allowed output power to obtain a power recovery curve.

[0275] Execute the terminal self-diagnosis function, check the working status of the internal circuit, communication module, and measurement circuit, and obtain the self-diagnosis results;

[0276] Compare and analyze the data before and after the islanding event, evaluate the effectiveness of anti-islanding measures, and obtain a measure evaluation report;

[0277] Based on the self-diagnosis results and measure evaluation reports, some operating parameters are automatically adjusted, including fault judgment thresholds and sampling strategies, to obtain parameter optimization solutions;

[0278] Generate a complete event report, including island cause analysis, system response assessment, recovery process records and improvement suggestions to form a recovery diagnosis report.

[0279] Specifically, the intelligent low-voltage branch monitoring terminal FC-5321 continuously monitors grid parameters. The terminal collects grid voltage and frequency data in real time through its AC voltage circuit and AC current circuit. The collected data is sampled at intervals of 0.5 seconds and compared with the preset normal operating parameter range. The normal voltage range is usually set to ±10% of the nominal voltage, and the frequency range is 50Hz±0.5Hz. The system determines whether these parameters have returned to the normal range and remain stable for a preset time (such as 5 minutes). This process obtains the grid status assessment result.

[0280] When the grid status assessment results indicate that the grid has returned to normal and is stable, the terminal starts the system recovery program. This process generates a recovery program activation signal, marking the beginning of the system's controlled recovery phase.

[0281] The system recovery adopts a phased strategy. In the first phase, the terminal sends a restart command to the photovoltaic inverter through the RS-485 interface. The command contains power limit information, requiring the inverter to limit the output power to within 20% of the rated power after restart. This stage obtains the first phase recovery data, including the inverter's response status and actual output power.

[0282] In the low-power operation state, the terminal closely monitors various system parameters, including voltage, current, power factor, etc. If the system operates stably in this limited state for 15 minutes without detecting any abnormalities, the terminal enters the second stage of recovery. In this stage, the terminal gradually increases the allowed output power, usually by 20% every 5 minutes, until it returns to full power operation. This process forms a power recovery curve, which records the time point of power increase and the corresponding system parameter changes.

[0283] At the same time, the terminal performs a comprehensive self-diagnosis procedure. This includes checking the integrity of the internal circuits, the connection status of the communication modules, the accuracy of the measurement loops, etc. In particular, the terminal verifies the working status of key components such as its AD sampling circuits, memory, clock, etc. Any detected anomalies will be recorded in the self-diagnosis results.

[0284] In addition, the terminal conducts in-depth analysis of data before and after the islanding event. It compares the changes in system parameters before and after the islanding occurs, and evaluates the response speed and effectiveness of anti-islanding measures. This analysis process generates a measure evaluation report, which provides a basis for future system optimization.

[0285] Based on the self-diagnosis results and measure evaluation reports, the terminal can automatically adjust some operating parameters. For example, it may adjust the fault judgment threshold, optimize the sampling strategy, or modify the communication retry mechanism. These adjustments form a parameter optimization scheme to improve the adaptability and reliability of the system.

[0286] The terminal generates a comprehensive recovery diagnostic report that covers the entire process of the islanding event, including possible cause analysis of the islanding event, system response process evaluation, detailed recovery process records, and improvement suggestions based on the event.

[0287] The photovoltaic anti-islanding monitoring method in the embodiment of the present invention is described above. The photovoltaic anti-islanding monitoring device in the embodiment of the present invention is described below. Figure 2 , an embodiment of the photovoltaic anti-islanding monitoring device in the embodiment of the present invention includes:

[0288] The monitoring module 201 is used to monitor the AC side of the photovoltaic system in real time and obtain monitoring data;

[0289] The processing module 202 is used to process and analyze the monitoring data to obtain processed data;

[0290] A judgment module 203 is used to judge the island state based on the processed data to obtain an island judgment result;

[0291] The reporting module 204 is used to perform data reporting according to the island determination result and obtain reporting confirmation information;

[0292] An execution module 205 is used to execute the anti-islanding control strategy based on the reported confirmation information to obtain a control execution result;

[0293] The diagnosis module 206 is used to perform system recovery and self-diagnosis according to the control execution result to obtain a recovery diagnosis report.

[0294] Through the cooperation of the above components, the intelligent low-voltage shunt monitoring terminal and high-precision sampling technology are used to achieve high-precision real-time monitoring of the electrical parameters on the AC side of the photovoltaic system, providing a reliable data basis for islanding state judgment. The built-in data processing module of the terminal is used to filter, calibrate and analyze the monitoring data, which improves the accuracy and availability of the data and provides more accurate input for islanding state judgment. By setting multiple parameter thresholds and combining power change trend analysis, multi-dimensional islanding state judgment is achieved, which improves the accuracy and reliability of islanding detection. The dual-mode communication module and multiple communication guarantee mechanism are used to ensure that islanding events and related data can be reported to the upper system in a timely and reliable manner. Based on the reported confirmation information, the system can quickly execute anti-islanding control strategies, including emergency shutdown, fault indication and equipment control, effectively reducing the risk of continuous islanding state. Through multi-stage system recovery procedures and self-diagnosis functions, the safe and controllable recovery of the photovoltaic system is achieved, while the system's adaptive ability is improved. Through comprehensive analysis and evaluation of islanding events, the system can automatically adjust operating parameters, continuously optimize anti-islanding strategies, and improve the long-term reliability and efficiency of the system.

[0295] above Figure 2 The photovoltaic anti-islanding monitoring device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The photovoltaic anti-islanding monitoring device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0296] Figure 3 3 is a schematic diagram of the structure of a photovoltaic anti-islanding monitoring device provided by an embodiment of the present invention. The photovoltaic anti-islanding monitoring device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the photovoltaic anti-islanding monitoring device 300. Furthermore, the processor 310 can be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the photovoltaic anti-islanding monitoring device 300 to implement the steps of the above-mentioned photovoltaic anti-islanding monitoring method.

[0297] The photovoltaic anti-islanding monitoring device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the photovoltaic anti-islanding monitoring device shown does not constitute a limitation on the photovoltaic anti-islanding monitoring device provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0298] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0299] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0300] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Photovoltaic anti-islanding monitoring method, characterized by: The method comprises the following steps: Step S1, real-time monitoring of the AC side of the photovoltaic system to obtain monitoring data; Step S2: Processing and analyzing the monitoring data to obtain processed data; Step S3, performing island state judgment based on the processed data to obtain an island judgment result; Step S4: reporting data according to the island determination result to obtain reporting confirmation information; Step S5, executing the anti-islanding control strategy based on the reported confirmation information to obtain a control execution result; Step S6: Perform system recovery and self-diagnosis according to the control execution result to obtain a recovery diagnosis report.

2. The photovoltaic anti-islanding monitoring method according to claim 1 is characterized in that: Step S1 specifically includes the following steps: Step A1: Use an intelligent low-voltage shunt monitoring terminal to connect the AC output end of the photovoltaic system to obtain a connection configuration; Step A2: Based on the connection configuration, measure electrical parameters through an AC voltage loop and an AC current loop to obtain original electrical data; sample the original electrical data to obtain sampled data; Step A3, using a hardware clock circuit with a temperature compensation function to time-stamp the sampled data to obtain time-stamped data; Step A4: storing the time stamp data in the internal memory of the terminal to obtain storage confirmation information; Step A5: Generate monitoring data according to the storage confirmation information, wherein the monitoring data includes three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power.

3. The photovoltaic anti-islanding monitoring method according to claim 1 is characterized in that: Step S2 specifically includes the following steps: Step B1, filtering and calibrating the monitoring data using a data processing module built into the terminal to obtain calibrated data; Step B2, calculating the total power, power factor and apparent power based on the calibrated data to obtain derived parameters; Step B3, performing electric energy measurement on the calibrated data to obtain electric energy data, wherein the electric energy data includes combined-phase forward active energy, reverse active energy, combined active electric energy, and four-quadrant reactive electric energy; Step B4, calculating the phase-by-phase forward and reverse active harmonic electric energy based on the calibrated data to obtain harmonic content data; Step B5, using the protection current fundamental component as the fault discrimination input to execute the fault detection algorithm to obtain fault indication information; Step B6: Freeze the calibrated data, the derived parameters, the electric energy data, the harmonic content data and the fault indication information according to a preset time interval to obtain processed data.

4. The photovoltaic anti-islanding monitoring method according to claim 1 is characterized in that: Step S3 specifically The steps include: Step C1, setting parameter thresholds under normal operating conditions to obtain a threshold setting set, wherein the threshold setting set includes a voltage range, a frequency range, and a power factor range; Step C1, comparing the processed data with the threshold setting set to obtain parameter deviation data; Step C2, determining whether the parameter deviation data exceeds a threshold range in a plurality of consecutive sampling periods, and obtaining a first determination result; Step C3, analyzing the power change trend in the processed data to obtain the power change rate; Step C4, determining whether the power change rate exceeds a preset threshold, and obtaining a second determination result; Step C5, using the phase voltage / no voltage detection function of the terminal to detect the voltage state and obtain the voltage detection result; Step C6: comprehensively determine the islanding state based on the first determination result, the second determination result and the voltage detection result to obtain an islanding determination result.

5. The photovoltaic anti-islanding monitoring method according to claim 1 is characterized in that: Step S4 comprises the following steps: Step D1, start the dual-mode communication module of the intelligent low-voltage branch monitoring terminal to obtain a communication module ready signal; Step D2, based on the communication module ready signal, sending the island event and related data through the main communication method to obtain a first sending result; Step D3, determining whether the first sending result has been confirmed within a preset time, and obtaining a first communication state; Step D4: If the first communication state is that no confirmation is received, switch to the backup communication mode to resend the data to obtain a second sending result; Step D5, integrating the occurrence time, duration, key parameter values ​​triggering islanding judgment and detailed electrical parameter records of the islanding event to obtain a complete event data packet; Step D6, uploading all frozen data related to the island event to the upper level system, and obtaining data upload confirmation; Step D7: Record the abnormal conditions in the entire communication process, form a communication log, and obtain reporting confirmation information.

6. The photovoltaic anti-islanding monitoring method according to claim 1, characterized in that: Step S5 comprises the following steps: Step E1, sending an emergency shutdown command to the photovoltaic inverter, and cutting off the power supply to the grid within a preset time, and obtaining inverter response information; Step E2, activating the fault indication function inside the terminal, lighting up the red fault indicator light, and obtaining a visual alarm status; Step E3, sending an alarm signal to the connected external device through the first RS-485 interface, and obtaining a response confirmation from the external device; Step E4: Communicate with the power station monitoring system using the second RS-485 interface to trigger the power station-level emergency response plan and obtain the power station response result; Step E5, executing the auxiliary topology analysis function, identifying other devices and lines within the impact range of the island, and obtaining impact range data; Step E6: sending control instructions to relevant equipment according to the impact range data, including cutting off non-critical loads and adjusting reactive power compensation devices, to obtain equipment control feedback; Step E7: Continuously monitor changes in system parameters, record all executed control operations, form a control strategy execution log, and obtain control execution results.

7. The photovoltaic anti-islanding monitoring method according to claim 1 is characterized in that: Step S6 comprises the following steps: Step F1, continuously monitor the grid parameters, determine whether the grid voltage and frequency return to normal and remain stable for a preset time, and obtain a grid status assessment result; Step F2: based on the grid status assessment result, start the system recovery program and obtain a recovery program activation signal; Step F3: Send a restart instruction to the photovoltaic inverter to limit its output power to not exceed a preset percentage of the rated power, and obtain the first stage recovery data; Step F4: monitor the system stability in the low-power operation state. If no abnormality is detected after the preset stable operation time, gradually increase the allowed output power to obtain a power recovery curve; Step F5, execute the terminal self-diagnosis function, check the working status of the internal circuit, communication module, and measurement loop, and obtain the self-diagnosis result; Step F6: Compare and analyze the data before and after the islanding event, evaluate the effectiveness of the anti-islanding measures, and obtain a measure evaluation report; Step F7: based on the self-diagnosis result and the measure evaluation report, automatically adjust some operating parameters, including fault judgment threshold and sampling strategy, to obtain a parameter optimization solution; Step F8: Generate a complete event report, including islanding cause analysis, system response assessment, recovery process record and improvement suggestions, to form a recovery diagnosis report.

8. The photovoltaic anti-islanding monitoring method according to claim 1, characterized in that: When the photovoltaic system works in the anti-islanding condition, the AC side of the photovoltaic system is monitored in real time through the intelligent low-voltage shunt monitoring terminal, specifically: The intelligent low-voltage shunt monitoring terminal adopts an integrated mutual inductor, which is connected to the AC output end of the photovoltaic system, and measures and monitors the electrical parameters of three-phase voltage, phase voltage, phase current, frequency, phase active power and phase reactive power through the AC voltage loop and the AC current loop; wherein, the voltage measurement range is 176V to 264V, and the accuracy is not greater than ±0.5%; the current measurement range is 1A to 600A, and the accuracy is not greater than ±0.5%; the frequency measurement range is 45Hz to 55Hz, and the accuracy is not greater than 0.01Hz; the monitoring data is sampled at intervals of 0.5s and stored in the internal memory of the monitoring terminal; When the intelligent low-voltage branch monitoring terminal processes and analyzes the collected monitoring data, the following method is adopted: the data processing module built into the terminal first filters and calibrates the original data to eliminate possible interference and errors; wherein the specific implementation method of filtering and calibrating the original data includes digital filtering and data calibration; The digital filtering is specifically as follows: a Butterworth low-pass filter is used for digital filtering; the Butterworth filter has a large flat passband response, almost no ripple in the passband, and can effectively suppress high-frequency noise; The specific implementation steps of digital filtering are as follows: a) Design filter: Set a 4th-order Butterworth low-pass filter with a cutoff frequency of 100 Hz to retain the fundamental frequency 50 Hz and low-order harmonic information in the power system and filter out high-frequency noise; use a 4th-order filter to provide sufficient attenuation slope to effectively suppress stop-band signals while ensuring passband flatness; b) Filter implementation: Use the bilinear transformation method to convert the analog filter into a digital filter. The differential equation obtained after the conversion is: y(n)=b0x(n)+b1x(n-1)+b2x(n-2)+b3x(n-3)+b4x(n-4)-a1y(n-1)-a2y(n-2)-a3y(n-3)-a4*y(n-4), Among them, x(n) is the input signal, y(n) is the output signal after filtering, bi and ai are the filter coefficients; c) Apply filter: Apply the above differential equation to the data of each sampling point in turn to obtain the filtered data sequence; in order to avoid transient response when the filter starts, fill the filter's historical data buffer with the first valid data point before the actual processing starts; The data calibration is specifically as follows: after filtering, the data is calibrated using a linear calibration method to compensate for system errors and improve measurement accuracy; The specific implementation steps of data calibration are as follows: a) Establishing a calibration model: Establishing a linear calibration model for each measurement channel: y=kx+b, the measurement channel includes a voltage measurement channel and a current measurement channel; wherein y is the calibrated value, x is the filtered measurement value, k is the proportionality coefficient, and b is the offset; b) Determine calibration parameters: Determine the values ​​of k and b by multi-point measurement of the standard source; The specific steps to determine the values ​​of k and b are as follows: select at least 5 standard value points evenly distributed within the measurement range; perform multiple measurements on each standard value point and take the average value as the measurement value of the point. Use the least squares method to fit the values ​​of k and b; c) Apply calibration: for each filtered data point x, apply the calibration formula y=kx+b to obtain the calibrated data y; d) Calibration verification: Regularly verify the calibration results using a standard source. If the error is found to exceed the preset threshold, the calibration parameters need to be re-determined; Through the above filtering and calibration steps, the quality and reliability of the original data can be effectively improved; that is: the Butterworth low-pass filter can effectively remove high-frequency noise while retaining the main characteristics of the signal, and the linear calibration can compensate for the inherent error of the system and improve the measurement accuracy; the derived parameters of total power, power factor, and apparent power are calculated; at the same time, electric energy metering is performed, including the calculation of the forward active power, reverse active power, and combined active power, as well as the calculation of the four-quadrant reactive power; for the harmonic content, the forward and reverse active harmonic power of each phase are calculated separately; the processed data is frozen at a preset time interval to form a variety of frozen data including instantaneous freezing, timed freezing, daily freezing, and monthly freezing. Based on the processed data, the islanding state is judged; first, the parameter thresholds under normal operating conditions are set, including the voltage range, frequency range, and power factor range; the real-time monitoring data is compared with the preset thresholds. If any parameter exceeds the threshold range within multiple consecutive sampling cycles, an islanding warning is triggered; at the same time, the power change trend is analyzed, and if a sudden power change is detected, an early warning will also be triggered. If the abnormal state lasts longer than the preset value in the high-frequency sampling mode, it is determined to be an islanding state; When an island state is detected, the terminal immediately starts the communication program and reports the island event and related data to the superior system. The terminal uses a dual-mode communication module of micro-power wireless RF and power line carrier HPLC to ensure the reliability of communication. The reported data includes the time of island occurrence, duration, key parameter values ​​that trigger island judgment, and detailed electrical parameter records for a certain period of time before and after the island. After confirming the islanding status and completing the data reporting, the system immediately executes the preset anti-islanding control strategy; the terminal sends an emergency shutdown command to the photovoltaic inverter, requiring it to cut off the power supply to the grid within 100ms; at the same time, the terminal activates its internal fault indication function, lights up the fault indicator light, and sends an alarm signal to the connected external device through the RS-485 interface; The system enters the recovery and self-diagnosis stage; the terminal continuously monitors the grid parameters, and when it detects that the grid voltage and frequency have returned to normal and remain stable for a preset time, it starts the system recovery program; the recovery program is carried out in stages, gradually increasing the allowable output power until full power operation is restored; during the recovery process, the terminal uses its self-diagnosis function to continuously check the working status of key components of the internal circuit, communication module, and measurement loop; the terminal generates a complete event report, including analysis of the cause of the islanding, system response evaluation, recovery process records and improvement suggestions, and uploads the report to the management platform.

9. A photovoltaic anti-islanding monitoring device, used to execute the photovoltaic anti-islanding monitoring method according to any one of claims 1 to 7, characterized in that: The device comprises: A monitoring module is used to monitor the AC side of the photovoltaic system in real time and obtain monitoring data; A processing module, used to process and analyze the monitoring data to obtain processed data; A judgment module, used for judging the island state based on the processed data to obtain an island judgment result; A reporting module, used to perform data reporting according to the island judgment result and obtain reporting confirmation information; An execution module, used for executing the anti-islanding control strategy based on the reported confirmation information to obtain a control execution result; The diagnosis module is used to perform system recovery and self-diagnosis according to the control execution result to obtain a recovery diagnosis report.

10. Photovoltaic anti-islanding monitoring equipment, characterized by: The photovoltaic anti-islanding monitoring device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the photovoltaic anti-islanding monitoring device to execute the photovoltaic anti-islanding monitoring method according to any one of claims 1 to 7.

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