Embedded fault recording system

Abstract

The invention relates to the technical field of power equipment operation and maintenance, and provides an embedded fault recording system which integrates a control device end, a monitoring equipment end, a power equipment end, a background control end and a user operation end, and constructs an electric energy early warning model to predict electric energy stability by monitoring electric energy information in a photovoltaic power station in real time. When the electric energy stability is abnormal, the system automatically carries out fault recording. The system further has the functions of data acquisition, processing, detection, model optimization and the like, accuracy and integrity of data acquisition are improved, abnormity is recognized and fault waveforms are recorded through a preset fault detection algorithm, and meanwhile, equipment configuration parameters are continuously optimized to improve accuracy and response speed of fault monitoring. In addition, the system further comprises an electric energy management module, a standby power switching module, an electric quantity monitoring module and the like, the power supply stability and reliability of the system are guaranteed, the fault monitoring capacity of the photovoltaic power station is effectively improved, and safe and stable operation of the power system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment operation and maintenance, and particularly relates to an embedded fault recording system. BACKGROUND

[0002] The embedded fault recording system is a highly integrated device designed for the stability and safety of photovoltaic power station power systems. The embedded fault recording system aims to monitor the voltage, current and other parameters in the power system in real time, and quickly record the fault waveform and related information when detecting abnormal conditions. By timely storing and uploading these data to the monitoring system, the embedded fault recording system provides strong technical support for the safe and stable operation of photovoltaic power stations.

[0003] The embedded fault recording system collects voltage, current and other parameters in the photovoltaic power station power system in real time through high-precision sensors, and uses internal high-performance processors to quickly process these data. When abnormal fluctuations or signs of failure are found in the power system, the system will immediately start the fault recording function to record the waveform changes and related parameters before and after the fault occurs. Then, the system stores the processed data in the non-volatile memory and uploads the key information to the remote monitoring center through the communication module, so that technical personnel can respond quickly and take appropriate maintenance measures.

[0004] Although the embedded fault recording system has shown significant advantages in improving the fault monitoring capability of photovoltaic power stations, it still faces the following technical difficulties in actual application: With the continuous expansion of the scale of photovoltaic power stations and the increase in the complexity of power systems, higher requirements are put forward for the real-time and accuracy of fault monitoring, and the existing fault recording system is difficult to meet these needs in terms of processing capacity and response speed. SUMMARY

[0005] In view of the deficiencies in the prior art, the present application provides an embedded fault recording system to solve the problem of higher requirements for real-time and accuracy of fault monitoring with the continuous expansion of the scale of photovoltaic power stations and the increase in the complexity of power systems.

[0006] To solve the above technical problems, the specific technical solutions of the present application are as follows:

[0007] The embedded fault recording system provided by the present application comprises a control device end, a monitoring device end, a power equipment end, a background control end and a user operation end, the control device end is in communication connection with the monitoring device end, the power equipment end, the background control end and the user operation end, the control device end receives the data of the power equipment end collected by the monitoring device end, and the control device end transmits the received data to the background control end and the user operation end after processing, and the control device end comprises:

[0008] The electric energy early warning module collects electric energy information in the photovoltaic power station, monitors each electric energy in the photovoltaic power station according to the electric energy information in the photovoltaic power station, and stores the monitored data into a database, calls the historical data of the electric energy information in the photovoltaic power station in the database, constructs an electric energy early warning model in the photovoltaic power station based on the historical data of the electric energy information in the photovoltaic power station in the database, uses the electric energy early warning model in the photovoltaic power station to predict the stability of the electric energy in the photovoltaic power station, and performs fault recording on the photovoltaic power station if the stability of the electric energy in the photovoltaic power station is lower than an expected stability value.

[0009] The data collection module sends monitoring equipment state detection information to the monitoring equipment end, determines the usable monitoring equipment according to the monitoring equipment state detection information fed back by the monitoring equipment end, matches the usable monitoring equipment with the preset fault recording equipment configuration information, generates monitoring equipment early warning information if the matching result of the usable monitoring equipment and the preset fault recording equipment configuration information is lower than a preset monitoring equipment completeness value, and collects data through the usable monitoring equipment.

[0010] The data processing module determines the unmatched monitoring equipment according to the matching result of the usable monitoring equipment and the preset fault recording equipment configuration information, calls the historical monitoring data corresponding to the unmatched monitoring equipment in the database, substitutes the historical monitoring data corresponding to the unmatched monitoring equipment into a monitoring data simulation model to generate simulation data of the unmatched monitoring equipment, substitutes the simulation data of the unmatched monitoring equipment and the data collected by the usable monitoring equipment into an electric energy fault detection model to generate an electric energy fault detection result, analyzes the data in the electric energy fault detection result to obtain an electric energy fault prediction result, determines an electric energy fault prediction period according to the electric energy fault prediction result, and collects electric energy data in the electric energy fault prediction period.

[0011] The data detection module stores the electric energy data in the electric energy fault prediction period, calls prediction period time information in the electric energy data in the electric energy fault prediction period, collects real-time electric energy data in the photovoltaic power station, compares the monitoring data corresponding to the prediction period time information in the real-time electric energy data in the photovoltaic power station with the electric energy data in the electric energy fault prediction period to obtain a prediction data comparison result, and the prediction data comparison result includes data prediction qualified and data prediction unqualified.

[0012] The model optimization module optimizes the preset fault recording equipment configuration parameters if the prediction data comparison result is data prediction unqualified, and uses the optimized preset fault recording equipment configuration parameters to perform fault recording on the electric energy in the photovoltaic power station.

[0013] Further, the embedded fault recording system provided by the application comprises an electric energy module.

[0014] System power supply monitoring module, for continuously monitoring the power supply state of the system main power supply;

[0015] Power management module, for converting, stabilizing and distributing the input power;

[0016] Standby power switching module, when detecting that the system main power supply is powered off, automatically switching to the power supply mode;

[0017] Electric quantity monitoring module: real-time monitoring of electric quantity, generating computer monitoring warning information when the electric quantity is lower than the set threshold.

[0018] Further, the embedded fault recording system provided by the application, the data acquisition module comprises:

[0019] Sensor initialization module, for starting current sensor and voltage sensor, and initializing and calibrating current sensor and voltage sensor;

[0020] Real-time data acquisition module, for real-time acquisition of current, voltage and frequency parameter data from photovoltaic power station power system;

[0021] Data processing packaging module, for preliminary processing of the collected raw data, and packaging into data packets for transmission;

[0022] Data transmission module, for transmitting the packaged data packets to the processing module through the internal interface.

[0023] Further, the embedded fault recording system provided by the application, the data processing module comprises:

[0024] Data receiving module, for receiving data packets transmitted by the data acquisition module;

[0025] Fault detection algorithm module, for analyzing the received data using a preset fault detection algorithm, and identifying abnormal conditions in the power system;

[0026] Fault waveform recording module, for automatically recording fault waveform information when detecting abnormal conditions, the fault waveform information including timestamp and fault parameters.

[0027] Further, the embedded fault recording system provided by the application, the data detection module comprises:

[0028] Data receiving module, for receiving recording data and fault information transmitted by the processing module;

[0029] Data saving module, for saving the received data using non-volatile memory;

[0030] Storage management module, for periodically backing up and compressing the stored data;

[0031] Storage space monitoring: for monitoring the remaining space of the memory, and issuing an alarm information when the remaining space of the memory is insufficient.

[0032] Further, the embedded fault recording system provided by the application, the model optimization module comprises:

[0033] The interface initialization module is used for starting the Ethernet or field bus interface to receive the data generated by the new energy power station in real time.

[0034] The data transmission module is used for transmitting the recording data and other important information to other monitoring devices or remote monitoring centers in the photovoltaic power station through the Ethernet or field bus interface.

[0035] The communication state monitoring module is used for continuously monitoring the communication state, and when detecting the communication abnormality, generating a warning information and recording the abnormal information.

[0036] Further, the embedded fault recording system provided by the application, the control device end further comprises:

[0037] The embedded fault recording system is deployed in the inverter, the busbar box and the component support of the photovoltaic power station.

[0038] The system is initialized and configured, and the system initialization and configuration comprises interface initialization and sensor calibration.

[0039] The data acquisition module acquires the current, voltage and frequency parameter data from the photovoltaic component and the power system in real time.

[0040] The data processing module analyzes the collected data, identifies the abnormal situation in the power system by using the preset fault detection algorithm, and automatically records the fault waveform information.

[0041] The beneficial effects of the application mainly include the following aspects:

[0042] The system can rapidly respond to the abnormal situation in the power system by real-time acquisition of the voltage, current and other parameters in the photovoltaic power system through high-precision sensors and rapid processing by the internal high-performance processor, thereby improving the real-time performance and accuracy of fault monitoring.

[0043] When detecting the abnormal fluctuation or fault sign in the power system, the system can immediately start the recording function, record the fault waveform and related information, and upload the key information to the remote monitoring center through the communication module, thereby providing an important basis for the timely response and maintenance measures of the technical personnel, and enhancing the safety and stability of the power system.

[0044] The system integrates multiple intelligent components such as power warning modules, data acquisition modules, data processing modules, etc., and can realize intelligent warning and detection of power stability in photovoltaic power stations through the construction of warning models, real-time monitoring and data analysis, thereby improving the accuracy of fault prediction.

[0045] The system has a model optimization module, which can automatically optimize the preset fault recording device configuration parameters in the case of unqualified comparison results of prediction data, thereby improving the adaptability and flexibility of the system and enhancing the efficient operation under different power system complexity and real-time requirements.

[0046] The data processing module can efficiently process and analyze the collected data to generate power fault prediction results, and verify the prediction results through the data detection module, thereby improving the accuracy and reliability of the data. At the same time, the system uses non-volatile memory to save data and has data backup and compression functions, thereby improving the security and efficiency of data storage.

[0047] The embedded fault recording system provided by the present application is not only suitable for photovoltaic power stations, but also can be widely applied to other new energy power stations and power systems, and has strong universality and expandability. Through real-time monitoring and fault warning, the system can discover and handle potential problems in advance, avoid the expansion of faults and the extension of downtime, thereby reducing the operation and maintenance cost and improving the economic benefit of the power station.

[0048] In summary, the present application designs an embedded fault recording system, which effectively improves the real-time performance and accuracy of photovoltaic power station fault monitoring, enhances the safety and stability of the power system, and has significant beneficial effects. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0050] Figure 1 The embedded fault recording system provided by the present application is not only suitable for photovoltaic power stations, but also can be widely applied to other new energy power stations and power systems, and has strong universality and expandability. Through real-time monitoring and fault warning, the system can discover and handle potential problems in advance, avoid the expansion of faults and the extension of downtime, thereby reducing the operation and maintenance cost and improving the economic benefit of the power station. DETAILED DESCRIPTION

[0051] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described below in connection with the embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application. The technical solutions provided by the embodiments of the present application are described in detail below in connection with the drawings.

[0052] In order to better understand the objects of the present application, the present application will be described in further detail below.

[0053] Please refer to Figure 1 The present application provides an embedded fault recording system, comprising:

[0054] The embedded fault recording system provided by the present application comprises a control device end, a monitoring device end, a power device end, a background control end and a user operation end. The control device end is in communication connection with the monitoring device end, the power device end, the background control end and the user operation end. The control device end receives the data of the power device end collected by the monitoring device end, processes the received data and transmits the processed data to the background control end and the user operation end. The control device end comprises:

[0055] The power energy warning module collects the power energy information in the photovoltaic power station, monitors each power energy in the photovoltaic power station according to the power energy information in the photovoltaic power station, stores the monitored data into a database, retrieves the historical data of the power energy information in the photovoltaic power station from the database, constructs a power energy warning model in the photovoltaic power station based on the historical data of the power energy information in the photovoltaic power station in the database, uses the power energy warning model in the photovoltaic power station to predict the stability of the power energy in the photovoltaic power station, and performs fault recording on the photovoltaic power station if the stability of the power energy in the photovoltaic power station is lower than the expected stability value.

[0056] Data collection: the module first collects the power energy information in the photovoltaic power station. The power energy information in the photovoltaic power station is obtained by real-time monitoring, which improves the real-time performance of the data.

[0057] Power energy monitoring: the collected power energy information is used to monitor each power energy in the photovoltaic power station. This step is an important link to improve the stability of the power energy.

[0058] Data storage: the monitored data is stored into a database for subsequent data analysis and model construction.

[0059] Historical data retrieval: the historical data of the power energy information in the photovoltaic power station is retrieved from the database.

[0060] Warning model construction: based on the retrieved historical data, a power energy warning model in the photovoltaic power station is constructed.

[0061] Electric energy stability prediction: using the constructed electric energy early warning model to predict the electric energy stability in the photovoltaic power station. This step is the basis for judging whether the electric energy is stable.

[0062] Fault recording triggering: if the prediction result shows that the electric energy stability in the photovoltaic power station is lower than the expected stable value, the module will trigger fault recording.

[0063] In summary, the electric energy early warning module improves the stability and safety of electric energy in the photovoltaic power station through real-time monitoring, data collection, storage, historical data retrieval, early warning model construction, electric energy stability prediction, and fault recording triggering.

[0064] The data collection module sends monitoring device state detection information to the monitoring device end, determines the usable monitoring devices according to the monitoring device state detection information feedback from the monitoring device end, matches the usable monitoring devices with the preset fault recording device configuration information, and if the matching result of the usable monitoring devices with the preset fault recording device configuration information is lower than the preset monitoring device completeness value, generates monitoring device early warning information, and collects data through the usable monitoring devices.

[0065] The working process of the data collection module is as follows:

[0066] State detection information sending: the module first sends monitoring device state detection information to the monitoring device end, which is to determine which monitoring devices are currently available.

[0067] Determination of usable monitoring devices: according to the state monitoring information feedback from the monitoring device end, determine which monitoring devices can be used.

[0068] Device configuration information matching: next, match the usable monitoring devices with the preset fault recording device configuration information. This step is to improve the monitoring devices to meet the needs of fault recording.

[0069] Matching result judgment: if the matching result of the usable monitoring devices with the preset fault recording device configuration information is lower than the preset monitoring device completeness value, it means that the current monitoring device configuration cannot fully meet the requirements of fault recording.

[0070] Early warning information generation: in the case that the matching result is lower than the preset completeness value, the module will generate monitoring device early warning information. This is to remind relevant personnel to pay attention to the configuration problem of the monitoring device.

[0071] Data Collection: Finally, the module collects data through the available monitoring devices. Although the matching result is lower than the preset complete value, the module still tries to collect data using the existing monitoring devices to improve the continuity and completeness of the data.

[0072] In summary, the data collection module realizes effective management and utilization of monitoring devices through the steps of sending status detection information, determining available monitoring devices, device configuration information matching, matching result judgment, early warning information generation, and data collection.

[0073] Data Processing Module: Based on the matching result of the available monitoring devices and the preset fault recording device configuration information, the module determines the non-matching monitoring devices, retrieves the historical monitoring data corresponding to the non-matching monitoring devices in the database, substitutes the historical monitoring data of the non-matching monitoring devices into the monitoring data simulation model to generate simulated data for the non-matching monitoring devices, substitutes the simulated data of the non-matching monitoring devices and the data collected by the available monitoring devices into the electric energy fault detection model to generate electric energy fault detection results, analyzes the data in the electric energy fault detection results to obtain electric energy fault prediction results, determines the electric energy fault prediction period based on the electric energy fault prediction results, and collects electric energy data in the electric energy fault prediction period.

[0074] The data processing module is a key part of the embedded fault recording system, responsible for processing and analyzing data collected by monitoring devices to predict and detect electric energy faults. Here is the workflow of the data processing module and detailed explanations of each step:

[0075] Matching of Monitoring Devices and Configuration Information: The data processing module first determines which monitoring devices are not matching based on the matching result of the available monitoring devices and the preset fault recording device configuration information. This step is the basis for improving the accuracy of subsequent data processing and analysis.

[0076] Retrieval of Historical Monitoring Data: For non-matching monitoring devices, the module retrieves the historical monitoring data corresponding to these devices in the database. Historical data is an important basis for building simulation models and predicting faults.

[0077] Generation of Simulated Data: Substitute the historical monitoring data of non-matching monitoring devices into the monitoring data simulation model to generate simulated data for these non-matching devices. Simulated data can fill the gaps in real-time data and improve the accuracy of fault prediction.

[0078] Electric Energy Fault Detection: Substitute the simulated data of non-matching monitoring devices into the electric energy fault detection model together with the real-time data collected by available monitoring devices. The model processes these data to generate electric energy fault detection results. This step is crucial for determining whether there is a fault in electric energy.

[0079] Analysis of fault prediction results: Analyze the data in the power failure detection results to obtain the power failure prediction results. The analysis process can extract the characteristics and trends of the fault, providing a basis for subsequent fault handling.

[0080] Determination of prediction period and data collection: According to the power failure prediction results, determine the prediction period of power failure occurrence. In the prediction period, the data collection module will collect the power data of the period for further analysis and processing. This step is to improve the ability to obtain key data in time when the fault occurs, providing support for rapid positioning and handling of faults.

[0081] In summary, the data processing module realizes the processing and analysis of power data, predicts and detects power failure, and provides strong guarantee for the safe and stable operation of photovoltaic power stations.

[0082] The data detection module stores the power data of the power failure prediction period, retrieves the prediction period time information in the power data of the power failure prediction period, collects real-time photovoltaic power station internal power data, and compares the monitoring data corresponding to the prediction period time information in the real-time photovoltaic power station internal power data with the power data of the power failure prediction period to obtain the prediction data comparison result. The prediction data comparison result includes data prediction qualified and data prediction unqualified.

[0083] Store power failure prediction period data: The data detection module first stores the power data of the power failure prediction period. This is to enable subsequent comparison with real-time data.

[0084] Retrieve prediction period time information: Then, retrieve the prediction period time information in the power data of the power failure prediction period. These time information is used to locate the corresponding monitoring data in the real-time data.

[0085] Collect real-time photovoltaic power station internal power data: Collect real-time photovoltaic power station internal power data. These data reflect the current and latest power station operation status.

[0086] Compare prediction and real-time data: In the real-time photovoltaic power station internal power data, find the monitoring data corresponding to the prediction period time information, and compare these data with the power data of the power failure prediction period.

[0087] Generate prediction data comparison result: After comparison, generate the prediction data comparison result. These results include two:

[0088] Data prediction qualified: Indicates that the difference between real-time data and prediction data is within an acceptable range, and the power station is running normally.

[0089] Data prediction unqualified: indicates that the difference between real-time data and predicted data exceeds the acceptable range, and there is an abnormal situation.

[0090] The main function of the data detection module is to verify the accuracy of the prediction by comparing the data of the prediction period with the real-time data, and to discover abnormal situations in a timely manner. This mechanism helps the operation and maintenance personnel of the photovoltaic power station to respond and handle potential problems in a timely manner, and improves the safe and stable operation of the power station.

[0091] Assuming that the system predicts that the power output of the power station will decrease in a certain time period, the data monitoring module will collect the power data of the power station in real time during this period and compare it with the predicted data. If the real-time data indeed shows that the power output has decreased and the decrease is consistent with the prediction, then the data prediction result is qualified. If the real-time data shows that the power output is significantly different from the prediction, then the data prediction result is unqualified, at which time the system will trigger further fault detection or alarm mechanism.

[0092] Model optimization module, if the prediction data comparison result is data prediction unqualified, the preset fault recording device configuration parameters are optimized, and the optimized preset fault recording device configuration parameters are used for fault recording of the power in the photovoltaic power station.

[0093] When the prediction data comparison result is data prediction unqualified, the model optimization module will perform the following operations: optimize the preset fault recording device configuration parameters: the model optimization module will analyze the reasons for the unqualified prediction data and adjust and optimize the preset fault recording device configuration parameters according to these reasons.

[0094] Use the optimized configuration parameters for fault recording: the optimized preset fault recording device configuration parameters will be used for more accurate fault recording of the power in the photovoltaic power station. This is done to improve the accuracy and efficiency of fault detection.

[0095] Specifically, the model optimization module involves the following sub-modules or functions:

[0096] Interface initialization module: start Ethernet or fieldbus interface to receive real-time data generated by new energy power station, improve real-time and accuracy of data.

[0097] Data transmission module: transmit the optimized recording data and other important information to other monitoring devices in the photovoltaic power station or remote monitoring center through Ethernet or fieldbus interface, realize data sharing and real-time monitoring.

[0098] Communication state monitoring module: continuously monitor the communication state, generate warning information and record abnormal information when detecting communication abnormality, improve the stability and reliability of data transmission.

[0099] The core role of the model optimization module is to continuously adjust and optimize the configuration parameters of the fault recording device to cope with the complexity and real-time requirements of the photovoltaic power station power system, thereby improving the accuracy and response speed of fault monitoring.

[0100] Specifically, the embedded fault recording system provided by the application comprises the power module.

[0101] The system power supply monitoring module is used for continuously monitoring the power supply state of the system main power.

[0102] The power management module is used for converting, stabilizing and distributing the input power.

[0103] The standby power switching module automatically switches to the power supply mode when detecting that the system main power is powered off.

[0104] The power monitoring module: real-time monitoring of power, when the power is lower than the set threshold, generating computer monitoring warning information.

[0105] Specifically, the embedded fault recording system provided by the application comprises the data acquisition module.

[0106] The sensor initialization module is used for starting the current sensor and the voltage sensor, and initializing and calibrating the current sensor and the voltage sensor.

[0107] The real-time data acquisition module acquires current, voltage and frequency parameter data from the photovoltaic power station power system in real time.

[0108] The data processing and packaging module preliminarily processes the collected original data and packages them into data packets for transmission.

[0109] The data transmission module transmits the packaged data packets to the processing module through the internal interface.

[0110] Specifically, the embedded fault recording system provided by the application comprises the data processing module.

[0111] The data receiving module is used for receiving the data packets transmitted by the data acquisition module.

[0112] The fault detection algorithm module is used for analyzing the received data using a preset fault detection algorithm to identify abnormal conditions in the power system.

[0113] The fault waveform recording module is used for automatically recording fault waveform information when detecting abnormal conditions, and the fault waveform information includes a timestamp and fault parameters.

[0114] Specifically, the embedded fault recording system provided by the application, the data detection module comprises:

[0115] The data receiving module is used for receiving the recording data and fault information transmitted by the processing module.

[0116] The data saving module is used for saving the received data by using a non-volatile memory.

[0117] The storage management module is used for regularly performing data backup and compression on the stored data.

[0118] Storage space monitoring: used for monitoring the remaining space of the memory, and issuing an alarm information when the remaining space of the memory is insufficient.

[0119] Specifically, the embedded fault recording system provided by the application, the model optimization module comprises:

[0120] The interface initialization module is used for starting the Ethernet or field bus interface to receive the real-time data generated by the new energy power station.

[0121] The data transmission module is used for transmitting the recording data and other important information to other monitoring devices or a remote monitoring center in the photovoltaic power station through the Ethernet or field bus interface.

[0122] The communication state monitoring module is used for continuously monitoring the communication state, generating a warning information and recording abnormal information when detecting the communication abnormality.

[0123] Specifically, the embedded fault recording system provided by the application, the control device end further comprises:

[0124] The embedded fault recording system is deployed in the inverter, the combiner box and the component support of the photovoltaic power station.

[0125] The system is initialized and configured, and the system initialization and configuration comprises interface initialization and sensor calibration.

[0126] The data acquisition module acquires the current, voltage and frequency parameter data from the photovoltaic component and the power system in real time.

[0127] The data processing module analyzes the collected data, identifies the abnormal condition in the power system by using a preset fault detection algorithm, and automatically records the fault waveform information.

[0128] The technical scheme of the application effectively solves the problem that the higher requirements on real-time performance and accuracy of fault monitoring are proposed due to the continuous expansion of the scale of the photovoltaic power station and the increase of the complexity of the power system.

[0129] The application provides an embedded fault recording system, which comprises a control device end, a monitoring equipment end, a power equipment end, a background control end and a user operation end, communication connections are established between the ends, and real-time transmission and processing of data are improved.

[0130] Electric energy information in the photovoltaic power station is collected, and an electric energy early warning model is constructed based on historical data to predict the stability of electric energy.

[0131] By sending state monitoring information to the monitoring equipment end, the available monitoring equipment is determined, and the equipment configuration information is matched.

[0132] The collected data is analyzed by using a preset fault detection algorithm, abnormal conditions in the power system are identified, and fault waveform information is automatically recorded.

[0133] If the prediction data comparison result is unqualified, the preset fault recording equipment configuration parameters are optimized, and the optimized parameters are used for fault recording.

[0134] The application comprises system power supply monitoring, electric energy management, standby power switching and electric quantity monitoring modules to improve the stability and reliability of system power supply and avoid affecting the real-time and accuracy of fault monitoring due to power supply problems.

[0135] The voltage, current and frequency parameter data in the photovoltaic power station power system are collected in real time by high-precision sensors, and are quickly processed by internal high-performance processors to improve the real-time and accuracy of data collection.

[0136] In summary, the application solves the problem of higher requirements for real-time and accuracy of fault monitoring due to expansion of the scale of the photovoltaic power station and increase of the complexity of the power system by integrated system architecture design, intelligent early warning and detection modules and efficient model optimization mechanism.

[0137] In the photovoltaic power station, the embedded fault recording terminal hardware device is deployed in the inverter, the bus box and the component support. The system initialization stage starts the Ethernet interface and performs zero point calibration of the current sensor voltage sensor, and the calibration error is controlled within the set percentage of the measurement range. The data acquisition module acquires the three-phase current, voltage and frequency parameters of the photovoltaic component output in real time through the calibrated sensor at a dynamic sampling frequency. The original data is packaged into a standardized data packet after being filtered by a FIR digital filter to eliminate high-frequency noise.

[0138] The power warning module periodically retrieves historical power quality data from the database, and constructs a voltage fluctuation rate prediction model based on time series analysis algorithm. When the model output stability index is lower than the preset threshold, the fault recording mechanism is triggered immediately. The data processing module uses wavelet transform algorithm to perform frequency spectrum analysis on real-time data stream. When the current mutation exceeds the set proportion, the fault waveform recording module automatically captures the complete waveform of the set frequency before and after the fault occurs, and associates the time stamp and fault type code with an accuracy of milliseconds.

[0139] When the monitoring device state is abnormal, the system retrieves the historical synchronous data of the device and inputs the monitoring data simulation model based on the LSTM network to generate alternative simulation data to maintain data integrity. The data detection module dynamically time warping matches the actual current value curve in the fault prediction period with the prediction curve, and if the average deviation rate exceeds the set tolerance, it is determined that the data prediction is unqualified. At this time, the model optimization module adjusts the sensitivity threshold and sampling frequency parameters of the fault detection algorithm through gradient descent algorithm iteration, and the optimized parameters take effect immediately in the subsequent data acquisition process.

[0140] The system power supply monitoring module detects the voltage fluctuation of the main power supply in real time. When the input voltage continuously drops below the set threshold for a set period of time, the backup power switching module enables the lithium iron phosphate battery pack to provide seamless power supply. The power monitoring module performs coulomb counting on the remaining capacity of the battery pack, and sends a warning code to the background when the capacity percentage is below the set safety threshold. The recording data is saved to the FRAM non-volatile memory through a circular storage strategy. The storage management module performs incremental backup daily and uses the LZ77 algorithm for monthly data compression. When the remaining storage space is below the set percentage, the automatic cleaning mechanism is activated.

[0141] The communication state monitoring module detects the Ethernet transmission delay in real time, and automatically switches to the LoRa wireless backup channel when the delay exceeds the set millisecond value. All fault waveforms and diagnostic reports are transmitted to the remote monitoring center through dual-channel redundant transmission. The center platform can retrieve the recording data waveform diagram and the corresponding device optimization parameter version number of any node in real time.

[0142] The implementation process dynamically improves the fault recognition accuracy while maintaining the continuous operation of the system through a closed-loop data acquisition, prediction and optimization mechanism.

[0143] The following is a technical analysis of the algorithms and models involved in the present invention:

[0144] Time series analysis algorithm (electric energy early warning model):

[0145] Function: Predict the stability of photovoltaic power station electric energy.

[0146] Principle: Based on historical electric energy data (voltage, current, harmonic distortion rate, etc.), a time series model is constructed, and the periodicity and trend in the data are learned through autoregressive integrated moving average (ARIMA) or long short-term memory network (LSTM).

[0147] Application: Input real-time current fluctuation data, and the model outputs the stability probability value in the future set period. If the probability value is lower than the preset threshold (such as 95%), trigger fault recording.

[0148] Technical significance: Early identification of potential faults such as voltage sag and frequency deviation, replacing the lagging nature of traditional threshold alarms.

[0149] Wavelet transform algorithm (fault detection algorithm):

[0150] Function: Identify abnormal mutations in power systems.

[0151] Principle: Decompose current / voltage signals into different frequency sub-bands, and detect mutation points (such as microsecond-level spikes caused by short circuits) of high-frequency components through modulus maximum value.

[0152] Application: Perform multi-scale analysis on real-time data streams, and determine abnormalities when high-frequency component energy exceeds the baseline set multiple.

[0153] Technical significance: Overcome the limitations of Fourier transform on non-stationary signals and accurately capture transient fault characteristics.

[0154] LSTM network (monitoring data simulation model):

[0155] Function: Generate simulated data for missing devices.

[0156] Principle: Long short-term memory network learns the long-term dependence of historical data through a gating mechanism, and predicts the theoretical data of a specific monitoring point during a fault period.

[0157] Application: When a current sensor fails, input the historical data of other sensors in the same group, and output the simulated current curve of the node.

[0158] Technical significance: Maintain the integrity of the input of the fault detection model, and avoid system misjudgment due to device failure.

[0159] Dynamic time warping (DTW, data comparison algorithm):

[0160] Function: Verify the deviation between predicted data and actual data.

[0161] Principle: Nonlinear alignment of two time series, calculate the minimum path distance as a similarity measure (e.g. predicted current curve vs actual current curve).

[0162] Application: If the DTW distance of two curves exceeds the set tolerance, mark it as "prediction failure".

[0163] Technical significance: Solve the comparison error caused by time series stretching and deformation, improve the reliability of verification.

[0164] Gradient descent method (model optimization module):

[0165] Function: Dynamically adjust fault detection parameters

[0166] Principle: Take the deviation of prediction results as the loss function, and update the parameters (such as fault judgment threshold, sampling frequency) along the negative gradient direction.

[0167] Application: When the DTW distance is out of tolerance, automatically reduce the current mutation detection threshold setting value, improve the sensitivity.

[0168] Technical significance: Realize the adaptability of the system under complex working conditions, reduce the cost of manual parameter adjustment.

[0169] Technical synergy effect:

[0170] Closed-loop optimization mechanism: Time series early warning, wavelet transform detection, DTW verification, gradient descent optimization form a closed loop, continuously improve the fault recognition rate.

[0171] Data redundancy design: LSTM simulation data and real-time acquisition data are input into the detection model in parallel, which guarantees the system robustness under extreme working conditions.

[0172] The above algorithms are mature methods in the field of industrial control. The present application solves the balance problem of real-time and accuracy of photovoltaic power station fault monitoring by unique combination and adaptation to the power scene. The specific parameters (such as the number of LSTM network layers, wavelet basis function selection) can be determined through experimental verification according to different power station scales, without departing from the core design idea of the present application.

[0173] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application also intends to include these modifications and variations, and the above described embodiments of the present application do not constitute a limitation on the scope of protection of the present application.

Claims

1. An embedded fault recording system, characterized in that, include: The system comprises a control device, a monitoring device, a power equipment, a back-end control terminal, and a user operation terminal. The control device establishes communication connections with these components. It receives data from the power equipment collected by the monitoring device, processes the received data, and then transmits it to the back-end control terminal and the user operation terminal. The control device includes: The power early warning module collects power information within the photovoltaic power station, monitors various power sources within the power station based on this information, and stores the monitored data in a database. It then retrieves historical power information data from the database and constructs a power early warning model for the power station based on this data. This model is used to predict the stability of the power supply within the power station. If the stability of the power supply is lower than the expected stable value, fault recording is performed on the power station. The data acquisition module sends monitoring equipment status detection information to the monitoring equipment terminal. Based on the monitoring equipment status detection information fed back by the monitoring equipment terminal, it determines the available monitoring equipment and matches the available monitoring equipment with the preset fault recording equipment configuration information. If the matching result between the available monitoring equipment and the preset fault recording equipment configuration information is lower than the preset monitoring equipment complete value, it generates monitoring equipment early warning information and collects data through the available monitoring equipment. The data processing module determines mismatched monitoring devices based on the matching results between available monitoring devices and preset fault recording device configuration information. It retrieves historical monitoring data corresponding to the mismatched monitoring devices from the database, substitutes the historical monitoring data corresponding to the mismatched monitoring devices into the monitoring data simulation model to generate simulated data for the mismatched monitoring devices, substitutes the simulated data of the mismatched monitoring devices and the data collected by available monitoring devices into the power fault detection model to generate power fault detection results, parses the data in the power fault detection results to obtain power fault prediction results, determines the power fault prediction period based on the power fault prediction results, and collects power data for the power fault prediction period. The data detection module stores the power data for the predicted power failure period, retrieves the predicted time information from the power data for the predicted power failure period, collects real-time power data within the photovoltaic power station, compares the monitoring data corresponding to the predicted time information in the real-time power data within the photovoltaic power station with the power data for the predicted power failure period, and obtains the prediction data comparison result, which includes whether the data prediction is qualified or unqualified. If the prediction data comparison result is unqualified, the model optimization module optimizes the preset fault recording equipment configuration parameters and uses the optimized preset fault recording equipment configuration parameters to perform fault recording of the power in the photovoltaic power station.

2. The embedded fault recording system as described in claim 1, characterized in that, The power module includes: The system power supply monitoring module is used to continuously monitor the power supply status of the system's main power. The power management module is used to convert, regulate, and distribute the input electrical energy; The backup power switching module automatically switches to the main power supply mode when it detects a power outage in the system. Battery monitoring module: Monitors battery level in real time and generates computer monitoring and warning information when the battery level is lower than a set threshold.

3. The embedded fault recording system as described in claim 1, characterized in that, The data acquisition module includes: The sensor initialization module is used to start the current sensor and voltage sensor, and to initialize and calibrate the current sensor and voltage sensor. The real-time data acquisition module collects current, voltage, and frequency parameter data from the photovoltaic power station's power system in real time. The data processing and encapsulation module performs preliminary processing on the collected raw data and encapsulates it into data packets for transmission. The data transmission module transmits the encapsulated data packets to the processing module through an internal interface.

4. The embedded fault recording system as described in claim 1, characterized in that, The data processing module includes: The data receiving module is used to receive data packets transmitted by the data acquisition module. The fault detection algorithm module is used to analyze the received data using a preset fault detection algorithm to identify abnormal situations in the power system. The fault waveform recording module is used to automatically record fault waveform information when an abnormal situation is detected. The fault waveform information includes timestamps and fault parameters.

5. The embedded fault recording system as described in claim 1, characterized in that, The data detection module includes: The data receiving module is used to receive waveform data and fault information transmitted by the processing module; A data storage module is used to store received data using non-volatile memory; The storage management module is used to periodically back up and compress the stored data; Storage space monitoring: Used to monitor the remaining space of the storage device and issue an alarm message when the remaining space of the storage device is insufficient.

6. The embedded fault recording system as described in claim 1, characterized in that, The model optimization module includes: The interface initialization module is used to start the Ethernet or fieldbus interface to receive real-time data generated by the new energy power plant; The data transmission module is used to transmit waveform recording data and other important information to other monitoring equipment or remote monitoring centers in the photovoltaic power plant via Ethernet or fieldbus interface; The communication status monitoring module is used to continuously monitor the communication status. When a communication anomaly is detected, it generates an early warning message and records the anomaly information.

7. The embedded fault recording system as described in claim 1, characterized in that, The control device also includes: Deploy embedded fault recording systems in inverters, combiner boxes, and module supports of photovoltaic power plants; The system is initialized and configured, including interface initialization and sensor calibration. The data acquisition module collects current, voltage, and frequency parameter data from photovoltaic modules and power systems in real time. The data processing module analyzes the collected data, uses a preset fault detection algorithm to identify abnormal situations in the power system, and automatically records fault waveform information.

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