Remote monitoring system of energy storage power station based on Internet of Things

Abstract

The invention relates to the field of power station monitoring, in particular to a remote monitoring system for an energy storage power station based on the Internet of Things, and the system comprises a monitoring execution module which is used for determining abnormal monitoring parameters; the abnormity evaluation module is used for setting a monitoring regulation and control scale and determining whether abnormity traceability analysis is carried out or not; the traceability analysis module is used for determining whether to adopt a collaborative traceability mode or a local traceability mode to carry out abnormal traceability analysis so as to determine whether to carry out adjustment aiming at the monitoring regulation and control scale; the collaborative traceability module is used for determining to adjust the monitoring regulation and control scale based on the demand interference parameter or the environment interference parameter according to the abnormal association difference index and the reference abnormal duration index; the local traceability module is used for determining whether to adjust the monitoring regulation and control scale of each abnormal monitoring parameter according to the abnormal continuous index; and the execution evaluation module is used for determining whether to execute optimization analysis or not. The monitoring quality and the resource utilization efficiency of the energy storage power station are both considered.

Description

Technical Field

[0001] This invention relates to the field of power plant monitoring, and more particularly to a remote monitoring system for an energy storage power plant based on the Internet of Things. Background Technology

[0002] To meet the stable demand for power supply, real-time monitoring and analysis of various relevant data from energy storage power stations are necessary to enable real-time and targeted adjustments to the station's operation. Energy storage power stations designed for renewable energy generation can effectively mitigate the fluctuations and instabilities in renewable energy output; therefore, remote monitoring of relevant data from energy storage power stations is crucial for renewable energy power supply. However, the monitoring process of energy storage power stations is affected by various factors, leading to fluctuations in the required accuracy of the acquired monitoring data. Specific analysis of anomalies is needed to assess whether adjustments to the monitoring scheme are necessary. Therefore, how to make targeted adjustments to the monitoring process of energy storage power stations based on the actual monitoring results, balancing resource utilization efficiency and monitoring quality, is a problem that urgently needs to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN116345585A discloses a smart control strategy method integrating a new energy power plant with energy storage, which optimizes the active and reactive resources of the new energy power plant, induction filter booster station, and energy storage station to achieve safer, more economical, and more efficient grid connection of new energy. As a core control strategy method applicable to plant-side monitoring systems, it can realize data uploading and command issuance between subsystems such as the energy storage energy management system, booster station monitoring system, new energy power generation monitoring system, and induction filter monitoring system, integrating application functions that were originally belonging to multiple isolated systems. However, the above solution has the following drawbacks: it fails to make targeted optimizations to the monitoring process of the energy storage power plant in real time based on the actual monitoring data, resulting in the inability to guarantee both the quality of the monitoring results and the resource utilization efficiency of the actual monitoring process. Summary of the Invention

[0004] To address this issue, the present invention provides a remote monitoring system for energy storage power stations based on the Internet of Things (IoT), which overcomes the problem in existing technologies that fail to combine actual monitoring data with real-time targeted optimization of the monitoring process of energy storage power stations, resulting in the inability to effectively balance the quality of monitoring results and the resource utilization efficiency of the actual monitoring process.

[0005] To achieve the above objectives, the present invention provides a remote monitoring system for an energy storage power station based on the Internet of Things, comprising: The monitoring execution module includes several execution edge nodes, which are used to perform the analysis and transmission tasks of various monitoring and analysis parameters of the target monitoring power station, and periodically detect the abnormal index of various monitoring and analysis parameters to identify abnormal monitoring parameters. An anomaly assessment module, which is connected to the monitoring execution module, is used to set the monitoring and control scale of each anomaly monitoring parameter based on the anomaly index and the linkage anomaly index, and to determine whether to conduct anomaly source tracing analysis for the target monitoring power station based on the reference anomaly index and the anomaly coverage ratio index. The source tracing analysis module, which is connected to the anomaly assessment module, is used to determine the anomaly source tracing status of the target monitoring power station based on the anomaly coverage ratio index and the anomaly coverage correlation index, and to determine whether to use a collaborative source tracing method or a local source tracing method for anomaly source tracing analysis based on the anomaly source tracing status, so as to determine whether to adjust the monitoring and control scale of each anomaly monitoring parameter. The collaborative tracing module, which is connected to the tracing analysis module, is used to determine the demand interference analysis or environmental interference analysis for the target monitoring power station based on the anomaly correlation difference index and the reference anomaly persistence index, so as to adjust the monitoring and control scale of each anomaly monitoring parameter based on the demand interference parameter or the environmental interference parameter. A local tracing module, which is connected to the tracing analysis module, is used to determine whether to adjust the monitoring and control scale for each abnormal monitoring parameter based on the abnormality continuity index. An execution evaluation module, which is connected to the anomaly evaluation module, the collaborative tracing module, and the local tracing module, is used to determine whether to perform execution optimization analysis for each execution edge node based on the node execution load coefficient.

[0006] Furthermore, the abnormality index of each monitoring and analysis parameter is periodically detected, wherein the abnormal monitoring parameter is the monitoring and analysis parameter whose abnormality index is greater than the preset abnormality index. The monitoring and control scale of any of the abnormal monitoring parameters is positively correlated with the abnormality index and linkage abnormality index of the corresponding abnormal monitoring parameters. The abnormality index of any of the monitoring and analysis parameters is determined based on the standard difference index determined for each monitoring and analysis parameter.

[0007] Furthermore, if the reference anomaly index is greater than the preset reference anomaly index or the anomaly coverage ratio index is greater than the preset anomaly coverage ratio index, then it is determined that anomaly tracing analysis should be performed on the target monitored power station. The reference anomaly index is determined based on the anomaly index of each anomaly monitoring parameter, and the anomaly coverage ratio index is determined based on the power plant monitoring equipment corresponding to each anomaly monitoring parameter.

[0008] Furthermore, the anomaly tracing status includes a first type of anomaly tracing status and a second type of anomaly tracing status, wherein, The target monitoring power station in the first type of abnormal tracing state is the target monitoring power station whose abnormal coverage ratio index is greater than the preset abnormal coverage ratio index or whose abnormal coverage correlation index is greater than the preset abnormal correlation index. The target monitoring power station in the second type of anomaly tracing state is the target monitoring power station whose anomaly coverage ratio index is less than or equal to the preset anomaly coverage ratio index and whose anomaly coverage correlation index is less than or equal to the preset anomaly coverage correlation index.

[0009] Furthermore, if the target monitoring power station is in a state of abnormality, a collaborative tracing approach is used to conduct abnormality tracing analysis on the target monitoring power station.

[0010] Furthermore, if the abnormal correlation difference index is less than or equal to the preset abnormal correlation difference index or the reference abnormality persistence index is less than or equal to the preset reference abnormality persistence index, demand interference analysis is performed on the target monitored power station. The monitoring and control scale of each abnormal monitoring parameter is increased and adjusted according to the demand intervention index. The increase in the monitoring and control scale is positively correlated with the demand intervention index.

[0011] Furthermore, the setting method of the demand interference index is determined based on the abnormal coverage ratio index; If the abnormal coverage ratio index is greater than the preset abnormal coverage ratio index, the demand intervention index is determined based on the abnormal coverage correlation index and the peak shaving deviation index. If the abnormal coverage ratio index is less than or equal to the preset abnormal coverage ratio index, the demand interference index is determined based on the equipment reference abnormal index.

[0012] Furthermore, if the anomaly correlation difference index is greater than the preset anomaly correlation difference index and the reference anomaly persistence index is greater than the preset reference anomaly persistence index, an environmental interference analysis is performed on the target monitored power station. The environmental interference index is determined based on the reference anomaly difference index and the stage interference difference index; The monitoring and control scale of each abnormal monitoring parameter is increased according to the environmental interference index, and the increase in the monitoring and control scale is positively correlated with the environmental interference index.

[0013] Furthermore, if the target monitoring power station is in a Class II anomaly tracing state, a local tracing approach is used to perform anomaly tracing analysis on the target monitoring power station, wherein... If the abnormal continuity index of an abnormal monitoring parameter is less than or equal to the preset abnormal continuity index, the monitoring and control scale of the abnormal monitoring parameter will be increased according to the abnormal continuity index. The increase in the monitoring and control scale is negatively correlated with the abnormality continuity index.

[0014] Furthermore, given that the tracing is complete, if any execution edge node has an execution load coefficient greater than the preset node execution load coefficient, then execution optimization analysis is performed on that execution edge node. The node linkage ratio index determines whether to perform secondary adjustments to the monitoring and control scale of each abnormal monitoring parameter corresponding to the execution edge node. If the node linkage ratio index of an abnormal monitoring parameter is greater than the preset node linkage ratio index, the monitoring and control scale of the abnormal monitoring parameter will be reduced based on the node execution load coefficient and the node linkage ratio index. The condition for completing the source tracing is that the target monitored power station completes the anomaly source tracing analysis.

[0015] Compared with the prior art, the beneficial effects of the present invention are that the technical solution of the present invention determines whether the monitoring accuracy requirement needs to be adjusted based on the actual monitoring situation of each monitoring and analysis parameter, and determines whether to conduct source tracing analysis for the current abnormal situation by combining the overall monitoring situation of the target monitoring power station and the equipment situation corresponding to the abnormal monitoring parameters. In this way, the monitoring and control scale of each abnormal monitoring parameter is adjusted in a targeted manner, so that the set monitoring scheme is adapted to the target monitoring power station. The present invention improves the monitoring quality of the target monitoring power station.

[0016] Furthermore, in this invention, when performing anomaly tracing analysis on a target monitoring power station, the anomaly tracing status of the target monitoring power station is determined by the anomaly coverage ratio index and the anomaly coverage correlation index. Based on the anomaly tracing status, it is determined whether to use a collaborative tracing method or a local tracing method for anomaly tracing analysis. This makes the adjustment decisions for the monitoring and control scale of each anomaly monitoring parameter more in line with the actual situation, thereby improving the data processing efficiency in the monitoring process of the target monitoring power station.

[0017] Furthermore, in this invention, for a target monitoring power station in a state of anomaly tracing, demand interference analysis or environmental interference analysis is determined based on the anomaly correlation difference index and the reference anomaly persistence index. The anomaly correlation difference index and the reference anomaly persistence index characterize the temporal differences and persistence of anomalies among different anomaly monitoring parameters. This allows for targeted settings in the subsequent analysis process of monitoring accuracy requirements for anomaly monitoring parameters. For cases with small temporal differences and mild persistence, the degree of interference from the power consumption process is analyzed. For cases with large temporal differences or severe persistence, a certain trend is observed, and the sensitivity to environmental changes is analyzed. This ensures that the adjustment decisions for the monitoring and control scale of each anomaly monitoring parameter are more in line with the actual situation, guaranteeing the adaptability of the set monitoring and control scale to the actual monitoring process.

[0018] Furthermore, in this invention, for a target monitoring power station in a state of abnormality tracing, since the abnormality of the current monitoring and analysis parameters of the target monitoring power station involves relatively few power station monitoring devices and the correlation between the power station monitoring devices involved is small, based on the continuity of the abnormality of each abnormal monitoring parameter, it is determined whether the current monitoring scheme can meet the monitoring detail requirements of the data characteristics, thereby making targeted adjustments to the monitoring process, and further ensuring the adaptability of the set monitoring and control scale to the actual monitoring process.

[0019] Furthermore, based on the node execution load coefficient, it is determined whether to perform execution optimization analysis for each execution edge node. The node execution load coefficient characterizes the data processing task burden of each execution edge node in the current stage. For execution edge nodes with a heavy data processing task burden in the current stage, the monitoring requirements of their corresponding abnormal monitoring parameters are further analyzed to avoid unnecessary data processing burden. Based on whether each execution edge node has many parameters that provide correlation analysis basis for its abnormal monitoring parameters, the monitoring and control scale of each abnormal monitoring parameter is appropriately reduced and adjusted so as to reduce the data processing burden of the execution edge nodes without affecting the abnormal analysis process of the target monitored power station. Attached Figure Description

[0020] Figure 1 This is a module connection diagram of the remote monitoring system for an energy storage power station based on the Internet of Things according to the present invention; Figure 2 This is a flowchart illustrating the process of determining the anomaly tracing status of the target monitored power station based on the anomaly coverage ratio index and the anomaly coverage correlation index according to the present invention. Figure 3 This is a flowchart illustrating the present invention's method for determining whether to employ collaborative or local tracing methods for anomaly tracing analysis based on the current anomaly tracing status. Figure 4 This is a flowchart illustrating the process of determining whether to perform execution optimization analysis on each execution edge node based on the node execution load coefficient. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0024] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] Please see Figures 1 to 4 As shown, the present invention provides a remote monitoring system for an energy storage power station based on the Internet of Things, comprising: The monitoring execution module includes several execution edge nodes, which are used to perform the analysis and transmission tasks of various monitoring and analysis parameters of the target monitoring power station, and periodically detect the abnormal index of various monitoring and analysis parameters to identify abnormal monitoring parameters. An anomaly assessment module, which is connected to the monitoring execution module, is used to set the monitoring and control scale of each anomaly monitoring parameter based on the anomaly index and the linkage anomaly index, and to determine whether to conduct anomaly source tracing analysis for the target monitoring power station based on the reference anomaly index and the anomaly coverage ratio index. The source tracing analysis module, which is connected to the anomaly assessment module, is used to determine the anomaly source tracing status of the target monitoring power station based on the anomaly coverage ratio index and the anomaly coverage correlation index, and to determine whether to use a collaborative source tracing method or a local source tracing method for anomaly source tracing analysis based on the anomaly source tracing status, so as to determine whether to adjust the monitoring and control scale of each anomaly monitoring parameter. The collaborative tracing module, which is connected to the tracing analysis module, is used to determine the demand interference analysis or environmental interference analysis for the target monitoring power station based on the anomaly correlation difference index and the reference anomaly persistence index, so as to adjust the monitoring and control scale of each anomaly monitoring parameter based on the demand interference parameter or the environmental interference parameter. A local tracing module, which is connected to the tracing analysis module, is used to determine whether to adjust the monitoring and control scale for each abnormal monitoring parameter based on the abnormality continuity index. An execution evaluation module, which is connected to the anomaly evaluation module, the collaborative tracing module, and the local tracing module, is used to determine whether to perform execution optimization analysis for each execution edge node based on the node execution load coefficient.

[0026] This invention is used for real-time optimization of the monitoring process of energy storage power stations for new energy power generation. It avoids the failure to ensure the resource utilization efficiency of data processing while adapting the monitoring scheme of the energy storage power station to the fluctuations caused by the new energy power generation and electricity demand. The energy storage power station for which the monitoring process is optimized in real time is called the target monitoring power station. The monitoring process of the target monitoring power station needs to monitor various monitoring and analysis parameters in real time. In this invention, the monitoring process of the target monitoring power station is set with several edge nodes. The edge node that performs the data processing task of any monitoring and analysis parameter is called the execution edge node. The data processing task includes, but is not limited to, the preprocessing, analysis and transmission tasks of the values ​​obtained for the corresponding monitoring and analysis parameters. Each execution edge node is assigned several data processing tasks for monitoring and analysis parameters. Each execution edge node must complete the preprocessing and analysis of the corresponding assigned monitoring and analysis parameters and transmit the preprocessed data and analysis results to the cloud center for monitoring and analyzing the target monitoring power station. The equipment within the target monitoring power station related to the monitoring and analysis parameters is referred to as the power station monitoring equipment. The monitoring and analysis parameters to be monitored in this invention include, but are not limited to: battery cell voltage, total current, SOC (remaining capacity), SOH (health status), single cluster temperature difference (≥5℃ triggers early warning), PCS output power, conversion efficiency, switching status, harmonic distortion rate, charge-discharge cycle count, energy conversion efficiency, system response time, real-time output of photovoltaic / wind power, grid connection point voltage, grid connection point frequency, real-time power load, load forecast curve, energy storage warehouse temperature and humidity, and light intensity. The preprocessing process requires cleaning and correcting the data obtained from each monitoring and analysis parameter. The analysis process requires detecting the abnormal index of the monitoring and analysis parameters and analyzing the correlation between each monitoring and analysis parameter. The transmission task requires transmitting a portion of the data obtained from each monitoring and analysis parameter that needs to be uploaded to the cloud center for operation and maintenance control of the target monitoring power station. How to monitor each monitoring and analysis parameter and how the cloud center performs operation and maintenance control of the target monitoring power station based on the obtained monitoring and analysis parameters are contents that are already known to those skilled in the art and will not be elaborated here. This invention utilizes several monitoring execution records. Each monitoring execution record records at least one instance of anomaly index, standard difference index, reference anomaly index, anomaly coverage ratio index, anomaly coverage correlation index, anomaly correlation difference index, reference anomaly persistence index, anomaly continuity index, node execution load coefficient, and node linkage ratio index for the optimized monitoring process of the target monitoring power station's operation monitoring scheme. Each monitoring execution record also has a corresponding qualified mark, which records whether the monitoring quality and analysis efficiency of the target monitoring power station meet the user's requirements.

[0027] Specifically, the abnormal index of each monitoring and analysis parameter is periodically detected, and the abnormal monitoring parameter is the monitoring and analysis parameter whose abnormal index is greater than the preset abnormal index. The monitoring and control scale of any of the abnormal monitoring parameters is positively correlated with the abnormality index and linkage abnormality index of the corresponding abnormal monitoring parameters. The abnormality index of any of the monitoring and analysis parameters is determined based on the standard difference index determined for each monitoring and analysis parameter.

[0028] In this invention, an anomaly assessment cycle is applied. The duration of the anomaly assessment cycle can be determined by the user. The higher the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station, the shorter the duration of the anomaly assessment cycle. An anomaly assessment cycle of 30 seconds is provided. At the end of each anomaly assessment cycle, the anomaly index of each monitoring and analysis parameter is obtained for detection. If the current time is the end time of an anomaly assessment period, for a single monitoring and analysis parameter, the anomaly index = the number of abnormal monitoring times for that monitoring and analysis parameter within the anomaly assessment period / the number of parameter monitoring times for that monitoring and analysis parameter within the anomaly assessment period. For a single parameter monitoring time, if the standard difference index determined by that parameter monitoring time for that monitoring and analysis parameter is greater than a preset standard difference index, then that parameter monitoring time is recorded as an abnormal monitoring time for that monitoring and analysis parameter. The standard difference index... a1 is the absolute value of the difference between the value obtained for this monitoring and analysis parameter at the monitoring time and the maximum value of the standard value range of this monitoring and analysis parameter; a2 is the absolute value of the difference between the value obtained for this monitoring and analysis parameter at the monitoring time and the minimum value of the standard value range of this monitoring and analysis parameter; b is the average of the maximum and minimum values ​​of the standard value range of this monitoring and analysis parameter. Each monitoring and analysis parameter has a corresponding parameter acquisition frequency and a corresponding standard value range. The parameter acquisition frequency is the number of times the value of the corresponding monitoring and analysis parameter is acquired per unit time. The maximum value of the standard value range is the maximum value allowed for the corresponding monitoring and analysis parameter during the operation of the target monitoring power station. The maximum value of the standard value range is the minimum value allowed for the corresponding monitoring and analysis parameter during the operation of the target monitoring power station. How to target each monitoring... The parameter acquisition frequency and corresponding standard value range of the control analysis parameters are content already known to those skilled in the art and will not be elaborated here. For a single abnormal monitoring parameter, the monitoring control scale and control reference index set for the abnormal monitoring parameter are positively correlated. The control reference index is the sum of the abnormal index and the linkage abnormal index. The linkage abnormal index is the average of the abnormal indices of each linkage abnormal parameter of the abnormal monitoring parameter. The linkage abnormal parameter is the abnormal monitoring parameter whose abnormal linkage index is less than the preset abnormal linkage index. The abnormal linkage index between any two abnormal monitoring parameters is the interval between the first abnormal monitoring time of the two abnormal monitoring parameters within the abnormal assessment period. The monitoring control scale = (parameter acquisition frequency after monitoring control is completed - parameter acquisition frequency before monitoring control is completed) / parameter acquisition frequency before monitoring control is completed. The values ​​of the preset anomaly index, preset standard difference index, and preset anomaly linkage index can be determined by the user based on the actual working scenario. For example, the user can set them based on the monitoring execution records. The higher the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station, the smaller the value of the preset anomaly index, the smaller the value of the preset standard difference index, and the smaller the value of the preset anomaly linkage index. One method for determining the preset anomaly index is to record the minimum value of the anomaly index of the abnormal monitoring parameters in the monitoring execution records that meet the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station as the preset anomaly index. One possible value for the preset anomaly index is 0.05. Another method for determining the preset standard difference index is to record the minimum value of the standard difference index determined for the corresponding monitoring analysis parameters at each anomaly monitoring time in the monitoring execution records that meet the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station as the preset standard difference index. Finally, one possible value for the preset anomaly linkage index is 3 seconds.

[0029] Specifically, if the reference anomaly index is greater than the preset reference anomaly index or the anomaly coverage ratio index is greater than the preset anomaly coverage ratio index, then it is determined that anomaly tracing analysis should be performed on the target monitored power station. The reference anomaly index is determined based on the anomaly index of each anomaly monitoring parameter, and the anomaly coverage ratio index is determined based on the power plant monitoring equipment corresponding to each anomaly monitoring parameter.

[0030] If the current time is the end time of an anomaly assessment cycle, after the anomaly monitoring parameters are determined, the reference anomaly index and the anomaly coverage ratio index are detected to determine whether to conduct anomaly source tracing analysis for the target monitoring power station. The reference anomaly index is the average of the anomaly indices of each determined anomaly monitoring parameter. The anomaly coverage ratio index = the number of anomaly monitoring devices in the target monitoring power station / the number of power station monitoring devices in the target monitoring power station. The anomaly monitoring devices are the power station monitoring devices corresponding to each currently determined anomaly monitoring parameter. The values ​​of the preset reference anomaly index and the preset anomaly coverage ratio index can be determined by the user based on the actual working scenario. For example, the user can set them based on the monitoring execution records. The higher the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station, the smaller the value of the preset reference anomaly index and the smaller the value of the preset anomaly coverage ratio index. A method for determining the value of the preset reference anomaly index is provided, in which the monitoring execution records for anomaly tracing analysis of the target monitoring power station are recorded as tracing reference records, and the average value of the reference anomaly index in the tracing reference records that meet the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset reference anomaly index. A method for determining the value of the preset anomaly coverage ratio index is provided, in which the average value of the anomaly coverage ratio index in the tracing reference records that meet the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset anomaly coverage ratio index.

[0031] Specifically, the anomaly tracing status includes a first type of anomaly tracing status and a second type of anomaly tracing status, wherein, The target monitoring power station in the first type of abnormal tracing state is the target monitoring power station whose abnormal coverage ratio index is greater than the preset abnormal coverage ratio index or whose abnormal coverage correlation index is greater than the preset abnormal correlation index. The target monitoring power station in the second type of anomaly tracing state is the target monitoring power station whose anomaly coverage ratio index is less than or equal to the preset anomaly coverage ratio index and whose anomaly coverage correlation index is less than or equal to the preset anomaly coverage correlation index.

[0032] In this process, if anomaly tracing analysis is performed on a target monitoring power station, the anomaly tracing status of the target monitoring power station is determined based on the anomaly coverage ratio index and the anomaly coverage correlation index. The anomaly coverage correlation index is calculated as the number of associated anomaly devices present in the target monitoring power station / the number of anomaly monitoring devices present in the target monitoring power station. For a single anomaly monitoring device, if the anomaly monitoring device has an electrical connection with any other anomaly monitoring device, then the anomaly monitoring device is recorded as an associated anomaly device. The value of the preset anomaly coverage correlation index can be determined by the user based on the actual working scenario. For example, the user can set it based on the monitoring execution record. A method for determining the value of the preset anomaly coverage correlation index is provided, which is the maximum value of the anomaly coverage correlation index of the target monitoring power station in the second anomaly tracing status in the tracing reference record that meets the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station.

[0033] Specifically, if the target monitoring power station is in a state of abnormality, a collaborative tracing approach is used to conduct abnormality tracing analysis on the target monitoring power station.

[0034] If the target monitoring power station is in the first type of abnormal tracing state, it indicates that the abnormal coverage ratio index is large or the abnormal coverage correlation index is large. This indicates that the abnormal situation of the current monitoring and analysis parameters of the target monitoring power station involves a large number of power station monitoring devices or the correlation between the power station monitoring devices is large. In this case, it is necessary to make a preliminary assessment of the cause of the fluctuation to determine whether there is a continuous or large-scale interference on the electricity demand side.

[0035] Specifically, if the abnormal correlation difference index is less than or equal to the preset abnormal correlation difference index or the reference abnormality persistence index is less than or equal to the preset reference abnormality persistence index, demand interference analysis is performed on the target monitored power station. The monitoring and control scale of each abnormal monitoring parameter is increased and adjusted according to the demand intervention index. The increase in the monitoring and control scale is positively correlated with the demand intervention index.

[0036] If the abnormal correlation difference index is less than or equal to the preset abnormal correlation difference index or the reference abnormality duration index is less than or equal to the preset reference abnormality duration index, it indicates that the differences between the current abnormal monitoring parameters at the time of the abnormality are small or the duration of the abnormality is short. This suggests that the abnormality is more likely caused by a linkage abnormality due to instantaneous changes in power demand or equipment malfunction. Therefore, the degree of control demand and the impact of control operations on the target monitored power station in the subsequent stage are analyzed to optimize the monitoring scheme in a timely manner, ensuring that the monitoring scheme for each monitoring and analysis parameter can meet the actual needs of the target energy storage power station in the subsequent monitoring process. The abnormal correlation difference index = the standard deviation between the abnormality index of each abnormal monitoring parameter and its linkage abnormality parameter / the standard deviation between each abnormal monitoring parameter and its linkage abnormality parameter. The average of the abnormal indices of the abnormal monitoring parameters and each linked abnormal parameter, the reference abnormal persistence index is the average of the abnormal persistence indices of each abnormal monitoring parameter. For a single abnormal monitoring parameter, the abnormal persistence index = the number of abnormal assessment periods corresponding to the abnormal monitoring parameter being determined as an abnormal monitoring parameter within the continuous assessment period / the number of abnormal assessment periods within the continuous assessment period. The end time of the continuous assessment period is the end time of the abnormal assessment period of the currently determined abnormal monitoring parameter. The duration of the continuous assessment period can be determined by the user according to the actual working scenario. The higher the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station, the longer the duration of the continuous assessment period. A value for the duration of the continuous assessment period is provided, which is 8 times the duration of the abnormal assessment period. The values ​​of the preset anomaly correlation difference index and the preset reference anomaly persistence index can be determined by the user according to the actual working scenario. For example, the user can set them according to the monitoring execution records. A method for determining the value of the preset anomaly correlation difference index is provided, in which the monitoring execution records for demand interference analysis of the target monitoring power station are recorded as interference reference records, and the minimum value of the anomaly correlation difference index in the interference reference records that meets the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset anomaly correlation difference index. A method for determining the value of the preset reference anomaly persistence index is provided, in which the minimum value of the reference anomaly persistence index in the interference reference records that meets the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset reference anomaly persistence index.

[0037] Specifically, the setting method of the demand interference index is determined based on the abnormal coverage ratio index; If the abnormal coverage ratio index is greater than the preset abnormal coverage ratio index, the demand intervention index is determined based on the abnormal coverage correlation index and the peak shaving deviation index. If the abnormal coverage ratio index is less than or equal to the preset abnormal coverage ratio index, the demand interference index is determined based on the equipment reference abnormal index.

[0038] If the abnormal coverage ratio index is greater than the preset abnormal coverage ratio index, it indicates that the current abnormal situation involves a large number of power station monitoring devices, indicating that the range of affected devices is large, and many parameters are affected by the instantaneous power grid regulation. By analyzing the regulation demand and the scope of impact, the degree of impact that each abnormal monitoring parameter may be affected is assessed. The first interference reference coefficient is determined based on the abnormal coverage correlation index and the peak shaving deviation index. The first interference reference coefficient is the sum of the abnormal coverage correlation index and the peak shaving deviation index. The peak shaving deviation index = |Rated output power of the target energy storage power station at the current moment - Actual grid output power of the target energy storage power station at the current moment| / Rated output power of the target energy storage power station at the current moment. The first interference reference coefficient is normalized. The demand interference index is positively correlated with the first interference reference coefficient after normalization. If the abnormal coverage ratio index is less than or equal to the preset abnormal coverage ratio index, it indicates that the current abnormal situation involves fewer power plant monitoring devices. By evaluating the overall abnormal situation of the power plant monitoring devices corresponding to each abnormal monitoring parameter, the degree of subsequent impact is assessed, thereby optimizing the monitoring scheme. For a single abnormal monitoring parameter, the equipment reference abnormal index is recorded as the second interference reference coefficient. The equipment reference abnormal index is the average of the abnormal indices of the abnormal monitoring parameters existing in the power plant monitoring devices corresponding to that abnormal monitoring parameter. The second interference reference coefficient is normalized, and the required interference index is positively correlated with the normalized second interference reference coefficient.

[0039] Specifically, if the anomaly correlation difference index is greater than the preset anomaly correlation difference index and the reference anomaly persistence index is greater than the preset reference anomaly persistence index, an environmental interference analysis is performed on the target monitored power station. The environmental interference index is determined based on the reference anomaly difference index and the stage interference difference index; The monitoring and control scale of each abnormal monitoring parameter is increased according to the environmental interference index, and the increase in the monitoring and control scale is positively correlated with the environmental interference index.

[0040] If the anomaly correlation difference index is greater than the preset anomaly correlation difference index and the reference anomaly persistence index is greater than the preset reference anomaly persistence index, it indicates that there are significant differences in the timing of anomalies among the currently existing abnormal monitoring parameters, and the duration of the anomalies is generally long. This suggests that the current anomalies are likely caused by independent changes in environmental conditions over a long period, requiring adjustments to the subsequent monitoring plan based on the degree of fluctuation caused by environmental impact. When conducting environmental interference analysis on the target monitoring power station, the environmental interference index of each abnormal monitoring parameter is detected. The environmental interference index characterizes the sensitivity of each abnormal monitoring parameter to environmental changes, thereby providing a basis for monitoring... The process is optimized so that, for a single abnormal monitoring parameter, the environmental interference index is the sum of the reference abnormality difference index and the stage interference difference index. The reference abnormality difference index is the standard deviation between the abnormality index of each abnormal environmental parameter and the abnormality index of each linked abnormal parameter / the average of the abnormality index of each abnormal environmental parameter and the abnormality index of each linked abnormal parameter. The stage interference difference index is the average of the standard deviation index of each abnormal environmental parameter at each abnormal monitoring time within the abnormal assessment period of the current abnormal monitoring parameter. The abnormal environmental parameter is the abnormal monitoring parameter whose corresponding category belongs to environmental parameters. The monitoring and analysis parameters whose corresponding category belongs to environmental parameters include, but are not limited to, temperature, light intensity, and wind speed.

[0041] Specifically, if the target monitoring power station is in a Class II anomaly tracing state, a local tracing method is used to conduct anomaly tracing analysis on the target monitoring power station. If the abnormal continuity index of an abnormal monitoring parameter is less than or equal to the preset abnormal continuity index, the monitoring and control scale of the abnormal monitoring parameter will be increased according to the abnormal continuity index. The increase in the monitoring and control scale is negatively correlated with the abnormality continuity index.

[0042] If the target monitoring power station is in the second-class anomaly tracing state, it indicates that the anomaly coverage ratio index and anomaly coverage correlation index of the target monitoring power station are both small. This indicates that the current monitoring and analysis parameters of the target monitoring power station involve few power station monitoring devices and the correlation between the power station monitoring devices is small. It also indicates that the current abnormal monitoring parameters do not have anomaly linkage. Based on the continuity of the abnormality of each abnormal monitoring parameter, it is determined whether the current monitoring scheme can meet the monitoring detail requirements of the data characteristics. For a single abnormal monitoring parameter, the abnormal continuity index is calculated as the average of the adjacent intervals of each combination of adjacent abnormal moments / the interval between adjacent monitoring moments of the abnormal monitoring parameter. Two adjacent abnormal monitoring moments within the abnormal assessment period of the abnormal monitoring parameter are recorded as an adjacent abnormal moment combination. For a single adjacent abnormal moment combination, the adjacent interval is the interval between two abnormal monitoring moments within that adjacent abnormal moment combination. The value of the preset abnormal continuity index can be determined by the user based on the actual working scenario. For example, the user can set it based on the monitoring execution records. A method for determining the value of the preset abnormal continuity index is provided, in which the monitoring execution records that increase the monitoring control scale of the abnormal monitoring parameter according to the abnormal continuity index are recorded as continuous reference records, and the average value of the abnormal continuity index in the continuous reference records that meets the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset abnormal continuity index.

[0043] Specifically, if, under the condition that source tracing is completed, there exists an execution load coefficient for any execution edge node that is greater than the preset execution load coefficient, then execution optimization analysis is performed on that execution edge node. The node linkage ratio index determines whether to perform secondary adjustments to the monitoring and control scale of each abnormal monitoring parameter corresponding to the execution edge node. If the node linkage ratio index of an abnormal monitoring parameter is greater than the preset node linkage ratio index, the monitoring and control scale of the abnormal monitoring parameter will be reduced based on the node execution load coefficient and the node linkage ratio index. The condition for completing the source tracing is that the target monitored power station completes the anomaly source tracing analysis.

[0044] Specifically, for a single execution edge node, the node execution load coefficient is the sum of the node anomaly ratio index and the node reference anomaly index of the execution edge node. The node anomaly ratio index is the number of categories of monitoring parameters identified as abnormal in the monitoring and analysis parameters corresponding to the execution edge node / the number of categories of monitoring and analysis parameters corresponding to the execution edge node. The node reference anomaly index is the average value of the monitoring and control scales of the monitoring and analysis parameters identified as abnormal in the monitoring and analysis parameters corresponding to the execution edge node. If the node execution load coefficient of the execution edge point is greater than the preset node execution load coefficient, it indicates that the data processing task borne by the execution edge node in the current stage is relatively heavy. Therefore, further analysis should be conducted on the monitoring requirements of the corresponding abnormal monitoring parameters to avoid unnecessary data processing burden. For any abnormal monitoring parameter corresponding to the execution edge node that requires performance optimization analysis, the node linkage ratio index = the number of associated abnormal parameters in the abnormal monitoring parameters corresponding to the execution edge node / the number of abnormal monitoring parameters corresponding to the execution edge node. If the node linkage ratio index of the abnormal monitoring parameter is greater than the preset node linkage ratio index, it indicates that the current execution edge node has a lot of parameters that provide a basis for correlation analysis for the abnormal monitoring parameter. Appropriately reducing the monitoring and control scale of the abnormal monitoring parameter can reduce the data processing burden of the execution edge node without affecting the abnormal analysis process of the target monitoring power station. The reduction value of the monitoring and control scale is positively correlated with the control reference index of the abnormal monitoring parameter. The control reference index is the sum of the node execution load coefficient and the node linkage ratio index of the abnormal monitoring parameter. The values ​​of the preset node execution load coefficient and the preset node linkage ratio index can be determined by the user according to the actual working scenario. For example, the user can set them according to the monitoring execution records. The higher the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station, the smaller the value of the preset node execution load coefficient. A method for determining the preset node execution load coefficient is provided, which is the minimum value of the node execution load coefficient of the execution edge node that performs execution optimization analysis in the monitoring execution records that meet the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station. A method for determining the preset node linkage ratio index is provided, which is the minimum value of the node linkage ratio index of the abnormal monitoring parameters that are adjusted for the monitoring control scale in the monitoring execution records that meet the user's requirements for the monitoring quality and analysis efficiency of the target monitoring power station.

[0045] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A remote monitoring system for an energy storage power station based on the Internet of Things, characterized in that, include: The monitoring execution module includes several execution edge nodes, which are used to perform the analysis and transmission tasks of various monitoring and analysis parameters of the target monitoring power station, and periodically determine abnormal monitoring parameters. An anomaly assessment module, which is connected to the monitoring execution module, is used to set the monitoring and control scale of each anomaly monitoring parameter based on the anomaly index and the linkage anomaly index, and to determine whether to perform anomaly source tracing analysis based on the reference anomaly index and the anomaly coverage ratio index. The source tracing analysis module, which is connected to the anomaly assessment module, is used to determine the anomaly source tracing status of the target monitoring power station based on the anomaly coverage ratio index and the anomaly coverage correlation index, so as to determine whether to use a collaborative source tracing method or a local source tracing method for anomaly source tracing analysis. The collaborative tracing module, which is connected to the tracing analysis module, is used to determine the monitoring and control scale of each anomaly monitoring parameter based on the demand interference parameter or the environmental interference parameter according to the anomaly correlation difference index and the reference anomaly persistence index. A local tracing module, which is connected to the tracing analysis module, is used to determine whether to adjust the monitoring and control scale for each abnormal monitoring parameter based on the abnormality continuity index. An execution evaluation module, which is connected to the anomaly evaluation module, the collaborative tracing module, and the local tracing module, is used to determine whether to perform execution optimization analysis based on the node execution load coefficient.

2. The remote monitoring system for an IoT-based energy storage power station according to claim 1, characterized in that, The abnormal index of each monitoring and analysis parameter is periodically detected, and the abnormal monitoring parameter is the monitoring and analysis parameter whose abnormal index is greater than the preset abnormal index. The monitoring and control scale of any of the abnormal monitoring parameters is positively correlated with the abnormality index and linkage abnormality index of the corresponding abnormal monitoring parameters. The abnormality index of any of the monitoring and analysis parameters is determined based on the standard difference index determined for each monitoring and analysis parameter.

3. The remote monitoring system for an IoT-based energy storage power station according to claim 1, characterized in that, If the reference anomaly index is greater than the preset reference anomaly index or the anomaly coverage ratio index is greater than the preset anomaly coverage ratio index, then it is determined that anomaly tracing analysis should be performed on the target monitored power station. The reference anomaly index is determined based on the anomaly index of each anomaly monitoring parameter, and the anomaly coverage ratio index is determined based on the power plant monitoring equipment corresponding to each anomaly monitoring parameter.

4. The remote monitoring system for an IoT-based energy storage power station according to claim 3, characterized in that, The anomaly tracing status includes a first-class anomaly tracing status and a second-class anomaly tracing status, wherein, The target monitoring power station in the first type of abnormal tracing state is the target monitoring power station whose abnormal coverage ratio index is greater than the preset abnormal coverage ratio index or whose abnormal coverage correlation index is greater than the preset abnormal correlation index. The target monitoring power station in the second type of anomaly tracing state is the target monitoring power station whose anomaly coverage ratio index is less than or equal to the preset anomaly coverage ratio index and whose anomaly coverage correlation index is less than or equal to the preset anomaly coverage correlation index.

5. The remote monitoring system for an IoT-based energy storage power station according to claim 4, characterized in that, If the target monitoring power station is in a Class I abnormal tracing state, then a collaborative tracing method is used to conduct abnormal tracing analysis on the target monitoring power station.

6. The remote monitoring system for an IoT-based energy storage power station according to claim 5, characterized in that, If the abnormal correlation difference index is less than or equal to the preset abnormal correlation difference index or the reference abnormality persistence index is less than or equal to the preset reference abnormality persistence index, demand interference analysis is performed on the target monitored power station. The monitoring and control scale of each abnormal monitoring parameter is increased and adjusted according to the demand intervention index. The increase in the monitoring and control scale is positively correlated with the demand intervention index.

7. The remote monitoring system for an IoT-based energy storage power station according to claim 6, characterized in that, The setting method of the demand intervention index is determined based on the abnormal coverage ratio index; If the abnormal coverage ratio index is greater than the preset abnormal coverage ratio index, the demand intervention index is determined based on the abnormal coverage correlation index and the peak shaving deviation index. If the abnormal coverage ratio index is less than or equal to the preset abnormal coverage ratio index, the demand interference index is determined based on the equipment reference abnormal index.

8. The remote monitoring system for an IoT-based energy storage power station according to claim 7, characterized in that, If the anomaly correlation difference index is greater than the preset anomaly correlation difference index and the reference anomaly persistence index is greater than the preset reference anomaly persistence index, an environmental interference analysis is performed on the target monitored power station. The environmental interference index is determined based on the reference anomaly difference index and the stage interference difference index; The monitoring and control scale of each abnormal monitoring parameter is increased according to the environmental interference index, and the increase in the monitoring and control scale is positively correlated with the environmental interference index.

9. The remote monitoring system for an IoT-based energy storage power station according to claim 4, characterized in that, If the target monitoring power station is in a Class II anomaly tracing state, then a local tracing method is used to perform anomaly tracing analysis on the target monitoring power station, wherein... If the abnormal continuity index of an abnormal monitoring parameter is less than or equal to the preset abnormal continuity index, the monitoring and control scale of the abnormal monitoring parameter will be increased according to the abnormal continuity index. The increase in the monitoring and control scale is negatively correlated with the abnormality continuity index.

10. The remote monitoring system for an IoT-based energy storage power station according to claim 1, characterized in that, If, under the condition that tracing is completed, there exists an execution edge node whose execution load coefficient is greater than the preset node execution load coefficient, then execution optimization analysis is performed on that execution edge node. The node linkage ratio index determines whether to perform secondary adjustments to the monitoring and control scale of each abnormal monitoring parameter corresponding to the execution edge node. If the node linkage ratio index of an abnormal monitoring parameter is greater than the preset node linkage ratio index, the monitoring and control scale of the abnormal monitoring parameter will be reduced based on the node execution load coefficient and the node linkage ratio index. The condition for completing the source tracing is that the target monitored power station completes the anomaly source tracing analysis.

Images