Multi-energy collaborative power management system

Abstract

The invention discloses a multi-energy collaborative power management system, and particularly relates to the technical field of power management, which comprises the following steps: acquiring scheduling response information when a standby power management system schedules energy equipment, and acquiring an equipment scheduling response delay rise coefficient; acquiring data communication loss information when the standby power supply management system performs data communication with the energy equipment, acquiring a data communication loss abnormal coefficient and a coupling decline trend hidden danger analysis model, and generating a coupling decline trend hidden danger index; the method comprises the following steps: intelligently sensing a descending trend hidden danger of coupling between a standby power supply management system and energy equipment, identifying a coupling problem as soon as possible, and obtaining a descending trend hidden danger signal generation proportion and an energy equipment importance degree correlation coefficient; and early warning classification of different degrees is carried out on the descending trend hidden danger of coupling between the standby power supply management system and the energy equipment, the coping capacity and the post-disaster recovery efficiency are improved, and efficient and reliable operation of the multi-energy cooperative system is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and more specifically, to a multi-energy coordinated power management system. Background Art

[0002] A multi-energy coordinated power management system usually refers to a system that achieves efficient and reliable energy management and supply by integrating and optimizing multiple energy forms (such as wind energy, solar energy, energy storage, etc.). The goal of this system is to improve energy utilization efficiency, reduce operating costs, and enhance system flexibility and reliability through multi-energy complementarity and coordinated control.

[0003] Although the existing multi-energy collaborative power management system is becoming increasingly mature in technology, it faces many challenges in remote areas, especially the lag in infrastructure funding compared to developed urban areas, which leads to the inability to achieve full coverage of power and communication networks. This lack of coverage directly affects the system's ability to cope with post-disaster recovery after natural disasters, especially the potential risk of a downward trend in the coupling between the backup power management system and basic energy equipment. In this case, the system may not be able to effectively coordinate the operation of different energy equipment, reducing the overall recovery efficiency. In addition, the existing monitoring methods lack the ability to capture and analyze the weak representation of the downward trend, so that potential risks cannot be identified in a timely manner, thereby exacerbating the delay in post-disaster reconstruction and even causing the risk of secondary damage. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-energy coordinated power management system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A multi-energy coordinated power management system, including an equipment dispatch response module, a data communication loss module, a model building module, a trend hidden danger generation module, and an early warning classification module;

[0007] The equipment dispatch response module is used to obtain the dispatch response information when the backup power management system dispatches the energy equipment, and obtain the equipment dispatch response delay rise coefficient according to the dispatch response information;

[0008] A data communication loss module is used to obtain data communication loss information when the backup power management system communicates data with the energy equipment, and obtain a data communication loss abnormality coefficient according to the data communication loss information;

[0009] A model building module is used to build a coupling decline trend hidden danger analysis model based on the equipment dispatch response delay increase coefficient and the data communication loss abnormality coefficient, conduct a comprehensive analysis of the coupling decline trend hidden dangers between the backup power management system and the energy equipment, and generate a coupling decline trend hidden danger index;

[0010] A trend hidden danger generation module is used to compare the coupling decline trend hidden danger index with a preset coupling decline trend hidden danger index threshold to determine whether there is a declining trend hidden danger in the coupling between the backup power management system and the current energy equipment;

[0011] The early warning classification module is used to classify the hidden dangers of the downward trend of the coupling between the backup power management system and the current energy equipment when there is a hidden danger of the downward trend of the coupling between the backup power management system and the current energy equipment, combined with the importance information of the energy equipment.

[0012] In a preferred embodiment, by obtaining the dispatch response information when the backup power management system dispatches the energy equipment, analyzing the dispatch response between the backup power management system and the energy equipment, and obtaining the equipment dispatch response delay increase coefficient, the degree of increase in the dispatch response delay between the backup power management system and the energy equipment is measured;

[0013] The logic for obtaining the device scheduling response delay rise coefficient is as follows:

[0014] Each time the backup power management system schedules the energy equipment, the time tc when the backup power management system sends the scheduling instruction and the time ts when the energy equipment executes the scheduling instruction are obtained, and the scheduling response value dxy is calculated. The expression is as follows: dxy = ts - tc; a scheduling response value data analysis set is constructed, and the scheduling response value data analysis set is marked as DXY = {dxy i}={dxy 1 ,dxy 2 ,...,dxy I}, where dxy i represents the dispatch response value calculated by the backup power management system when dispatching the energy equipment for the i-th time, i={1,2,...,I}, I is a positive integer;

[0015] Calculate the average dispatch response time The expression is as follows

[0016] Calculate the standard deviation dxyσ of the scheduling response time, the expression is as follows

[0017] Calculate the scheduling response time deviation coefficient cv, the expression is as follows

[0018] Calculate the abnormal coefficient Zdxy of the scheduling response time i , the expression is as follows

[0019] Calculate the trend coefficient qs, the expression is as follows where tc i It indicates the time when the backup power management system sends the scheduling instruction for the i-th time. is the average value of the time when the backup power management system sends the scheduling instruction, and the expression is as follows

[0020] Calculate the device scheduling response delay rise coefficient Sbxy, the expression is as follows Where max(0,qs) is the maximum value acquisition function. If qs≤0, then max(0,qs) takes the value of 0. If qs>0, then max(0,qs) takes the value of qs. 1 、a 2 、a 3 represents the preset proportionality factor, and a 1 、a 2 、a 3 Both are greater than 0.

[0021] In a preferred embodiment, by acquiring data communication loss information when the backup power management system and the energy device communicate data, analyzing the data communication loss between the backup power management system and the energy device, and acquiring the data communication loss abnormality coefficient, the degree of abnormal data communication loss between the backup power management system and the energy device is measured;

[0022] The logic for obtaining the data communication loss anomaly coefficient is as follows:

[0023] When the backup power management system communicates data with the energy equipment, multiple monitoring cycles are preset, and the packet loss rate in different monitoring cycles is calculated. The calculation expression is as follows: where dsl t represents the packet loss rate in the t-th monitoring cycle, s1 represents the number of packets lost when the backup power management system communicates with the energy equipment in the t-th monitoring cycle, s2 represents the total number of packets when the backup power management system communicates with the energy equipment in the t-th monitoring cycle, t={1,2,...,T}, T is a positive integer;

[0024] Calculate the exponentially weighted moving average of the packet loss rate. The calculation expression is as follows: t+1 =α*dsl t+1 +(1-α)*Eds t , of which Eds trepresents the exponentially weighted moving average of the packet loss rate in the tth monitoring period, α represents the preset smoothing factor, dsl t+1 represents the packet loss rate of the t+1th monitoring period, Eds t+1 represents the exponentially weighted moving average of the packet loss rate in the t+1th monitoring period;

[0025] The packet loss rate and the exponentially weighted moving average of the packet loss rate in different monitoring periods are taken as a set of feature data, and the Euclidean distance between the feature data in the t-th monitoring period and the remaining feature data in the j-th monitoring period is calculated. The calculation expression is as follows: where d t,j represents the Euclidean distance between the feature data of the tth monitoring period and the remaining jth monitoring periods, dsl t represents the packet loss rate in the tth monitoring period, Eds t represents the exponentially weighted moving average of the packet loss rate in the tth monitoring period, dsl j represents the packet loss rate in the remaining j-th monitoring period, Eds j represents the exponentially weighted moving average of the packet loss rate in the remaining j-th monitoring period, j = {1, 2, ..., T-1};

[0026] Compare the Euclidean distance between the feature data of the t-th monitoring period and the remaining j-th monitoring period with the preset distance threshold. When the Euclidean distance between the feature data of the t-th monitoring period and the remaining j-th monitoring period is less than the distance threshold, add the corresponding j-th monitoring period to the neighbor set of the t-th monitoring period. Calculate the local density, the expression is as follows where ld t represents the local density of the tth monitoring period, ZS t It represents the accumulated value of the Euclidean distance within the neighbor set corresponding to the t-th monitoring period;

[0027] Calculate the packet loss outlier value LOF of the tth monitoring period t , the expression is as follows where ld k represents the local density of the kth monitoring period in the neighbor set corresponding to the tth monitoring period, k = {1, 2, ..., K}, K is a positive integer;

[0028] The data packet loss anomaly values ​​of T monitoring cycles are compared with the preset anomaly thresholds respectively, and the data packet loss anomaly values ​​greater than the anomaly thresholds are accumulated and calculated to obtain the data communication loss anomaly coefficient Sjtc.

[0029] In a preferred embodiment, a coupling downtrend hidden danger analysis model is constructed according to the device scheduling response delay increase coefficient and the data communication loss abnormality coefficient to generate a coupling downtrend hidden danger index Ohxz. The formula of the model is as follows: Where b 1 , b 2 They represent the preset proportional coefficients of the device scheduling response delay increase coefficient and the data communication loss abnormality coefficient, respectively, and b 1 , b 2 Both are greater than 0.

[0030] In a preferred embodiment, if the coupling decreasing trend hidden danger index is greater than the coupling decreasing trend hidden danger index threshold, a coupling decreasing trend hidden danger signal is generated;

[0031] If the coupling downward trend hidden danger index is less than or equal to the coupling downward trend hidden danger index threshold, there is no need to generate a coupling downward trend hidden danger signal.

[0032] In a preferred embodiment, the number of times V1 of coupling downward trend hidden danger signals are generated is obtained, and the proportion of downward trend hidden danger signals generated Yhbl is calculated, and the expression is as follows: Where V2 represents the total number of energy devices.

[0033] In a preferred embodiment, the energy equipment importance information includes an energy equipment importance correlation coefficient;

[0034] The logic for obtaining the correlation coefficient of energy equipment importance is as follows:

[0035] Obtain relevant assessment data of energy equipment including operating status data, environmental data, load data, energy consumption data, and fault data;

[0036] Calculate the energy equipment correlation coefficient xgx between the target energy equipment and the remaining energy equipment. The expression is as follows Where X n represents the nth correlation evaluation data of the target energy device, X represents the average value of the correlation evaluation data of the target energy device, and Y m,n represents the nth correlation evaluation data of the remaining mth energy device, represents the average value of the correlation evaluation data of the remaining m-th energy equipment, n={1,2,...,N}, N is a positive integer;

[0037] Calculate the energy equipment importance correlation coefficient Nysb, the expression is as follows where xgx h,m represents the energy equipment correlation coefficient between the h-th target energy equipment and the remaining m-th energy equipment, represents the preset energy equipment correlation coefficient threshold, h={1,2,...,H}, H is a positive integer, and m={1,2,...,H-1}.

[0038] In a preferred embodiment, a coupling downward trend hidden danger warning classification model is constructed according to the downward trend hidden danger signal generation ratio and the energy equipment importance correlation coefficient, and a coupling downward trend hidden danger warning classification index Oyzf is generated. The model is based on the following formula Oyzf=ln(r 1 *Yhbl+r 2 *Nysb+1), where r 1 、r 2 They represent the preset proportional coefficients of the generation ratio of downward trend hidden danger signals and the correlation coefficient of the importance of energy equipment, and r 1 、r 2 Both are greater than 0.

[0039] In a preferred embodiment, the coupling downward trend hidden danger warning classification index is compared with a preset coupling downward trend hidden danger warning classification index threshold. If the coupling downward trend hidden danger warning classification index is greater than the coupling downward trend hidden danger warning classification index threshold, a first-level warning signal is generated; if the coupling downward trend hidden danger warning classification index is less than or equal to the coupling downward trend hidden danger warning classification index threshold, a second-level warning signal is generated.

[0040] Technical effects and advantages of the present invention:

[0041] 1. The present invention obtains the dispatch response information when the backup power management system dispatches the energy equipment, obtains the equipment dispatch response delay increase coefficient, obtains the data communication loss information when the backup power management system communicates data with the energy equipment, and obtains the data communication loss abnormality coefficient, constructs a coupling decline trend hidden danger analysis model, generates a coupling decline trend hidden danger index, intelligently perceives the coupling decline trend hidden danger between the backup power management system and the energy equipment, identifies and solves the coupling problem early, reduces the equipment failure rate, reduces the maintenance and repair costs, and improves the energy utilization efficiency and post-disaster recovery capabilities as well as the energy management level in remote areas.

[0042] 2. The present invention obtains the correlation coefficient between the generation ratio of downward trend hidden danger signals and the importance of energy equipment from an overall perspective, constructs a coupling downward trend hidden danger warning classification model, generates a coupling downward trend hidden danger warning classification index, and classifies the coupling downward trend hidden dangers between the backup power management system and the energy equipment into different degrees of warning, thereby improving the response capability and post-disaster recovery efficiency, and promoting the efficient and reliable operation of the multi-energy collaborative system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0044] Figure 1 Schematic diagram of the structure of the system of the embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Embodiment: The present invention provides as Figure 1 A multi-energy coordinated power management system shown includes an equipment scheduling response module, a data communication loss module, a model building module, a trend hidden danger generation module, and an early warning classification module;

[0047] The equipment dispatch response module is used to obtain the dispatch response information when the backup power management system dispatches the energy equipment, and obtain the equipment dispatch response delay rise coefficient according to the dispatch response information;

[0048] A data communication loss module is used to obtain data communication loss information when the backup power management system communicates data with the energy equipment, and obtain a data communication loss abnormality coefficient according to the data communication loss information;

[0049] A model building module is used to build a coupling decline trend hidden danger analysis model based on the equipment dispatch response delay increase coefficient and the data communication loss abnormality coefficient, conduct a comprehensive analysis of the coupling decline trend hidden dangers between the backup power management system and the energy equipment, and generate a coupling decline trend hidden danger index;

[0050] A trend hidden danger generation module is used to compare the coupling decline trend hidden danger index with a preset coupling decline trend hidden danger index threshold to determine whether there is a declining trend hidden danger in the coupling between the backup power management system and the current energy equipment;

[0051] The early warning classification module is used to classify the potential danger of a decreasing trend in the coupling between the backup power management system and the current energy equipment in combination with the importance information of the energy equipment when there is a potential danger of a decreasing trend in the coupling between the backup power management system and the energy equipment;

[0052] The equipment dispatch response module is used to obtain the dispatch response information when the backup power management system dispatches the energy equipment, and obtain the equipment dispatch response delay rise coefficient according to the dispatch response information;

[0053] When the backup power management system dispatches energy equipment and its dispatch response is delayed, the following effects may occur:

[0054] Energy equipment does not respond in time: Backup power systems are usually activated in emergency situations (such as after a disaster or equipment failure). If there is a delay in the dispatch response, energy equipment may not be able to start or adjust its operating status in time, resulting in the inability to supply emergency energy as expected, affecting the normal operation of key areas;

[0055] Reduced system reliability: Delayed dispatch response may indicate problems with the system's communication or control links. This delay will affect the overall coordination and reliability of the system, especially in multi-energy collaborative scenarios, where various energy devices need to respond quickly and work together to ensure the continuity and stability of power supply.

[0056] Reduced energy efficiency: Delayed scheduling may result in the inability to adjust the operating mode of energy equipment in a timely manner according to real-time demand, resulting in energy equipment being unable to work efficiently. For example, intermittent energy such as wind or solar energy may have significantly reduced energy utilization efficiency after missing the best time;

[0057] Reduced coupling between the backup power system and equipment: Delayed dispatch response indicates that there is a problem with the coordination between the backup power system and energy equipment, which will affect the system's accurate control of the status of energy equipment. In the long run, reduced coupling may lead to reduced operating efficiency and safety of the overall system;

[0058] Therefore, by obtaining the dispatch response information when the backup power management system dispatches the energy equipment, analyzing the dispatch response situation between the backup power management system and the energy equipment, and obtaining the equipment dispatch response delay increase coefficient, the degree of increase in the dispatch response delay between the backup power management system and the energy equipment is measured;

[0059] The logic for obtaining the device scheduling response delay rise coefficient is as follows:

[0060] Each time the backup power management system schedules the energy equipment, the time tc when the backup power management system sends the scheduling instruction and the time ts when the energy equipment executes the scheduling instruction are obtained, and the scheduling response value dxy is calculated. The expression is as follows: dxy = ts - tc; a scheduling response value data analysis set is constructed, and the scheduling response value data analysis set is marked as DXY = {dxy i}={dxy 1 ,dxy 2 ,...,dxy I}, where dxy i represents the dispatch response value calculated by the backup power management system when dispatching the energy equipment for the i-th time, i={1,2,...,I}, I is a positive integer;

[0061] Calculate the average dispatch response time The expression is as follows

[0062] Calculate the standard deviation dxyσ of the scheduling response time, the expression is as follows

[0063] Calculate the scheduling response time deviation coefficient cv, the expression is as follows

[0064] The dispatch response time deviation coefficient is used to measure the relative volatility of the dispatch response time;

[0065] Calculate the abnormal coefficient Zdxy of the scheduling response time i , the expression is as follows

[0066] The dispatch response time anomaly coefficient is used to measure the impact of the dispatch response time anomaly on the overall dispatch response;

[0067] Calculate the trend coefficient qs, the expression is as follows where tc i It represents the time when the backup power management system sends the scheduling instruction for the i-th time, and tc1 is the average time when the backup power management system sends the scheduling instruction. The expression is as follows

[0068] The trend coefficient is used to measure the changing trend of the energy equipment dispatch response time over time, and can be used to help identify the dispatch response delay when the backup power management system dispatches the energy equipment;

[0069] Calculate the device scheduling response delay rise coefficient Sbxy, the expression is as follows Sbxy = a 1 *cv+a 2 * Where max(0,qs) is the maximum value acquisition function. If qs≤0, then max(0,qs) takes the value of 0. If qs>0, then max(0,qs) takes the value of qs. 1 、a 2 、a 3 represents the preset proportionality factor, and a 1 、a 2 、a 3 All are greater than 0;

[0070] It should be noted that a 1 、a 2 、a 3Set it according to the actual situation. For example, adopt the expert empowerment method, that is, invite experts in relevant fields to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0071] The equipment dispatch response delay increase coefficient is used to measure the increase in the dispatch response delay between the backup power management system and the energy equipment. When the equipment dispatch response delay increase coefficient is larger, it indicates that the dispatch response delay increase between the backup power management system and the energy equipment is more serious, and the probability of the existence of a downward trend hidden danger in the coupling between the backup power management system and the energy equipment is greater; conversely, when the equipment dispatch response delay increase coefficient is smaller, it indicates that the coupling between the backup power management system and the energy equipment is stronger, the dispatch response is more timely, the potential risk is lower, and the probability of the existence of a downward trend hidden danger in the coupling between the backup power management system and the energy equipment is smaller;

[0072] A data communication loss module is used to obtain data communication loss information when the backup power management system communicates data with the energy equipment, and obtain a data communication loss abnormality coefficient according to the data communication loss information;

[0073] When data is lost during data communication between the backup power management system and the energy equipment, the following effects may occur:

[0074] Lack of energy equipment status monitoring: Due to communication interruption or data loss, the backup power management system cannot monitor the real-time status of energy equipment in a timely manner, which may result in energy equipment operation abnormalities not being discovered in time, increasing the risk of system failure;

[0075] Reduced energy efficiency: When the system cannot obtain the load information or energy consumption data of energy equipment in real time, it may lead to inaccurate or untimely scheduling of backup power supply, resulting in unreasonable energy distribution, thus reducing the energy utilization efficiency of the backup power supply system;

[0076] Increased system safety risks: Under high load or emergency conditions, if the backup power dispatch system cannot obtain real-time data from energy equipment, it may lead to unbalanced power load distribution, which in turn may cause safety risks such as overload and power outage;

[0077] Reduced data consistency and control accuracy: Lost data may contain control instructions or energy equipment status information. The loss of this information will affect the system's precise control of energy equipment, which may lead to energy equipment misoperation or status deviation, thus affecting the coordination and accuracy of the overall system;

[0078] Therefore, by obtaining the data communication loss information when the backup power management system and the energy equipment communicate data, the data communication loss situation between the backup power management system and the energy equipment is analyzed, and the data communication loss abnormality coefficient is obtained to measure the abnormal degree of data communication loss between the backup power management system and the energy equipment;

[0079] The logic for obtaining the data communication loss anomaly coefficient is as follows:

[0080] When the backup power management system communicates data with the energy equipment, multiple monitoring cycles are preset, and the packet loss rate in different monitoring cycles is calculated. The calculation expression is as follows: where dsl t represents the packet loss rate in the t-th monitoring cycle, s1 represents the number of packets lost when the backup power management system communicates with the energy equipment in the t-th monitoring cycle, s2 represents the total number of packets when the backup power management system communicates with the energy equipment in the t-th monitoring cycle, t={1,2,...,T}, T is a positive integer;

[0081] Calculate the exponentially weighted moving average of the packet loss rate. The calculation expression is as follows: t+1 =α*dsl t+1 +(1-α)*Eds t , of which Eds t represents the exponentially weighted moving average of the packet loss rate in the tth monitoring period, α represents the preset smoothing factor, dsl t+1 represents the packet loss rate of the t+1th monitoring period, Eds t+1 represents the exponentially weighted moving average of the packet loss rate in the t+1th monitoring period;

[0082] It should be noted that the initial value of the exponentially weighted moving average of the packet loss rate is the packet loss rate of the first monitoring period, that is, when t = 1, Eds 1 =dsl 1 , α represents a preset smoothing factor, and a reference value can be given by a person skilled in the art with reference to the number of data packets for data communication between the backup power management system and the energy device;

[0083] The packet loss rate and the exponentially weighted moving average of the packet loss rate in different monitoring periods are taken as a set of feature data, and the Euclidean distance between the feature data in the t-th monitoring period and the remaining feature data in the j-th monitoring period is calculated. The calculation expression is as follows: where d t,j represents the Euclidean distance between the feature data of the tth monitoring period and the remaining jth monitoring periods, dsl trepresents the packet loss rate in the tth monitoring period, Eds t represents the exponentially weighted moving average of the packet loss rate in the tth monitoring period, dsl j represents the packet loss rate in the remaining j-th monitoring period, Eds j represents the exponentially weighted moving average of the packet loss rate in the remaining j-th monitoring period, j = {1, 2, ..., T-1};

[0084] Compare the Euclidean distance between the feature data of the t-th monitoring period and the remaining j-th monitoring period with the preset distance threshold. When the Euclidean distance between the feature data of the t-th monitoring period and the remaining j-th monitoring period is less than the distance threshold, add the corresponding j-th monitoring period to the neighbor set of the t-th monitoring period. Calculate the local density, the expression is as follows where ld t represents the local density of the tth monitoring period, ZS t It represents the accumulated value of the Euclidean distance within the neighbor set corresponding to the t-th monitoring period;

[0085] Calculate the packet loss outlier value LOF of the tth monitoring period t , the expression is as follows where ld k represents the local density of the kth monitoring period in the neighbor set corresponding to the tth monitoring period, k = {1, 2, ..., K}, K is a positive integer;

[0086] The data packet loss anomaly values ​​of T monitoring cycles are compared with the preset anomaly thresholds respectively, and the data packet loss anomaly values ​​greater than the anomaly thresholds are accumulated and calculated to obtain the data communication loss anomaly coefficient Sjtc;

[0087] The data communication loss anomaly coefficient is used to measure the degree of data communication loss anomaly between the backup power management system and the energy equipment. When the data communication loss anomaly coefficient is larger, it indicates that the degree of data communication loss anomaly between the backup power management system and the energy equipment is more serious, and the probability of the existence of a downward trend hidden danger in the coupling between the backup power management system and the energy equipment is greater; conversely, when the data communication loss anomaly coefficient is smaller, it indicates that the degree of data communication loss anomaly between the backup power management system and the energy equipment is smaller, indicating that the coupling between the backup power management system and the energy equipment is stronger, the data communication is more stable, the potential risk is lower, and the probability of the existence of a downward trend hidden danger in the coupling between the backup power management system and the energy equipment is smaller;

[0088] A model building module is used to build a coupling decline trend hidden danger analysis model based on the equipment dispatch response delay increase coefficient and the data communication loss abnormality coefficient, conduct a comprehensive analysis of the coupling decline trend hidden dangers between the backup power management system and the energy equipment, and generate a coupling decline trend hidden danger index;

[0089] According to the equipment scheduling response delay increase coefficient and data communication loss abnormality coefficient, a coupling decline trend hidden danger analysis model is constructed to generate the coupling decline trend hidden danger index Ohxz. The formula based on the model is as follows Where b 1 , b 2 They represent the preset proportional coefficients of the device scheduling response delay increase coefficient and the data communication loss abnormality coefficient, respectively, and b 1 , b 2 All are greater than 0;

[0090] It should be noted that before constructing the coupling decline trend hazard analysis model, it is necessary to ensure that the equipment scheduling response delay increase coefficient and the data communication loss abnormality coefficient are normalized. Common normalization methods include Min-Max normalization, Z-Score normalization, etc. 1 , b 2 Set it according to the actual situation. For example, adopt the expert empowerment method, that is, invite experts in relevant fields to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0091] It can be seen from the above calculation expression that the larger the equipment scheduling response delay increase coefficient and the larger the data communication loss abnormality coefficient, the larger the coupling downward trend hidden danger index, indicating that the probability of the coupling downward trend hidden danger between the backup power management system and the energy equipment is greater. On the contrary, the smaller the equipment scheduling response delay increase coefficient and the smaller the data communication loss abnormality coefficient, the smaller the coupling downward trend hidden danger index, indicating that the coupling between the backup power management system and the energy equipment is strong, the potential risk is low, and the probability of the coupling downward trend hidden danger between the backup power management system and the energy equipment is smaller.

[0092] A trend hidden danger generation module is used to compare the coupling decline trend hidden danger index with a preset coupling decline trend hidden danger index threshold to determine whether there is a declining trend hidden danger in the coupling between the backup power management system and the current energy equipment;

[0093] If the coupling decreasing trend hidden danger index is greater than the coupling decreasing trend hidden danger index threshold, a coupling decreasing trend hidden danger signal is generated to indicate the coupling decreasing trend hidden danger between the backup power management system and the current energy equipment, so that the operator can quickly identify it;

[0094] If the coupling downward trend hidden danger index is less than or equal to the coupling downward trend hidden danger index threshold, there is no need to generate a coupling downward trend hidden danger signal and no special intervention is required;

[0095] The early warning classification module is used to classify the potential danger of a decreasing trend in the coupling between the backup power management system and the current energy equipment in combination with the importance information of the energy equipment when there is a potential danger of a decreasing trend in the coupling between the backup power management system and the energy equipment;

[0096] Get the number of times V1 of coupling downward trend hidden danger signal is generated, and calculate the proportion of downward trend hidden danger signal generation Yhbl. The expression is as follows Where V2 represents the total number of energy devices;

[0097] The proportion of the downward trend hidden danger signal generation is used to measure the proportion of energy equipment that generates coupling downward trend hidden danger signals to the total number of energy equipment. The greater the proportion of downward trend hidden danger signal generation, the deeper the degree of early warning of the downward trend hidden danger of coupling between the backup power management system and the energy equipment;

[0098] The energy equipment importance information includes energy equipment importance correlation coefficient;

[0099] The energy equipment importance correlation coefficient is used to measure the correlation between the energy equipment that generates the coupling decline trend hidden danger signal and other energy equipment. For example, the reduction of the output power of the power generation equipment may cause the load energy equipment that relies on the power to fail to operate normally; a transformer needs maintenance, which may affect the operation of the generator connected to it; when the energy equipment importance correlation coefficient is larger, it indicates that the energy equipment is more important, and the greater the impact of the coupling decline trend between it and the backup power management system, the deeper the warning level;

[0100] The logic for obtaining the correlation coefficient of energy equipment importance is as follows:

[0101] Obtain relevant assessment data of energy equipment including operating status data, environmental data, load data, energy consumption data, and fault data;

[0102] It should be noted that the operating status data of energy equipment includes operating time, downtime, etc., environmental data includes temperature, humidity, air pressure, vibration, etc., load data includes load power, equipment utilization rate, etc., energy consumption data includes energy consumption, energy consumption cost, etc., and fault data includes fault frequency, repair time, etc.;

[0103] Calculate the energy equipment correlation coefficient xgx between the target energy equipment and the remaining energy equipment. The expression is as follows Where X nrepresents the nth correlation evaluation data of the target energy equipment, represents the average value of the correlation evaluation data of the target energy equipment, Y m,n represents the nth correlation evaluation data of the remaining mth energy device, represents the average value of the correlation evaluation data of the remaining m-th energy equipment, n={1,2,...,N}, N is a positive integer;

[0104] It should be noted that the target energy device refers to any energy device among all energy devices, and the remaining energy devices refer to other energy devices except the target energy device. Before calculating the energy device correlation coefficient, it is necessary to ensure that the correlation evaluation data are normalized;

[0105] Calculate the energy equipment importance correlation coefficient Nysb, the expression is as follows where xgx h,m represents the energy equipment correlation coefficient between the h-th target energy equipment and the remaining m-th energy equipment, represents the preset energy equipment correlation coefficient threshold, h={1,2,...,H}, H is a positive integer, m={1,2,...,H-1};

[0106] According to the generation ratio of downward trend hidden danger signals and the correlation coefficient of energy equipment importance, a coupled downward trend hidden danger warning classification model is constructed to generate a coupled downward trend hidden danger warning classification index Oyzf. The model is based on the following formula: Oyzf = ln(r 1 *Yhbl+r 2 *Nysb+1), where r 1 、r 2 They represent the preset proportional coefficients of the generation ratio of downward trend hidden danger signals and the correlation coefficient of the importance of energy equipment, and r 1 、r 2 All are greater than 0;

[0107] It should be noted that before constructing the coupled downward trend hidden danger warning classification model, it is necessary to ensure that the downward trend hidden danger signal generation ratio and the energy equipment importance correlation coefficient are normalized. Commonly used normalization methods include Min-Max normalization, Z-Score normalization, etc. 1 , b 2 Set it according to the actual situation. For example, adopt the expert empowerment method, that is, invite experts in relevant fields to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0108] It can be seen from the above calculation expression that the greater the proportion of downward trend hidden danger signal generation and the greater the correlation coefficient of the importance of energy equipment, the greater the coupling downward trend hidden danger warning classification index, indicating that the overall downward trend hidden danger of coupling between the backup power management system and the energy equipment is more serious, and the degree of early warning intervention is deeper; conversely, the smaller the proportion of downward trend hidden danger signal generation and the smaller the correlation coefficient of the importance of energy equipment, the smaller the coupling downward trend hidden danger warning classification index is, indicating that the overall downward trend hidden danger of coupling between the backup power management system and the energy equipment is relatively gentle, and the degree of early warning intervention can be appropriately reduced;

[0109] The coupling downward trend hidden danger warning classification index is compared with the preset coupling downward trend hidden danger warning classification index threshold. If the coupling downward trend hidden danger warning classification index is greater than the coupling downward trend hidden danger warning classification index threshold, a first-level warning signal is generated, indicating that many energy equipment have potential coupling problems, and the situation is relatively serious and needs to be dealt with urgently; if the coupling downward trend hidden danger warning classification index is less than or equal to the coupling downward trend hidden danger warning classification index threshold, a second-level warning signal is generated, and the current coupling problem is still within an acceptable range, but vigilance and enhanced monitoring are still required;

[0110] It should be noted that the level of early warning intervention for the first-level early warning signal is deeper than that for the second-level early warning signal;

[0111] The present invention obtains the dispatch response information of the backup power management system when dispatching the energy equipment, obtains the equipment dispatch response delay rising coefficient, obtains the data communication loss information when the backup power management system communicates data with the energy equipment, and obtains the data communication loss abnormality coefficient, constructs a coupling decline trend hidden danger analysis model, generates a coupling decline trend hidden danger index, intelligently perceives the coupling decline trend hidden danger between the backup power management system and the energy equipment, identifies and solves the coupling problem early, reduces the equipment failure rate, reduces the maintenance and repair costs, and improves the energy utilization efficiency and post-disaster recovery capabilities as well as the energy management level in remote areas.

[0112] The present invention obtains the correlation coefficient of the downward trend hidden danger signal generation ratio and the importance of energy equipment from an overall perspective, constructs a coupling downward trend hidden danger warning classification model, generates a coupling downward trend hidden danger warning classification index, and classifies the coupling downward trend hidden dangers between the backup power management system and the energy equipment into different degrees of warning, thereby improving the response capability and post-disaster recovery efficiency, and promoting the efficient and reliable operation of the multi-energy collaborative system.

[0113] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0114] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0115] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0116] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-energy coordinated power management system, characterized in that: It includes equipment dispatch response module, data communication loss module, model building module, trend hidden danger generation module, and early warning classification module; The equipment dispatch response module is used to obtain the dispatch response information when the backup power management system dispatches the energy equipment, and obtain the equipment dispatch response delay rise coefficient according to the dispatch response information; A data communication loss module is used to obtain data communication loss information when the backup power management system communicates data with the energy equipment, and obtain a data communication loss abnormality coefficient according to the data communication loss information; A model building module is used to build a coupling decline trend hidden danger analysis model based on the equipment dispatch response delay increase coefficient and the data communication loss abnormality coefficient, conduct a comprehensive analysis of the coupling decline trend hidden dangers between the backup power management system and the energy equipment, and generate a coupling decline trend hidden danger index; A trend hidden danger generation module is used to compare the coupling decline trend hidden danger index with a preset coupling decline trend hidden danger index threshold to determine whether there is a declining trend hidden danger in the coupling between the backup power management system and the current energy equipment; The early warning classification module is used to classify the hidden dangers of the downward trend of the coupling between the backup power management system and the current energy equipment when there is a hidden danger of the downward trend of the coupling between the backup power management system and the current energy equipment, combined with the importance information of the energy equipment.

2. The multi-energy coordinated power management system according to claim 1, characterized in that: By obtaining the dispatch response information when the backup power management system dispatches the energy equipment, analyzing the dispatch response situation between the backup power management system and the energy equipment, and obtaining the equipment dispatch response delay increase coefficient, the degree of increase in the dispatch response delay between the backup power management system and the energy equipment is measured; The logic for obtaining the device scheduling response delay rise coefficient is as follows: Each time the backup power management system schedules the energy equipment, the time tc when the backup power management system sends the scheduling instruction and the time ts when the energy equipment executes the scheduling instruction are obtained, and the scheduling response value dxy is calculated. The expression is as follows: dxy = ts - tc; a scheduling response value data analysis set is constructed, and the scheduling response value data analysis set is marked as DXY = {dxy i }={dxy1,dxy2,...,dxy I }, where dxy i represents the dispatch response value calculated by the backup power management system when dispatching the energy equipment for the i-th time, i={1,2,...,I}, I is a positive integer; Calculate average dispatch response time The expression is as follows Calculate the standard deviation dxyσ of the scheduling response time, the expression is as follows Calculate the scheduling response time deviation coefficient cv, the expression is as follows Calculate the abnormal coefficient Zdxy of the scheduling response time i , the expression is as follows Calculate the trend coefficient qs, the expression is as follows where tc i It indicates the time when the backup power management system sends the scheduling instruction for the i-th time. is the average value of the time when the backup power management system sends the scheduling instruction, and the expression is as follows Calculate the device scheduling response delay rise coefficient Sbxy, the expression is as follows Wherein max(0,qs) is the maximum value acquisition function. If qs≤0, max(0,qs) takes the value of 0. If qs>0, max(0,qs) takes the value of qs. a1, a2, and a3 represent preset proportional coefficients, and a1, a2, and a3 are all greater than 0.

3. The multi-energy coordinated power management system according to claim 1, characterized in that: By acquiring the data communication loss information when the backup power management system communicates with the energy equipment, analyzing the data communication loss between the backup power management system and the energy equipment, and acquiring the data communication loss abnormality coefficient, the abnormal degree of data communication loss between the backup power management system and the energy equipment is measured; The logic for obtaining the data communication loss anomaly coefficient is as follows: When the backup power management system communicates data with the energy equipment, multiple monitoring cycles are preset, and the packet loss rate in different monitoring cycles is calculated. The calculation expression is as follows: where dsl t represents the packet loss rate in the t-th monitoring cycle, s1 represents the number of packets lost when the backup power management system communicates with the energy equipment in the t-th monitoring cycle, s2 represents the total number of packets when the backup power management system communicates with the energy equipment in the t-th monitoring cycle, t={1,2,...,T}, T is a positive integer; Calculate the exponentially weighted moving average of the packet loss rate. The calculation expression is as follows: t+1 =α*dsl t+1 +(1-α)*Eds t , of which Eds t represents the exponentially weighted moving average of the packet loss rate in the tth monitoring period, α represents the preset smoothing factor, dsl t+1 represents the packet loss rate of the t+1th monitoring period, Eds t+1 represents the exponentially weighted moving average of the packet loss rate in the t+1th monitoring period; The packet loss rate and the exponentially weighted moving average of the packet loss rate in different monitoring periods are taken as a set of feature data, and the Euclidean distance between the feature data in the t-th monitoring period and the remaining feature data in the j-th monitoring period is calculated. The calculation expression is as follows: where d t,j represents the Euclidean distance between the feature data of the tth monitoring period and the remaining jth monitoring periods, dsl t represents the packet loss rate in the tth monitoring period, Eds t represents the exponentially weighted moving average of the packet loss rate in the tth monitoring period, dsl j represents the packet loss rate in the remaining j-th monitoring period, Eds j represents the exponentially weighted moving average of the packet loss rate in the remaining j-th monitoring period, j = {1, 2, ..., T-1}; Compare the Euclidean distance between the feature data of the t-th monitoring cycle and the remaining j-th monitoring cycle with the preset distance threshold. When the Euclidean distance between the feature data of the t-th monitoring cycle and the remaining j-th monitoring cycle is less than the distance threshold, add the corresponding j-th monitoring cycle to the neighbor set of the t-th monitoring cycle. Calculate the local density, the expression is as follows where ld t represents the local density of the tth monitoring period, ZS t It represents the accumulated value of the Euclidean distance within the neighbor set corresponding to the t-th monitoring period; Calculate the packet loss outlier value LOF of the tth monitoring period t , the expression is as follows where ld k represents the local density of the kth monitoring period in the neighbor set corresponding to the tth monitoring period, k = {1, 2, ..., K}, K is a positive integer; The data packet loss anomaly values ​​of T monitoring cycles are compared with the preset anomaly thresholds respectively, and the data packet loss anomaly values ​​greater than the anomaly thresholds are accumulated and calculated to obtain the data communication loss anomaly coefficient Sjtc.

4. The multi-energy coordinated power management system according to claim 1, characterized in that: According to the equipment scheduling response delay increase coefficient and data communication loss abnormality coefficient, a coupling decline trend hidden danger analysis model is constructed to generate the coupling decline trend hidden danger index Ohxz. The formula based on the model is as follows Wherein b1 and b2 represent the preset proportional coefficients of the device scheduling response delay increase coefficient and the data communication loss abnormality coefficient, respectively, and b1 and b2 are both greater than 0.

5. The multi-energy coordinated power management system according to claim 4, characterized in that: If the coupling downward trend hidden danger index is greater than the coupling downward trend hidden danger index threshold, a coupling downward trend hidden danger signal is generated; If the coupling downward trend hidden danger index is less than or equal to the coupling downward trend hidden danger index threshold, there is no need to generate a coupling downward trend hidden danger signal.

6. The multi-energy coordinated power management system according to claim 5, characterized in that: Get the number of times V1 of the coupling downward trend hidden danger signal is generated, and calculate the downward trend hidden danger signal generation ratio Yhbl. The expression is as follows Where V2 represents the total number of energy devices.

7. The multi-energy coordinated power management system according to claim 1, characterized in that: The energy equipment importance information includes energy equipment importance correlation coefficient; The logic for obtaining the correlation coefficient of energy equipment importance is as follows: Obtain relevant assessment data of energy equipment including operating status data, environmental data, load data, energy consumption data, and fault data; Calculate the energy equipment correlation coefficient xgx between the target energy equipment and the remaining energy equipment. The expression is as follows Where X n represents the nth correlation evaluation data of the target energy equipment, represents the average value of the correlation evaluation data of the target energy equipment, Y m,n represents the nth correlation evaluation data of the remaining mth energy device, represents the average value of the correlation evaluation data of the remaining m-th energy equipment, n={1,2,...,N}, N is a positive integer; Calculate the energy equipment importance correlation coefficient Nysb, the expression is as follows where xgx h,m represents the energy equipment correlation coefficient between the h-th target energy equipment and the remaining m-th energy equipment, represents the preset energy equipment correlation coefficient threshold, h={1,2,...,H}, H is a positive integer, and m={1,2,...,H-1}.

8. The multi-energy coordinated power management system according to claim 7, characterized in that: According to the generation ratio of downward trend hidden danger signals and the correlation coefficient of the importance of energy equipment, a coupled downward trend hidden danger warning classification model is constructed to generate a coupled downward trend hidden danger warning classification index Oyzf. The model is based on the following formula: Oyzf=ln(r1*Yhbl+r2*Nysb+1), where r1 and r2 represent the preset proportional coefficients of the generation ratio of downward trend hidden danger signals and the correlation coefficient of the importance of energy equipment, respectively, and r1 and r2 are both greater than 0.

9. The multi-energy coordinated power management system according to claim 8, characterized in that: The coupling downward trend hidden danger warning classification index is compared with the preset coupling downward trend hidden danger warning classification index threshold, and if the coupling downward trend hidden danger warning classification index is greater than the coupling downward trend hidden danger warning classification index threshold, a first-level warning signal is generated; If the coupling downward trend hidden danger warning classification index is less than or equal to the coupling downward trend hidden danger warning classification index threshold, a secondary warning signal is generated.

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