An intelligent operation and maintenance system of a solar photovoltaic energy storage system

Abstract

The present application relates to the technical field of ecological product accounting, and particularly relates to an intelligent operation and maintenance system of a solar photovoltaic energy storage system, which comprises a state judgment module for judging the running state of the solar photovoltaic energy storage system, specifically: for the collected real-time operation data, the running data is compared with the safe running data threshold value, and the running state of the solar photovoltaic energy storage system is judged according to the comparison result; and the stability scoring function of the solar photovoltaic energy storage system is constructed, the collected real-time operation data is taken as the variable of the function, and the running state of the solar photovoltaic energy storage system is judged according to the function scoring result; an intelligent operation and maintenance module for intelligently operating and maintaining the solar photovoltaic energy storage system, specifically: based on the system running state result output by the state judgment module, the corresponding operation and maintenance level is adaptively selected, and the corresponding operation and maintenance decision is executed according to the operation and maintenance level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of system operation and maintenance, in particular to an intelligent operation and maintenance system of a solar photovoltaic energy storage system. BACKGROUND

[0002] With the large-scale development of renewable energy, the proportion of solar photovoltaic systems in the power supply structure is increasing, especially in distributed photovoltaic and photovoltaic storage integrated systems, how to realize stable and efficient operation and maintenance has become the focus of technical research and development.

[0003] In the prior art, the solar photovoltaic energy storage system generally adopts periodic manual inspection or early warning strategy based on fixed rules for operation and maintenance, which has the following problems:

[0004] Processing is extensive: the conventional early warning mechanism usually makes threshold judgment on a single parameter, and does not fully consider the interaction between parameters, resulting in that part of the critical state cannot be identified in time

[0005] Single operation and maintenance strategy: the existing system mostly adopts unified operation and maintenance logic, and lacks the ability to dynamically select different operation and maintenance strategies according to the operation state, resulting in that the processing method does not match the fault level, which may cause resource waste or over-control;

[0006] System stability depends on manual intervention: in most abnormal operation scenarios, manual remote processing is still needed, which is low in operation and maintenance efficiency and slow in emergency response. SUMMARY

[0007] In view of the above problems, the present application is proposed.

[0008] To solve the above technical problems, the present application provides the following technical scheme: an intelligent operation and maintenance system of a solar photovoltaic energy storage system, comprising,

[0009] A state judgment module is used to judge the operation state of the solar photovoltaic energy storage system, specifically:

[0010] Collecting real-time operation data of the system, and judging the operation state of the system through two-layer judgment mechanism based on the real-time operation data, including,

[0011] For the collected real-time operation data, comparing the operation data with the safe operation data threshold, judging the operation state of the solar photovoltaic energy storage system according to the comparison result, and

[0012] By constructing a stability score function of the solar photovoltaic energy storage system, and taking the collected real-time operation data as the variable of the function, judging the operation state of the solar photovoltaic energy storage system according to the function score result;

[0013] The intelligent operation and maintenance module is used for intelligent operation and maintenance of the solar photovoltaic energy storage system, and specifically comprises:

[0014] Based on the system operation state result output by the state judgment module, a corresponding operation and maintenance level is adaptively selected, and a corresponding operation and maintenance decision is executed according to the operation and maintenance level, including,

[0015] When the system is in a normal operation state, a daily adjustment mechanism is triggered, and a light-weight adjustment operation is executed;

[0016] When the system is in an abnormal operation state, an emergency operation and maintenance mechanism is triggered, and an adaptive operation and maintenance adjustment is executed according to the emergency state;

[0017] After the operation and maintenance decision is executed, it is judged whether the executed operation and maintenance decision is effective through abnormality monitoring.

[0018] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, the safe operation data threshold is specifically as follows:

[0019] Real-time operation data of the system is extracted from the database, including photovoltaic real-time output power , real-time state of charge of the energy storage system battery , and real-time output power change rate of the energy storage system ;

[0020] Safe operation data thresholds of the solar photovoltaic energy storage system are set, including upper limit and lower limit of the photovoltaic real-time output power threshold, upper limit and lower limit of the real-time state of charge of the energy storage system battery threshold, and upper limit and lower limit of the real-time output power change rate of the energy storage system threshold.

[0021] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, the operation state of the solar photovoltaic energy storage system is determined according to the comparison result, specifically as follows:

[0022] If the photovoltaic real-time output power comparison result satisfies the formula , it indicates that the corresponding photovoltaic output power of the solar photovoltaic energy storage system is abnormal, and the abnormal state is sent to the operation and maintenance decision intelligent selection module, otherwise it indicates that the corresponding photovoltaic output power of the solar photovoltaic energy storage system is normal;

[0023] If the real-time state of charge of the energy storage system battery comparison result satisfies the formula If the real-time state of charge of the energy storage system battery of the solar photovoltaic energy storage system is abnormal, the abnormal state is sent to the intelligent operation and maintenance decision selection module; otherwise, the real-time state of charge of the energy storage system battery of the solar photovoltaic energy storage system is normal.

[0024] If the real-time output power of the energy storage system meets the formula If the real-time output power of the energy storage system meets the formula

[0025] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, the stability score function of the solar photovoltaic energy storage system is specifically as follows:

[0026] Based on the set safety operation data threshold of the solar photovoltaic energy storage system, the stability score function is constructed, and then

[0027]

[0028] Based on the collected real-time operation data, the stability score function of the solar photovoltaic energy storage system is constructed, and then

[0029]

[0030] wherein, , respectively represent the upper limit and the lower limit of the set real-time output power threshold of the photovoltaic, , respectively represent the upper limit and the lower limit of the set real-time state of charge threshold of the energy storage system battery, , respectively represent the upper limit and the lower limit of the set real-time output power change rate threshold of the energy storage system, represents the real-time output power of the photovoltaic, represents the real-time state of charge of the energy storage system battery, represents the real-time output power change rate of the energy storage system, , , represents a weight coefficient, represents the stability score function constructed by the set safety operation data threshold, represents the stability score function constructed by the collected real-time operation data.

[0031] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, wherein: the judging the operation state of the solar photovoltaic energy storage system according to the function score result is specifically as follows:

[0032] According to the set safety threshold interval , and the fault threshold interval , the safety threshold interval is within the upper and lower limits of the set threshold, the fault threshold interval is outside the upper and lower limits of the set threshold, and a state judgment function is set.

[0033] If the output result of the state judgment function satisfies the formula , it indicates that the current operation state of the photovoltaic energy storage system is a normal operation state.

[0034] If the output result of the state judgment function satisfies the formula , it indicates that the current operation state of the photovoltaic energy storage system is an abnormal operation state.

[0035] If the output result of the state judgment function satisfies the formula , and , it indicates that the current operation state of the photovoltaic energy storage system is a fluctuation state.

[0036] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, wherein: the executing the corresponding operation and maintenance decision according to the operation and maintenance level is specifically as follows:

[0037] According to the received system operation data in the current period, including three parameters of photovoltaic output power, energy storage state of charge and output power fluctuation rate, the parameters are continuously sampled through a monitoring mechanism based on a time sliding window, forming a continuous operation data sampling sequence.

[0038] According to the operation data contained in the sampling sequence, the trend factor corresponding to each parameter is calculated, including trend mean, change slope and change standard deviation; the trend factors of each parameter are aggregated to construct a system operation trend feature vector in the current period.

[0039] According to the comparison between the constructed operation trend feature vector and the preset safety threshold interval, if the operation trend feature in the current period falls within the corresponding safety interval, it is determined that the system operation state in the current period is stable, the system remains in a normal operation state, and enters the next data sampling period.

[0040] For a plurality of sampling periods in succession, a system running trend feature vector sequence is constructed respectively, and it is judged whether there is a trend behavior deviating from the edge of the safety threshold interval in succession in the sequence, if it is judged that there is a trend deviation, a daily adjustment mechanism is triggered.

[0041] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, the daily adjustment mechanism is specifically as follows:

[0042] According to the direction of the trend deviation, the maximum power point tracking control parameter is fine-tuned to improve the power generation stability of the photovoltaic system, the charging and discharging strategy of the energy storage system is adjusted synchronously to optimize the charge fluctuation range of the energy storage system, and the sensitivity of the control parameter of the output power change is adjusted;

[0043] After the daily adjustment mechanism adjustment operation is performed, the running data sampling and trend feature construction stage of the next cycle is entered, the current adjustment is judged according to the new round of trend feature vector whether the system returns to stability, if the trend feature vector returns to the center interval or the deviation degree is reduced, the adjustment effect of this round is recorded, the deviation state is reset, and the daily monitoring state is re-entered;

[0044] Through the judgment of the data trend of each sampling period and the continuous iteration of the system adjustment based on the trend slight deviation, the continuous optimization and operation and maintenance of the system in the daily running state are realized.

[0045] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, the daily adjustment mechanism is specifically as follows:

[0046] If it is judged that the current abnormality is a first-level emergency state, a high-priority protection strategy is immediately executed, and the high-priority protection strategy has,

[0047] The photovoltaic output inverter is disconnected to isolate the power generation end, the battery pack is forced to reduce power or stop charging and discharging, the grid-connected end output path is cut off or switched to an island operation mode, and the safety of the system core unit is ensured;

[0048] If it is judged that the current abnormality is a second-level emergency state, a system load reduction strategy for a critical state is executed, and the system load reduction strategy for a critical state has,

[0049] The maximum power point tracking power, the battery pack charging and discharging rate limit, and the inverter output power setting soft limit are dynamically adjusted downward to realize the buffer adjustment of the system running state to the safety interval.

[0050] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, the emergency state is specifically judged as follows:

[0051] The system emergency level is set according to the eigenvalue in the trend eigenvalue vector, and the offset of the eigenvalue in the trend eigenvalue vector relative to the safety interval If the eigenvalue is set, then

[0052] The offset threshold of the eigenvalue relative to the safety interval is set If the offset of the eigenvalue relative to the safety interval meets the formula compared with the set offset threshold, it indicates that the current abnormal state is a first-level emergency state, otherwise it indicates that the current abnormal state is a second-level emergency state.

[0053] The offset of the eigenvalue in the trend eigenvalue vector relative to the safety interval is achieved by changing the value of The difference between the current eigenvalue and the maximum value of the corresponding safety interval, and the minimum value, is calculated respectively, and then

[0054] If the current eigenvalue exceeds the maximum value of the safety interval, the difference between the current eigenvalue and the maximum value is taken as the offset of the current eigenvalue relative to the safety interval.

[0055] If the current eigenvalue is lower than the minimum value of the safety interval, the difference between the current eigenvalue and the minimum value is taken as the offset of the current eigenvalue relative to the safety interval.

[0056] As a preferred scheme of the intelligent operation and maintenance system of the solar photovoltaic energy storage system, the abnormality monitoring and recovery is specifically as follows:

[0057] The trend change of the adjusted abnormal parameters in multiple sampling periods is continuously tracked, the trend eigenvalue sequence in the recovery period is constructed, and it is judged whether the system returns to the normal range under control intervention.

[0058] If the trend factor gradually tends to the safety threshold central interval in the recovery period, it is determined that the current round of emergency treatment takes effect, the system state is temporarily stable, the current round of emergency treatment strategy, response time and effect index are recorded, and the emergency operation and maintenance period is ended.

[0059] If the trend still cannot return to stable in continuous sampling periods and shows an aggravating trend, the system alarm is triggered, the abnormal state signal is pushed to the artificial operation and maintenance end, and the artificial intervention stage is entered.

[0060] The beneficial effects of the present application are:

[0061] The present application realizes fine identification and state classification of the solar photovoltaic energy storage system operation state by adopting the state judgment mechanism based on the multi-parameter weighted stability scoring function, so that the system can make more accurate operation and maintenance judgment according to the comprehensive trend characteristics, effectively improves the reliability of operation and maintenance response and the stability of system operation.

[0062] By adopting the operation trend feature vector constructed by the trend factor, dynamic tracking and trend analysis of the system operation state in the time sequence are realized, the sensitivity in early abnormal fluctuation identification of the system is improved, and basic data support is provided for active operation and maintenance;

[0063] By adopting the multi-level emergency state level division mechanism based on the trend offset, fast and hierarchical response to the abnormal state of the solar photovoltaic energy storage system is realized, the system failure expansion caused by excessive intervention or response delay is effectively avoided, and the operation safety and economy of the system are ensured;

[0064] By adopting the recovery period trend monitoring mechanism, real-time quantitative evaluation of the emergency operation and maintenance effect is realized, the intelligent level of the processing closed loop is improved, the system state is avoided to be in the marginal fluctuation interval for a long time, and the sustainability and closed loop management of the system operation and maintenance effect are helpful. BRIEF DESCRIPTION OF DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor. Among them:

[0066] Figure 1 It is a whole method step structure schematic diagram of the intelligent operation and maintenance system of the solar photovoltaic energy storage system of the present application. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0068] Embodiment 1

[0069] Reference Figure 1 For the first embodiment of the present application, an intelligent operation and maintenance system of a solar photovoltaic energy storage system is provided, which comprises a state judgment module and an intelligent operation and maintenance module;

[0070] Specifically, the status judgment module is used to collect real-time operating data of the solar photovoltaic energy storage system and judge the operating status of the solar photovoltaic energy storage system based on the real-time operating data; the intelligent operation and maintenance module adaptively executes operation and maintenance decisions based on the operating status of the solar photovoltaic energy storage system to realize intelligent operation and maintenance of the system.

[0071] Furthermore, the status judgment module extracts real-time operating data from the database corresponding to the solar photovoltaic energy storage system, determines the operating status of the solar photovoltaic energy storage system based on the collected real-time operating data, and sends the operating status of the solar photovoltaic energy storage system to the intelligent operation and maintenance module.

[0072] The intelligent operation and maintenance module executes corresponding operation and maintenance decisions based on the received operating status of the solar photovoltaic energy storage system, thereby realizing intelligent operation and maintenance of the solar energy storage system.

[0073] Furthermore, the real-time operating data is extracted from the database corresponding to the solar photovoltaic energy storage system, including the real-time output power of the photovoltaic system. Real-time state of charge of batteries in energy storage systems and the real-time output power change rate of the energy storage system Based on the collected real-time operational data, the operating status of the solar photovoltaic energy storage system is determined, as follows:

[0074] The determination of the operating status of a solar photovoltaic energy storage system is achieved through a two-layer judgment mechanism, including:

[0075] Judging the operating status of a solar photovoltaic energy storage system based on a single real-time operating data point and judging the operating status based on comprehensive real-time operating data, we have:

[0076] Determining the operational status of a solar photovoltaic energy storage system based on a single real-time operational data point involves comparing the collected real-time operational data with safe operational thresholds. The operational status of the solar photovoltaic energy storage system is then determined based on the comparison result.

[0077] Set safe operation data thresholds for solar photovoltaic energy storage systems, including the upper limit of real-time photovoltaic output power threshold. and threshold lower limit Upper limit of real-time state of charge threshold for energy storage system batteries and threshold lower limit And the upper limit of the real-time output power change rate threshold of the energy storage system. and threshold lower limit And by comparing the collected operational data with the set safe operational data thresholds, we have:

[0078] If the photovoltaic real-time output power comparison result meets the formula , it indicates that the corresponding photovoltaic output power of the solar photovoltaic energy storage system is running abnormally, and the abnormal state is sent to the operation and maintenance decision intelligent selection module, otherwise it indicates that the corresponding photovoltaic output power of the solar photovoltaic energy storage system is running normally;

[0079] If the energy storage system battery real-time state of charge comparison result meets the formula , it indicates that the corresponding energy storage system battery real-time state of charge of the solar photovoltaic energy storage system is abnormal, and the abnormal state is sent to the operation and maintenance decision intelligent selection module, otherwise it indicates that the corresponding energy storage system battery real-time state of charge of the solar photovoltaic energy storage system is normal;

[0080] If the energy storage system real-time output power comparison result meets the formula , it indicates that the corresponding energy storage system real-time output power of the solar photovoltaic energy storage system is running abnormally, and the abnormal state is sent to the operation and maintenance decision intelligent selection module, otherwise it indicates that the corresponding energy storage system real-time output power of the solar photovoltaic energy storage system is running normally.

[0081] The running state of the photovoltaic energy storage system is judged by comprehensively analyzing the real-time running data, which is realized by constructing a stability score function of the solar photovoltaic energy storage system, taking the collected real-time running data as the variables of the function, and judging the running state of the solar photovoltaic energy storage system according to the function score result, specifically:

[0082] Based on the set safety running data threshold of the solar photovoltaic energy storage system, the stability score function is constructed, that is,

[0083]

[0084] At the same time, based on the collected real-time running data, the stability score function of the solar photovoltaic energy storage system is constructed, that is,

[0085]

[0086] Wherein, , respectively represent the set upper limit and lower limit of the photovoltaic real-time output power threshold, , respectively represent the set upper limit and lower limit of the energy storage system battery real-time state of charge threshold, , respectively represent the set upper limit and lower limit of the energy storage system real-time output power change rate threshold, represents the photovoltaic real-time output power, represents the energy storage system battery real-time state of charge, represents the real-time output power change rate of the energy storage system, , , represents the weight coefficient, which is set by the implementer according to the actual application scenario, represents the stability score function constructed by setting the safe operation data threshold, represents the stability score function constructed by collecting real-time operation data, which is used to judge the operation state of the photovoltaic energy storage system according to the comprehensive real-time operation data, specifically:

[0087] According to the set safe threshold interval , and the fault threshold interval , the safe threshold interval is within the upper and lower limits of the set threshold, the fault threshold interval is outside the upper and lower limits of the set threshold, and the state judgment function , the constructed stability score function is used as the input data of the state judgment function, and the output result of the state judgment function is used to judge the operation state of the photovoltaic energy storage system, then,

[0088] If the output result of the state judgment function satisfies the formula , it indicates that the current operation state of the photovoltaic energy storage system is normal operation state;

[0089] If the output result of the state judgment function satisfies the formula , it indicates that the current operation state of the photovoltaic energy storage system is abnormal operation state;

[0090] If the output result of the state judgment function satisfies the formula and , it indicates that the current operation state of the photovoltaic energy storage system is fluctuation state.

[0091] It should be noted that the selection of the state function is selected by the implementer according to the actual application scenario, including dynamic selection according to the safe threshold interval, if the safe threshold interval is composed of the real-time output power of the photovoltaic, then the judgment is based on the real-time output power of the photovoltaic in the stability score function, if the safe threshold interval is composed of the real-time state of charge of the energy storage system battery, then the judgment is based on the real-time state of charge of the energy storage system battery in the stability score function, if the safe threshold interval is composed of the real-time output power change rate of the energy storage system, then the judgment is based on the real-time output power change rate of the energy storage system in the stability score function.

[0092] Further, the intelligent operation and maintenance module is used to adaptively select operation and maintenance decisions for intelligent operation and maintenance of the system according to the operation state of the solar photovoltaic energy storage system, including daily operation and maintenance decisions and emergency operation and maintenance decisions, which are implemented as follows:

[0093] The daily operation and maintenance decision is an operation and maintenance decision executed for the solar photovoltaic energy storage system in the daily operation state, and then,

[0094] According to the received system operation data in the current period, including three parameters of photovoltaic output power, energy storage state of charge and output power fluctuation rate, through a monitoring mechanism based on a time sliding window, the parameters are continuously sampled to form a continuous operation data sampling sequence;

[0095] According to the operation data contained in the sampling sequence, the trend factor corresponding to each parameter is calculated, including the trend mean, the change slope and the change standard deviation; the trend factors of each parameter are aggregated to construct a system operation trend feature vector in the current period;

[0096] According to the comparison between the constructed operation trend feature vector and the preset safety threshold interval, if the operation trend features in the current period all fall within the corresponding safety interval, it is determined that the system operation state in the current period is stable, the system remains in the normal operation state, and enters the next data sampling period;

[0097] For a plurality of continuous sampling periods, a system operation trend feature vector sequence is constructed, and it is judged whether there is a trend behavior deviating from the edge of the safety threshold interval in the sequence, if it is determined that there is a trend deviation, a daily adjustment mechanism is triggered to perform a light adjustment operation, specifically:

[0098] According to the direction of the trend deviation, the maximum power point tracking control parameter is fine-tuned to improve the power generation stability of the photovoltaic system; the charging and discharging strategy of the energy storage system is adjusted synchronously to optimize the charge fluctuation range of the energy storage system; and the sensitivity of the control parameter of the output end power change is adjusted to reduce the system volatility;

[0099] After executing the daily adjustment mechanism adjustment operation, the operation data sampling and trend feature construction stage of the next period is entered, and whether the current adjustment achieves system recovery is judged according to the new round of trend feature vector, if the trend feature vector returns to the center interval or the deviation degree is reduced, the adjustment effect of this round is recorded, the deviation state is reset, and the daily monitoring state is reentered;

[0100] Through the judgment of the data trend of each sampling period and the continuous iteration of the system adjustment based on the light deviation of the trend, the continuous optimization and operation and maintenance of the system in the daily operation state are realized.

[0101] It should be noted that for the monitoring mechanism in the daily operation and maintenance, a time sliding window sampling mechanism is set to monitor the real-time operation data of the system, specifically:

[0102] Setting a time sliding window , wherein, represents the current time is the end point, the length of the time sliding window is , and the step length is ;

[0103] According to the time points in the time window, three parameters of photovoltaic output power, energy storage state of charge, and output power fluctuation rate are collected, and the mean value, change rate, and standard deviation corresponding to each parameter are calculated;

[0104] Based on the calculated mean value, change rate, and standard deviation, a system operation trend feature vector is constructed, specifically, the calculated mean value, change rate, and standard deviation are combined to construct the system operation trend feature vector;

[0105] According to the comparison between the constructed system operation trend feature vector and the set safety threshold interval, if the feature values in the operation trend feature vector in the current period all meet the corresponding safety interval, it is determined that the system operation state in the current period is stable, and the system remains in a normal operation state, and enters the next data sampling period.

[0106] For the judgment of the direction of the trend deviation in daily operation and maintenance, the deviation direction of the feature values in the system operation trend feature vector relative to the safety interval is determined, including the feature values being lower than the minimum value of the safety interval and the feature values being higher than the maximum value of the safety interval.

[0107] Further, the emergency operation and maintenance is an operation and maintenance decision taken by the solar photovoltaic energy storage system in an abnormal state, specifically:

[0108] According to the comparison between the constructed operation trend feature vector and the set safety interval, if the feature values in the operation trend feature vector exceed the safety interval, it is determined that the system has an operation abnormality in the current period, triggering the emergency operation and maintenance mechanism;

[0109] Before executing the emergency operation and maintenance mechanism, the trend feature vector generated in the current sampling period is quickly discriminated, the severity level of the current abnormal state is determined according to the index, direction, and degree of deviation, and the corresponding emergency treatment strategy is selected according to the severity level, specifically:

[0110] The system emergency state level is set according to the deviation of the feature values in the trend feature vector relative to the safety interval , that is,

[0111] The deviation threshold of the feature values relative to the safety interval is set as , if the deviation of the feature values relative to the safety interval compared with the set deviation threshold satisfies the formula , it indicates that the current abnormal state is a first-level emergency state, otherwise it indicates that the current abnormal state is a second-level emergency state.

[0112] It should be noted that the offset of the characteristic value in the trend feature vector relative to the safety interval is achieved by changing the value of The selection of different characteristic values is achieved by changing the value of

[0113] If the current characteristic value exceeds the maximum value of the safety interval, the difference between the current characteristic value and the maximum value is taken as the offset of the current characteristic value relative to the safety interval.

[0114] If the current characteristic value is lower than the minimum value of the safety interval, the difference between the current characteristic value and the minimum value is taken as the offset of the current characteristic value relative to the safety interval.

[0115] If it is determined that the current anomaly is a first-level emergency state, a high-priority protection strategy is immediately executed, and there is

[0116] Disconnect the photovoltaic output inverter to isolate the generation end, force the battery pack to reduce power or stop charging and discharging, cut off the grid-connected end output path or switch to island operation mode, and ensure the safety of the system core unit.

[0117] If it is determined that the current anomaly is a second-level emergency state, a system load reduction strategy for critical states is executed, and there is

[0118] Dynamically reduce the maximum power point tracking power, battery pack charging and discharging rate limit, inverter output power setting soft limit, etc., to realize the buffer adjustment of the system running state to the safety interval.

[0119] After executing the emergency processing strategy, enter the abnormality monitoring recovery phase, which is specifically:

[0120] Continuously track the trend change of the abnormality parameter in the next several sampling periods, construct the trend feature sequence in the recovery period, and determine whether the system returns to the normal range under control intervention.

[0121] If the trend factor gradually tends to the safety threshold center interval in the recovery period, it is determined that the current round of emergency processing takes effect, the system state becomes temporarily stable, the emergency processing strategy, response time and effect index of this round are recorded, and the emergency operation and maintenance cycle is ended.

[0122] If the trend still fails to stabilize and intensifies in the continuous several sampling periods, a system alarm is triggered, an abnormal state signal is pushed to the artificial operation and maintenance end, and the artificial intervention phase is entered.

[0123] Further, the functions can be implemented in a form of software function units and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or partially or parts of the technical solutions can be embodied in a form of a software product, and the computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods according to the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0124] The logic and / or steps represented in the flowcharts and / or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logic functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or in conjunction with such instruction execution system, apparatus or device. For the purpose of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus or device, or in conjunction with such instruction execution system, apparatus or device.

[0125] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by electronic conversion of the scanned data, and then editing, interpreting or otherwise processing the data as necessary, and then storing the data in a computer memory.

[0126] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent operation and maintenance system of a solar photovoltaic energy storage system, characterized in that: a state judgment module is configured to judge the running state of the solar photovoltaic energy storage system, specifically: collecting real-time running data of the system and judging the running state of the system through two-layer judgment mechanism, including, comparing the collected real-time running data with the safe running data threshold, and judging the running state of the solar photovoltaic energy storage system according to the comparison result, and constructing a stability scoring function of the solar photovoltaic energy storage system, and taking the collected real-time running data as the variable of the function, and judging the running state of the solar photovoltaic energy storage system according to the function score result; an intelligent operation and maintenance module is configured to intelligently operate and maintain the solar photovoltaic energy storage system, specifically: based on the system running state result output by the state judgment module, the corresponding operation and maintenance level is adaptively selected, and the corresponding operation and maintenance decision is executed according to the operation and maintenance level, including, when the system is in a normal running state, triggering a daily adjustment mechanism to execute a light-weight adjustment operation; when the system is in an abnormal running state, triggering an emergency operation and maintenance mechanism to adaptively adjust according to the emergency state; after the execution of the operation and maintenance decision, it is judged whether the executed operation and maintenance decision is effective through abnormality monitoring; the stability scoring function of the solar photovoltaic energy storage system is specifically as follows: based on the set safe running data threshold of the solar photovoltaic energy storage system, the stability scoring function is constructed, then, the safe running data threshold is specifically as follows: the running state of the solar photovoltaic energy storage system is judged according to the comparison result, specifically as follows: the running state of the solar photovoltaic energy storage system is judged according to the function score result, specifically as follows: the corresponding operation and maintenance decision is executed according to the operation and maintenance level, specifically as follows: based on the received system running data in the current period, including photovoltaic output power, energy storage state of charge and output power fluctuation rate, through a monitoring mechanism based on a time sliding window, the parameters are continuously sampled to form a continuous running data sampling sequence; according to the running data contained in the sampling sequence, the trend factor corresponding to each parameter is calculated, including trend mean, change slope and change standard deviation; the trend factors of each parameter are aggregated to construct a system running trend feature vector in the current period; the constructed running trend feature vector is compared with the preset safety threshold interval, if the running trend feature in the current period falls within the corresponding safety interval, it is determined that the system running state in the current period is stable, the system remains in a normal running state, and enters the next data sampling period; for a plurality of continuous sampling periods, the system running trend feature vector sequence is constructed, and it is judged whether there is a trend behavior continuously deviating from the edge of the safety threshold interval in the sequence, if it is determined that there is a trend deviation, the daily adjustment mechanism is triggered. The daily adjustment mechanism is specifically as follows: ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ; at the same time, based on the collected real-time operation data, the stability score function of the solar photovoltaic energy storage system is constructed, wherein, , respectively represent the set upper and lower threshold values of the photovoltaic real-time output power, , respectively represent the set upper and lower threshold values of the energy storage system battery real-time state of charge, , respectively represent the set upper and lower threshold values of the energy storage system real-time output power change rate, represents the photovoltaic real-time output power, represents the energy storage system battery real-time state of charge, represents the energy storage system real-time output power change rate, , , represents the weight coefficient, represents the stability score function constructed by the set safe operation data threshold values, represents the stability score function constructed by the collected real-time operation data. 2.The intelligent operation and maintenance system of a solar photovoltaic energy storage system according to claim 1, characterized in that, ​ extracting real-time running data of the system from the database, including, real-time output power of the photovoltaic , real-time state of charge of the battery of the energy storage system , and real-time output power change rate of the energy storage system ; Respectively set the safety operation data threshold of the solar photovoltaic energy storage system, including the upper limit of the photovoltaic real-time output power threshold And the lower limit of the threshold The upper limit of the energy storage system battery real-time state of charge threshold And the lower limit of the threshold And the upper limit of the energy storage system real-time output power change rate threshold And the lower limit of the threshold . 3.The intelligent operation and maintenance system of a solar photovoltaic energy storage system according to claim 2, characterized in that, ​ If the real-time output power of the photovoltaic satisfies the formula , it indicates that the photovoltaic output power of the solar photovoltaic energy storage system is abnormal, and the abnormal state is sent to the operation and maintenance decision intelligent selection module, otherwise it indicates that the photovoltaic output power of the solar photovoltaic energy storage system is normal. If the real-time state of charge of the energy storage system battery meets the formula , it indicates that the real-time state of charge of the energy storage system battery corresponding to the solar photovoltaic energy storage system is abnormal, and the abnormal state is sent to the operation and maintenance decision intelligent selection module, otherwise it indicates that the real-time state of charge of the energy storage system battery corresponding to the solar photovoltaic energy storage system is normal. If the real-time output power comparison result of the energy storage system satisfies the formula , it indicates that the real-time output power of the solar photovoltaic energy storage system corresponding to the energy storage system is abnormal, and the abnormal state is sent to the operation and maintenance decision intelligent selection module, otherwise it indicates that the real-time output power of the solar photovoltaic energy storage system corresponding to the energy storage system is normal. 4.The intelligent operation and maintenance system of a solar photovoltaic energy storage system according to claim 3, characterized in that, ​ According to the set safety threshold interval , and the fault threshold interval , the safety threshold interval is within the set upper and lower threshold range, the fault threshold interval is outside the set upper and lower threshold range, and a state judgment function , the constructed stability score function is input data of the state judgment function, and the running state of the photovoltaic energy storage system is judged according to the output result of the state judgment function, that is, If the output result of the state judgment function satisfies the formula , it indicates that the current running state of the photovoltaic energy storage system is a normal running state. If the output result of the state judgment function satisfies the formula , it indicates that the current running state of the photovoltaic energy storage system is an abnormal running state. If the output result of the state judgment function satisfies the formula and , it indicates that the current operating state of the photovoltaic energy storage system is a fluctuation state. 5.The intelligent operation and maintenance system of a solar photovoltaic energy storage system according to claim 4, characterized in that, ​ ​ ​ ​ ​ 6.The intelligent operation and maintenance system of a solar photovoltaic energy storage system according to claim 5, characterized in that, ​ According to the direction of the trend deviation, fine-tune the maximum power point tracking control parameters to improve the power generation stability of the photovoltaic system; simultaneously adjust the charge and discharge strategy of the energy storage system to optimize the charge fluctuation range of the energy storage system; and adjust the sensitivity of the control parameters of the output power change; After the daily adjustment mechanism adjustment operation is performed, the next cycle of operation data sampling and trend feature construction phase is entered, and whether the current adjustment achieves system return to stability is judged according to the new round of trend feature vector. If the trend feature vector re-centers the interval or the deviation degree is reduced, the adjustment effect of the current round is recorded, the deviation state is reset, and the daily monitoring state is re-entered. Through the judgment of the data trend of each sampling period and the continuous iteration of system adjustment based on the trend slight deviation, the continuous optimization and operation and maintenance of the system in the daily operation state are realized. 7.The intelligent operation and maintenance system of a solar photovoltaic energy storage system according to claim 6, characterized in that, The emergency state adaptive operation and maintenance adjustment is as follows: If it is judged that the current abnormality is a first-level emergency state, a high-priority protection strategy is immediately executed, that is, The photovoltaic output inverter is disconnected to isolate the power generation end, the battery pack is forced to reduce power or stop charging and discharging, the grid-connected end output path is cut off or switched to island operation mode, and the safety of the system core unit is ensured; If it is judged that the current abnormality is a second-level emergency state, a system load reduction strategy for critical state is executed, that is, The maximum power point tracking power, the battery pack charge and discharge rate limit, and the inverter output power setting soft limit are dynamically adjusted to realize the buffer adjustment of the system operation state to the safety interval. 8.The intelligent operation and maintenance system of a solar photovoltaic energy storage system according to claim 7, characterized in that, The emergency state is specifically judged as follows: The system emergency state level is set according to the eigenvalue in the trend eigenvector, which is the offset of the eigenvalue in the trend eigenvector relative to the safety interval If the setting is made, then Setting a threshold of offset of characteristic value relative to safety interval If the offset of characteristic value relative to safety interval satisfies the formula compared with the set threshold of offset, it indicates that the current abnormal state is a first emergency state, otherwise it indicates that the current abnormal state is a second emergency state. The offset of the characteristic value in the trend characteristic vector relative to the safety interval is, by changing the value of the selection of different characteristic values is realized, and the difference between the current characteristic value and the maximum value and the minimum value of the corresponding safety interval is calculated, respectively, that is, If the current feature value exceeds the maximum value of the safety interval, the difference between the current feature value and the maximum value is taken as the deviation of the current feature value relative to the safety interval; If the current feature value is lower than the minimum value of the safety interval, the difference between the current feature value and the minimum value is taken as the deviation of the current feature value relative to the safety interval. 9.The intelligent operation and maintenance system of a solar photovoltaic energy storage system according to claim 8, characterized in that, The abnormality monitoring recovery is as follows: The trend changes of the adjusted abnormality parameters in multiple sampling periods are continuously tracked, the trend feature sequence in the recovery period is constructed, and whether the system returns to the normal range under control intervention is judged; If the trend factor gradually tends to the center interval of the safety threshold in the recovery period, it is judged that the current round of emergency treatment is effective, the system state is temporarily stable, the current round of emergency treatment strategy, response time and effect index are recorded, and the emergency operation and maintenance cycle is ended; If the trend still cannot return to stability in continuous sampling periods and shows an aggravating trend, the system alarm is triggered, the abnormal state signal is pushed to the artificial operation and maintenance end, and the artificial intervention stage is entered.

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