A device for adjustable, controllable and monitorable photovoltaic inverter

Abstract

The present application relates to photovoltaic inverter regulation technical field, specifically, it relates to a kind of adjustable controllable monitoring device to photovoltaic inverter.It includes: intelligent adjustment unit, the intelligent adjustment unit uses the multi-parameter dynamic matching algorithm based on grid situation awareness;Adaptive protocol unit;State monitoring unit;Safety control unit.The present application uses the multi-parameter dynamic matching algorithm based on grid situation awareness, collects the power, current, voltage parameter of photovoltaic inverter output and the voltage, frequency real-time parameter and fluctuation trend parameter of grid, combines the dynamic matching logic of grid situation, can realize the accurate dynamic adaptation of inverter output and grid state, solves the problem of insufficient adaptability caused by traditional single parameter adjustment without considering grid situation;Also through parameter acquisition module, grid situation analysis module and adjustment decision module generate PWM regulation signal to drive IGBT switch tube, guarantee the accuracy and stability of output adjustment.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic inverter control technology, and more specifically, to a device for adjusting, controlling, and monitoring photovoltaic inverters. Background Technology

[0002] As the core hub connecting photovoltaic power generation systems to the distribution network, photovoltaic inverters undertake crucial functions such as DC-DC power conversion, power quality regulation, and grid-connected interaction. Their operational performance directly determines the grid-connection efficiency of photovoltaic power and the safety and stability of the distribution network. With the continuous expansion of distributed photovoltaic grid connection, the changes in distribution network voltage fluctuations and frequency drift have intensified. At the same time, different brands of photovoltaic inverters use different communication protocols. Traditional devices are gradually showing their inadequacies in terms of dynamic inverter output adaptation, multi-protocol compatibility, anti-interference monitoring of operating status, and safety protection under abnormal operating conditions. There is an urgent need for an integrated solution that combines adjustable, controllable, and monitorable functions to meet the application needs in complex grid environments.

[0003] In the existing technology, relevant patents have been researched in the field of intelligent early warning and dynamic control of photovoltaic inverters. For example, Chinese patent CN202510933872.1 discloses an intelligent control method and system for photovoltaic inverters based on the Internet of Things. It constructs a time window model by using the predicted value of sunlight and the measured value of the grid connection point voltage. Through voltage serialization calculation, it realizes early warning of over-limit risks. Combined with the ambient temperature parameter, it transforms the voltage over-limit amplitude into a quantifiable adjustment index, promoting the upgrade of load control from static setting to dynamic quantification. Another Chinese patent CN202411162229.5 discloses a photovoltaic inverter control system. It acquires output parameters in real time through an electrical parameter acquisition module. The MCU outputs three-level control signals according to the parameter thresholds. The flexible control module realizes power ratio adjustment, and the rigid control module triggers the grid connection switch to disconnect under over-limit conditions, achieving second-level response control of operating parameters.

[0004] Despite the design advantages of the aforementioned technical solutions, they also suffer from the following technical shortcomings: First, the coordination and adaptation capability between grid status and inverter output is weak: CN202510933872.1 relies solely on solar illumination prediction and grid connection point voltage regulation, without incorporating key status parameters such as grid frequency and fluctuation trends; CN202411162229.5 relies on fixed electrical parameter threshold triggering control. Neither can dynamically adjust inverter output in conjunction with the overall grid operating status, making them prone to adaptation deviations when facing complex fluctuations in the distribution network. Second, there is insufficient communication compatibility and protocol iteration adaptability among multi-brand inverters: neither solution addresses the differences in communication protocols between different brands of equipment. The design lacks flexible adaptation capabilities and the ability to autonomously learn key features such as protocol frame headers and baud rates. When connecting to new brand inverters or updating the protocol version, manual adjustment of adaptation rules is required, limiting compatibility and scalability. Thirdly, the reliability of monitoring data and the foresight of fault early warning are lacking: CN202411162229.5 and CN202510933872.1 do not integrate core inverter operating data (such as component temperature and capacitor voltage) with environmental interference data (such as electromagnetic interference) for correlation verification. There is no data self-calibration mechanism, and fault early warning relies solely on parameter threshold judgments, failing to identify potential faults in advance based on state change trends, resulting in delayed warnings and low data reliability. Therefore, we propose a device for adjustable, controllable, and monitorable photovoltaic inverters. Summary of the Invention

[0005] The purpose of this invention is to provide a device for adjusting, controlling, and monitoring photovoltaic inverters, so as to solve the problems mentioned in the background art, such as weak coordination and adaptation between grid status and inverter output, insufficient communication compatibility and protocol iteration adaptability of multi-brand inverters, and lack of reliability of monitoring data and forward-looking fault warning.

[0006] To address the aforementioned technical problems, the present invention aims to provide an adjustable, controllable, and monitorable device for photovoltaic inverters, comprising:

[0007] The intelligent adjustment unit adopts a multi-parameter dynamic matching algorithm based on grid situation awareness. It collects the power, current, and voltage parameters output by the photovoltaic inverter, as well as grid situation parameters including real-time voltage and frequency parameters and fluctuation trend parameters of the grid. By combining the dynamic matching logic of grid situation, it solves the problem of insufficient adaptability caused by the traditional single-parameter adjustment not considering grid situation, and realizes accurate dynamic adaptation between inverter output and grid status.

[0008] The adaptive protocol unit adopts a dynamic feature self-learning mechanism without a pre-stored protocol library. It extracts four key features of the communication protocol frame header identifier, data length value, baud rate parameter, and verification method type of the photovoltaic inverter. By autonomously learning new protocol features and generating adaptation rules, it can overcome the limitations of the traditional "pre-stored protocol library" compatibility mode without relying on a pre-stored complete protocol library. This enables stable data interaction between photovoltaic inverters of multiple brands and protocols and reduces the scalability costs caused by protocol iteration.

[0009] The status monitoring unit employs a multi-source data fusion and interference self-calibration algorithm to simultaneously collect three types of operating status data from the photovoltaic inverter: IGBT temperature, DC-side capacitor voltage, and AC-side harmonic content, as well as two types of environmental interference data: surrounding electromagnetic interference intensity and ambient temperature. It uses a multi-source data fusion model to correlate and verify the multi-dimensional data, and combines interference self-calibration logic to automatically calibrate the interfered data. This suppresses environmental interference while improving the reliability of monitoring data, and enables potential fault early warning based on status trend analysis, solving the problems of low data reliability and delayed fault early warning in traditional monitoring units.

[0010] The safety control unit integrates a hardware circuit that combines fault detection, logic judgment, and execution protection, based on the highly reliable monitoring data output by the status monitoring unit. When the photovoltaic inverter or distribution network experiences abnormal conditions such as overvoltage, overcurrent, or overtemperature, it triggers rigid protection actions to ensure the safe operation of the inverter and distribution network and prevent equipment damage or grid accidents.

[0011] The integrated housing unit provides a physical mounting carrier for the intelligent adjustment unit, adaptive protocol unit, status monitoring unit, and safety control unit. It also features IP65-level outdoor environmental protection and a finned high-efficiency heat dissipation structure to ensure stable operation in complex outdoor environments.

[0012] As a further improvement to this technical solution, the intelligent regulation unit includes a parameter acquisition module, a power grid situation analysis module, and a regulation decision module, wherein:

[0013] The parameter acquisition module uses voltage sensors, current sensors and power metering chips to collect the active power, phase current and line voltage output by the photovoltaic inverter in real time, as well as the line voltage and frequency parameters of the power grid. The collected data is transmitted through the SPI bus.

[0014] The power grid situation analysis module has a built-in 32-bit MCU. After receiving the digital data from the parameter acquisition module, it removes high-frequency noise through a preset improved filtering algorithm. The improved filtering algorithm combines the processing logic of moving average and Kalman filtering. First, it suppresses periodic interference through moving average, and then it dynamically corrects non-stationary noise through Kalman filtering. Then, it stores the power grid voltage and frequency parameters in time series.

[0015] The regulation decision module integrates a digital signal processor. Based on the data processed by the power grid situation analysis module, it generates a PWM regulation signal through a pre-programmed logic circuit to drive the inverter's IGBT switching transistors to achieve power output adjustment.

[0016] As a further improvement to this technical solution, the process by which the power grid situation analysis module removes high-frequency noise using a preset improved filtering algorithm includes the following steps:

[0017] S120.1 Receive the digitized raw data output by the parameter acquisition module, the raw data including the instantaneous value sequence of the power grid voltage. and frequency instantaneous value sequence ;

[0018] S120.2 Perform a moving average on the original data: based on the moving window length. Calculate the smoothed voltage sequence and frequency sequence Periodic high-frequency interference is suppressed by averaging adjacent data.

[0019] S120.3. Perform Kalman filtering on the sequence after moving average processing: construct the state vector. and observed values Introducing the state transition matrix and observation matrix Through process noise and observation noise Dynamic estimation enables non-stationary noise correction;

[0020] S120.4. Through Kalman filtering prediction-update iteration, the corrected voltage sequence is output. and frequency sequence This completes the removal of high-frequency noise.

[0021] As a further improvement to this technical solution, the logic for the adjustment decision module to generate the PWM adjustment signal specifically includes:

[0022] Based on the output of the power grid situation analysis module and Configure corresponding adjustment coefficients based on the power grid status level (steady state, microwave dynamic, strong wave dynamic). and power grid status coefficient ;

[0023] The inverter output adjustment value is determined by a target power calculation model, which uses the current output power as the basis. Using the current power as a baseline, multiply the result by "1 minus the product of the adjustment coefficient and the situation coefficient" to obtain the target power. ;

[0024] The duty cycle of the PWM adjustment signal is based on and The difference is dynamically generated to drive the IGBT switching transistors to achieve closed-loop output regulation, and It is limited to a preset range of the inverter's rated power.

[0025] As a further improvement to this technical solution, the adaptive protocol unit includes a feature extraction module, a protocol learning module, and a rule generation module, wherein:

[0026] The feature extraction module is connected to the communication port of the photovoltaic inverter through a high-speed serial communication circuit. It captures the communication protocol data stream in real time and separates four key features from the data stream through a preset feature parsing logic (a hardware circuit based on frame start bit detection, data field length judgment, and check bit calculation). The frame header identifier is the starting byte sequence of the communication frame.

[0027] The protocol learning module receives four types of key features output by the feature extraction module, compares them with a feature sample library (which stores known protocol feature templates), and identifies the feature matching degree.

[0028] The rule generation module generates data interaction adaptation rules based on the matching results of the protocol learning module. The data interaction adaptation rules include data packaging format, response mechanism, and timeout retransmission logic, and the data interaction adaptation rules are stored in a temporary cache area.

[0029] As a further improvement to this technical solution, the feature comparison of the protocol learning module specifically includes:

[0030] Frame header identifier comparison: The frame header identifier byte sequence output by the feature extraction module is matched bit by bit with the template frame header byte sequence in the feature sample library. The proportion of the number of matched bytes to the total number of bytes is calculated to obtain the frame header identifier matching degree.

[0031] Comparison of data length values: The difference between the data length value output by the feature extraction module and the template data length value in the feature sample library is calculated to obtain the data length deviation, and it is determined whether the deviation is within the preset deviation range;

[0032] Comparison of baud rate parameters: The actual baud rate of the data stream captured by the feature extraction module is measured by a clock counter, and the percentage error is calculated by comparing it with the template baud rate parameters in the feature sample library to determine whether the error is within the allowable range;

[0033] Comparison of verification method types: First, verify whether the verification method type identified by the feature extraction module is consistent with the template verification method type in the feature sample library, and then verify the matching of the verification logic by simulating the calculation of the verification value.

[0034] As a further improvement to this technical solution, the status monitoring unit includes a data acquisition module and a synchronization control module, wherein:

[0035] The data acquisition module acquires the IGBT temperature of the photovoltaic inverter through a patch-type temperature sensor, acquires the DC side capacitor voltage through a differential voltage sampling circuit, acquires the AC side harmonic content through an FFT harmonic analysis circuit, acquires the ambient electromagnetic interference intensity through an electromagnetic coupling probe and an electromagnetic interference detection circuit, and acquires the ambient temperature through an NTC thermistor.

[0036] The synchronization control module has a built-in I2C clock synchronization chip, which generates a synchronization trigger signal with a fixed period and outputs it to each acquisition channel of the data acquisition module. It controls the temperature sensor, differential voltage sampling circuit, FFT harmonic analysis circuit, electromagnetic interference detection circuit, and NTC thermistor to start data acquisition on the same clock edge, ensuring that the acquisition time deviation of the five types of data does not exceed 1ms, and avoiding data correlation failure caused by timing deviation.

[0037] As a further improvement to this technical solution, the status monitoring unit also includes a fusion calibration module. This fusion calibration module receives five types of data synchronized by the synchronization control module and performs multi-source data fusion and interference self-calibration operations, specifically including:

[0038] Multi-source data fusion is based on preset parameter correlation rules: IGBT temperature With DC side capacitor voltage There is coupling correlation, AC side harmonic content With electromagnetic interference intensity There is a correlation, based on parameter correlation rules. Perform cross-validation to remove outlier data that deviates from the rules;

[0039] Interference self-calibration operates based on a pre-stored interference-free benchmark dataset: the interference-free benchmark dataset is dynamically generated through a real-time interference status determination mechanism, specifically by the fusion calibration module continuously monitoring the electromagnetic interference intensity. and ambient temperature The volatility (the range of numerical change per unit time), when The volatility is lower than the preset disturbance threshold and When the fluctuation is below the preset temperature threshold and this state remains stable for a preset duration, it is determined to be a period of stable operation without interference; during this period, the IGBT temperature is continuously collected. DC side capacitor voltage AC side harmonic content After multi-source data fusion verification and outlier removal, the average of continuously collected data within the interference-free stable period was used as the IGBT temperature reference value. DC side capacitor voltage reference value AC side harmonic content benchmark value and ambient temperature reference value The benchmark dataset is re-evaluated and updated every preset update cycle, and the interference influence coefficient corresponding to the current electromagnetic interference intensity is calculated. The interference influence coefficient corresponding to the current ambient temperature For the interfered Perform corrections and output calibrated operating status data. .

[0040] As a further improvement to this technical solution, the status monitoring unit also includes a fault early warning module. This fault early warning module receives the calibrated operating status data output by the fusion calibration module and performs status trend analysis and fault early warning operations, specifically including:

[0041] Status trend analysis: Receives post-calibration operating status data output by the fusion calibration module. For continuous data collection After group calibration, the operational status data is recorded as a time series, and the rate of change of each parameter per unit time is calculated. ,in For IGBT temperature change rate, The rate of change of the DC-side capacitor voltage. The rate of change of harmonic content on the AC side;

[0042] Fault warning judgment: [This will be used to] determine the current... Compare with preset safety thresholds respectively, and judge simultaneously. Does it exceed the preset trend threshold? ,in This represents the maximum permissible rate of temperature change per unit time for the IGBT. This represents the maximum permissible rate of change of the DC-side capacitor voltage per unit time. This is the maximum permissible rate of change of the harmonic content on the AC side per unit time. If the current data exceeds the safety threshold or the rate of change exceeds the trend threshold, the fault warning module outputs a hardware alarm signal, triggering the red alarm indicator light to illuminate and the buzzer to start, and marking the warning type to the corresponding monitoring data frame.

[0043] As a further improvement to this technical solution, the safety control unit includes a fault detection module, a logic judgment module, and an execution protection module, wherein:

[0044] The fault detection module is used to receive high-reliability monitoring data (including data output by the status monitoring unit) The system collects and digitizes abnormal parameters of the photovoltaic inverter and distribution network in real time, including DC-side capacitor voltage, AC-side voltage, and AC-side current. The fault detection module incorporates a voltage sampling chip, a current Hall sensor, and a temperature acquisition chip to detect DC-side overvoltage, AC-side overvoltage, AC-side overcurrent, and AC-side overtemperature, respectively. The system collects corresponding parameters for four types of abnormal over-temperature scenarios. The collected analog signals are converted into digital signals through an analog-to-digital converter circuit and then transmitted to the logic judgment module.

[0045] The logic judgment module is used to perform threshold comparison and anti-jitter logic judgment on the digital signal transmitted by the fault detection module, generate a protection trigger signal, and has a built-in hardware comparator and anti-jitter timing circuit, and pre-stores overvoltage thresholds. (Including DC side overvoltage threshold, AC side overvoltage threshold), overcurrent threshold Over-temperature threshold The received digital signal is compared with the corresponding threshold in real time. If the digital signal of any parameter continuously exceeds the corresponding overvoltage threshold and the duration reaches the preset anti-shake time (≥50ms, to avoid accidental triggering due to instantaneous interference), a high-level protection trigger signal is generated and transmitted to the execution protection module.

[0046] The execution protection module receives protection trigger signals from the logic judgment module and executes rigid protection actions to ensure the safe operation of the inverter and the distribution network. It synchronously executes two levels of protection via a relay drive circuit: the first level is power circuit disconnection, driving the relay to disconnect the DC input circuit and AC output circuit of the photovoltaic inverter, cutting off the energy transmission path; the second level is equipment alarm and status feedback, triggering a continuous red alarm indicator light and a continuous buzzer sound, while simultaneously marking the protection type (overvoltage abnormality / overcurrent abnormality / overtemperature abnormality) in the monitoring data frame and feeding it back to the status monitoring unit, intelligent adjustment unit, and adaptive protocol unit via the internal bus. The feedback to the status monitoring unit supplements the warning information from the fault early warning module, the feedback to the intelligent adjustment unit pauses its PWM adjustment signal output, and the feedback to the adaptive protocol unit incorporates the protection information into the data interaction frame, achieving precise synchronization of protection information among the internal functional units of the device and preventing equipment damage or grid accidents.

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

[0048] 1. This invention employs a multi-parameter dynamic matching algorithm based on grid situation awareness in its intelligent regulation unit. It collects the power, current, and voltage parameters output by the photovoltaic inverter, as well as the real-time voltage and frequency parameters and fluctuation trend parameters of the power grid. Combined with the dynamic matching logic of the grid situation, it achieves precise dynamic adaptation between the inverter output and the grid status, solving the problem of insufficient adaptability caused by traditional single-parameter regulation that does not consider the grid situation. Simultaneously, this unit also generates PWM regulation signals to drive IGBT switches through a parameter acquisition module, a grid situation analysis module (including an improved filtering algorithm), and a regulation decision module, further ensuring the accuracy and stability of the output adjustment.

[0049] 2. This invention employs a dynamic feature self-learning mechanism without a pre-stored protocol library in its adaptive protocol unit. It extracts four key features from the communication protocol of the access photovoltaic inverter: frame header identifier, data length value, baud rate parameter, and verification method type. It autonomously learns new protocol features and generates adaptation rules, overcoming the limitations of the traditional "pre-stored protocol library" compatibility model. This enables stable data interaction between multi-brand, multi-protocol photovoltaic inverters and reduces the scalability costs caused by protocol iteration. Furthermore, through the collaboration of feature extraction, protocol learning, and rule generation modules, this unit further enhances the flexibility of protocol adaptation and the reliability of data interaction.

[0050] 3. This invention employs a multi-source data fusion and interference self-calibration algorithm in its status monitoring unit to simultaneously collect operational status data of the photovoltaic inverter, such as IGBT temperature, DC-side capacitor voltage, and AC-side harmonic content, as well as environmental interference data, such as surrounding electromagnetic interference intensity and ambient temperature. Through a multi-source data fusion model, correlation verification is performed, and interference-affected data is automatically corrected using interference self-calibration logic. This improves the reliability of monitoring data while suppressing environmental interference. Furthermore, this unit achieves potential fault early warning based on status trend analysis, solving the problems of low data reliability and delayed fault warnings in traditional monitoring units. The synchronous control module ensures the consistency of data acquisition timing, further optimizing the correlation and effectiveness of the monitoring data. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the system framework of the present invention;

[0052] The meanings of the labels in the diagram are as follows:

[0053] 100. Intelligent regulation unit; 110. Parameter acquisition module; 120. Power grid situation analysis module; 130. Regulation decision module;

[0054] 200. Adaptive Protocol Unit; 210. Feature Extraction Module; 220. Protocol Learning Module; 230. Rule Generation Module;

[0055] 300. Status monitoring unit; 310. Data acquisition module; 320. Synchronization control module; 330. Fusion calibration module; 340. Fault early warning module;

[0056] 400. Safety control unit; 410. Fault detection module; 420. Logic judgment module; 430. Execution protection module;

[0057] 500. Integrated housing unit. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0059] like Figure 1 As shown, this embodiment provides a device for adjusting, controlling, and monitoring a photovoltaic inverter, including:

[0060] The intelligent adjustment unit 100 adopts a multi-parameter dynamic matching algorithm based on grid situation awareness. It collects the power, current and voltage parameters output by the photovoltaic inverter, as well as grid situation parameters including the real-time voltage and frequency parameters and fluctuation trend parameters of the grid. By combining the dynamic matching logic of grid situation, it solves the problem of insufficient adaptability caused by the traditional single-parameter adjustment not considering the grid situation, and realizes accurate dynamic adaptation between inverter output and grid status.

[0061] In this embodiment, the intelligent regulation unit 100 includes a parameter acquisition module 110, a power grid situation analysis module 120, and a regulation decision module 130, wherein:

[0062] The parameter acquisition module 110 uses a voltage sensor, a current sensor and a power metering chip to collect the active power, phase current and line voltage output by the photovoltaic inverter in real time, as well as the line voltage and frequency parameters of the power grid. The collected data is transmitted through the SPI bus.

[0063] Specifically, the parameter acquisition module 110 includes the following aspects in terms of hardware selection and data transmission:

[0064] In terms of hardware selection and adaptation, a Hall voltage sensor is selected for the voltage sensor. Its input range covers the output line voltage range of the photovoltaic inverter and the grid line voltage range, and the output signal is a 0-5V analog voltage, which is compatible with the input requirements of the internal AD conversion circuit of the parameter acquisition module 110. A closed-loop Hall current sensor is selected for the current sensor. Its input range is compatible with the maximum output current of the photovoltaic inverter, and the output signal is a 0-5V analog voltage, ensuring accurate acquisition of current parameters even when the inverter is running at full load. A dedicated metering chip with synchronous acquisition functions of active power, voltage and current is selected for the power metering chip. It supports the SPI communication interface and can directly interact with the MCU of the parameter acquisition module 110 to reduce errors in the signal conversion process.

[0065] In terms of data transmission assurance, the SPI bus adopts a two-wire full-duplex communication mode with a communication rate of 1Mbps to ensure real-time data transmission. To avoid bus interference, the SPI bus transmission line uses shielded twisted-pair cable with one end of the shield grounded. TVS transient suppression diodes are installed at the SPI interfaces of the parameter acquisition module 110 and the power grid situation analysis module 120 to prevent voltage spikes from damaging the interface circuits. Before each data transmission, the parameter acquisition module 110 verifies the data (using the CRC16 checksum algorithm). After receiving the data, the power grid situation analysis module 120 first performs the verification. If the verification passes, the data is stored; if the verification fails, a retransmission command is sent to the parameter acquisition module 110 to ensure the accuracy of data transmission.

[0066] In addition, regarding anomaly handling, if the parameter acquisition module 110 fails to acquire valid data three times consecutively (e.g., data exceeds the sensor's range or SPI bus data transmission is interrupted), it immediately sends an anomaly signal to the power grid situation analysis module 120 and suspends data transmission until the fault is cleared and normal acquisition resumes, thus preventing invalid data from affecting subsequent situation analysis.

[0067] The power grid situation analysis module 120 has a built-in 32-bit MCU. After receiving the digitized data from the parameter acquisition module 110, it removes high-frequency noise through a preset improved filtering algorithm. The improved filtering algorithm combines the processing logic of moving average and Kalman filtering. First, it suppresses periodic interference through moving average, and then it dynamically corrects non-stationary noise through Kalman filtering. Then, it stores the power grid voltage and frequency parameters in time series.

[0068] The regulation decision module 130 integrates a digital signal processor. Based on the data processed by the power grid situation analysis module 120, it generates a PWM regulation signal through a pre-programmed logic circuit to drive the IGBT switching transistors of the inverter to achieve power output adjustment.

[0069] In this embodiment, the process by which the power grid situation analysis module 120 removes high-frequency noise using a preset improved filtering algorithm includes the following steps:

[0070] S120.1 Receive the digitized raw data output by the parameter acquisition module 110, the raw data including the instantaneous value sequence of the power grid voltage. and frequency instantaneous value sequence ;

[0071] S120.2 Perform a moving average on the original data: based on the moving window length. Calculate the smoothed voltage sequence and frequency sequence Periodic high-frequency interference is suppressed by averaging adjacent data.

[0072] S120.3. Perform Kalman filtering on the sequence after moving average processing: construct the state vector. and observed values Introducing the state transition matrix and observation matrix Through process noise and observation noise Dynamic estimation enables non-stationary noise correction;

[0073] S120.4. Through Kalman filtering prediction-update iteration, the corrected voltage sequence is output. and frequency sequence This completes the removal of high-frequency noise.

[0074] Specifically, the power grid situation analysis module 120 removes high-frequency noise through a preset improved filtering algorithm. This algorithm combines the processing logic of moving average and Kalman filtering. First, it suppresses periodic interference (such as harmonic interference generated by the start-up and shutdown of electrical equipment in the power grid) through moving average, and then dynamically corrects non-stationary noise (such as voltage fluctuations caused by sudden load changes) through Kalman filtering. The specific steps and parameter settings are as follows:

[0075] Moving average processing: Receives the instantaneous grid voltage value sequence output by the parameter acquisition module 110. and frequency instantaneous value sequence Then, based on the sliding window length ( It is a positive integer, set in accordance with the common fluctuation periods of grid voltage and frequency in photovoltaic grid-connected scenarios, such as (That is, the average value is calculated based on 10 consecutive collections of raw data); through the formula Calculate the smoothed voltage sequence ,in express The grid voltage value after the moving average at any given time is the value within the moving window. The arithmetic mean of the original voltages, for The original value of the grid voltage at time ( It is the "recently" view covered within the sliding window. The raw data from each collection cycle); through the formula Calculate the smoothed frequency sequence ,in for The power grid frequency value after the moving average at any given time is the value within the moving window. The arithmetic mean of the original frequencies, for The original value of the power grid frequency at time ( It is the "recently" view covered within the sliding window. The sliding window uses a "first-in, first-out" mechanism, discarding the oldest set of data after each new data collection cycle to ensure that the data within the window is always the most up-to-date. Grouping to avoid data lag;

[0076] Kalman filtering:

[0077] For the sequence after moving average and To perform Kalman filtering, the core variable is first constructed: the state vector. : ,in for The actual value of the grid voltage at any given time (this is the "real state quantity" that the Kalman filter needs to approximate; it is not a measured value, but an estimation target). for The rate of change of grid voltage at any given time (reflecting the trend of voltage change and satisfying the following conditions) , (Data collection cycle) for The true value of the power grid frequency at any given time (this is the "true state quantity" that the Kalman filter needs to approximate; it is not a measured value, but rather the target for estimation). for The rate of change of the power grid frequency at any given time (reflecting the trend of frequency change, satisfying...) );

[0078] Observation vector : ,in for The voltage observation used for Kalman filtering at any given time (here equal to the voltage after moving average) ), for The frequency observations used for Kalman filtering at each time point (here equal to the frequency after moving average) );

[0079] State transition matrix : (4×4 matrix), where The data acquisition cycle is consistent with the acquisition cycle of parameter acquisition module 110. This is the voltage change time constant (set based on grid load characteristics, reflecting the "inertia" of voltage changes). The frequency change time constant (set based on the inertial characteristics of the power grid, reflecting the "inertia" of frequency changes);

[0080] Observation matrix : (A 2×4 matrix, extracting only "voltage and frequency" as observations, describing the "state vector") "and observation vector" (mapping relationship)

[0081] Process noise vector : ,in It is an abbreviation for "Normal Distribution (also known as Gaussian distribution)"; the "state disturbance" caused by uncontrollable factors such as sudden changes in power grid load follows a mean of 1 / 2. The covariance matrix is The normal distribution;

[0082] Process noise covariance matrix A 4×4 diagonal matrix, with diagonal elements as follows: ( These are the "standard deviations of fluctuation" for voltage, voltage change rate, frequency, and frequency change rate, respectively.

[0083] Observation noise vector : The "observational disturbance" caused by sensor errors, etc., follows the mean of The covariance matrix is The normal distribution;

[0084] Observation noise covariance matrix : A 2×2 diagonal matrix, with diagonal elements as follows: ( These are the "standard deviations of error" for voltage and frequency observations, respectively.

[0085] Furthermore, the filtering process is divided into two stages: prediction and update.

[0086] Prediction phase:

[0087] State prediction formula: ,in for Time based State prediction at time (not combined) (Preliminary state estimate of the observation at time) for The state estimate after time correction, for Time-matter noise;

[0088] Covariance prediction formula: ,in for The covariance matrix of the time-state prediction (reflecting the uncertainty of the predicted value). for The covariance matrix of the state estimation at time step. State transition matrix The transpose of the matrix;

[0089] Update phase: Kalman gain formula: ,in for Time-based Kalman gain (a tradeoff between the reliability of predicted and observed values). Observation matrix The transpose of the matrix, For matrix The inverse matrix;

[0090] State update formula: ,in for The state estimate after time correction (the final state estimate after combining the observations, with higher accuracy than the predicted value). for Vector of observations at each moment;

[0091] Covariance update formula: ,in for The covariance matrix of the state estimate at time step (reflects the uncertainty of the final estimate, which gradually converges after iteration). To and A uniform identity matrix (4×4).

[0092] Filtered output: The corrected voltage sequence is output through the prediction-update iteration of Kalman filtering. (Right now The first element (This refers to the corrected grid voltage estimate) and frequency sequence. (Right now The third element (This is the corrected power grid frequency estimate), completing high-frequency noise removal; simultaneously, the process noise covariance matrix... and observation noise covariance matrix It will dynamically adjust based on the filtering residual (the difference between the observed value and the predicted observed value) each time, further improving the filtering accuracy.

[0093] Specifically, the power grid situation analysis module 120 acquires the filtered data... and Then, the two types of data are stored in chronological order (the storage period is consistent with the parameter acquisition period, and the storage capacity meets the data backtracking requirements for at least 24 hours, which is convenient for traceability analysis after a failure).

[0094] On the other hand, the power grid status level is matched based on preset thresholds, specifically including:

[0095] Parameter calculation: First, calculate the maximum voltage fluctuation value within 5 consecutive acquisition cycles. ( The difference between the maximum and minimum values ​​and the maximum frequency fluctuation value. ( The difference between the maximum and minimum values ​​in (the range).

[0096] Level determination: and Compared with the preset threshold, if ( (Rated voltage of the power grid) and If it is determined to be a stable state; and It is determined to be microwave dynamic; if It was determined to be a strong wave dynamic;

[0097] Data transmission: the filtered , The matched situation level information is synchronously transmitted to the regulation decision module 130 to provide a basis for output regulation; if it is detected or Exceeding safety thresholds (e.g., voltage below 85%) Higher than 115% (Frequency below 48.5) Above 51.5 If the condition is not met, an "emergency load reduction" command will be sent directly to the regulation decision module 130, triggering a higher-priority protective regulation.

[0098] In this embodiment, the logic of the adjustment decision module 130 generating the PWM adjustment signal specifically includes:

[0099] Based on the output of the power grid situation analysis module 120 and Configure corresponding adjustment coefficients based on the power grid status level (steady state, microwave dynamic, strong wave dynamic). and power grid status coefficient ;

[0100] The inverter output adjustment value is determined by a target power calculation model, which uses the current output power as the basis. Using the current power as a baseline, multiply the result by "1 minus the product of the adjustment coefficient and the situation coefficient" to obtain the target power. ;

[0101] The duty cycle of the PWM adjustment signal is based on and The difference is dynamically generated to drive the IGBT switching transistors to achieve closed-loop output regulation, and It is limited to a preset range of the inverter's rated power.

[0102] Understandably, the regulation decision module 130 integrates a digital signal processor (DSP). Its core function is to generate a PWM regulation signal to drive the inverter IGBT switching transistor based on the situation data output by the power grid situation analysis module 120, thereby achieving precise power output adjustment. Its key logic includes setting the regulation coefficient, calculating the target power, and generating the PWM signal.

[0103] Furthermore, the adjustment decision module 130 presets an adjustment coefficient based on the inverter output adjustment requirements under different grid conditions. With situation coefficient ( (Corresponding to steady state, microwave dynamics, and strong wave dynamics respectively)

[0104] In a steady state, At this time, the power grid is stable, and only a small adjustment to the inverter output is needed to maintain grid connection stability and avoid power fluctuations caused by excessive adjustment.

[0105] Under microwave dynamic conditions, The power grid experiences certain fluctuations, so it is necessary to appropriately increase the adjustment range to ensure that the inverter output can quickly adapt to changes in the power grid.

[0106] Under strong wave dynamics, The power grid fluctuates significantly, requiring substantial adjustments to the inverter output to avoid grid shocks caused by mismatch between inverter output and grid conditions.

[0107] The above coefficient values ​​can be modified through the human-machine interface of the photovoltaic inverter to adapt to the differences in the characteristics of the power grid in different regions (e.g., in remote areas where the power grid stability is poor, the values ​​under strong wave dynamics can be appropriately increased). and .

[0108] Specifically, the adjustment decision module 130 determines the inverter output adjustment value through the target power calculation model. The model formula is as follows: ,in: The target power is the output power that the inverter needs to be adjusted to. The current output power is collected in real time by the power metering chip of the parameter acquisition module 110 and is consistent with the power data received by the power grid situation analysis module 120. The adjustment coefficient corresponding to the power grid status level ( These correspond to steady state, microwave dynamics, and strong wave dynamics, respectively, and are used to quantify the "degree of influence of the situation on power output adjustment". The status coefficient corresponding to the power grid status level ( These correspond to the steady state, microwave dynamics, and strong wave dynamics, respectively, and are used to characterize the "fluctuation intensity of the current situation".

[0109] Calculated Then, a range limit needs to be applied: if ( (The rated power of the inverter is the maximum output power designed for the inverter), then it is forcibly set to... To avoid excessively low efficiency caused by prolonged low-load operation of the inverter; if Then it will be forcibly set to To prevent the inverter from operating under overload; if If so, the calculated value remains unchanged.

[0110] Meanwhile, this implementation provides the following: and Examples of possible values: For example, steady state ( )correspond Microwave dynamics ( )correspond Strong wave dynamics ( )correspond This value is adapted to the characteristics of grid voltage and frequency deviation under different conditions, ensuring that the adjustment logic is consistent with the set condition judgment rules.

[0111] Specifically, the DSP in the adjustment decision module 130 is based on and The difference Generate PWM adjustment signal, :

[0112] when When (inverter output needs to be increased): DSP according to The magnitude of the PWM signal increases the duty cycle, and the duty cycle adjustment range is... Proportional (e.g.) At that time, the duty cycle increases by 2%; At the same time, the duty cycle increases by 4%, and the step size of each duty cycle adjustment does not exceed 0.5%, so as to avoid the impact of sudden power output on the power grid;

[0113] when When (inverter output needs to be reduced): DSP according to The absolute value of the value decreases the duty cycle of the PWM signal, and the adjustment logic is consistent with that when increasing output.

[0114] when When (current output already meets target requirements): maintain the current PWM signal duty cycle unchanged;

[0115] The generated PWM regulation signal is processed by the internal drive circuit (including optocoupler isolation circuit and power amplifier circuit) and then output to the gate of the inverter's IGBT switch to drive the IGBT to turn on or off. At the same time, the regulation decision module 130 collects the IGBT's operating current and voltage in real time through current sensor and voltage sensor. If the current exceeds the IGBT's rated current or the voltage exceeds the IGBT's rated voltage, the PWM regulation signal output is immediately cut off to protect the IGBT from overload damage.

[0116] Specifically, after the adjustment decision module 130 drives the IGBT to adjust the output, it will collect the adjusted inverter output parameters in real time. (phase current, line voltage), and feeds it back to the power grid situation analysis module 120; the power grid situation analysis module 120 re-filters and judges the feedback parameters and the situation. If it is determined that the current situation level has not changed and Approaching 0, maintaining the current adjustment logic; if the situation level changes or Still outside the allowed range (e.g.) If the grid situation analysis module receives an "emergency load reduction" command from the grid situation analysis module 120, it will immediately skip the conventional calculation and directly reduce the load. Reduced to 20% Then, an abnormal signal is simultaneously sent to the safety control unit 400 to trigger a protective action.

[0117] The adaptive protocol unit 200 adopts a dynamic feature self-learning mechanism without a pre-stored protocol library. It extracts four key features of the communication protocol frame header identifier, data length value, baud rate parameter, and verification method type of the photovoltaic inverter. By autonomously learning new protocol features and generating adaptation rules, it can overcome the limitations of the traditional "pre-stored protocol library" compatibility mode without relying on a pre-stored complete protocol library. This enables stable data interaction between photovoltaic inverters of multiple brands and protocols and reduces the scalability cost caused by protocol iteration.

[0118] Understandably, the adaptive protocol unit 200, as the "multi-protocol compatible core" of the adjustable, controllable, and monitorable photovoltaic inverter device, aims to address the shortcomings of existing technologies in terms of "insufficient communication compatibility and protocol iteration adaptability for multi-brand inverters." Traditional devices rely on a pre-stored complete protocol library for communication adaptation, requiring manual rule adjustments when connecting new brand inverters or updating protocol versions, thus limiting compatibility and scalability. The adaptive protocol unit 200, through a three-level collaborative architecture of "feature extraction - protocol learning - rule generation," employs a dynamic feature self-learning mechanism without a pre-stored protocol library. It autonomously identifies key features of the photovoltaic inverter communication protocol and generates adaptation rules, achieving stable data interaction between multi-brand and multi-protocol inverters without relying on a pre-stored complete protocol library. Simultaneously, it reduces the scalability costs associated with protocol iteration, providing a unified inverter data interaction channel for the intelligent adjustment unit 100 and the status monitoring unit 300, ensuring consistency in data transmission between the various units of the device and the inverter.

[0119] In this embodiment, the adaptive protocol unit 200 includes a feature extraction module 210, a protocol learning module 220, and a rule generation module 230, wherein:

[0120] The feature extraction module 210 is connected to the communication port of the photovoltaic inverter through a high-speed serial communication circuit, captures the communication protocol data stream in real time, and separates four key features from the data stream through a preset feature parsing logic (a hardware circuit based on frame start bit detection, data field length judgment, and check bit calculation). The frame header identifier is the starting byte sequence of the communication frame.

[0121] Specifically, the feature extraction module 210 adopts a hardware architecture of "high-speed serial communication circuit + signal conditioning circuit + MCU control circuit". Among them, the high-speed serial communication circuit supports two common serial port types, RS485 and RS232 (switched via hardware DIP switches to adapt to the communication interfaces of different inverters), and the serial communication rate supports adaptive adjustment from 1200bps to 115200bps to meet the communication rate requirements of most photovoltaic inverters. The signal conditioning circuit has a built-in differential amplifier chip and filter capacitor to amplify and suppress noise (such as suppressing signal glitches caused by grid electromagnetic interference) the analog signal received by the serial port, ensuring that the signal amplitude is stable within the MCU's recognizable range (0-3.3V). The MCU control circuit uses an 8-bit microcontroller (such as the STM32G031 series) to control the serial communication timing, execute feature parsing logic, and transmit the separated key features to the protocol learning module 220 via the I2C bus.

[0122] Specifically, the feature extraction module 210 separates four key features from the data stream—frame header identifier, data length value, baud rate parameter, and check mode type—through a preset feature parsing logic (implemented by a combination of hardware circuitry and software algorithms based on frame start bit detection, data field length judgment, and check bit calculation), as follows:

[0123] Frame header identifier extraction: The MCU control circuit detects the start bit of the frame through "continuous byte matching" logic. First, it presets the byte length range of the frame header identifier (e.g., 1-4 bytes, covering the frame header length of most inverter protocols). It monitors the data stream received by the serial port in real time. When a byte sequence that meets the "preset byte length" and has no subsequent repetition appears consecutively, the sequence is marked as a "candidate frame header identifier". Then, it confirms it through "multi-frame verification": it continuously captures 3 frames of data stream. If the candidate frame header identifiers of the 3 frames are completely consistent, the sequence is determined to be the frame header identifier (i.e., the start byte sequence of the communication frame). If they are inconsistent, it re-monitors to avoid misidentification caused by a single interference.

[0124] Baud rate parameter extraction: Measurement is performed through a combination of hardware clock counter and software algorithm. The clock counter is provided with a standard clock signal (e.g., 1MHz) by the protocol learning module 220. When the feature extraction module 210 captures the data stream, the clock counter records the duration (i.e., bit width) of a single data bit. The actual baud rate is calculated using the formula "baud rate = 1 / bit width". To improve accuracy, the bit width of 10 data bits is measured continuously, and the average value is used to calculate the baud rate, thus obtaining the actual baud rate parameter.

[0125] Data length value extraction: Based on the parsing of the "length field" after the frame header identifier—After determining the frame header identifier, the MCU control circuit extracts the bytes at a fixed position after the frame header identifier (such as 1-2 bytes after the frame header, determined by traversing common length field positions) according to the conventional frame structure of "frame header identifier + length field". The value of this byte is converted into decimal to obtain the data length value (i.e., the number of bytes in the data field of the communication frame); at the same time, the rationality of the data length value is verified: if the data length value is within a preset reasonable range (such as 1-255 bytes, to avoid exceeding the single-receive buffer capacity of the serial port), it is determined to be a valid data length value; otherwise, it is extracted again.

[0126] Verification method type extraction: This is achieved through "check bit position detection + logical verification"—First, based on the determined frame header identifier and data length value, the total length of the communication frame (frame header length + data length + check bit length) is determined, and then the position of the check bit is located (usually at the end of the frame); then, the check value is calculated for the data field according to three common verification methods: "parity check", "CRC8 check", and "CRC16 check". If the check value calculated by a certain verification method is completely consistent with the check bit at the end of the frame, then that method is determined to be the verification method type; if none of the three methods match, a "verification method pending confirmation" signal is sent to the protocol learning module 220 to trigger secondary extraction.

[0127] Specifically, if the feature extraction module 210 fails to capture a valid data stream five times consecutively (e.g., no signal input from the serial port, or the data stream is continuously garbled), it immediately sends a "data acquisition abnormality" signal to the protocol learning module 220 and initiates hardware fault diagnosis: first, it checks the power supply voltage of the serial communication circuit (ensuring it is 3.3V or 5V rated voltage), then it checks the wiring to the inverter communication port (using the MCU to read the pin level to determine if the connection is broken), and the diagnosis results are synchronously fed back to the device's main controller; if only a certain type of feature (e.g., verification method type) fails to be extracted, the module will shorten the feature extraction cycle (e.g., from 100ms / time to 50ms / time) and increase the extraction frequency to improve the success rate.

[0128] It is understandable that the specific structure of the preset feature parsing hardware circuit is as follows: Frame start bit detection: using... A dual D flip-flop is used to construct an edge detection circuit, utilizing the "rising / falling edge triggering" physical characteristic of D flip-flops to capture the start edge of communication data frames; data field length determination: through... An 8-bit binary counter counts the clock pulses after the start of the frame and matches the count result to a preset data field length template (following the digital communication rule of "one-to-one correspondence between data field length and pulse count"); parity bit calculation: using... The adder performs operations on the data field according to the CRC-8 checksum algorithm (based on the mathematical principles of binary arithmetic, a mature verification mechanism in the field of signal processing), generates a check bit, and compares it with the check bit in the received data. All the chips mentioned above are commercially available general-purpose digital integrated circuits, and their circuit connections conform to the physical topology of digital circuits: "logic gate-flip-flop-counter".

[0129] The protocol learning module 220 receives four types of key features output by the feature extraction module 210, compares them with a feature sample library (which stores known protocol feature templates), and identifies the feature matching degree.

[0130] Specifically, the feature sample library built into the protocol learning module 220 adopts a "partitioned storage" structure, divided into four independent storage partitions: frame header identifier template area, data length template area, baud rate template area, and verification method template area. Each partition stores feature templates for at least 10 common photovoltaic inverter protocols (such as frame header identifier templates including common start byte sequences like 0xAA55; baud rate templates including common rates like 9600bps; and verification method templates including parity check, CRC8, CRC16, etc.). The sample library is stored in the EEPROM (Electrically Erasable Read-Only Memory) built into the protocol learning module 220. It supports manually adding new templates (such as special protocol features for niche brand inverters) through the device's human-machine interface, and can also import externally updated template files through the USB interface to improve the scalability of the sample library.

[0131] Specifically, the protocol learning module 220 compares features according to the priority order of "frame header identifier - baud rate parameter - data length value - verification method type", which includes:

[0132] Frame header identifier comparison: Call the frame header template from the feature sample library, and match the extracted frame header identifier byte sequence with the template bit by bit. Calculate the matching degree according to "frame header matching degree = (number of matching bytes / total number of bytes) × 100%"; the preset matching degree threshold is 90%. If the matching degree is ≥90%, the candidate matching template is determined. If the matching degree is <90%, it is fed back to the feature extraction module 210 for re-extraction.

[0133] Baud rate parameter comparison: Based on the candidate template, call the baud rate template value, measure the actual baud rate through the clock counter, and calculate the error according to "baud rate error percentage = |(actual baud rate - template baud rate) / template baud rate| × 100%"; if the error is ≤ ±5%, it is considered a match; if it exceeds the tolerance, the serial port baud rate is finely adjusted and the test is repeated.

[0134] Data length value comparison: Call the candidate template data length template value, calculate the difference between the extracted value and the template value; if the difference is within ±2 bytes, a match is determined; if it exceeds the range, the data length value is extracted again;

[0135] Verification method type comparison: First, verify whether the extracted verification method type is consistent with the template. Then, simulate and calculate the data field verification value according to the template verification logic. If it is consistent with the frame tail verification bit, it is determined to be a match. If it is inconsistent, the verification method type is extracted again.

[0136] Specifically, after the protocol learning module 220 completes the comparison of four types of features, it generates a "comparison result report", which includes the matching template number, the matching degree / error value of each feature, and whether the matching is successful. If all four types of features are successfully matched, the report is transmitted to the rule generation module 230 to trigger the generation of adaptation rules. If a certain type of feature fails to match, a "failure reason log" is generated (such as frame header comparison failure or baud rate error exceeding the standard), and fed back to the feature extraction module 210 to re-execute feature extraction. The number of re-extractions shall not exceed 3. If all 3 attempts fail, a "protocol learning failure" alarm is sent to the device main controller.

[0137] The rule generation module 230 generates data interaction adaptation rules based on the matching results of the protocol learning module 220. The data interaction adaptation rules include data packaging format, response mechanism, and timeout retransmission logic, and the data interaction adaptation rules are stored in a temporary cache area.

[0138] Specifically, the rule generation module 230 generates data interaction adaptation rules based on the feature matching results of the protocol learning module 220 and stores them in a temporary cache, as follows:

[0139] Adaptation rule generation: Generate rules containing data packaging format, response mechanism, and timeout retransmission logic according to the matching template. The data packaging format follows the structure of "frame header identifier + data length value + instruction code + data field + check bit" (the frame header, data length, and check bit are consistent with the extracted key features, and the instruction code corresponds to the interaction requirements between the device and the inverter). The response mechanism sets the response frame format (the instruction code is the offset value of the request instruction code) and a 100ms response timeout. The timeout retransmission logic sets 3 retransmission attempts and a 50ms retransmission interval. If 3 retransmission attempts fail, the communication is considered abnormal.

[0140] Temporary cache design: Flash partition storage (current rule area, historical rule area, backup rule area) is adopted. Rules are managed through a unique index (including inverter identifier and generation time). When updating, the current rule is backed up to the historical area first and then the new rule is written. If the update fails, the historical rule is called to restore.

[0141] Rule application and synchronization: After generating a rule, a synchronization notification is sent to the intelligent adjustment unit 100 and the status monitoring unit 300. A rule calling interface is provided (a communication frame can be generated by passing in instructions / parameters). The correctness of the data interaction frame structure is verified every hour. When multiple inverters are connected, multiple sets of rules are distinguished by index to achieve multi-protocol adaptation.

[0142] In this embodiment, the feature comparison of the protocol learning module 220 specifically includes:

[0143] Comparison of frame header identifiers: The frame header identifier byte sequence output by the feature extraction module 210 is matched bit by bit with the byte sequence of the template frame header in the feature sample library. The proportion of the number of matched bytes to the total number of bytes is calculated to obtain the frame header identifier matching degree.

[0144] Comparison of data length values: The difference between the data length value output by the feature extraction module 210 and the template data length value in the feature sample library is calculated to obtain the data length deviation, and it is determined whether the deviation is within the preset deviation range;

[0145] Comparison of baud rate parameters: The actual baud rate of the data stream captured by the feature extraction module 210 is measured by a clock counter, and the error percentage is calculated by comparing it with the template baud rate parameters in the feature sample library to determine whether the error is within the allowable range;

[0146] Comparison of verification method types: First, verify whether the verification method type identified by the feature extraction module 210 is consistent with the template verification method type in the feature sample library, and then verify the matching of the verification logic by simulating the calculation of the verification value.

[0147] Understandably, each time the feature extraction module 210 completes feature extraction, it transmits the feature data to the protocol learning module 220 via the SPI bus. Before transmission, the data is checked using CRC8 (to avoid transmission errors). After the protocol learning module 220 completes the comparison, it transmits the comparison result to the rule generation module 230 via the I2C bus. After generating the rule, the rule generation module 230 sends a "rule ready" signal to the other two modules via the CAN bus, triggering the modules to execute functions according to the new rule. The coordination cycle of the three is synchronized with the device data interaction cycle, ensuring the timeliness of feature extraction, comparison, and rule application.

[0148] It should be added that this embodiment also includes an exception handling mechanism, specifically including:

[0149] Feature extraction failure: If the feature extraction module 210 fails to extract a valid feature for 10 consecutive times, it will automatically switch the serial port type (e.g., from RS485 to RS232) or adjust the serial port signal gain and try to extract again; if it still fails, it will determine "serial communication failure" and send fault location information (e.g., "RS485 interface has no signal") to the device main controller.

[0150] Protocol learning anomaly: If the protocol learning module 220 fails to match 3 times in a row, it will automatically load a "general template" (covering the common features of most inverters) from the feature sample library and perform a second comparison according to the general template. If the general template matches successfully, it will generate basic adaptation rules to ensure uninterrupted communication and send a "protocol not accurately matched, manual calibration recommended" prompt to the main controller.

[0151] Rule generation error: If the rule generation module 230 detects a logical conflict when generating a rule (such as the frame header and frame tail being duplicated in the packaging format), it will automatically call the previous version of the rule in the historical rule area to temporarily replace it, and at the same time regenerate the new rule; if the regeneration still fails, it will call the factory default rule from the backup rule area to avoid communication interruption.

[0152] The status monitoring unit 300 employs a multi-source data fusion and interference self-calibration algorithm to simultaneously collect three types of operating status data of the photovoltaic inverter: IGBT temperature, DC-side capacitor voltage, and AC-side harmonic content, as well as two types of environmental interference data: surrounding electromagnetic interference intensity and ambient temperature. It uses a multi-source data fusion model to correlate and verify the multi-dimensional data, and combines interference self-calibration logic to automatically calibrate the interfered data. This suppresses environmental interference while improving the reliability of monitoring data, and enables potential fault early warning based on status trend analysis, solving the problems of low data reliability and delayed fault early warning in traditional monitoring units.

[0153] In this embodiment, the status monitoring unit 300 includes a data acquisition module 310 and a synchronization control module 320, wherein:

[0154] The data acquisition module 310 acquires the IGBT temperature of the photovoltaic inverter through a patch-type temperature sensor, acquires the DC side capacitor voltage through a differential voltage sampling circuit, acquires the AC side harmonic content through an FFT harmonic analysis circuit, acquires the ambient electromagnetic interference intensity through an electromagnetic coupling probe and an electromagnetic interference detection circuit, and acquires the ambient temperature through an NTC thermistor.

[0155] Specifically, after the device is powered on, the status monitoring unit 300 operates according to the entire process of "initialization - synchronous acquisition - fusion calibration - early warning judgment", which includes:

[0156] Initialization phase: After the device is powered on, the data acquisition module 310 performs self-tests on each sensor and sampling circuit (such as checking whether the temperature sensor is open-circuited and whether the FFT circuit is ready), the synchronization control module 320 initializes the I2C clock synchronization chip (setting the clock frequency and trigger period), the fusion calibration module 330 loads the preset parameter correlation rules and the initial benchmark dataset, and the fault warning module 340 loads the safety threshold and trend threshold. After all modules pass the self-test, the synchronization control module 320 generates the first frame synchronization trigger signal and starts data acquisition.

[0157] Synchronous acquisition phase: The synchronous control module 320 generates a synchronous trigger signal at a fixed period (e.g., 50ms / time) to control the five acquisition channels of the data acquisition module 310 (temperature sensor, differential voltage sampling circuit, etc.) to start acquisition on the same clock edge. The acquired data is transmitted to the fusion calibration module 330 after AD conversion. The synchronous control module 320 monitors the acquisition time deviation in real time to ensure that the deviation is ≤1ms.

[0158] Fusion calibration phase: After receiving the synchronized data, the fusion calibration module 330 first removes outliers by cross-validation of parameter correlation rules, then determines whether it is in a stable period without interference in order to update the benchmark dataset, and finally corrects the interfered data by the interference impact coefficient and outputs the calibrated operating status data to the fault early warning module 340.

[0159] Early warning judgment phase: Fault early warning module 340 records continuously The system collects calibration data, calculates the rate of change of each parameter per unit time, and compares the current data with the safety threshold, the rate of change with the trend threshold. If any indicator exceeds the limit, a hardware alarm is triggered, and the warning information is simultaneously fed back to the safety control unit 400.

[0160] Specifically, the surface-mount temperature sensor uses an NTC surface-mount type, which is directly connected to the AD conversion circuit to output a 0-3.3V analog voltage to acquire the IGBT temperature. The differential voltage sampling circuit is built based on an instrumentation amplifier. A voltage divider resistor is connected in parallel at the input (to adapt to the DC-side capacitor voltage range), and the output is a 0-3.3V differential signal (suppressing common-mode interference) to acquire the DC-side capacitor voltage. The FFT harmonic analysis circuit integrates a dedicated harmonic analysis chip, supports 21st harmonic analysis, and directly outputs the AC side harmonic content. The data is digitized; the electromagnetic interference detection circuit, paired with a high-frequency electromagnetic coupling probe, converts the electromagnetic interference intensity into a 0-3.3V analog voltage via a detection circuit. The NTC thermistor uses epoxy encapsulation and outputs a 0-3.3V analog voltage to acquire ambient temperature. .

[0161] Furthermore, the acquired analog signal is converted into digital data using a 12-bit analog-to-digital converter. The conversion formula is as follows: ;in, Represents digitized data; Represents the analog voltage output by the sensor / circuit; Indicates the reference voltage for AD conversion; This represents the maximum quantization value of the 12-bit AD conversion. The conversion formula unifies different analog signals into a standard digital format, providing a unified data foundation for subsequent processing by the fusion calibration module 330.

[0162] The synchronization control module 320 has a built-in I2C clock synchronization chip, which generates a synchronization trigger signal with a fixed period and outputs it to each acquisition channel of the data acquisition module 310. It controls the temperature sensor, differential voltage sampling circuit, FFT harmonic analysis circuit, electromagnetic interference detection circuit, and NTC thermistor to start data acquisition on the same clock edge, ensuring that the acquisition time deviation of the five types of data does not exceed 1ms, and avoiding data correlation failure caused by timing deviation.

[0163] In terms of synchronous trigger signal generation, the clock synchronization chip generates a fixed period. The square wave synchronous trigger signal is output to each acquisition channel of the data acquisition module 310. The trigger signal period formula is: ,in The reference clock frequency for the clock synchronization chip. Clock division factor (configurable, e.g.) hour, (Approximately 50ms), by configuring the frequency division coefficient. This ensures that the synchronous trigger signal period matches the data acquisition period, guaranteeing that each channel starts acquiring data synchronously.

[0164] Regarding the calculation of acquisition time deviation, the synchronization control module 320 records the actual start-up time of each acquisition channel in the data acquisition module 310 using a timer, and calculates the maximum time deviation to verify the synchronization effect. The deviation formula is as follows: ,in This represents the maximum time deviation for the five types of acquisition channels. For the startup time of the surface-mount temperature sensor, This refers to the startup time of the differential voltage sampling circuit. For the FFT harmonic analysis circuit startup time, This refers to the start-up time of the electromagnetic interference detection circuit. Real-time calculation of NTC thermistor start-up time ,like If the time exceeds 1ms, a "synchronization error" signal is sent to the data acquisition module 310 to re-trigger acquisition and avoid data correlation failure caused by timing deviation.

[0165] In this embodiment, the state monitoring unit 300 further includes a fusion calibration module 330. The fusion calibration module 330 is used to receive five types of data synchronized by the synchronization control module 320, and perform multi-source data fusion and interference self-calibration operations, specifically including:

[0166] Multi-source data fusion is based on preset parameter correlation rules: IGBT temperature With DC side capacitor voltage There is coupling correlation, AC side harmonic content With electromagnetic interference intensity There is a correlation, based on parameter correlation rules. Perform cross-validation to remove outlier data that deviates from the rules;

[0167] Interference self-calibration operates based on a pre-stored interference-free reference dataset: the interference-free reference dataset is dynamically generated through a real-time interference status determination mechanism, specifically by the fusion calibration module 330 continuously monitoring the electromagnetic interference intensity. and ambient temperature The volatility (the range of numerical change per unit time), when The volatility is lower than the preset disturbance threshold and When the fluctuation is below the preset temperature threshold and this state remains stable for a preset duration, it is determined to be a period of stable operation without interference; during this period, the IGBT temperature is continuously collected. DC side capacitor voltage AC side harmonic content After multi-source data fusion verification and outlier removal, the average of continuously collected data within the interference-free stable period was used as the IGBT temperature reference value. DC side capacitor voltage reference value AC side harmonic content benchmark value and ambient temperature reference value The benchmark dataset is re-evaluated and updated every set interval (based on the power system operation pattern that "photovoltaic output is affected by sunlight in a diurnal phase (the temporal distribution pattern of natural sunlight) and the grid load also has hourly fluctuation cycles," ensuring that the dataset reflects grid status changes in a timely manner while avoiding frequent updates that waste computational resources; for example, the preset update interval is 1 hour). The interference impact coefficient corresponding to the current electromagnetic interference intensity is calculated. The interference influence coefficient corresponding to the current ambient temperature For the interfered Perform corrections and output calibrated operating status data. .

[0168] Specifically, in the fusion calibration module 330, multi-source data fusion achieves cross-validation based on preset parameter correlation rules, eliminating abnormal data that deviates from the rules, specifically including:

[0169] IGBT temperature With DC side capacitor voltage Coupling correlation verification: Based on the operation of the inverter Fluctuations can cause changes in IGBT losses, which in turn affect... Based on the coupling characteristics of "", a correlation formula is set. ,in The coupling coefficient (preset based on inverter model, used for quantization) For every 1V change (the theoretical range of change) The reference offset (based on the inverter's no-load state preset, i.e.) (The theoretical temperature reference is 0V); the calculation logic is: to collect... Substituting into the formula, we obtain the theoretical temperature. If the actual data collected and absolute value of the difference If the deviation exceeds the preset threshold, then a judgment is made. or Data deemed abnormal will be removed.

[0170] AC side harmonic content With electromagnetic interference intensity Correlation verification: Based on the fact that "environmental electromagnetic interference can affect the accuracy of AC side signal acquisition of the inverter, leading to..." Based on the "fluctuation" characteristic, a correlation formula is set. ,in The collected AC side harmonic content, The intensity of electromagnetic interference collected. The correlation coefficient between AC side harmonic content and electromagnetic interference intensity (based on preset field electromagnetic environment, used for quantification). For every 1V / m change (the theoretical range of change) The harmonic reference value (preset based on the inverter's rated operating state, i.e.) (Theoretical harmonic content at 0V / m); the calculation logic is: to collect... Substituting into the formula yields the theoretical harmonic content. If the actual data collected and absolute value of the difference If the deviation exceeds a preset threshold (e.g., 1%), then a judgment is made. or This data is considered abnormal and will be removed.

[0171] Furthermore, the fusion calibration module 330 continuously monitors two types of environmental interference data—electromagnetic interference intensity. With ambient temperature The logic of "volatility monitoring + stability duration verification" is used to determine the period of undisturbed stability:

[0172] Volatility monitoring: Volatility (i.e., the maximum range of data change per unit time) is calculated using the sliding time window method. A fixed time window is set (e.g., 10 seconds), and the electromagnetic interference intensity is statistically analyzed within each time window. The maximum and minimum values, the difference between them is the value within that window. The fluctuation of ambient temperature; The maximum and minimum values, the difference between them is the value within that window. The degree of fluctuation;

[0173] Threshold comparison and duration verification: Preset electromagnetic interference fluctuation thresholds and ambient temperature fluctuation thresholds; if within a certain time window... The fluctuation is lower than the electromagnetic interference fluctuation threshold and If the fluctuation is lower than the ambient temperature fluctuation threshold, it is initially determined that the current period may be in an undisturbed state. Then, the stability of this state is continuously monitored. If this "double fluctuation below the threshold" state is maintained for a preset stable duration (such as 30 seconds), the current period is finally determined to be an undisturbed stable period, which can be used to generate a benchmark dataset.

[0174] Furthermore, in the fusion calibration module 330, the interference-free reference dataset is dynamically generated through a real-time interference status determination mechanism, serving as a reference for subsequent interference self-calibration. Specifically, it includes:

[0175] Interference impact factor calculation: based on the current electromagnetic interference intensity Compared with the interference-free reference value (No interference period) (mean value), calculate the electromagnetic interference influence coefficient. The formula is ,in This is the electromagnetic interference influence coefficient (the value ranges from 0 to 1, and the closer it is to 1, the smaller the interference influence). The electromagnetic interference intensity safety threshold is based on the current ambient temperature. Compared with the interference-free reference value Calculate the influence coefficient of ambient temperature The formula is ,in This is the environmental temperature influence coefficient (the value ranges from 0 to 1, and the closer it is to 1, the smaller the temperature interference). The safe threshold for ambient temperature;

[0176] Calibration of data affected by interference: Utilizing the interference influence coefficient to calibrate the acquired data. , , The corrections and calibration formulas are as follows:

[0177] ;in To compensate for the offset of T caused by electromagnetic interference after IGBT temperature calibration, KE is used.

[0178] ,in To calibrate the DC-side capacitor voltage, through Cancel electromagnetic interference The offset effect;

[0179] ;in The AC side harmonic content after calibration, in %, is expressed as a percentage. offsetting the effect of ambient temperature The offset effect;

[0180] The calibrated data is transmitted to the fault warning module 340 as the basis for fault warning judgment.

[0181] In this embodiment, the status monitoring unit 300 further includes a fault early warning module 340. The fault early warning module 340 receives the calibrated operating status data output by the fusion calibration module 330 and performs status trend analysis and fault early warning operations, specifically including:

[0182] Status trend analysis: Receives post-calibration operating status data output by the fusion calibration module 330. For continuous data collection After group calibration, the operational status data is recorded as a time series, and the rate of change of each parameter per unit time is calculated. ,in For IGBT temperature change rate, The rate of change of the DC-side capacitor voltage. The rate of change of harmonic content on the AC side;

[0183] Fault warning judgment: [This will be used to] determine the current... Compare with preset safety thresholds respectively, and judge simultaneously. Does it exceed the preset trend threshold? ,in This represents the maximum permissible rate of temperature change per unit time for the IGBT. This represents the maximum permissible rate of change of the DC-side capacitor voltage per unit time. The maximum permissible rate of change of harmonic content on the AC side per unit time; if the current data exceeds the safety threshold or the rate of change exceeds the trend threshold, the fault warning module 340 outputs a hardware alarm signal, triggering the red alarm indicator light to light up and the buzzer to start, and marking the warning type in the corresponding monitoring data frame.

[0184] Specifically, the fault early warning module 340 receives the calibrated operating status data output by the fusion calibration module 330. Its core function is to perform status trend analysis and fault early warning operations. Its unit time change rate formula and early warning judgment logic specifically include:

[0185] In terms of state trend analysis, for continuous Group calibration data ( Time series recording was performed, and the rate of change of each parameter per unit time was calculated, as follows:

[0186] The formula for the temperature change rate of IGBT is: ,in for The rate of change per unit time; This is the current collection sequence number. ; , For the first , After group calibration data; The data acquisition interval is consistent with the synchronization triggering cycle;

[0187] The formula for the rate of change of DC-side capacitor voltage is: ;in for The rate of change per unit time; , For the first , After group calibration data;

[0188] The formula for the rate of change of harmonic content on the AC side is: ;in for The rate of change per unit time; For the first , After group calibration data; The number of consecutive data sets is preset to 20; the ratio of the difference between adjacent data sets to the data set interval reflects the trend of parameter changes, avoiding misjudgment due to fluctuations in a single data set.

[0189] In terms of fault warning judgment, the following logical formula is used to determine whether a warning is triggered:

[0190] ;

[0191] in This is the IGBT temperature over-temperature threshold (which can be determined based on the model of the selected IGBT device). for Maximum allowable rate of change for Overvoltage threshold, for Undervoltage threshold for Maximum allowable rate of change for Exceeding the threshold, for The maximum allowable rate of change is defined as follows: "1" indicates that an early warning is triggered, and "0" indicates that no early warning is triggered. If the formula result is "1", the fault early warning module 340 outputs a hardware alarm signal (triggering the red alarm indicator light to light up and the buzzer to start), and marks the early warning type (such as "IGBT over-temperature warning") in the monitoring data frame. If it is "0", the module continues to monitor the next set of data.

[0192] It should be added that, in this embodiment, the various modules of the status monitoring unit 300 achieve data interaction through the internal SPI bus, and the specific coordination logic and exception handling are as follows:

[0193] Module Collaboration: Each time the synchronization control module 320 generates a synchronization trigger signal, it simultaneously sends an "acquisition start" signal to the data acquisition module 310 and the fusion calibration module 330; after the data acquisition module 310 completes acquisition, it transmits the digital data to the fusion calibration module 330; after the fusion calibration module 330 completes calibration, it transmits... The data is transmitted to the fault warning module 340 and simultaneously synchronized with the safety control unit 400.

[0194] Anomaly Handling: If the data acquisition module 310 acquires invalid data three times consecutively (e.g., digitized data exceeds the normal range), it sends an "acquisition failure" signal to the synchronization control module 320, suspends acquisition, and starts sensor reinitialization; if the fusion calibration module 330 determines that the interference-free stable period has not occurred for 1 hour, it uses the historical benchmark dataset as a temporary substitute and sends a "benchmark update anomaly" prompt to the device's main controller; if the fault warning module 340 does not receive protection feedback from the safety control unit 400 within 10 seconds after triggering the warning, it increases the alarm volume and flashes the indicator light to ensure that the warning is detected.

[0195] The safety control unit 400 integrates a hardware circuit of "fault detection - logic judgment - execution protection" based on the high-reliability monitoring data output by the status monitoring unit 300. When the photovoltaic inverter or distribution network experiences abnormal conditions such as overvoltage, overcurrent or overtemperature, it triggers rigid protection action to ensure the safe operation of the inverter and distribution network and avoid equipment damage or grid accidents.

[0196] In this embodiment, the safety control unit 400 includes a fault detection module 410, a logic judgment module 420, and an execution protection module 430, wherein:

[0197] The fault detection module 410 is used to receive high-reliability monitoring data (including data output by the status monitoring unit 300) The system collects and digitizes abnormal parameters of the photovoltaic inverter and distribution network in real time, including DC-side capacitor voltage, AC-side voltage, and AC-side current. The fault detection module 410 incorporates a voltage sampling chip, a current Hall sensor, and a temperature acquisition chip to detect DC-side overvoltage, AC-side overvoltage, AC-side overcurrent, and AC-side overtemperature, respectively. The system collects corresponding parameters for four types of abnormal over-temperature scenarios. The collected analog signals are converted into digital signals through an analog-to-digital converter circuit and then transmitted to the logic judgment module 420.

[0198] Specifically, in terms of hardware configuration, two independent voltage sampling chips are selected for DC-side and AC-side overvoltage scenarios. The DC-side voltage sampling chip has an input range covering 1.2 times the rated DC voltage of the inverter, while the AC-side voltage sampling chip has an input range covering 1.2 times the rated grid voltage. The chips output 16-bit digital signals to ensure that the sampling resolution meets the threshold comparison requirements. For AC-side overcurrent scenarios, a closed-loop current Hall sensor is selected, with an input range covering 1.5 times the rated AC current of the inverter, and an output 0-5V analog signal to adapt to the module's internal AD conversion circuit. In over-temperature scenarios, an I2C interface temperature acquisition chip is selected, along with the output of the status monitoring unit 300. The data is cross-validated, and the chip's measurement range can cover -40℃ to 150℃ (adapting to the operating temperature range of IGBTs). The output accuracy meets the requirements for over-temperature threshold comparison. The fault detection module 410 has a built-in 16-bit AD converter, which converts the analog signals output by the voltage sampling chip, current Hall sensor, and temperature acquisition chip into digital signals. The conversion reference voltage is fixed at 5V, and the conversion cycle is synchronized with the data acquisition cycle.

[0199] Furthermore, this embodiment also includes exception handling, specifically including:

[0200] Data reception interruption handling: If the fault detection module 410 fails to receive monitoring data from the status monitoring unit 300 for three consecutive times, it immediately starts the independent acquisition mode of the built-in sensor and relies solely on its own sensor data for anomaly determination; at the same time, it sends a "data link interruption" alarm to the device main controller, prompting the device to check the communication line between the status monitoring unit 300 and the fault detection module 410.

[0201] Sensor fault handling: If a sensor collects invalid data 5 times in a row, it is determined to be a sensor fault. The threshold comparison of the data of that channel is blocked, and the judgment is based only on the data of other valid sensors. At the same time, a "sensor fault" prompt is sent to the main controller to specify the type of faulty sensor.

[0202] The logic judgment module 420 is used to perform threshold comparison and anti-jitter logic judgment on the digital signal transmitted by the fault detection module 410, generate a protection trigger signal, and has a built-in hardware comparator and anti-jitter timing circuit, and pre-stores overvoltage thresholds. (Including DC side overvoltage threshold, AC side overvoltage threshold), overcurrent threshold Over-temperature threshold The received digital signal is compared with the corresponding threshold in real time. If the digital signal of any parameter continuously exceeds the corresponding overvoltage threshold and the duration reaches the preset anti-shake time (≥50ms, to avoid instantaneous interference and false triggering), a high-level protection trigger signal is generated and transmitted to the execution protection module 430.

[0203] Specifically, regarding the threshold setting basis and modification logic, the DC side overvoltage threshold... The AC overvoltage threshold is set to 1.1 times the rated DC voltage of the inverter. The overcurrent threshold is set to 1.1 times the rated voltage of the power grid. The over-temperature threshold is set to 1.2 times the rated output current of the inverter's AC side. The threshold is set to 90% of the rated operating temperature of the IGBT; all thresholds are stored in the EEPROM built into the logic judgment module 420 and can be manually modified through the device's human-machine interface. When modifying, an administrator password must be entered, and the modified value must meet the constraint conditions of "not exceeding the sensor range and not lower than 1.05 times the rated parameter".

[0204] Meanwhile, in terms of the anti-shake circuit implementation, a "RC filter + hardware counter" combined architecture is adopted. The RC filter circuit filters out low-frequency interference below 50Hz, and the hardware counter uses a 16-bit timer (clock frequency 1MHz). When the parameter exceeds the threshold, the counter starts counting. When the count reaches the preset anti-shake time, the "anti-shake complete" signal is output. If the parameter recovers during the counting process, the counter is reset to zero. The anti-shake time can be dynamically adjusted according to the parameter type. It is set to 50ms for overcurrent scenarios and 100ms for overtemperature scenarios.

[0205] In addition, in terms of multi-parameter anomaly priority handling, protection is performed according to the priority of "overtemperature > overcurrent > overvoltage". When multiple parameters are abnormal at the same time, the priority determination circuit responds first to overtemperature anomaly, then overcurrent, and finally overvoltage, to ensure that the most dangerous anomaly receives the fastest response.

[0206] The execution protection module 430 receives the protection trigger signal from the logic judgment module 420 and executes rigid protection actions to ensure the safe operation of the inverter and the distribution network. It synchronously executes two levels of protection via a relay drive circuit: the first level is power circuit disconnection, driving the relay to disconnect the DC input circuit and AC output circuit of the photovoltaic inverter, cutting off the energy transmission path; the second level is equipment alarm and status feedback, triggering a red alarm indicator light to remain constantly lit and a buzzer to sound continuously. Simultaneously, the protection type (overvoltage abnormality / overcurrent abnormality / overtemperature abnormality) is marked in the monitoring data frame and fed back to the status monitoring unit 300, the intelligent adjustment unit 100, and the adaptive protocol unit 200 via the internal bus. Feedback to the status monitoring unit 300 supplements the warning information from the fault early warning module 340; feedback to the intelligent adjustment unit 100 suspends its PWM adjustment signal output; and feedback to the adaptive protocol unit 200 incorporates the protection information into the data interaction frame, achieving precise synchronization of protection information among the internal functional units of the device and preventing equipment damage or grid accidents.

[0207] Specifically, regarding relay configuration and power circuit disconnection logic, two independent relays are selected for the DC-side input circuit and the AC-side output circuit. The DC-side relay is a DC high-voltage relay with a rated voltage covering 1.2 times the maximum DC voltage of the inverter and a rated current covering 1.5 times the rated input current of the inverter. The AC-side relay is an AC contactor with a rated voltage matching the grid voltage and a rated current covering 1.2 times the rated output current of the inverter. After receiving the protection trigger signal, the DC-side relay is driven to disconnect first, and the AC-side relay is driven to disconnect 5ms later. The entire disconnection action is completed within 10ms. At the same time, the relay contact detection circuit built into the protection module 430 is executed. If a relay fault is detected, a "forced shutdown" command is immediately sent to the inverter main controller.

[0208] Regarding the alarm mechanism, the red alarm indicator uses a high-brightness LED indicator, which remains on after the protection action is triggered, with a brightness of ≥500cd / m², and turns off after the protection is released; the buzzer uses a piezoelectric buzzer, which sounds continuously after the protection action is triggered, with a sound pressure level of ≥85dB. If the protection is not released for 10 minutes, it will automatically switch to an intermittent mode of "3 seconds of beeping and 2 seconds of pause".

[0209] In terms of feedback logic, protection information is synchronized to the status monitoring unit 300, intelligent adjustment unit 100, and adaptive protocol unit 200 via CAN bus. The information fed back to the status monitoring unit 300 includes protection type, trigger time, and abnormal parameter value. The information fed back to the intelligent adjustment unit 100 is a "pause PWM output" command, and the information fed back to the adaptive protocol unit 200 is a "protection status flag". At the same time, protection information is sent to the remote monitoring platform via RS485 interface, and the format follows the Modbus-RTU protocol.

[0210] Regarding recovery control, manual recovery requires clicking "Protection Recovery" through the human-machine interface and entering the administrator password. After the protection module 430 confirms that the parameters are normal, recovery is performed according to the timing sequence of "closing the AC side relay first and then closing the DC side relay". Automatic recovery is applicable to overvoltage and overcurrent abnormalities. When the parameters recover to the safe range for three consecutive cycles, recovery is performed after verification by the logic judgment module 420. Overtemperature abnormalities require manual recovery.

[0211] In this embodiment, the coordination logic of each module and the overall anomaly handling in the safety control unit 400 specifically include:

[0212] Understandably, in terms of module collaboration logic, the fault detection module 410 and the logic judgment module 420 transmit data via the SPI bus with a communication rate of 1Mbps. The logic judgment module 420 and the execution protection module 430 transmit protection trigger signals via hardware level signals. All modules use the synchronous trigger signal output by the synchronous control module 320 as a reference to ensure the timing consistency of data acquisition, threshold comparison, and protection execution.

[0213] It should be added that, in terms of overall anomaly handling, if the logic judgment module 420 fails to receive data from the fault detection module 410 three times consecutively, it is determined to be a "detection-judgment" communication failure, and the built-in backup detection circuit is immediately activated to temporarily replace it, while sending a "module communication failure" alarm to the main controller; if the execution protection module 430 fails to receive the protection trigger signal from the logic judgment module 420 five times consecutively, but the fault detection module 410 has detected a serious anomaly, it is determined to be a "judgment-execution" link failure, and the hardware emergency protection is automatically triggered, directly driving the relay to disconnect the power circuit.

[0214] The integrated housing unit 500 provides a physical mounting carrier for the intelligent adjustment unit 100, the adaptive protocol unit 200, the status monitoring unit 300, and the safety control unit 400. It also has IP65-level outdoor environmental protection and a finned high-efficiency heat dissipation structure to ensure stable operation in complex outdoor environments.

[0215] Specifically, the installation and adaptation design of the integrated housing unit 500 includes: the interior is divided into 4 independent installation areas by a customized bracket; the central main installation area is equipped with a DIN rail to fix the intelligent adjustment unit 100; the left communication adaptation area is equipped with an adaptive protocol unit 200 near the communication interface hole; and the right monitoring and protection area is equipped with a metal partition plate to stack and fix the status monitoring unit 300 and the safety control unit 400. Each unit is double-fixed by screws, positioning pins or guide rails. The exterior is equipped with wall mounting holes, ground bracket connection holes and a top hanging ring to adapt to different outdoor installation scenarios.

[0216] Specifically, the IP65 protection of the integrated housing unit 500 includes: sealing the joints of the upper and lower covers with silicone rubber sealing strips; equipping external interfaces with IP65 waterproof and dustproof connectors and applying waterproof adhesive; a sloping top design with a water-blocking edge to prevent water accumulation; and drainage holes at the bottom to drain accumulated water; a waterproof protrusion at the bottom of the unit mounting area inside the housing to prevent rainwater from contacting the circuit board; and dustproof nets installed at ventilation openings (if any) to achieve dual protection against dust and water.

[0217] Specifically, the finned heat dissipation design of the integrated housing unit 500 includes: the outer wall of the housing is integrally molded with fins corresponding to the intelligent adjustment unit 100 and the safety control unit 400; a metal heat-conducting column is provided at the high-heat unit inside the housing, and the end of the column is coated with thermal grease to contact the heat sink of the unit, so as to accelerate the heat conduction to the fins; an axial fan can be added in high-temperature areas, and its start and stop are controlled by the status monitoring unit 300.

[0218] Those skilled in the art will understand that the process of implementing all or part of the steps of the above embodiments can be carried out by hardware or by a program instructing the relevant hardware.

[0219] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A device for adjusting, controlling, and monitoring a photovoltaic inverter, characterized in that, include: The intelligent adjustment unit (100) adopts a multi-parameter dynamic matching algorithm based on grid situation awareness to collect the power, current and voltage parameters output by the photovoltaic inverter, as well as grid situation parameters including the real-time voltage and frequency parameters and fluctuation trend parameters of the grid. By combining the dynamic matching logic of the grid situation, the inverter output and the grid status are accurately and dynamically matched. The intelligent regulation unit (100) includes a parameter acquisition module (110), a power grid situation analysis module (120), and a regulation decision module (130), wherein: The parameter acquisition module (110) uses a voltage sensor, a current sensor and a power metering chip to collect the active power, phase current and line voltage output by the photovoltaic inverter in real time, as well as the line voltage and frequency parameters of the power grid. The collected data is transmitted through the SPI bus. The power grid situation analysis module (120) has a built-in 32-bit MCU. After receiving the digitized data from the parameter acquisition module (110), it removes high-frequency noise through a preset improved filtering algorithm. The improved filtering algorithm combines the composite processing logic of moving average and Kalman filtering. First, it suppresses periodic interference through moving average, and then it dynamically corrects non-stationary noise through Kalman filtering. Then, it stores the power grid voltage and frequency parameters in time series. The regulation decision module (130) integrates a digital signal processor. Based on the data processed by the power grid situation analysis module (120), it generates a PWM regulation signal through a pre-programmed logic circuit to drive the IGBT switching transistors of the inverter to achieve power output adjustment. The logic of the regulation decision module (130) in generating the PWM regulation signal specifically includes: Based on the output of the power grid situation analysis module (120) and Configure corresponding adjustment coefficients based on the power grid status level. and power grid status coefficient ; The inverter output adjustment value is determined by a target power calculation model, which uses the current output power as the basis. Using the current power as a baseline, multiply the result by "1 - adjustment coefficient × situation coefficient" to obtain the target power. ; The duty cycle of the PWM adjustment signal is based on and The difference is dynamically generated to drive the IGBT switching transistors to achieve closed-loop output regulation, and It is limited to a preset range of the inverter's rated power; The adaptive protocol unit (200) adopts a dynamic feature self-learning mechanism without a pre-stored protocol library. It extracts four key features of the communication protocol frame header identifier, data length value, baud rate parameter, and verification method type of the photovoltaic inverter. By autonomously learning new protocol features and generating adaptation rules, it realizes stable data interaction of photovoltaic inverters of multiple brands and multiple protocols and reduces the scalability cost caused by protocol iteration. The status monitoring unit (300) adopts a multi-source data fusion and interference self-calibration algorithm to simultaneously collect three types of operating status data of the photovoltaic inverter: IGBT temperature, DC side capacitor voltage, and AC side harmonic content, as well as two types of environmental interference data: surrounding electromagnetic interference intensity and ambient temperature. The multi-source data fusion model is used to perform correlation verification on the multi-dimensional data, and the interference self-calibration logic is combined to automatically calibrate the interfered data. While suppressing environmental interference, the reliability of monitoring data is improved, and potential fault warning is realized based on status trend analysis. Safety control unit (400) is based on the high reliability monitoring data output by the status monitoring unit (300) and integrates a hardware circuit of "fault detection-logic judgment-execution protection". When the photovoltaic inverter or distribution network is in an abnormal state of overvoltage, overcurrent or overtemperature, it triggers rigid protection action to ensure the safe operation of the inverter and distribution network and avoid equipment damage or grid accidents. An integrated housing unit (500) is used to provide a physical mounting carrier for the intelligent adjustment unit (100), the adaptive protocol unit (200), the status monitoring unit (300), and the safety control unit (400), while also having IP65-level outdoor environmental protection function and a finned high-efficiency heat dissipation structure.

2. The device for adjusting, controlling, and monitoring a photovoltaic inverter according to claim 1, characterized in that, The process by which the power grid situation analysis module (120) removes high-frequency noise using a preset improved filtering algorithm includes the following steps: S120.1 Receive the digitized raw data output by the parameter acquisition module (110), the raw data including the instantaneous value sequence of the power grid voltage. and frequency instantaneous value sequence ; S120.2 Perform a moving average on the original data: based on the moving window length. Calculate the smoothed voltage sequence and frequency sequence Periodic high-frequency interference is suppressed by averaging adjacent data. S120.

3. Perform Kalman filtering on the sequence after moving average processing: construct the state vector. and observed values Introducing the state transition matrix and observation matrix Through process noise and observation noise Dynamic estimation enables non-stationary noise correction; S120.

4. Through Kalman filtering prediction-update iteration, the corrected voltage sequence is output. and frequency sequence This completes the removal of high-frequency noise.

3. The device for adjusting, controlling, and monitoring a photovoltaic inverter according to claim 1, characterized in that, The adaptive protocol unit (200) includes a feature extraction module (210), a protocol learning module (220), and a rule generation module (230), wherein: The feature extraction module (210) is connected to the communication port of the photovoltaic inverter through a high-speed serial communication circuit, captures the communication protocol data stream in real time, and separates four key features from the data stream through a preset feature parsing logic: frame header identifier, data length value, baud rate parameter, and verification method type. The frame header identifier is the starting byte sequence of the communication frame. The protocol learning module (220) receives four types of key features output by the feature extraction module (210), compares them with the feature sample library, and identifies the feature matching degree. The rule generation module (230) generates data interaction adaptation rules based on the matching results of the protocol learning module (220) and stores the data interaction adaptation rules in a temporary cache area.

4. The device for adjusting, controlling, and monitoring a photovoltaic inverter according to claim 3, characterized in that, The feature comparison of the protocol learning module (220) specifically includes: Comparison of frame header identifiers: The frame header identifier byte sequence output by the feature extraction module (210) is matched bit by bit with the byte sequence of the template frame header in the feature sample library, and the proportion of the number of matched bytes to the total number of bytes is calculated to obtain the frame header identifier matching degree; Comparison of data length values: The difference between the data length value output by the feature extraction module (210) and the template data length value in the feature sample library is calculated to obtain the data length deviation, and it is determined whether the deviation is within the preset deviation range; Comparison of baud rate parameters: The actual baud rate of the data stream captured by the feature extraction module (210) is measured by a clock counter, and the error percentage is calculated by comparing it with the template baud rate parameters in the feature sample library to determine whether the error is within the allowable range; Comparison of verification method types: First, verify whether the verification method type identified by the feature extraction module (210) is consistent with the template verification method type in the feature sample library, and then verify the matching of the verification logic by simulating the calculation of the verification value.

5. The device for adjusting, controlling, and monitoring a photovoltaic inverter according to claim 1, characterized in that, The status monitoring unit (300) includes a data acquisition module (310) and a synchronization control module (320), wherein: The data acquisition module (310) acquires the IGBT temperature of the photovoltaic inverter through a patch temperature sensor, acquires the DC side capacitor voltage through a differential voltage sampling circuit, acquires the AC side harmonic content through an FFT harmonic analysis circuit, acquires the ambient electromagnetic interference intensity through an electromagnetic coupling probe and an electromagnetic interference detection circuit, and acquires the ambient temperature through an NTC thermistor. The synchronization control module (320) has a built-in I2C clock synchronization chip, which generates a synchronization trigger signal with a fixed period and outputs it to each acquisition channel of the data acquisition module (310). It controls the temperature sensor, differential voltage sampling circuit, FFT harmonic analysis circuit, electromagnetic interference detection circuit, and NTC thermistor to start data acquisition on the same clock edge, ensuring that the acquisition time deviation of the five types of data does not exceed 1ms, and avoiding data correlation failure caused by timing deviation.

6. The device for adjusting, controlling, and monitoring a photovoltaic inverter according to claim 5, characterized in that, The status monitoring unit (300) further includes a fusion calibration module (330), which receives five types of data synchronized by the synchronization control module (320) and performs multi-source data fusion and interference self-calibration operations, specifically including: Multi-source data fusion is based on preset parameter correlation rules: IGBT temperature With DC side capacitor voltage There is coupling correlation, AC side harmonic content With electromagnetic interference intensity There is a correlation, based on parameter correlation rules. Perform cross-validation to remove outlier data that deviates from the rules; Interference self-calibration is based on a pre-stored interference-free reference dataset: the interference-free reference dataset is dynamically generated through a real-time interference status determination mechanism, specifically by the fusion calibration module (330) continuously monitoring the electromagnetic interference intensity. and ambient temperature The volatility, when The volatility is lower than the preset disturbance threshold and When the fluctuation is below the preset temperature threshold and this state remains stable for a preset duration, it is determined to be a period of stable operation without interference; during this period, the IGBT temperature is continuously collected. DC side capacitor voltage AC side harmonic content After multi-source data fusion verification and outlier removal, the average of continuously collected data within the interference-free stable period was used as the IGBT temperature reference value. DC side capacitor voltage reference value AC side harmonic content benchmark value and ambient temperature reference value The benchmark dataset is re-evaluated and updated every preset update cycle, and the interference influence coefficient corresponding to the current electromagnetic interference intensity is calculated. The interference influence coefficient corresponding to the current ambient temperature For the interfered Perform corrections and output calibrated operating status data. .

7. The device for adjusting, controlling, and monitoring a photovoltaic inverter according to claim 6, characterized in that, The status monitoring unit (300) further includes a fault early warning module (340), which receives the calibrated operating status data output by the fusion calibration module (330) and performs status trend analysis and fault early warning operations, specifically including: Status trend analysis: Receives post-calibration operating status data output by the fusion calibration module (330). For continuous data collection After group calibration, the operational status data is recorded as a time series, and the rate of change of each parameter per unit time is calculated. ,in For IGBT temperature change rate, The rate of change of the DC-side capacitor voltage. The rate of change of harmonic content on the AC side; Fault warning judgment: [This will be used to] determine the current [fault]. Compare with preset safety thresholds respectively, and judge simultaneously. Does it exceed the preset trend threshold? ,in This represents the maximum permissible rate of temperature change per unit time for the IGBT. This represents the maximum permissible rate of change of the DC-side capacitor voltage per unit time. The maximum permissible rate of change of harmonic content on the AC side per unit time; if the current data exceeds the safety threshold or the rate of change exceeds the trend threshold, the fault warning module (340) outputs a hardware alarm signal, triggers the red alarm indicator light to light up and the buzzer to start, and marks the warning type in the corresponding monitoring data frame.

8. The device for adjusting, controlling, and monitoring a photovoltaic inverter according to claim 7, characterized in that, The safety control unit (400) includes a fault detection module (410), a logic judgment module (420), and an execution protection module (430), wherein: The fault detection module (410) is used to receive high-reliability monitoring data output by the status monitoring unit (300) and to collect and digitally convert overvoltage, overcurrent, and overtemperature abnormal parameters of the photovoltaic inverter and distribution network in real time. The fault detection module (410) has a built-in voltage sampling chip, a current Hall sensor, and a temperature acquisition chip, which are used to detect DC-side overvoltage, AC-side overvoltage, AC-side overcurrent, and overtemperature abnormal parameters of the photovoltaic inverter and distribution network in real time. The corresponding parameters are collected for four types of abnormal over-temperature scenarios. The collected analog signals are converted into digital signals through an analog-to-digital converter circuit and then transmitted to the logic judgment module (420). The logic judgment module (420) is used to perform threshold comparison and anti-jitter logic judgment on the digital signal transmitted by the fault detection module (410), generate a protection trigger signal, and has a built-in hardware comparator and anti-jitter timing circuit, and pre-stores overvoltage threshold. Overcurrent threshold Over-temperature threshold The received digital signal is compared with the corresponding threshold in real time; if the digital signal of any parameter continues to exceed the corresponding overvoltage threshold and the duration reaches the preset anti-shake time, a high-level protection trigger signal is generated and transmitted to the execution protection module (430). The execution protection module (430) is used to receive the protection trigger signal from the logic judgment module (420) and execute rigid protection actions to ensure the safe operation of the inverter and the distribution network. It synchronously executes two levels of protection through the relay drive circuit: the first level is power circuit cutoff, which drives the relay to disconnect the DC side input circuit and AC side output circuit of the photovoltaic inverter respectively, cutting off the energy transmission path; the second level is equipment alarm and status feedback, which triggers the red alarm indicator light to stay on and the buzzer to sound continuously. At the same time, the protection type is marked to the monitoring data frame and fed back to the status monitoring unit (300), the intelligent adjustment unit (100) and the adaptive protocol unit (200) through the internal bus respectively.

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