CN120879969A - Remote monitoring system of energy storage power station based on Internet of things - Google Patents
Remote monitoring system of energy storage power station based on Internet of thingsInfo
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Abstract
The invention relates to the field of power station monitoring, in particular to a remote monitoring system of an energy storage power station based on the Internet of things, which comprises a monitoring execution module, an abnormality evaluation module, a tracing analysis module, a collaborative tracing module and a local tracing module, wherein the monitoring execution module is used for determining abnormal monitoring parameters, the abnormality evaluation module is used for setting monitoring regulation scales and determining whether abnormal tracing analysis is carried out, the tracing analysis module is used for determining whether abnormal tracing analysis is carried out in a collaborative tracing mode or a local tracing mode so as to determine whether regulation is carried out on the monitoring regulation scales, the collaborative tracing module is used for determining whether regulation is carried out on the monitoring regulation scales according to an abnormality association difference index and a reference abnormality persistence index, the collaborative tracing module is used for determining whether regulation is carried out on the monitoring regulation scales based on a demand interference parameter or an environment interference parameter, the local tracing module is used for determining whether regulation is carried out on the monitoring regulation scales of each abnormal monitoring parameter according to the abnormality persistence index, and the execution evaluation module is used for determining whether to carry out optimization analysis. The invention gives consideration to the monitoring quality and the resource utilization efficiency of the energy storage power station.
Description
Technical Field
The invention relates to the field of power station monitoring, in particular to a remote monitoring system of an energy storage power station based on the Internet of things.
Background
In order to meet the stable requirement of power supply, real-time monitoring analysis is required to be carried out on all relevant data of the energy storage power station, and real-time and targeted regulation and control are carried out on the operation process of the energy storage power station. The energy storage power station facing the new energy power generation process can effectively stabilize fluctuation and instability of new energy power generation output, so that the remote monitoring effect on the related data of the energy storage power station has important significance for the new energy power supply process. Because the monitoring process of the energy storage power station is influenced by various factors, the monitoring accuracy requirement of the acquired monitoring data fluctuates, specific analysis needs to be carried out aiming at abnormal conditions, and whether adjustment aiming at a monitoring scheme is needed or not is evaluated. Therefore, how to combine the actually obtained monitoring results to make targeted adjustment on the monitoring process of the energy storage power station so as to consider the resource utilization efficiency and the monitoring quality is a problem to be solved by the person skilled in the art.
Chinese patent publication No. CN116345585A discloses a new energy power station and energy storage three-station integrated intelligent control strategy method, and the new energy power station, the induction filtering booster station and the energy storage station are optimally controlled in active and reactive resources, so that the new energy grid connection is safer, more economical and more efficient. As a core control strategy method suitable for a monitoring system at a station end, the method can realize data uploading and instruction issuing among subsystems such as an energy storage energy management system, a booster station monitoring system, a new energy power generation monitoring system, an induction filtering monitoring system and the like, and integrates application functions originally belonging to a plurality of isolated systems. The scheme has the following defects that the monitoring process of the energy storage power station cannot be optimized in a targeted manner in real time by combining the actually obtained monitoring condition, so that the monitoring process of the energy storage power station cannot guarantee the quality of the monitoring result of the energy storage power station and the resource utilization efficiency of the actual monitoring process.
Disclosure of Invention
Therefore, the invention provides a remote monitoring system of an energy storage power station based on the Internet of things, which is used for solving the problem that the monitoring process of the energy storage power station cannot be effectively considered to be the quality of the monitoring result of the energy storage power station and the resource utilization efficiency of the actual monitoring process due to the fact that the monitoring process of the energy storage power station cannot be specifically optimized in real time in combination with the actually acquired monitoring situation in the prior art.
In order to achieve the above object, the present invention provides a remote monitoring system of an energy storage power station based on internet of things, comprising:
the monitoring execution module comprises a plurality of execution edge nodes, is used for executing analysis and transmission tasks of each monitoring analysis parameter of the target monitoring power station, and periodically detects abnormality indexes of each monitoring analysis parameter so as to determine the abnormality monitoring parameters;
the abnormality evaluation module is connected with the monitoring execution module and is used for setting a monitoring regulation scale of each abnormal monitoring parameter based on the abnormality index and the linkage abnormality index, and determining whether to perform abnormality traceability analysis on the target monitoring power station according to the reference abnormality index and the abnormality coverage duty ratio index;
The tracing analysis module is connected with the anomaly evaluation module and used for determining an anomaly tracing state of the target monitoring power station according to the anomaly coverage duty ratio index and the anomaly coverage association index, determining whether to perform anomaly tracing analysis in a collaborative tracing mode or a local tracing mode according to the anomaly tracing state so as to determine whether to adjust the monitoring regulation scale of each anomaly monitoring parameter;
The collaborative traceability module is connected with the traceability analysis module and is used for determining to perform demand interference analysis or environment interference analysis on the target monitoring power station according to the abnormality association difference index and the reference abnormality duration index so as to adjust the monitoring regulation scale of each abnormal monitoring parameter based on the demand interference parameter or the environment interference parameter;
the local tracing module is connected with the tracing analysis module and used for determining whether to adjust the monitoring regulation scale of each abnormal monitoring parameter according to the abnormal continuous index;
And the execution evaluation module is respectively connected with the abnormality evaluation module, the collaborative tracing module and the local tracing module and is used for determining whether to execute optimization analysis on each execution edge node according to the node execution load coefficient.
Further, detecting the abnormality indexes of the monitoring analysis parameters periodically, wherein the abnormality monitoring parameters are monitoring analysis parameters with abnormality indexes larger than preset abnormality indexes;
The monitoring regulation and control scale of any abnormal monitoring parameter is in positive correlation with the abnormal index and linkage abnormal index of the corresponding abnormal monitoring parameter respectively, and the abnormal index of any monitoring analysis parameter is determined according to the standard difference index determined for each time of the corresponding monitoring analysis parameter.
Further, if the reference abnormality index is larger than the preset reference abnormality index or the abnormality coverage duty ratio index is larger than the preset abnormality coverage duty ratio index, determining to perform abnormality traceability analysis on the target monitoring power station;
the reference abnormality index is determined according to abnormality indexes of the abnormal monitoring parameters, and the abnormal coverage duty ratio index is determined according to power station monitoring equipment corresponding to the abnormal monitoring parameters.
Further, the anomaly traceability states include a class of anomaly traceability states and a class of anomaly traceability states, wherein,
The target monitoring power station in the abnormal tracing state is a target monitoring power station with an abnormal coverage duty ratio index larger than a preset abnormal coverage duty ratio index or an abnormal coverage association index larger than a preset abnormal coverage association index;
The target monitoring power station in the second-class anomaly traceability state is the target monitoring power station with the anomaly coverage duty ratio index smaller than or equal to the preset anomaly coverage duty ratio index and the anomaly coverage association index smaller than or equal to the preset anomaly coverage association index.
Further, if the target monitoring power station is in an abnormal tracing state, performing abnormal tracing analysis on the target monitoring power station by adopting a collaborative tracing mode.
Further, if the abnormality association difference index is smaller than or equal to a preset abnormality association difference index or the reference abnormality duration index is smaller than or equal to a preset reference abnormality duration index, carrying out demand interference analysis on the target monitoring power station;
Increasing and adjusting the monitoring regulation scale of each abnormal monitoring parameter according to the demand interference index;
and the increase value of the monitoring regulation scale and the demand interference index are in positive correlation.
Further, the setting mode of the demand interference index is determined according to the abnormal coverage duty ratio index;
If the abnormal coverage duty ratio index is larger than the preset abnormal coverage duty ratio index, determining a demand interference index according to the abnormal coverage association index and the peak shaving deviation index;
If the abnormal coverage duty ratio index is smaller than or equal to the preset abnormal coverage duty ratio index, determining a demand interference index according to the equipment reference abnormal index.
Further, if the abnormality association difference index is greater than the preset abnormality association difference index and the reference abnormality duration index is greater than the preset reference abnormality duration index, performing environmental interference analysis with respect to the target monitoring power station, wherein,
The environment interference index is determined according to the reference abnormal difference index and the phase interference difference index;
And increasing and adjusting the monitoring regulation scale of each abnormal monitoring parameter according to the environment interference index, wherein the increasing value of the monitoring regulation scale and the environment interference index are in positive correlation.
Further, if the target monitoring power station is in the second class abnormal traceability state, performing abnormal traceability analysis on the target monitoring power station by adopting a local traceability mode, wherein,
If the abnormal continuous index of the abnormal monitoring parameter is smaller than or equal to the preset abnormal continuous index, increasing and adjusting the monitoring regulation scale of the abnormal monitoring parameter according to the abnormal continuous index;
and the increasing value of the monitoring regulation scale and the abnormal continuous index are in negative correlation.
Further, under the condition of tracing completion, if the node execution load coefficient of any execution edge node is larger than the preset node execution load coefficient, executing optimization analysis on the execution edge node, wherein,
Determining whether to perform secondary adjustment on the monitoring regulation scale of each abnormal monitoring parameter corresponding to the execution edge node based on the node linkage duty ratio index;
If the node linkage duty ratio index of the abnormal monitoring parameter is larger than the preset node linkage duty ratio index, reducing and adjusting the monitoring regulation scale of the abnormal monitoring parameter based on the node execution load coefficient and the node linkage duty ratio index;
and the tracing completion condition is that the target monitoring power station completes abnormal tracing analysis.
Compared with the prior art, the method has the beneficial effects that whether the monitoring precision needs to be adjusted or not is determined according to the actual monitoring conditions of each monitoring analysis parameter, whether the current abnormal condition is subjected to traceable analysis is determined by combining the overall monitoring condition of the target monitoring power station and the equipment condition corresponding to the abnormal monitoring parameter, so that the monitoring regulation and control scale of each abnormal monitoring parameter is subjected to targeted regulation, the adaptation degree of the set monitoring scheme to the target monitoring power station is improved, and the monitoring quality of the target monitoring power station is improved.
Further, when the anomaly traceability analysis is performed on the target monitoring power station, the anomaly traceability state of the target monitoring power station is determined through the anomaly coverage duty ratio index and the anomaly coverage association index, and whether the anomaly traceability analysis is performed in a collaborative traceability mode or a local traceability mode is determined according to the anomaly traceability state, so that the adjustment decision of the monitoring regulation and control scale of each anomaly monitoring parameter is more in line with the actual situation, and the data processing efficiency in the monitoring process of the target monitoring power station is improved.
Further, in the invention, aiming at the target monitoring power station in an abnormality tracing state, the requirement interference analysis or the environment interference analysis aiming at the target monitoring power station is determined according to the abnormality association difference index and the reference abnormality duration index, the abnormal time sequence difference and the duration condition among different abnormality monitoring parameters are represented through the abnormality association difference index and the reference abnormality duration index, so that the follow-up analysis process aiming at the monitoring precision requirement of the abnormality monitoring parameters is provided with pertinence, the interference degree of the power receiving process is analyzed under the condition that the time sequence difference is smaller and the duration condition is lighter, the sensitivity degree of the environment change is analyzed under the condition that the time sequence difference is larger or the duration condition is heavier, and the adjustment decision of the monitoring control scale of each abnormality monitoring parameter is more accordant with the actual condition, thereby ensuring the adaptation degree of the set monitoring control scale to the actual monitoring process.
Further, in the invention, aiming at the target monitoring power station in an abnormal tracing state, because the abnormal conditions of the monitoring analysis parameters existing in the target monitoring power station currently relate to less power station monitoring equipment and the related power station monitoring equipment have smaller association, based on the continuity of the abnormal conditions of the abnormal monitoring parameters, whether the current monitoring scheme can meet the monitoring detail requirement of the data characteristics of the data is judged, so that the targeted adjustment is carried out on the monitoring process, and the adaptation degree of the set monitoring regulation scale to the actual monitoring process is further ensured.
Further, whether to perform optimization analysis on each execution edge node is determined according to the node execution load coefficient, the data processing task burden of each execution edge node in the current stage is represented by the node execution load coefficient, the execution edge node with heavy data processing task burden in the current stage is further analyzed for the monitoring requirement of the corresponding abnormal monitoring parameters, unnecessary data processing burden is avoided, the relevant analysis basis is provided according to whether each execution edge node has more parameters for the abnormal monitoring parameters, and the monitoring regulation and control scale of each abnormal monitoring parameter is properly reduced and regulated, so that the data processing burden of the execution edge node is reduced under the condition that the abnormal analysis process of a target monitoring power station is not influenced.
Drawings
FIG. 1 is a block diagram of a remote monitoring system of an energy storage power station based on the Internet of things;
FIG. 2 is a flow chart of determining an abnormal traceability state of the target monitoring power station according to an abnormal coverage duty ratio index and an abnormal coverage association index;
FIG. 3 is a flowchart of determining whether to perform anomaly tracing analysis by adopting a collaborative tracing mode or a local tracing mode according to the anomaly tracing state;
FIG. 4 is a flow chart of the present invention for determining whether to perform optimization analysis for each executing edge node based on node execution load factor.
Detailed Description
The invention will be further described with reference to examples for the purpose of making the objects and advantages of the invention more apparent, it being understood that the specific examples described herein are given by way of illustration only and are not intended to be limiting.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In addition, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected through an intermediate medium, or in communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 to 4, the present invention provides a remote monitoring system of an energy storage power station based on the internet of things, comprising:
the monitoring execution module comprises a plurality of execution edge nodes, is used for executing analysis and transmission tasks of each monitoring analysis parameter of the target monitoring power station, and periodically detects abnormality indexes of each monitoring analysis parameter so as to determine the abnormality monitoring parameters;
the abnormality evaluation module is connected with the monitoring execution module and is used for setting a monitoring regulation scale of each abnormal monitoring parameter based on the abnormality index and the linkage abnormality index, and determining whether to perform abnormality traceability analysis on the target monitoring power station according to the reference abnormality index and the abnormality coverage duty ratio index;
The tracing analysis module is connected with the anomaly evaluation module and used for determining an anomaly tracing state of the target monitoring power station according to the anomaly coverage duty ratio index and the anomaly coverage association index, determining whether to perform anomaly tracing analysis in a collaborative tracing mode or a local tracing mode according to the anomaly tracing state so as to determine whether to adjust the monitoring regulation scale of each anomaly monitoring parameter;
The collaborative traceability module is connected with the traceability analysis module and is used for determining to perform demand interference analysis or environment interference analysis on the target monitoring power station according to the abnormality association difference index and the reference abnormality duration index so as to adjust the monitoring regulation scale of each abnormal monitoring parameter based on the demand interference parameter or the environment interference parameter;
the local tracing module is connected with the tracing analysis module and used for determining whether to adjust the monitoring regulation scale of each abnormal monitoring parameter according to the abnormal continuous index;
And the execution evaluation module is respectively connected with the abnormality evaluation module, the collaborative tracing module and the local tracing module and is used for determining whether to execute optimization analysis on each execution edge node according to the node execution load coefficient.
The method is used for optimizing the monitoring process of the energy storage power station for new energy power generation in real time, avoiding fluctuation caused by new energy power generation and power consumption requirements in time for a monitoring scheme set by the energy storage power station, ensuring the resource utilization efficiency of data processing, recording the energy storage power station which is optimized in real time for the monitoring process as a target monitoring power station, monitoring each monitoring analysis parameter for the monitoring process of the target monitoring power station, setting a plurality of edge nodes for the monitoring process of the target monitoring power station, recording the edge nodes of the data processing task for executing any monitoring analysis parameter as executing edge nodes, wherein the data processing task comprises but is not limited to preprocessing, analyzing and transmitting the numerical value acquired for the corresponding monitoring analysis parameter, the data processing task for distributing a plurality of monitoring analysis parameters correspondingly for any executing edge nodes, and transmitting the preprocessed data and analysis results to a center for monitoring and analyzing the target monitoring power station;
The invention relates to a power station monitoring device, which is used for monitoring analysis parameters, wherein the monitoring analysis parameters to be monitored in a target monitoring power station comprise, but are not limited to, battery single voltage, total current, SOC (residual electric quantity), SOH (health), single cluster temperature difference (more than or equal to 5 ℃ trigger early warning), PCS output power, conversion efficiency, switching state, harmonic distortion rate, charge-discharge cycle times, energy conversion efficiency, system response time, photovoltaic/wind power real-time output, grid-connected point voltage, grid-connected point frequency, real-time electricity load, load prediction curve, energy storage bin temperature and humidity and illumination intensity;
The invention is applied with a plurality of monitoring execution records, wherein any one monitoring execution record records at least one abnormality index, standard difference index, reference abnormality index, abnormal coverage duty ratio index, abnormal coverage association index, abnormal association difference index, reference abnormality duration index, abnormal continuous index, node execution load coefficient and node linkage duty ratio index of the optimizing monitoring process of the operation monitoring scheme of the target monitoring power station, each monitoring execution record corresponds to a qualified mark, and the qualified mark records whether the monitoring quality and analysis efficiency of the target monitoring power station meet the requirements of users.
Specifically, detecting an abnormality index of each monitoring analysis parameter periodically, wherein the abnormality monitoring parameter is a monitoring analysis parameter with an abnormality index larger than a preset abnormality index;
The monitoring regulation and control scale of any abnormal monitoring parameter is in positive correlation with the abnormal index and linkage abnormal index of the corresponding abnormal monitoring parameter respectively, and the abnormal index of any monitoring analysis parameter is determined according to the standard difference index determined for each time of the corresponding monitoring analysis parameter.
The method comprises the steps that a cyclic abnormality evaluation period is applied, a user can determine the duration of the abnormality evaluation period by himself, the higher the user has a higher requirement on the monitoring quality and analysis efficiency of a target monitoring power station, the shorter the duration of the abnormality evaluation period is, the duration of the abnormality evaluation period is provided, the abnormality evaluation period is 30s, and the end time of each abnormality evaluation period is used for acquiring abnormality indexes of monitoring analysis parameters for detection;
if the current time is the end time of an anomaly evaluation period, for a single monitoring analysis parameter, the anomaly index=the number of anomaly monitoring times of the monitoring analysis parameter existing in the anomaly evaluation period/the number of parameter monitoring times of the monitoring analysis parameter existing in the anomaly evaluation period, for a single parameter monitoring time, if the standard deviation index determined by the parameter monitoring time for the monitoring analysis parameter is greater than a preset standard deviation index, the parameter monitoring time is recorded as the anomaly monitoring time of the monitoring analysis parameter, and the standard deviation index is recorded as the anomaly monitoring time of the monitoring analysis parameter A1 is the absolute value of the difference between the value acquired by the monitoring analysis parameter and the maximum value of the standard value range of the monitoring analysis parameter at the parameter monitoring moment, a2 is the absolute value of the difference between the value acquired by the monitoring analysis parameter and the minimum value of the standard value range of the monitoring analysis parameter at the parameter monitoring moment, b is the average value of the maximum value and the minimum value of the standard value range of the monitoring analysis parameter, each monitoring analysis parameter is correspondingly provided with a parameter acquisition frequency and a corresponding standard value range, the parameter acquisition frequency is the acquisition frequency of the value of the corresponding monitoring analysis parameter in unit time, the maximum value of the standard value range is the maximum value allowed by the corresponding monitoring analysis parameter in the operation process of the target monitoring power station, the maximum value of the standard value range is the minimum value allowed by the corresponding monitoring analysis parameter in the operation process of the target monitoring power station, and how the parameter acquisition frequency and the corresponding standard value range are set for each monitoring analysis parameter is the content mastered by the technicians in the field; for a single abnormal monitoring parameter, the monitoring regulation scale and the regulation reference index set for the abnormal monitoring parameter are in positive correlation, the regulation reference index is the sum of the abnormal index and the linkage abnormal index, the linkage abnormal index is the average value of the abnormal indexes of the linkage abnormal parameters of the abnormal monitoring parameter, the linkage abnormal parameter is the abnormal monitoring parameter of which the abnormal linkage index of the abnormal monitoring parameter is smaller than the preset abnormal linkage index, the abnormal linkage index between any two abnormal monitoring parameters is the interval duration between the first abnormal monitoring time of the two abnormal monitoring parameters in the abnormal evaluation period, and the monitoring regulation scale= (the parameter acquisition frequency after monitoring regulation is finished-the parameter acquisition frequency before monitoring regulation is finished)/the parameter acquisition frequency before monitoring regulation is finished;
The preset abnormal index, the preset standard deviation index and the value of the preset abnormal linkage index can be determined according to an actual working scene, for example, the user can set according to a monitoring execution record, the higher the requirement of the user on the monitoring quality and analysis efficiency of the target monitoring power station is, the smaller the value of the preset abnormal index is, the smaller the value of the preset abnormal linkage index is, the value of the preset abnormal index is provided, the minimum value of the abnormal index of the abnormal monitoring parameter in the monitoring execution record meeting the requirement of the user on the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset abnormal index, the value of the preset abnormal index is provided, the value of the preset abnormal index is 0.05, the minimum value of the standard deviation index determined by each abnormal monitoring moment in the monitoring execution record meeting the requirement of the user on the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset standard deviation index, and the preset abnormal linkage index is provided as the preset abnormal value of 3s.
Specifically, if the reference abnormality index is greater than a preset reference abnormality index or the abnormality coverage duty ratio index is greater than a preset abnormality coverage duty ratio index, determining to perform abnormality traceability analysis on the target monitoring power station;
the reference abnormality index is determined according to abnormality indexes of the abnormal monitoring parameters, and the abnormal coverage duty ratio index is determined according to power station monitoring equipment corresponding to the abnormal monitoring parameters.
If the current time is the ending time of an anomaly evaluation period, detecting a reference anomaly index and an anomaly coverage duty ratio index after finishing the determination of anomaly monitoring parameters so as to determine whether to perform anomaly traceability analysis on a target monitoring power station, wherein the reference anomaly index is the average value of the anomaly indexes of the determined anomaly monitoring parameters, the anomaly coverage duty ratio index=the number of anomaly monitoring devices existing in the target monitoring power station/the number of power station monitoring devices existing in the target monitoring power station, and the anomaly monitoring devices are power station monitoring devices corresponding to the currently determined anomaly monitoring parameters;
The method comprises the steps of determining the values of a preset reference abnormality index and a preset abnormality coverage duty ratio index according to an actual working scene, for example, setting the values of the preset reference abnormality index according to a monitoring execution record, wherein the smaller the values of the preset reference abnormality index are, the smaller the values of the preset abnormality coverage duty ratio index are, recording the monitoring execution record of performing abnormality tracing analysis on a target monitoring power station as a tracing reference record, recording the average value of the reference abnormality index in the tracing reference record meeting the requirements of the user on the monitoring quality and the analysis efficiency of the target monitoring power station as a preset reference abnormality index, providing the value of the preset abnormality coverage duty ratio index, and recording the average value of the abnormality coverage duty ratio index in the tracing reference record meeting the requirements of the user on the monitoring quality and the analysis efficiency of the target monitoring power station as a preset abnormality coverage duty ratio index.
Specifically, the anomaly traceability states include a class of anomaly traceability states and a class of anomaly traceability states, wherein,
The target monitoring power station in the abnormal tracing state is a target monitoring power station with an abnormal coverage duty ratio index larger than a preset abnormal coverage duty ratio index or an abnormal coverage association index larger than a preset abnormal coverage association index;
The target monitoring power station in the second-class anomaly traceability state is the target monitoring power station with the anomaly coverage duty ratio index smaller than or equal to the preset anomaly coverage duty ratio index and the anomaly coverage association index smaller than or equal to the preset anomaly coverage association index.
If the anomaly traceability analysis is performed on the target monitoring power station, an anomaly traceability state where the target monitoring power station is located is determined based on an anomaly coverage duty ratio index and an anomaly coverage association index, the anomaly coverage association index=the number of associated anomaly devices existing in the target monitoring power station/the number of anomaly monitoring devices existing in the target monitoring power station, and if the anomaly monitoring device is electrically connected with any other anomaly monitoring device, the anomaly monitoring device is recorded as an associated anomaly device, the value of the preset anomaly coverage association index is determined according to an actual working scene, for example, the user can set according to a monitoring execution record, the value method of the preset anomaly coverage association index is provided, and the maximum value of the anomaly coverage association index of the target monitoring power station in the second-class anomaly traceability state in the traceability reference record meeting the requirements of the user on the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset anomaly coverage association index.
Specifically, if the target monitoring power station is in an abnormal tracing state, performing abnormal tracing analysis on the target monitoring power station by adopting a collaborative tracing mode.
If the target monitoring power station is in the abnormal tracing state, the abnormal coverage ratio index is larger or the abnormal coverage association index is larger, and further, the abnormal condition of the monitoring analysis parameters existing in the target monitoring power station currently is indicated to relate to more power station monitoring equipment or the association among the related power station monitoring equipment is larger, and under the condition, preliminary evaluation needs to be made for reasons causing fluctuation to determine whether continuous or larger interference exists at the power utilization demand side.
Specifically, if the abnormality association difference index is smaller than or equal to a preset abnormality association difference index or the reference abnormality duration index is smaller than or equal to a preset reference abnormality duration index, performing demand interference analysis on the target monitoring power station;
Increasing and adjusting the monitoring regulation scale of each abnormal monitoring parameter according to the demand interference index;
and the increase value of the monitoring regulation scale and the demand interference index are in positive correlation.
Wherein if the abnormality association difference index is smaller than or equal to the preset abnormality association difference index or the reference abnormality duration index is smaller than or equal to the preset reference abnormality duration index, the difference between the abnormality monitoring parameters existing at present and the abnormality index of each linkage abnormality parameter is smaller or the existence time of the abnormality condition is shorter, and further the abnormality condition existing at this time is larger, which may be caused by instantaneous power demand change or equipment abnormality, therefore, the regulation demand degree of the target monitoring power station and the influence degree of the regulation operation in the subsequent stage are analyzed, optimization is performed in time for the monitoring scheme, it is ensured that the monitoring scheme for each monitoring analysis parameter can meet the actual need of the target energy storage power station in the subsequent monitoring process, the abnormality association difference index = average value of the standard difference/each abnormality monitoring parameter and the abnormality index of each linkage abnormality parameter between the abnormality monitoring parameters and the abnormality index of each abnormality parameter of each abnormality monitoring parameter of each abnormality duration index, the abnormality duration index = duration time for each abnormality parameter of each abnormality is determined as the average value of the abnormality monitoring parameter of each abnormality, the abnormality parameter of each abnormality parameter within the abnormality duration = duration time, the abnormality parameter is determined as the duration of the monitoring parameter, the current evaluation time is determined as the number of the abnormality parameter, the number of the abnormality parameter is determined within the current evaluation time, the time is determined, the time of the user is determined that the time of the current evaluation period is high, and the time of the user is constant, and the time is determined, and the period of the abnormality is determined, providing a value of the duration of the continuous evaluation period, wherein the duration of the continuous evaluation period is 8 times of the duration of the abnormal evaluation period;
The preset abnormality association difference index and the preset reference abnormality duration index are determined according to an actual working scene, for example, the user can set according to a monitoring execution record, a preset abnormality association difference index value method is provided, a monitoring execution record for carrying out demand interference analysis on a target monitoring power station is recorded as an interference reference record, the minimum value of the abnormality association difference index in the interference reference record meeting the requirements of the user on the monitoring quality and analysis efficiency of the target monitoring power station is recorded as a preset abnormality association difference index, a preset reference abnormality duration index value method is provided, and the minimum value of the reference abnormality duration index in the interference reference record meeting the requirements of the user on the monitoring quality and analysis efficiency of the target monitoring power station is recorded as a preset reference abnormality duration index.
Specifically, the setting mode of the demand interference index is determined according to the abnormal coverage duty ratio index;
If the abnormal coverage duty ratio index is larger than the preset abnormal coverage duty ratio index, determining a demand interference index according to the abnormal coverage association index and the peak shaving deviation index;
If the abnormal coverage duty ratio index is smaller than or equal to the preset abnormal coverage duty ratio index, determining a demand interference index according to the equipment reference abnormal index.
If the abnormal coverage duty ratio index is larger than a preset abnormal coverage duty ratio index, indicating that the current existing abnormal situation relates to more power station monitoring equipment, indicating that the affected equipment range is larger, and that more parameters are subjected to instantaneous influence of power grid regulation, evaluating the influence degree possibly suffered by each subsequent abnormal monitoring parameter through analyzing regulation and control requirements and influence ranges, and determining a first interference reference coefficient according to the abnormal coverage association index and the peak shaving deviation index, wherein the first interference reference coefficient is the sum of the abnormal coverage association index and the peak shaving deviation index, and the peak shaving deviation index= |is the rated output power of the target energy storage power station at the current moment-the actual power grid output power of the target energy storage power station at the current moment|/the rated output power of the target energy storage power station at the current moment, and carrying out normalization processing on the first interference reference coefficient, wherein the required interference index and the first interference reference coefficient for completing normalization processing are in positive correlation;
If the abnormal coverage duty ratio index is smaller than or equal to the preset abnormal coverage duty ratio index, the condition that the current abnormal condition relates to less power station monitoring equipment is indicated, the subsequent influence degree is evaluated through evaluating the overall abnormal condition of the power station monitoring equipment corresponding to each abnormal monitoring parameter, the equipment reference abnormal index is recorded as a second interference reference coefficient aiming at a single abnormal monitoring parameter by the optimized monitoring scheme, the equipment reference abnormal index is the average value of the abnormal indexes of the abnormal monitoring parameters of the power station monitoring equipment corresponding to the abnormal monitoring parameter, the second interference reference coefficient is subjected to normalization processing, and the required interference index and the second interference reference coefficient subjected to normalization processing are in positive correlation.
Specifically, if the abnormality related difference index is greater than the preset abnormality related difference index and the reference abnormality duration index is greater than the preset reference abnormality duration index, performing environmental interference analysis with respect to the target monitoring power station,
The environment interference index is determined according to the reference abnormal difference index and the phase interference difference index;
And increasing and adjusting the monitoring regulation scale of each abnormal monitoring parameter according to the environment interference index, wherein the increasing value of the monitoring regulation scale and the environment interference index are in positive correlation.
If the abnormality association difference index is greater than the preset abnormality association difference index and the reference abnormality duration index is greater than the preset reference abnormality duration index, the present abnormality monitoring parameter has a large difference between abnormal moments and the time of occurrence of the abnormality is generally longer, and further the present abnormality is indicated to be more likely to be caused by independent change caused by environmental condition change within a longer time range, adjustment is required to be made for a subsequent monitoring scheme based on the fluctuation degree caused by environmental influence, when environmental interference analysis is performed for a target monitoring power station, the environmental interference index of each abnormality monitoring parameter is detected, the sensitivity degree of each abnormality monitoring parameter to environmental transformation is represented by the environmental interference index, optimization is made for a monitoring process, the environmental interference index is the sum of the reference abnormality difference index and the phase interference difference index, the reference abnormality difference index=the standard difference between the abnormality indexes of each abnormality environment parameter and each linkage abnormality parameter, the phase difference index is the average value of the abnormality environmental parameter and the abnormality index of each linkage abnormality parameter, the phase difference index is the environmental interference parameter corresponding to the abnormal parameter in the abnormality evaluation period of the current abnormality monitoring parameter, and the temperature difference of each abnormality monitoring parameter belongs to the abnormality monitoring parameter, and the corresponding temperature difference of each abnormality parameter belongs to the abnormality monitoring parameter is not limited to the abnormality parameter, and the abnormality parameter belongs to the type.
Specifically, if the target monitoring power station is in a second-class abnormal traceability state, performing abnormal traceability analysis on the target monitoring power station by adopting a local traceability mode, wherein,
If the abnormal continuous index of the abnormal monitoring parameter is smaller than or equal to the preset abnormal continuous index, increasing and adjusting the monitoring regulation scale of the abnormal monitoring parameter according to the abnormal continuous index;
and the increasing value of the monitoring regulation scale and the abnormal continuous index are in negative correlation.
If the target monitoring power station is in a second-class anomaly traceability state, the abnormal coverage ratio index and the abnormal coverage association index of the target monitoring power station are smaller, the abnormal condition of the monitoring analysis parameters existing at present of the target monitoring power station is indicated to be related to less power station monitoring equipment, the association among the related power station monitoring equipment is smaller, the abnormal condition of the currently acquired abnormal monitoring parameters is indicated to be free of abnormal linkage, and whether the current monitoring scheme can meet the monitoring detail requirement of the data characteristics of the data is judged according to the continuity of the abnormal condition of each abnormal monitoring parameter;
For a single anomaly monitoring parameter, the anomaly continuous index=the average value of adjacent interval durations of each adjacent anomaly time combination/the interval duration between adjacent parameter monitoring moments of the anomaly monitoring parameter, the two adjacent anomaly monitoring moments in an anomaly evaluation period for judging the anomaly monitoring parameter are recorded as an adjacent anomaly time combination, for the single adjacent anomaly time combination, the adjacent interval moments are interval durations between the two anomaly monitoring moments in the adjacent anomaly time combination, the preset value of the anomaly continuous index can be determined according to an actual working scene, for example, the user can set according to a monitoring execution record, a preset value method of the anomaly continuous index is provided, the monitoring execution record for increasing and adjusting the monitoring regulation scale of the anomaly monitoring parameter according to the anomaly continuous index is recorded as a continuous reference record, and the average value of the anomaly continuous index in the continuous reference record meeting the requirements of the user on the monitoring quality and analysis efficiency of a target monitoring power station is recorded as the preset anomaly continuous index.
Specifically, under the condition of tracing completion, if the node execution load coefficient of any execution edge node is larger than the preset node execution load coefficient, executing optimization analysis on the execution edge node, wherein,
Determining whether to perform secondary adjustment on the monitoring regulation scale of each abnormal monitoring parameter corresponding to the execution edge node based on the node linkage duty ratio index;
If the node linkage duty ratio index of the abnormal monitoring parameter is larger than the preset node linkage duty ratio index, reducing and adjusting the monitoring regulation scale of the abnormal monitoring parameter based on the node execution load coefficient and the node linkage duty ratio index;
and the tracing completion condition is that the target monitoring power station completes abnormal tracing analysis.
For a single execution edge node, the node execution load coefficient is the sum of a node abnormality duty ratio index of the execution edge node and a node reference abnormality index, the node abnormality duty ratio index=the number of categories of abnormality monitoring parameters determined in monitoring analysis parameters corresponding to the execution edge node/the number of categories of monitoring analysis parameters corresponding to the execution edge node, the node reference abnormality index is the average value of monitoring regulation and control scales of the abnormality monitoring parameters determined in the monitoring analysis parameters corresponding to the execution edge node, if the node execution load coefficient of the execution edge point is greater than a preset node execution load coefficient, the node execution load coefficient of the execution edge point is higher than the preset node execution load coefficient, so that the data processing task borne by the execution edge node in the current stage is heavier, and further analysis is performed for the monitoring requirement of the abnormality monitoring parameters corresponding to the execution edge node, so that unnecessary data processing burden is avoided;
For any abnormal monitoring parameter corresponding to an execution edge node needing to perform optimization analysis, the node linkage duty ratio index=the number of associated abnormal parameters serving as the abnormal monitoring parameter/the number of abnormal monitoring parameters corresponding to the execution edge node in the abnormal monitoring parameters corresponding to the execution edge node, if the node linkage duty ratio index of the abnormal monitoring parameter is larger than a preset node linkage duty ratio index, the current execution edge node is indicated to have a parameter providing relevant analysis basis for the abnormal monitoring parameter, and the monitoring regulation scale of the abnormal monitoring parameter is properly reduced and regulated, so that the data processing burden of the execution edge node can be reduced under the condition that the abnormal analysis process of a target monitoring power station is not influenced, the reduction value of the monitoring regulation scale and the regulation reference index of the abnormal monitoring parameter are in positive correlation relation, and the regulation reference index is the sum of the node execution load factor of the abnormal monitoring parameter and the node linkage duty ratio index;
The method comprises the steps that a preset node execution load coefficient and a preset node linkage duty ratio index are valued, a user can determine according to an actual working scene, for example, the user can set according to a monitoring execution record, the higher the user has to monitor quality and analysis efficiency of a target monitoring power station, the smaller the value of the preset node execution load coefficient is, a preset node execution load coefficient valued method is provided, the minimum value of the node execution load coefficient of an execution edge node which is used for executing optimization analysis in the monitoring execution record which meets the requirement of the user on the monitoring quality and analysis efficiency of the target monitoring power station is recorded as the preset node execution load coefficient, and the minimum value of the node linkage duty ratio index which meets the requirement of the user on the monitoring quality and analysis efficiency of the target monitoring power station and is secondarily adjusted on the monitoring regulation and control scale is recorded as the preset node linkage duty ratio index.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
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