Remote alarm device for monitoring abnormity of photovoltaic energy storage power station

Abstract

The invention discloses a remote alarm device for monitoring the abnormity of a photovoltaic energy storage power station, and relates to the technical field of remote alarm, and the device comprises an array information obtaining module which is used for extracting the layout information of a photovoltaic module, an inverter and a battery pack; the performance degradation analysis module is used for identifying an equipment performance degradation area; the neighborhood attenuation concentration analysis module is used for extracting a attenuation concentration value; the newly added scheme identification module is used for dynamically optimizing a remote alarm equipment layout scheme according to the recession concentrated value; and the remote alarm module is used for updating an alarm device array by using the optimization scheme, and realizing real-time monitoring and accurate alarm of an abnormal area. The technical problem that dynamic adaptation cannot be performed according to the actual operation state of the equipment in the prior art, and the anomaly detection precision and the fault response efficiency are affected is solved, and the technical effects of accurate monitoring and real-time remote alarm of the high-risk area and improvement of the anomaly detection precision and the fault response efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote alarms, and in particular to a remote alarm device for abnormal monitoring of a photovoltaic energy storage power station. Background Art

[0002] With the large-scale deployment and long-term operation of photovoltaic energy storage power stations, the gradual decline in equipment performance and abnormal risks have become increasingly prominent. Especially in complex operating environments, key equipment such as photovoltaic panels, inverters and battery packs are prone to varying degrees of performance degradation.

[0003] Most existing remote alarm systems are statically deployed, that is, in the early stage of power station construction, alarm equipment is fixedly deployed based on empirical data and standardized design. However, as the operation time of photovoltaic energy storage power stations increases, the degree of equipment loss, abnormal frequency and risk distribution in each area gradually change. Traditional alarm systems cannot dynamically adapt according to the actual operating status of photovoltaic energy storage power stations, and cannot timely focus on monitoring performance degradation areas, resulting in lags in abnormal detection and alarm response, affecting the stable operation of power stations. Summary of the invention

[0004] The present application provides a remote alarm device for abnormal monitoring of photovoltaic energy storage power stations, which is used to solve the technical problem in the prior art that the traditional alarm system cannot dynamically adapt according to the actual operating status of the equipment, affecting the accuracy of abnormal detection and fault response efficiency.

[0005] The present application provides a remote alarm device for abnormal monitoring of a photovoltaic energy storage power station, the device comprising: an array information acquisition module, used to acquire a photovoltaic component layout array, an inverter layout array and a battery group layout array of a target photovoltaic energy storage power station; an abnormal monitoring data extraction module, used to extract a photovoltaic component historical abnormal monitoring data set, an inverter historical abnormal monitoring data set and a battery group historical abnormal monitoring data set of the photovoltaic component layout array, the inverter layout array and the battery group layout array within a preset historical window; a performance attenuation analysis module, used to perform a device performance attenuation distribution neighborhood analysis on the photovoltaic component layout array, the inverter layout array and the battery group layout array based on the photovoltaic component historical abnormal monitoring data set, the inverter historical abnormal monitoring data set and the battery group historical abnormal monitoring data set, to obtain a photovoltaic component performance attenuation distribution neighborhood set, an inverter performance attenuation distribution neighborhood set and a battery group performance attenuation distribution neighborhood set; a neighborhood attenuation concentration analysis module, It is used to perform neighborhood attenuation concentration analysis based on the neighborhood set of photovoltaic component performance attenuation distribution, the neighborhood set of inverter performance attenuation distribution and the neighborhood set of battery group performance attenuation distribution, and obtain the neighborhood attenuation concentration value set of photovoltaic components, the neighborhood attenuation concentration value set of inverter performance and the neighborhood attenuation concentration value set of battery group performance; a new scheme identification module is used to obtain the remote alarm device layout array of the target photovoltaic energy storage power station, and identify the new equipment scheme for the remote alarm device layout array based on the neighborhood attenuation concentration value size of the photovoltaic component neighborhood attenuation concentration value set, the inverter performance neighborhood attenuation concentration value set and the battery group performance neighborhood attenuation concentration value set, and obtain the target equipment addition scheme; a remote alarm module is used to use the target equipment addition scheme to add equipment to the remote alarm device layout array, obtain an updated remote alarm device layout array, and use the updated remote alarm device layout array to remotely alarm the abnormal monitoring situation of the target photovoltaic energy storage power station.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The remote alarm device for abnormal monitoring of photovoltaic energy storage power stations provided in the present application relates to the field of remote alarm technology. The array information acquisition module extracts the layout information and historical abnormal data of photovoltaic components, inverters and battery packs; the performance decay analysis module identifies the equipment performance decay area; the neighborhood decay concentration analysis module extracts the decay concentration value; the new scheme identification module dynamically optimizes the remote alarm equipment layout scheme according to the decay concentration value; the remote alarm module updates the alarm equipment array using the optimization scheme to achieve real-time monitoring and accurate alarm of abnormal areas, solving the technical problem that the traditional alarm system in the prior art cannot be dynamically adapted according to the actual operating status of the equipment, affecting the accuracy of abnormal detection and the efficiency of fault response. By dynamically optimizing the layout scheme of the remote alarm equipment, the areas with high performance decay and abnormal frequency in the photovoltaic energy storage power station are accurately covered, and real-time and accurate abnormal monitoring and rapid alarm are achieved, thereby improving the technical effect of the abnormal detection accuracy and fault response efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic diagram of the structure of a remote alarm device for abnormal monitoring of a photovoltaic energy storage power station provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for performing neighborhood analysis of equipment performance degradation distribution in a performance degradation analysis module of a remote alarm device for abnormal monitoring of a photovoltaic energy storage power station provided in an embodiment of the present application.

[0009] Explanation of reference numerals: array information acquisition module 10 , abnormal monitoring data extraction module 20 , performance attenuation analysis module 30 , neighborhood attenuation centralized analysis module 40 , new solution identification module 50 , remote alarm module 60 . DETAILED DESCRIPTION

[0010] The present application provides a remote alarm device for abnormal monitoring of photovoltaic energy storage power stations, which is used to solve the technical problem in the prior art that the traditional alarm system cannot dynamically adapt according to the actual operating status of the equipment, affecting the accuracy of abnormal detection and fault response efficiency.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable in appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, the process, method, system, product or server comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides a remote alarm device for abnormal monitoring of a photovoltaic energy storage power station, the device comprising: The array information acquisition module 10 is used to acquire the photovoltaic component layout array, inverter layout array and battery group layout array of the target photovoltaic energy storage power station.

[0014] Specifically, the main function of the array information acquisition module 10 of the present application is to obtain the layout array information of various key equipment in the target photovoltaic energy storage power station, including the photovoltaic module layout array, inverter layout array and battery pack layout array, to provide basic data support for subsequent abnormal monitoring and equipment performance analysis.

[0015] First of all, the photovoltaic module layout array refers to the installation arrangement of photovoltaic panels and their spatial distribution in the power station. Photovoltaic modules are the core components of photovoltaic power generation systems and directly affect the power generation efficiency of power stations. Therefore, obtaining the layout information of photovoltaic modules not only helps to understand the layout relationship between the components, but also reflects the possible performance differences in different regions. For example, the layout angle, orientation and shielding relationship between photovoltaic modules and the surrounding environment (such as buildings, trees, etc.) will affect their power generation effect. The array information acquisition module can scan the physical position of photovoltaic modules through sensors or automation systems, and record its precise spatial coordinates, direction angles, installation tilt angles and other parameters.

[0016] Next, obtain the inverter layout array. The inverter is a device that converts the direct current generated by the photovoltaic module into alternating current, and plays a key role in the photovoltaic energy storage power station. Inverters of different models and locations may have different workloads and decay rates, so accurately obtaining the inverter layout information is crucial for analyzing the attenuation degree and potential failures of the equipment. The array information acquisition module can ensure that the data of all inverters can be effectively collected based on the installation location, power capacity and role of the inverter in the power station.

[0017] Finally, the battery pack layout array refers to the layout and structure of the battery pack (used to store the electricity generated by photovoltaic power generation). The layout of the battery pack has a direct impact on the energy storage efficiency and system stability. Factors such as the battery's charge and discharge frequency and temperature environment will also affect its performance degradation. The array information acquisition module monitors the layout, connection method and battery health status of the battery pack to ensure real-time acquisition of the battery pack's working status information.

[0018] In actual operation, the array information acquisition module may achieve this goal through a variety of technical means. For example, drones can be used in conjunction with lidar technology to scan the location of photovoltaic modules and inverters, or the layout parameters of each device can be collected in real time through an automated monitoring system and sensor network. This information is transmitted to the back-end processing platform through the data acquisition system, providing an accurate data basis for subsequent performance analysis, abnormal monitoring and equipment optimization.

[0019] Through these steps, the array information acquisition module lays the foundation for subsequent performance degradation analysis and optimization of remote alarm solutions, ensuring that the photovoltaic energy storage power station can monitor the operating status of each device in real time during actual operation and issue timely risk warnings.

[0020] The abnormal monitoring data extraction module 20 is used to extract the photovoltaic component historical abnormal monitoring data set, the inverter historical abnormal monitoring data set and the battery group historical abnormal monitoring data set of the photovoltaic component layout array, the inverter layout array and the battery group layout array within a preset historical window.

[0021] It should be understood that the core function of the abnormal monitoring data extraction module 20 of the present application is to extract historical abnormal monitoring data from each device array of the photovoltaic energy storage power station. These data can reflect the operating status and potential abnormal conditions of each device (photovoltaic components, inverters and battery packs) within a specific time window, and provide necessary data support for subsequent performance analysis and equipment health assessment.

[0022] First, the extraction of the historical abnormal monitoring data set of photovoltaic modules involves real-time collection of the operating data of each photovoltaic module. The operating status of photovoltaic modules is affected by many factors, such as ambient temperature, light intensity, shading, etc. Therefore, the abnormal monitoring data extraction module needs to obtain relevant real-time data from the sensor network of the device, including parameters such as voltage, current, and power output. At the same time, it is also necessary to combine the environmental data of the device, such as light intensity and temperature, to determine whether it has an abnormal state. The module will collect and store this data as a historical abnormal monitoring data set of photovoltaic modules according to the preset historical time window (usually a few days, weeks or months). These data can help the system identify whether there is a fault in the photovoltaic module, such as surface damage, wiring problems or performance degradation of the photovoltaic module.

[0023] Secondly, the extraction of the inverter historical abnormal monitoring data set focuses on the output and working status of the inverter. The main task of the inverter is to convert the direct current generated by the photovoltaic module into alternating current and output it to the power grid in a stable manner. The abnormal monitoring data extraction module extracts its real-time monitoring data, including output power, current, voltage, frequency and other parameters, through the communication interface with the inverter. During operation, the inverter may be abnormal due to load fluctuations, internal faults or external factors. By analyzing these historical monitoring data, the module can identify the possible fault types of the inverter, such as overload, undervoltage, overtemperature, inverter damage and other problems. In addition, the working state of the inverter may be affected by factors such as ambient temperature and load changes. Extracting historical data is helpful for detailed fault analysis and prediction.

[0024] Finally, the extraction of the battery pack historical abnormal monitoring data set involves the monitoring of the battery pack's charge and discharge status. The battery pack is an important component of the photovoltaic energy storage system, responsible for storing the electrical energy generated by the photovoltaic modules and releasing it when needed. Battery performance degradation and failure are usually closely related to factors such as the number of charge and discharge cycles, temperature and voltage abnormalities. The abnormal monitoring data extraction module extracts real-time monitoring data of the battery from the battery management system (BMS), including information such as battery voltage, temperature, charge / discharge rate, and battery health status. By extracting these historical data, the module can promptly detect potential problems of the battery pack, such as capacity decay, overcharging, over-discharging, etc., thereby providing data support for subsequent maintenance and optimization.

[0025] During the data extraction process, the module will collect the operation data of each device in batches in combination with the preset historical window. The setting of the preset historical window can be adjusted according to actual needs and the operation cycle of the device to ensure that sufficient data within the time span can be obtained. These historical data will be standardized, noise will be removed, and potential anomalies will be marked to form a corresponding abnormal monitoring data set, providing accurate input data for subsequent performance degradation analysis, anomaly detection and risk assessment.

[0026] The performance attenuation analysis module 30 is used to perform equipment performance attenuation distribution neighborhood analysis on the photovoltaic component layout array, the inverter layout array and the battery group layout array based on the photovoltaic component historical abnormality monitoring data set, the inverter historical abnormality monitoring data set and the battery group historical abnormality monitoring data set, and obtain the photovoltaic component performance attenuation distribution neighborhood set, the inverter performance attenuation distribution neighborhood set and the battery group performance attenuation distribution neighborhood set.

[0027] Further, such as Figure 2 As shown, the performance degradation analysis module 30 is also used to perform the following steps: P31: Based on the photovoltaic component historical abnormality monitoring data set, the abnormal frequency of photovoltaic components in the photovoltaic component layout array is counted to obtain a photovoltaic component abnormal frequency distribution array; P32: Q photovoltaic component abnormal frequencies are randomly extracted from the photovoltaic component abnormal frequency distribution array, where Q is an integer greater than or equal to 1; P33: Based on the Q photovoltaic component abnormal frequencies, combined with the photovoltaic component abnormal frequency distribution array, the photovoltaic component layout array is subjected to equipment performance attenuation distribution neighborhood analysis to obtain the photovoltaic component performance attenuation distribution neighborhood set; P34: Based on the inverter historical abnormality monitoring data set and the battery pack historical abnormality monitoring data set, the inverter layout array and the battery pack layout array are subjected to equipment performance attenuation distribution neighborhood analysis to obtain the inverter performance attenuation distribution neighborhood set and the battery pack performance attenuation distribution neighborhood set.

[0028] It should be understood that the main function of the performance degradation analysis module 30 of the present application is to evaluate the performance degradation of each device in the photovoltaic energy storage power station by analyzing the historical abnormal monitoring data, and further provide data support for subsequent equipment optimization and fault prediction. This module obtains the performance degradation distribution information of the equipment by analyzing the abnormal frequency and performance changes of photovoltaic components, inverters and battery packs in a specific historical time window, providing a basis for formulating reasonable maintenance plans and alarm plans.

[0029] First, the module counts the abnormal frequencies of all PV modules based on the historical abnormal monitoring data set of PV modules. The abnormal frequency refers to the number of times each PV module has an abnormality within a specified historical time window. The abnormality can be in various forms, such as power drop, voltage fluctuation, temperature abnormality, etc. By counting these abnormal frequencies, the module can identify which PV modules show a higher failure frequency and generate a PV module abnormal frequency distribution array, that is, the abnormal frequency summary data of these modules.

[0030] Based on the statistical distribution array of PV module abnormality frequencies, the module will randomly extract the abnormal frequencies of Q PV modules, where Q is an integer greater than or equal to 1. The Q randomly extracted modules represent a portion of modules that may have potential problems. The purpose of this step is to further analyze the performance degradation trend by selecting representative module samples and provide sufficient data for subsequent equipment analysis.

[0031] Furthermore, based on the abnormal frequencies of the extracted Q photovoltaic modules and combined with the abnormal frequency distribution array of photovoltaic modules, the module performs a performance attenuation distribution neighborhood analysis on the photovoltaic module layout array. The performance attenuation distribution neighborhood analysis is to identify equipment areas with severe losses by analyzing the performance change trends of the selected components and surrounding components. The core of the neighborhood analysis is to evaluate the performance attenuation pattern of the equipment in space by examining the spatial relationship and operating status between photovoltaic modules, revealing which areas of photovoltaic modules have experienced performance degradation, and predicting possible failure risks. Through this method, a neighborhood set of photovoltaic module performance attenuation distribution is generated, which includes components with severe performance attenuation and their adjacent components, and becomes the basis for subsequent fault diagnosis and optimization of alarm facilities.

[0032] In addition to photovoltaic modules, inverters and battery packs are also key equipment in photovoltaic energy storage power stations. In order to fully evaluate the operating status of the entire system, the module also performs performance attenuation distribution neighborhood analysis on the inverter array and battery array array based on the inverter historical abnormal monitoring data set and the battery pack historical abnormal monitoring data set. The performance attenuation of the inverter is usually closely related to factors such as load fluctuations and internal component losses, while the attenuation of the battery pack is related to factors such as the number of charge and discharge times and temperature fluctuations. By analyzing these data, the module can identify the inverter and battery pack areas with more serious performance degradation, and generate the corresponding inverter performance attenuation distribution neighborhood set and battery pack performance attenuation distribution neighborhood set. This information can provide strong support for the optimization of equipment maintenance, performance prediction and early warning systems in power stations.

[0033] Furthermore, step P33 of the embodiment of the present application also includes: P33-1: Verify the neighborhood centers of the Q photovoltaic component abnormal frequencies. If the verification is passed, use the Q photovoltaic component abnormal frequencies as Q initial neighborhood centers; P33-2: Calculate the Q data differences between the abnormal frequency of each photovoltaic component in the photovoltaic component abnormal frequency distribution array and the Q initial neighborhood centers respectively, add each photovoltaic component abnormal frequency to the neighborhood of the initial neighborhood center corresponding to the maximum value of the Q data similarities, and obtain Q initial neighborhoods; P33-3: According to the positions of each photovoltaic component in the photovoltaic component layout array, with the goal of minimizing the cost function, iteratively update the Q initial neighborhoods and the Q initial neighborhood centers until the preset update stop condition is met, and obtain Q target neighborhood centers and Q target neighborhoods; wherein, the preset update stop condition is that the difference between the cost values ​​of the two cost functions of two adjacent iterations is less than or equal to the preset cost value difference and / or the number of iterations meets the preset number of iterations. P33-4: The area constituted by photovoltaic components corresponding to the abnormal frequencies of multiple photovoltaic components in each of the Q target neighborhoods is taken as a photovoltaic component performance attenuation distribution neighborhood to obtain the photovoltaic component performance attenuation distribution neighborhood set.

[0034] Furthermore, step P33-3 of the embodiment of the present application also includes: Taking into account the data difference and the distribution location of photovoltaic modules, a cost function is constructed, where the cost function is: ;in, For the value of is the abnormal frequency of the jth PV module in the i-th initial neighborhood, is the distribution position of the PV module corresponding to the abnormal frequency of the jth PV module in the i-th initial neighborhood, is the abnormal frequency of the PV modules at the center of the i-th initial neighborhood, is the distribution position of the photovoltaic module corresponding to the i-th initial neighborhood center, To balance the weights of abnormal frequency and distribution location, is the total number of abnormal frequencies of PV panels contained in the i-th initial neighborhood.

[0035] Optionally, based on the abnormal frequencies of the Q photovoltaic components, the specific process of performing a neighborhood analysis of the distribution of equipment performance attenuation on the photovoltaic component layout array in combination with the abnormal frequency distribution array of the photovoltaic components may be, first, verifying the neighborhood center of the Q randomly extracted abnormal frequencies of photovoltaic components. The neighborhood center is the initial reference point for neighborhood analysis, indicating the central position of the abnormal frequency. The neighborhood center verification aims to ensure that the Q randomly extracted abnormal frequencies of photovoltaic components can reasonably represent the concentrated area of ​​performance attenuation. Verification includes data integrity check (excluding missing or outliers), statistical distribution analysis (ensuring coverage of the main abnormal frequency range and uniform distribution), similarity verification (cluster analysis and outlier check), and neighborhood coverage verification (ensuring that the neighborhood of the initial point can effectively cover the performance degradation area of ​​the layout array). If the verification passes, the Q abnormal frequencies are used as the initial neighborhood center, otherwise they are re-extracted and verified to ensure the reliability and representativeness of the initial neighborhood center.

[0036] Next, the data difference between the abnormal frequency of each photovoltaic component in the photovoltaic component abnormal frequency distribution array and the Q initial neighborhood centers is calculated. The data difference is used to measure the degree of deviation between the abnormal frequency of each component and the initial neighborhood center, and can be calculated using Euclidean distance or other similarity algorithms. The abnormal frequency of each photovoltaic component is assigned to the neighborhood of the initial neighborhood center corresponding to the maximum data similarity, forming Q initial neighborhoods. For example, if the difference between photovoltaic component A and neighborhood center 1 is the smallest, A is included in the neighborhood corresponding to neighborhood center 1. This division method ensures that each component is reasonably classified into the most similar neighborhood.

[0037] In order to further optimize the neighborhood division, the module iteratively updates the abnormal frequency and distribution location of the PV modules with the goal of minimizing the cost function. The expression of the cost function is: ;in, is the cost value, indicating the cost of the overall partition. is the abnormal frequency of the jth PV module in the i-th initial neighborhood, is the distribution position of the PV module corresponding to the abnormal frequency of the jth PV module in the i-th initial neighborhood, is the abnormal frequency of the PV modules at the center of the i-th initial neighborhood, is the distribution position of the photovoltaic module corresponding to the i-th initial neighborhood center, To balance the weights of abnormal frequency and distribution location, is the total number of abnormal frequencies of PV panels contained in the i-th initial neighborhood.

[0038] The cost function is optimized through iterative updates. Each iteration recalculates the parameters of each neighborhood center (anomaly frequency and distribution location) and re-divides the neighborhood according to the minimum cost principle. This process continues until the preset update stop condition is met, such as the difference between the cost values ​​of two consecutive iterations is less than the preset threshold, or the maximum number of iterations is reached.

[0039] After completing the iterative optimization, the module determines the final Q target neighborhood centers and their corresponding neighborhoods as the performance attenuation distribution neighborhoods of the PV modules. Specifically, the PV modules corresponding to the abnormal frequencies of multiple PV modules contained in each target neighborhood constitute an independent performance attenuation area. These area sets form the neighborhood set of PV module performance attenuation distribution, providing a basis for subsequent performance evaluation, fault prediction, and alarm layout optimization.

[0040] The neighborhood attenuation concentration analysis module 40 is used to perform neighborhood attenuation concentration analysis based on the neighborhood set of photovoltaic component performance attenuation distribution, the neighborhood set of inverter performance attenuation distribution and the neighborhood set of battery pack performance attenuation distribution, and obtain the neighborhood attenuation concentration value set of photovoltaic component, the neighborhood attenuation concentration value set of inverter performance and the neighborhood attenuation concentration value set of battery pack performance.

[0041] Furthermore, the neighborhood attenuation centralized analysis module 40 is further configured to perform the following steps: P41: Traverse the photovoltaic module performance attenuation distribution neighborhood set, the inverter performance attenuation distribution neighborhood set and the battery pack performance attenuation distribution neighborhood set to perform edge data cleaning, and obtain the cleaned photovoltaic module performance attenuation distribution neighborhood set, the cleaned inverter performance attenuation distribution neighborhood set and the cleaned battery pack performance attenuation distribution neighborhood set after removing the edge values ​​with small distribution amounts; P42: Perform mean processing on the cleaned photovoltaic module performance attenuation distribution neighborhood set, the cleaned inverter performance attenuation distribution neighborhood set and the cleaned battery pack performance attenuation distribution neighborhood set to obtain the photovoltaic module neighborhood attenuation concentrated value set, the inverter performance neighborhood attenuation concentrated value set and the battery pack performance neighborhood attenuation concentrated value set.

[0042] Specifically, the main function of the neighborhood attenuation concentration analysis module 40 of the present application is to perform further neighborhood attenuation concentration analysis on these devices based on the performance attenuation distribution neighborhood set of photovoltaic components, inverters and battery packs, and finally obtain the neighborhood attenuation concentration value set of photovoltaic components, inverters and battery packs.

[0043] First, the module traverses the neighborhood set of photovoltaic module performance attenuation distribution, the neighborhood set of inverter performance attenuation distribution, and the neighborhood set of battery pack performance attenuation distribution, and cleans the edge values ​​that account for a small proportion of the data. Edge values ​​usually refer to those device points with weak performance attenuation and small data distribution, which may generate noise due to sensor errors or environmental interference.

[0044] By setting the distribution threshold, these invalid data are removed. For example, in the neighborhood set of photovoltaic modules, if the performance attenuation value in a certain area is extremely low and only contains a small number of devices, it will be regarded as irrelevant data and removed. Similarly, for the neighborhood sets of inverters and battery packs, edges are removed according to the data distribution. After cleaning, the neighborhood sets of performance attenuation distribution of cleaned photovoltaic modules, the neighborhood sets of performance attenuation distribution of cleaned inverters, and the neighborhood sets of performance attenuation distribution of cleaned battery packs are obtained. These sets remove invalid data and ensure the accuracy of subsequent analysis.

[0045] After the cleaning is completed, the module performs mean processing on the cleaned data set and further extracts the neighborhood attenuation concentrated value set of each device. Mean processing is a statistical method that aims to calculate the average level of device performance attenuation values ​​in each performance attenuation distribution neighborhood, reflecting the overall performance degradation of the neighborhood. For example, in the photovoltaic module neighborhood, the performance attenuation values ​​of all photovoltaic modules are averaged. Similarly, the performance attenuation concentrated values ​​of the inverter and battery pack are calculated by the same method to generate the inverter performance neighborhood attenuation concentrated value set and the battery pack performance neighborhood attenuation concentrated value set. These concentrated values ​​represent the concentrated trend of performance degradation in each device area, which helps to identify areas where equipment performance is severely degraded and provide data support for subsequent abnormal warnings and equipment maintenance.

[0046] Finally, the neighborhood attenuation concentration analysis module 40 accurately extracts the neighborhood attenuation concentration value set of photovoltaic components, inverters and battery packs through edge cleaning and mean calculation. These results can provide accurate decision-making basis for the operation and maintenance personnel of photovoltaic energy storage power stations, help locate equipment areas with more serious performance degradation, and provide strong support for the dynamic optimization of remote alarm facilities and equipment maintenance strategies.

[0047] The new scheme identification module 50 is used to obtain the remote alarm equipment layout array of the target photovoltaic energy storage power station, identify the new equipment scheme for the remote alarm equipment layout array based on the neighborhood attenuation concentration value set of the photovoltaic component neighborhood attenuation concentration value set, the inverter performance neighborhood attenuation concentration value set and the battery pack performance neighborhood attenuation concentration value set, and obtain the target equipment addition scheme.

[0048] Furthermore, the new solution identification module 50 is further configured to perform the following steps: P51: Based on the PV module performance attenuation distribution neighborhood set, the inverter performance attenuation distribution neighborhood set and the battery pack performance attenuation distribution neighborhood set, the remote alarm device layout array is neighborhood matched to obtain the PV module remote alarm device layout neighborhood set, the inverter remote alarm device layout neighborhood set and the battery pack remote alarm device layout neighborhood set; P52: Count the distribution quantity and distribution uniformity of the PV module remote alarm device layout neighborhood set, the inverter remote alarm device layout neighborhood set and the battery pack remote alarm device layout neighborhood set, and after weighted analysis, obtain the PV module remote alarm device neighborhood layout coefficient set, the inverter remote alarm device neighborhood layout coefficient set and the battery pack remote alarm device layout neighborhood set. P53: According to the neighborhood attenuation concentration value of the PV module neighborhood attenuation concentration value set, the inverter performance neighborhood attenuation concentration value set and the battery pack performance neighborhood attenuation concentration value set, the PV module remote alarm device neighborhood layout coefficient set, the inverter remote alarm device neighborhood layout coefficient set and the battery pack remote alarm device neighborhood layout coefficient set are matched and analyzed to obtain a set of new solutions for PV module neighborhood equipment, a set of new solutions for inverter neighborhood equipment and a set of new solutions for battery pack neighborhood equipment; P54: The set of new solutions for PV module neighborhood equipment, the set of new solutions for inverter neighborhood equipment and the set of new solutions for battery pack neighborhood equipment are summarized to obtain the target device new solution.

[0049] It should be understood that the main function of the new scheme identification module 50 of the present application is to dynamically identify new equipment schemes based on the neighborhood attenuation concentration value set of photovoltaic components, inverters and battery packs, combined with the current deployment of remote alarm equipment, to meet the needs of accurate monitoring of equipment performance attenuation areas.

[0050] Firstly, the module performs neighborhood matching on the current remote alarm device deployment array based on the neighborhood set of photovoltaic module performance attenuation distribution, the neighborhood set of inverter performance attenuation distribution and the neighborhood set of battery pack performance attenuation distribution.

[0051] In this process, the system matches the performance degradation neighborhoods of PV modules, inverters and battery packs with the remote alarm equipment deployment areas one by one, generating the PV module remote alarm equipment deployment neighborhood set, the inverter remote alarm equipment deployment neighborhood set and the battery pack remote alarm equipment deployment neighborhood set. This one-to-one matching relationship is associated through the device location coordinates and neighborhood performance data to ensure that each performance degradation area can find the currently covered alarm equipment area, providing a clear data basis for subsequent analysis.

[0052] After completing the neighborhood matching, the module conducts detailed statistical analysis on the matched PV module remote alarm device deployment neighborhood set, inverter remote alarm device deployment neighborhood set, and battery group remote alarm device deployment neighborhood set, focusing on the distribution quantity and distribution uniformity. Among them, the distribution quantity represents the actual deployment of remote alarm devices in each neighborhood, reflecting the coverage of the alarm devices. The distribution uniformity measures the distribution density of the devices in each neighborhood and identifies whether there is an imbalance in the deployment of alarm devices.

[0053] Based on the statistical results, through the weighted analysis method, the distribution quantity and uniformity are comprehensively considered to generate quantitative coefficients for each neighborhood, forming a set of neighborhood layout coefficients for photovoltaic module remote alarm equipment, a set of neighborhood layout coefficients for inverter remote alarm equipment, and a set of neighborhood layout coefficients for battery pack remote alarm equipment. These coefficients reflect the advantages and disadvantages of the current layout of alarm equipment in each neighborhood, providing data support for the identification of subsequent new solutions.

[0054] Next, based on the neighborhood attenuation concentration values ​​of the PV module neighborhood attenuation concentration value set, the inverter performance neighborhood attenuation concentration value set, and the battery pack performance neighborhood attenuation concentration value set, a matching analysis is performed on the alarm device deployment coefficient set obtained in the previous step. The neighborhood attenuation concentration value represents the severity of the equipment decay in each performance decay neighborhood. The larger the value, the more obvious the performance decay and the higher the risk. The system compares the attenuation concentration value of each neighborhood with the corresponding deployment coefficient to find out the high attenuation areas with insufficient monitoring coverage and less equipment deployment. Through matching analysis, key areas that cannot be fully covered by the current alarm equipment are identified, and a set of new solutions for PV module neighborhood equipment, a set of new solutions for inverter neighborhood equipment, and a set of new solutions for battery pack neighborhood equipment are generated, clarifying the location and type of new alarm equipment that needs to be added.

[0055] Finally, the module summarizes the set of new solutions for PV module neighborhood devices, inverter neighborhood devices, and battery pack neighborhood devices to form the final target device addition solution. This solution covers the deployment planning of new alarm devices in areas where PV modules, inverters, and battery packs have serious performance degradation, including the specific location, quantity, and type of equipment deployment. Through this solution, the remote alarm system can dynamically adapt to the current performance degradation trend, accurately cover high-risk areas, and improve the monitoring accuracy and response capabilities of photovoltaic energy storage power stations.

[0056] Furthermore, step P53 of the embodiment of the present application also includes: P53-1: Pre-build a matching analyzer, and use the matching analyzer to perform a one-to-one mapping matching analysis on the PV module neighborhood attenuation concentration value set and the PV module remote alarm device neighborhood layout coefficient set, respectively, to obtain a set of new solutions for PV module neighborhood equipment; P53-2: Use the matching analyzer to perform a matching analysis on the inverter performance neighborhood attenuation concentration value set and the battery pack performance neighborhood attenuation concentration value set, and the corresponding inverter remote alarm device neighborhood layout coefficient set and battery pack remote alarm device neighborhood layout coefficient set, to obtain a set of new solutions for the inverter neighborhood equipment and a set of new solutions for the battery pack neighborhood equipment.

[0057] In a possible embodiment of the present application, in order to accurately identify new equipment solutions, a pre-built matching analyzer can be used to achieve a one-to-one mapping matching analysis between the attenuation concentration value set of photovoltaic components, inverters and battery packs and the neighborhood layout coefficient set of the current remote alarm device. This process ensures that the high attenuation area corresponds accurately to the alarm device requirements by building an efficient matching mechanism, thereby obtaining a new solution set.

[0058] First, a matching analyzer is pre-built as the core tool for performing one-to-one mapping matching analysis. Based on the algorithm model, the matching analyzer matches the neighborhood attenuation concentration value set of photovoltaic components with the neighborhood layout coefficient set of photovoltaic component remote alarm devices one by one. Among them, the neighborhood attenuation concentration value set represents the severity of performance degradation in each neighborhood of the photovoltaic component. The larger the concentration value, the more obvious the performance degradation of the photovoltaic components in the area, which needs to be monitored. The neighborhood layout coefficient set of the remote alarm device reflects the layout of the current alarm equipment in each neighborhood, including the quantitative results of the number of equipment and coverage effect.

[0059] The matching analyzer identifies high attenuation areas in the neighborhood of PV modules by comparing the numerical differences and weight priorities of the two, and matches the layout of existing alarm equipment. For areas with high attenuation concentration values ​​but low alarm equipment layout coefficients, they are identified as weak monitoring areas, and a set of new equipment addition plans for the neighborhood of PV modules is generated. This plan clarifies the demand for new equipment in high attenuation areas of PV modules, including the location, quantity and monitoring range of new alarm equipment.

[0060] Next, the matching analyzer further performs matching analysis between the inverter and the battery pack, and matches the inverter performance neighborhood attenuation concentration value set and the battery pack performance neighborhood attenuation concentration value set with their corresponding inverter remote alarm device neighborhood layout coefficient set and battery pack remote alarm device neighborhood layout coefficient set one by one. The matching degree between the performance attenuation concentration value and the layout coefficient of each neighborhood is calculated through the algorithm model, and the high attenuation area with insufficient monitoring is identified. For example, when the attenuation concentration value of a certain inverter neighborhood is much higher than that of other areas, but the corresponding layout coefficient is low, it is identified as an area with new equipment demand. Finally, a set of new inverter neighborhood equipment addition solutions and a set of new battery pack neighborhood equipment addition solutions are generated respectively to determine the specific areas and layout solutions where new alarm equipment needs to be added.

[0061] Through the above steps, the performance degradation areas of photovoltaic modules, inverters and battery packs are accurately matched with the current remote alarm equipment layout, and high attenuation areas with weak monitoring are identified. The final generated set of new solutions for photovoltaic module neighborhood equipment, inverter neighborhood equipment and battery pack neighborhood equipment provide a scientific basis for the dynamic optimization of remote alarm equipment, ensuring that photovoltaic energy storage power stations can achieve real-time and accurate monitoring of key areas, effectively improving the system's abnormal detection capabilities and stability.

[0062] The remote alarm module 60 is used to use the target device addition plan to add equipment to the remote alarm device deployment array, obtain an updated remote alarm device deployment array, and use the updated remote alarm device deployment array to remotely alarm the abnormal monitoring situation of the target photovoltaic energy storage power station.

[0063] Specifically, the main function of the remote alarm module 60 of the present application is to dynamically optimize the existing remote alarm device deployment array and deploy new equipment based on the target device addition plan, and finally form an updated remote alarm device deployment array to enhance the abnormal monitoring capability of the photovoltaic energy storage power station and realize accurate remote alarm.

[0064] First, the target device addition plan is received, which specifies the new location, quantity and layout strategy of alarm devices in high attenuation areas of photovoltaic modules, inverters and battery packs. According to the plan, the module optimizes and adjusts the current remote alarm device layout array and newly arranges the equipment.

[0065] In the process of new deployment, relying on the equipment deployment algorithm, combined with the attenuation concentration value and deployment coefficient of each area, the location of the new equipment is dynamically allocated to ensure the uniformity and effectiveness of the distribution of the new equipment. For example, in the neighborhood of photovoltaic modules, if the attenuation concentration value of a certain area is high and the current alarm equipment deployment coefficient is low, the new alarm equipment will be deployed in this area first to fill the monitoring gap.

[0066] After the new equipment is deployed, the module will generate and update the remote alarm equipment deployment array. This array not only includes the location information of the new equipment, but also re-optimizes the deployment of existing alarm equipment to form a more efficient monitoring network. Through dynamic adjustment and equipment addition, the alarm equipment can be more scientifically laid out in space, effectively covering the performance degradation areas of photovoltaic modules, inverters and battery packs, and solving the problems of unbalanced monitoring and blind spots.

[0067] Based on the updated alarm device array, real-time monitoring and remote alarm are carried out for abnormal monitoring of photovoltaic energy storage power stations. When abnormalities are detected in photovoltaic modules, inverters or battery packs, such as power output decline, voltage fluctuations, temperature abnormalities, etc., the system will immediately trigger the alarm mechanism and remotely transmit the fault information to the operation and maintenance center, providing accurate fault location and diagnosis basis for operation and maintenance personnel.

[0068] In summary, the embodiments of the present application have at least the following technical effects: This application obtains the layout information and historical abnormal data of photovoltaic modules, inverters and battery packs, and identifies high decay areas based on performance decay distribution neighborhood analysis and attenuation concentration value analysis; combined with the existing alarm equipment layout array, the equipment layout plan is dynamically optimized through new plan identification, and the new layout and array update are completed, so as to realize accurate monitoring and real-time remote alarm of high-risk areas, and improve the accuracy of anomaly detection and fault response efficiency.

[0069] It achieves the technical effect of real-time, accurate abnormal monitoring and rapid alarm, improving the system's fault response capability and monitoring resource utilization efficiency.

[0070] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0072] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A remote alarm device for abnormal monitoring of photovoltaic energy storage power stations, characterized in that: The device comprises: An array information acquisition module is used to acquire the photovoltaic module layout array, inverter layout array and battery group layout array of the target photovoltaic energy storage power station; An abnormal monitoring data extraction module is used to extract the photovoltaic component historical abnormal monitoring data set, the inverter historical abnormal monitoring data set and the battery group historical abnormal monitoring data set of the photovoltaic component layout array, the inverter layout array and the battery group layout array within a preset historical window respectively; A performance attenuation analysis module is used to perform equipment performance attenuation distribution neighborhood analysis on the photovoltaic component layout array, the inverter layout array and the battery group layout array based on the photovoltaic component historical abnormality monitoring data set, the inverter historical abnormality monitoring data set and the battery group historical abnormality monitoring data set, and obtain the photovoltaic component performance attenuation distribution neighborhood set, the inverter performance attenuation distribution neighborhood set and the battery group performance attenuation distribution neighborhood set; A neighborhood attenuation concentration analysis module is used to perform neighborhood attenuation concentration analysis based on the neighborhood set of photovoltaic component performance attenuation distribution, the neighborhood set of inverter performance attenuation distribution, and the neighborhood set of battery pack performance attenuation distribution, to obtain a neighborhood attenuation concentration value set of photovoltaic components, a neighborhood attenuation concentration value set of inverter performance, and a neighborhood attenuation concentration value set of battery pack performance; A new scheme identification module is used to obtain the remote alarm device layout array of the target photovoltaic energy storage power station, identify the new equipment scheme for the remote alarm device layout array based on the neighborhood attenuation concentration value set of the photovoltaic component neighborhood attenuation concentration value set, the inverter performance neighborhood attenuation concentration value set, and the battery pack performance neighborhood attenuation concentration value set, and obtain the target equipment new scheme; The remote alarm module is used to use the target device addition plan to add equipment to the remote alarm device deployment array, obtain an updated remote alarm device deployment array, and use the updated remote alarm device deployment array to remotely alarm the abnormal monitoring situation of the target photovoltaic energy storage power station.

2. The remote alarm device for abnormal monitoring of a photovoltaic energy storage power station according to claim 1, characterized in that: The performance degradation analysis module is also used for: Based on the photovoltaic component historical abnormality monitoring data set, abnormal frequency statistics of photovoltaic components in the photovoltaic component layout array are performed to obtain a photovoltaic component abnormality frequency distribution array; Randomly extracting Q photovoltaic component abnormal frequencies from the photovoltaic component abnormal frequency distribution array, where Q is an integer greater than or equal to 1; Based on the abnormal frequencies of the Q photovoltaic modules, and in combination with the abnormal frequency distribution array of the photovoltaic modules, a neighborhood analysis of equipment performance attenuation distribution is performed on the photovoltaic module layout array to obtain a neighborhood set of the photovoltaic module performance attenuation distribution; Based on the inverter historical abnormality monitoring data set and the battery pack historical abnormality monitoring data set, the inverter layout array and the battery pack layout array are subjected to equipment performance attenuation distribution neighborhood analysis to obtain the inverter performance attenuation distribution neighborhood set and the battery pack performance attenuation distribution neighborhood set.

3. The remote alarm device for abnormal monitoring of a photovoltaic energy storage power station according to claim 2, characterized in that: The performance degradation analysis module is also used for: Performing neighborhood center verification on the Q photovoltaic module abnormal frequencies, and if the verification passes, using the Q photovoltaic module abnormal frequencies as Q initial neighborhood centers; Respectively calculating the difference between the abnormal frequency of each photovoltaic module in the abnormal frequency distribution array of the photovoltaic module and the Q data of the Q initial neighborhood centers, adding each photovoltaic module abnormal frequency to the neighborhood of the initial neighborhood center corresponding to the maximum value of the Q data similarities, and obtaining Q initial neighborhoods; According to the position of each photovoltaic module in the photovoltaic module layout array, the Q initial neighborhoods and the Q initial neighborhood centers are iteratively updated with the goal of minimizing the cost function until a preset update stop condition is met, thereby obtaining Q target neighborhood centers and Q target neighborhoods; An area constituted by photovoltaic components corresponding to a plurality of abnormal frequencies of photovoltaic components in each of the Q target neighborhoods is taken as a neighborhood of photovoltaic component performance attenuation distribution, to obtain a neighborhood set of photovoltaic component performance attenuation distribution.

4. The remote alarm device for abnormal monitoring of a photovoltaic energy storage power station according to claim 3, characterized in that: The performance degradation analysis module is also used for: Taking into account the data difference and the distribution location of photovoltaic modules, a cost function is constructed, where the cost function is: ； in, For the value of is the abnormal frequency of the jth PV module in the i-th initial neighborhood, is the distribution position of the PV module corresponding to the abnormal frequency of the jth PV module in the i-th initial neighborhood, is the abnormal frequency of the PV modules at the center of the i-th initial neighborhood, is the distribution position of the photovoltaic module corresponding to the i-th initial neighborhood center, To balance the weights of abnormal frequency and distribution location, is the total number of abnormal frequencies of PV panels contained in the i-th initial neighborhood.

5. The remote alarm device for abnormal monitoring of a photovoltaic energy storage power station according to claim 4, characterized in that: The preset update stop condition is that the difference between the cost values ​​of the two cost functions of two adjacent iterations is less than or equal to a preset cost value difference and / or the number of iterations meets the preset number of iterations.

6. The remote alarm device for abnormal monitoring of a photovoltaic energy storage power station according to claim 1, characterized in that: The neighborhood attenuation centralized analysis module is also used for: Traversing the photovoltaic module performance attenuation distribution neighborhood set, the inverter performance attenuation distribution neighborhood set, and the battery pack performance attenuation distribution neighborhood set to clean edge data, and obtaining the cleaned photovoltaic module performance attenuation distribution neighborhood set, the cleaned inverter performance attenuation distribution neighborhood set, and the cleaned battery pack performance attenuation distribution neighborhood set after removing edge values ​​with small distribution amounts; The neighborhood set of performance attenuation distribution of cleaning photovoltaic components, the neighborhood set of performance attenuation distribution of cleaning inverters, and the neighborhood set of performance attenuation distribution of cleaning battery packs are averaged to obtain the neighborhood attenuation concentrated value set of photovoltaic components, the neighborhood attenuation concentrated value set of inverter performance, and the neighborhood attenuation concentrated value set of battery pack performance.

7. The remote alarm device for abnormal monitoring of a photovoltaic energy storage power station according to claim 1, characterized in that: The newly added solution identification module is also used for: Based on the photovoltaic module performance attenuation distribution neighborhood set, the inverter performance attenuation distribution neighborhood set and the battery pack performance attenuation distribution neighborhood set, the remote alarm device layout array is neighborhood matched to obtain the photovoltaic module remote alarm device layout neighborhood set, the inverter remote alarm device layout neighborhood set and the battery pack remote alarm device layout neighborhood set; Counting the distribution quantity and distribution uniformity of the photovoltaic module remote alarm device layout neighborhood set, the inverter remote alarm device layout neighborhood set, and the battery group remote alarm device layout neighborhood set, and after weighted analysis, obtaining the photovoltaic module remote alarm device neighborhood layout coefficient set, the inverter remote alarm device neighborhood layout coefficient set, and the battery group remote alarm device neighborhood layout coefficient set; According to the neighborhood attenuation concentrated value sizes of the photovoltaic component neighborhood attenuation concentrated value set, the inverter performance neighborhood attenuation concentrated value set, and the battery group performance neighborhood attenuation concentrated value set, matching analysis is performed on the photovoltaic component remote alarm device neighborhood layout coefficient set, the inverter remote alarm device neighborhood layout coefficient set, and the battery group remote alarm device neighborhood layout coefficient set to obtain a photovoltaic component neighborhood device new addition solution set, an inverter neighborhood device new addition solution set, and a battery group neighborhood device new addition solution set; The photovoltaic module neighborhood device new addition solution set, the inverter neighborhood device new addition solution set and the battery pack neighborhood device new addition solution set are summarized to obtain the target device new addition solution.

8. The remote alarm device for abnormal monitoring of a photovoltaic energy storage power station according to claim 7, characterized in that: The newly added solution identification module is also used for: Pre-constructing a matching analyzer, using the matching analyzer to perform a one-to-one mapping matching analysis on the photovoltaic component neighborhood attenuation concentration value set and the photovoltaic component remote alarm device neighborhood layout coefficient set, respectively, to obtain a set of new photovoltaic component neighborhood device addition solutions; The matching analyzer is used to perform matching analysis on the inverter performance neighborhood attenuation concentrated value set and the battery pack performance neighborhood attenuation concentrated value set, and the corresponding inverter remote alarm device neighborhood layout coefficient set and the battery pack remote alarm device neighborhood layout coefficient set, to obtain the inverter neighborhood device new addition plan set and the battery pack neighborhood device new addition plan set.

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