Information entropy-based dynamic peak capacity analysis method and system for generator sets

Abstract

An improved information entropy-based dynamic peak capacity analysis method and system for generator sets, characterized in that the method comprises the following steps: collecting performance indicator data of each generator set at each moment within a region, so as to form a performance indicator data matrix; normalizing the performance indicator data at a set moment and an offset value of the performance indicator data between adjacent moments, so as to respectively obtain a normalized performance indicator matrix and a normalized offset performance indicator matrix; calculating static and dynamic analysis peak capacities of each performance indicator of the generator set; and on the basis of the obtained peak capacities of the performance indicators and offset performance indicators, calculating an integrated peak capacity of each performance indicator of the generator set. By means of dynamically analyzing peak capacities at different moments, the present invention provides effective analysis rules for the power peak of generator sets, thereby determining the peak capacities of the generator sets at the current moment and different moments.

Description

A generator set peak capacity dynamic analysis method and system based on information entropy TECHNICAL FIELD

[0001] The present application relates to the field of generator set power peak capacity dynamic analysis, in particular to a generator set peak capacity dynamic analysis method and system based on information entropy. BACKGROUND

[0002] Electric power peak is related to the normal development of social economy, with the continuous economic recovery, continuous improvement of people's quality of life, frequent extreme weather phenomena, power supply gap occurs from time to time, and the pressure of electric power peak is huge; it is of great significance to analyze the peak capacity of the generator set.

[0003] CN117973848A constructs an electric power system peak resilience evaluation index system from the time dimension, the spatial dimension and the electric power system characteristic dimension; the combined weighting method is used to solve the weight of the electric power system peak resilience evaluation index system under the extreme risk event, and the peak resilience is scored.

[0004] CN117764307A formulates peak decision in the meteorological disaster early warning stage from the angles of meteorological disaster prediction, electric power peak decision generation and digital twin simulation.

[0005] CN117728418A electric power system peak capacity optimization method based on source network load storage integrated decision, proposes feasible supporting wind power, photovoltaic and energy storage capacity under different power generation scales.

[0006] CN116154867A proposes a dynamic piecewise linear climbing model, more accurately evaluates the real-time climbing capacity of the unit, obtains a more realistic and feasible day-ahead unit dispatching table, and simulates a realistic and feasible generator set dispatching scheme.

[0007] CN117895544A constructs an adjusting power source to promote peak saturation effect model to improve the rationality of electric power system dispatching.

[0008] The above-mentioned patents do not involve time series dynamic peak capacity analysis of generator sets, and the peak capacity of generator sets at different times will change greatly, which is not only reflected in the peak capacity at a certain moment, but also related to the peak capacity between different moments; developing dynamic analysis of generator sets can solve the problem of unit peak analysis under time series, which helps to master the peak capacity of generator sets at present and at different moments, and understand the future development trend.

[0009] Therefore, a generator set peak capacity dynamic analysis method is needed, which realizes dynamic analysis of the peak capacity of the generator set by assigning corresponding dynamic weights. SUMMARY

[0010] In order to solve the problems in the prior art, the application provides a generator set peak capacity dynamic analysis method based on information entropy, corresponding weights are distributed to index information, index changes at different times are considered, corresponding dynamic weights are distributed, and dynamic analysis of the generator set peak capacity is realized.

[0011] The application adopts the technical scheme as follows.

[0012] A generator set peak capacity dynamic analysis method based on improved information entropy comprises the following steps.

[0013] Step 1: collect performance index data of each generator set at a set time in a region to form a performance index data matrix;

[0014] Step 2: normalize the performance index data at the set time and the offset value of the performance index data between adjacent times to obtain a performance index normalized matrix and an offset performance index normalized matrix respectively;

[0015] Step 3: calculate the weight of each performance index and offset performance index to obtain a performance index standard matrix and an offset performance index standard matrix;

[0016] Step 4: calculate the peak capacity of each performance index and offset performance index of the generator set;

[0017] Step 5: based on the peak capacity of the performance index and offset performance index obtained in step 4, the fusion peak capacity of the generator set is calculated.

[0018] The application further comprises the following preferred schemes:

[0019] In step 1, the performance index includes generator set peak efficiency, power generation contribution rate, peak average load rate, peak available hours, electric coal inventory target completion rate, available coal storage days, fuel health degree, boiler equipment health degree, steam turbine equipment health degree, thermal system health degree and electrical equipment health degree.

[0020] In step 3, the calculation formula of the weight of each performance index and offset performance index is as follows:

[0021] ,

[0022] wherein, , wherein respectively represent the number of generator sets, the number of evaluation indexes and the number of different times, represents the first time; , respectively represent the first time under the first The offset performance index weight of the i-th performance index; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment. The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment.

[0023] In step 4, the calculation formula of the peak capacity of each performance index and offset performance index of the generator set is as follows:

[0024] ,

[0025] , The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment; The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment. The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment. The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment. The normalized value of the offset value of the i-th performance index of the j-th generator set at the k-th moment.

[0026] The calculation formula of the improved information entropy between each performance index and the positive and negative ideal solutions of the performance index of each generator set is as follows:

[0027] ,

[0028] Among them, The information entropy of each performance index and the positive and negative ideal solutions at the k-th moment; The information entropy of each performance index and the positive and negative ideal solutions at the k-th moment; The information entropy of each performance index and the positive and negative ideal solutions at the k-th moment. The information entropy of each performance index and the positive and negative ideal solutions at the k-th moment. The information entropy of each performance index and the positive and negative ideal solutions at the k-th moment. The information entropy of each performance index and the positive and negative ideal solutions at the k-th moment.

[0029] The calculation formula of the improved information entropy between each performance index and the positive and negative ideal solutions of the performance index of each generator set is as follows:

[0030] ,

[0031] Among them, The information entropy of each performance index and the positive and negative ideal solutions at the k-th moment; The information entropy of each performance index and the positive and negative ideal solutions at the k-th moment; The offset effectiveness index of each generator set is offset from the improved information entropy of the positive and negative ideal solutions. 、 The positive and negative ideal solutions of the first offset effectiveness index are respectively obtained.

[0032] In step 5, the fusion peak capacity calculation formula of the generator set is as follows:

[0033] 、

[0034] Among them, 、 are the effectiveness and offset effectiveness weight coefficients respectively, 、 indicate the fusion peak capacity of the first generator set at the first moment, 、 indicate the peak capacity of the effectiveness index and the offset effectiveness index of each generator set at the first moment respectively.

[0035] Meanwhile, the application also provides a dynamic analysis system for the peak capacity of a generator set based on improved information entropy, comprising a data acquisition module, a data processing module, a static and dynamic peak capacity calculation module and a fusion peak capacity calculation module.

[0036] The data acquisition module is used for acquiring the effectiveness index data of each generator set at each moment in a region to form an effectiveness index data matrix.

[0037] The data processing module is used for normalizing the effectiveness index data at each moment and the change of the effectiveness index data between different moments to obtain normalized matrices respectively.

[0038] The static and dynamic peak capacity calculation module is used for calculating the static and dynamic analysis peak capacity of each effectiveness index of the generator set.

[0039] The fusion peak capacity calculation module is used for calculating the fusion peak capacity of each effectiveness index of the generator set.

[0040] A terminal comprises a processor and a storage medium; the storage medium is used for storing instructions; the processor is used for operating according to the instructions to perform the steps of the method according to any one of the embodiments.

[0041] A computer readable storage medium, which stores a computer program, the program is executed by a processor to realize the steps of the method according to any one of the embodiments.

[0042] ​The beneficial effect of the present application is that, compared with the prior art, the present application issues the generator set peak capacity index at different times, allocates the corresponding weight through the index information amount, simultaneously considers the problem that the two equal distances in the traditional information entropy cannot be evaluated and sorted, calculates the effectiveness index at the same time and at different times based on the improved information entropy, realizes the dynamic analysis of the generator set peak capacity by allocating the corresponding dynamic weight at different times, the present application not only considers the peak capacity analysis at a certain time, but also dynamically analyzes the deviation trend at different times, provides effective analysis rules for the generator set power peak, masters the peak capacity of the generator set at the current time and at different times, and verifies the effectiveness of the index and the analysis method through data. BRIEF DESCRIPTION OF DRAWINGS

[0043] Fig. 1 is a flow chart of a dynamic analysis method of a generator set peak capacity based on information entropy in the present application;

[0044] Fig. 2 is a block diagram of a dynamic analysis system of a generator set peak capacity based on information entropy in the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, but not all the embodiments. Based on the spirit of the present application, other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0046] The present application provides a dynamic analysis method of a generator set peak capacity based on information entropy, as shown in Fig. 1, which is a flow chart of a dynamic analysis method of a generator set peak capacity based on information entropy in the present application, the method comprising the following steps:

[0047] Step 1: Collect the effectiveness index data of each generator set at a set time in the region to form an effectiveness index data matrix;

[0048] In an embodiment of the present application, in step 1, the effectiveness index includes the generator set peak efficiency, the power contribution rate, the peak average load rate, the peak available hours, the coal inventory target completion rate, the available coal days, the fuel health degree, the boiler equipment health degree, the steam turbine equipment health degree, the thermal system health degree and the electrical equipment health degree.

[0049] In the present embodiment, the effectiveness index data of each generator set at each time in the region is collected, and the T1, T2, T3 and T4 of each generator set at each time are calculated ​Four indicators, with dates of January 1, 2020, May 1, 2020, September 1, 2020, and December 1, 2020. ,get OK Column data matrix , Indicates the first At this moment Line number The results of the performance indicators are shown in Table 1.1, where "unit" refers to the generator set.

[0050] Table 1.1 Performance Indicator Data

[0051]

[0052] Based on the raw data, four time points and four indicators were selected. At the same time point, each unit changed only one indicator compared to the previous unit. Unit 5 had the best indicators, and the indicator data of Unit 6 and Unit 4, and Unit 7 and Unit 3 were the same. At different time points, one indicator of each unit was increased compared to the previous time. The data of the four indicators were divided into multiple orders of magnitude.

[0053] Step 2: Normalize the performance indicator data at the set time and the offset values ​​of the performance indicator data between adjacent time points to obtain the performance indicator normalization matrix and the offset performance indicator normalization matrix, respectively.

[0054] In one embodiment of the present invention, the calculation formula for the performance index normalization matrix in step 2 is as follows:

[0055] ,

[0056] ,

[0057] in, They represent the first The generator set Each evaluation indicator is in The original value and normalized value at time point ,in These represent the number of generator sets, the number of evaluation indicators, and the number of different times, respectively. Indicates the first time.

[0058] Considering, for example, a positive indicator in The value at time is The result after processing using the range transformation method is: ,exist The time value is , the result after processing by range transformation method is also ; similarly, the results of and time after processing by linear proportional transformation method are ; the results after processing by normalization method are . The above dimensionless process increment information is lost, and this problem also exists when other commonly used static dimensionless methods are used. Therefore, in this paper, the data at each time and the change of the data at different times are normalized.

[0059] In an embodiment of the present application, in step 2, the calculation of the performance indicator data change dynamic normalization matrix includes:

[0060] In step 2, the calculation of the performance indicator data change dynamic normalization matrix includes:

[0061] Step S201: Calculate the offset matrix of the performance indicator data matrix: ,

[0062] Wherein, represents the offset value of each performance indicator of each generator at time, and is defined as ;

[0063] Step S202: Calculate the proportion of each value in the offset matrix in the offset matrix to obtain the normalization matrix of the offset matrix.

[0064] ,

[0065] represents the normalized value of the offset value of the th generator and the th evaluation index at time.

[0066] In this embodiment, the normalization matrix at each time is shown in Table 1.2, and the offset normalization matrix between different times is shown in Table 1.3.

[0067] Table 1.2 Normalization matrix at each time

[0068]

[0069] Table 1.3 Offset normalization matrix between different times

[0070]

[0071] Step 3: Calculate the weights of each performance indicator and the offset performance indicator to obtain the performance indicator normalization matrix and the offset performance indicator normalization matrix.

[0072] In one embodiment of the present invention, the calculation formulas for each performance indicator and the weight of the offset performance indicator are as follows:

[0073] ,

[0074] ,

[0075] in, ,in These represent the number of generator sets, the number of evaluation indicators, and the number of different times, respectively. Indicates the first time; , They represent the first At this moment Individual performance indicators and their weights; Indicates the first The generator set Each evaluation indicator is in The normalized value at time; Indicates the first The generator set The offset of each evaluation indicator is at The normalized values ​​at time points are shown in Table 1.4.

[0076] Table 1.4 Performance Indicators and Weights of Offset Performance Indicators

[0077]

[0078] Step 4: Calculate the peak capacity of each performance index and the deviation performance index of the generator set.

[0079] In one embodiment of the present invention, the calculation formulas for the peak capacity of each performance index and the deviation performance index of the generator set are as follows:

[0080] ,

[0081] , They represent the first Time of the first Peak capacity of individual generator set performance indicators and peak capacity of deviation performance indicators. The respective Time of the first For each generator unit's performance indicators, The respective the moment the distance between each offset effectiveness index of the generator set and the positive and negative ideal solutions.

[0082] Preferably, the calculation formula of the improved information entropy between each effectiveness index of the generator set and the positive and negative ideal solutions of the effectiveness index is as follows:

[0083] ,

[0084] wherein, the maximum and minimum values of each column index in Table 1.2 are taken respectively, to obtain , the distance between each offset effectiveness index of the generator set and the positive and negative ideal solutions of the offset effectiveness index; the maximum and minimum values of each column index in Table 1.3 are taken respectively, to obtain , the positive and negative ideal solutions of the effectiveness index of the 3rd generator set at the moment of 2020.01.01.

[0085] Preferably, the calculation formula of the improved information entropy between each offset effectiveness index of the generator set and the positive and negative ideal solutions of the offset effectiveness index is as follows:

[0086] ,

[0087] wherein, the distance between each offset effectiveness index of the generator set and the positive and negative ideal solutions of the offset effectiveness index; the maximum and minimum values of each column index in Table 1.3 are taken respectively, to obtain , the positive and negative ideal solutions of the offset effectiveness index of the 3rd generator set at the moment of 2020.01.01.

[0088] Information entropy is a non-symmetrical measure that can measure the distance between two probability distributions and does not satisfy the triangle inequality, so it can solve the defect that the points on the midline of the two ends cannot be effectively sorted. However, the logarithmic function in the relative entropy formula requires positive data values, so on the basis of the original formula, the true number is added with the absolute value, and the numerator and denominator are also added with which has little effect on the calculation result and can ensure that it can be normally calculated when there is zero value data. As shown in Table 1.3, the 3rd unit at 2020.01.01 and the 1st unit at 2020.05.01 cannot obtain the difference sorting value. This paper uses the improved information entropy to solve the defect that the points on the midline of the two ends cannot be effectively sorted, and obtains the effective sorting result as shown in Table 1.6. The sorting value of the 3rd unit at 2020.01.01 is 0.1, and the sorting value of the 1st unit at 2020.05.01 is 0.102.

[0089] ​​​​Step 5: Based on the performance indicators and the peak capacity of the offset performance indicators obtained in step 4, the integrated peak capacity of the generator set is calculated.

[0090] In an embodiment of the present application, the formula for calculating the integrated peak capacity of the generator set is as follows:

[0091]

[0092] wherein, are the performance and offset performance weight coefficients respectively, represents the integrated peak capacity of the generator set at the i-th moment, , respectively, represent the performance indicator ranking result of the i-th generator set at the i-th moment and the peak capacity of the offset performance indicator, respectively, and are taken as The performance indicator ranking result, the offset performance indicator ranking result and the final integrated ranking result are shown in Table 1.5, Table 1.6 and Table 1.7, respectively.

[0093] Table 1.5 Performance indicator ranking result

[0094] Table 1.6 Offset performance indicator ranking result

[0095] It should be noted that the "2020.05.01" list represents the data change from 2020.01.01 to 2020.05.01, and the others are sequentially extended, and "2020.01.01" represents itself.

[0096] Table 1.7 Final integrated ranking result

[0097] From the selection characteristics of the original data, at the same moment, the peak capacity ranking value should gradually increase from unit 1 to unit 5, and then gradually decrease to unit 7, and the results of unit 7, unit 3, unit 6 and unit 4 should be consistent; at different moments, the indicator value of the same unit should gradually increase, and the peak capacity ranking value should gradually increase; the orders of magnitude of different indicators are different, and the change of the peak capacity ranking value should not be affected by the change of the dimension. From the result table 1.7, it can be seen that the results all meet the above characteristics, verifying the correctness and rationality of the algorithm.

[0098] ​​​​​​​​In another embodiment of the present application, the effectiveness of the algorithm is illustrated using 5 sets of 11 performance indicator data of the generator units, including generator unit peak power, power generation contribution rate, peak average load rate, peak available hours, coal inventory target completion rate, available coal storage days, fuel health, boiler equipment health, steam turbine equipment health, thermal system health, and electrical equipment health. The raw data is shown in Table 1.8, the static and dynamic information entropy and weight results are shown in Table 1.9, and the final ranking results are shown in Table 1.10.

[0099] Table 1.8 Raw data

[0100]

[0101] Table 1.9 Static and dynamic information entropy and weight

[0102] Table 1.10 Comprehensive ranking results based on information entropy

[0103] On 2020.01.01, most of the indicators of unit 1 are higher than those of unit 3, on 2020.05.01, the indicators of the unit decrease, and the changes at the two time points further aggravate the bottom of the peak capacity, on 2020.09.01 and 2020.12.01, the peak capacity of unit 1 gradually rises, and the ranking results of unit 1 fluctuate greatly, which is related to the fluctuation of the indicators of the unit at different time points. The above results verify the effectiveness of the method for dynamic analysis of the peak capacity of the unit.

[0104] Through the above embodiments, the rationality and effectiveness of the analysis method are proved.

[0105] Meanwhile, the present application also realizes a generator unit peak capacity dynamic analysis system based on improved information entropy, as shown in Figure 2 is a block diagram of a generator unit peak capacity dynamic analysis system based on information entropy in the present application, the system includes a data acquisition module, a data processing module, a static and dynamic peak capacity calculation module, and a fusion peak capacity calculation module;

[0106] The data acquisition module is used for acquiring the performance indicator data of each generator unit at each time in the region, to form a performance indicator data matrix;

[0107] The data processing module is used for normalizing the performance indicator data at each time and the change dynamics of each performance indicator data between different times, to obtain normalized matrices respectively;

[0108] The static and dynamic peak capacity calculation module is configured to calculate the static and dynamic analysis peak capacity of each performance index of the generator set.

[0109] The fusion peak capacity calculation module is configured to calculate the fusion peak capacity of each performance index of the generator set.

[0110] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0111] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a magneto-optical or other optical medium, and / or any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0112] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0113] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0114] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A dynamic analysis method for the peak capacity of generator sets based on improved information entropy, characterized in that, The method includes the following steps: Step 1: Collect the performance index data of each generator group in the region at a set time to form a performance index data matrix; Step 2: Normalize the performance indicator data at the set time and the offset values ​​of the performance indicator data between adjacent time points to obtain the performance indicator normalization matrix and the offset performance indicator normalization matrix, respectively. Step 3: Calculate the weights of each performance indicator and the offset performance indicator to obtain the performance indicator normalization matrix and the offset performance indicator normalization matrix. Step 4: Calculate the peak capacity of each performance index and the deviation performance index of the generator set. Step 5: Based on the peak capabilities of the performance indicators and offset performance indicators obtained in Step 4, calculate the combined peak capability of the generator set.

2. The method for dynamic analysis of generator peak capacity based on improved information entropy according to claim 1, characterized in that: In step 1, the performance indicators include generator unit peak efficiency, power generation contribution rate, peak average load rate, peak available hours, coal inventory target completion rate, available coal storage days, fuel health, boiler equipment health, turbine equipment health, thermal system health, and electrical equipment health.

3. The method for dynamic analysis of generator peak capacity based on improved information entropy according to claim 1, characterized in that: In step 3, the calculation formulas for each performance indicator and the weight of the offset performance indicator are as follows: ， in, ,in These represent the number of generator sets, the number of evaluation indicators, and the number of different times, respectively. Indicates the first time; They represent the first At this moment Individual performance indicators and their weights; Indicates the first The generator set Each evaluation indicator is in The normalized value at time; Indicates the first The generator set The offset of each evaluation indicator is at The normalized value at time.

4. The method for dynamic analysis of generator peak capacity based on improved information entropy according to claim 1, characterized in that: In step 4, the calculation formulas for the peak capacity of each performance index and the deviation performance index of the generator set are as follows: ， They represent the first Time of the first Peak capacity of individual generator set performance indicators and peak capacity of deviation performance indicators. The respective Time of the first For each generator unit's performance indicators, The respective Time of the first The distance between each offset performance index of each generator set and the positive and negative ideal solutions of the offset.

5. The method for dynamic analysis of generator peak capacity based on improved information entropy according to claim 4, characterized in that: The formulas for calculating the improved information entropy between the performance indicators and the positive and negative ideal solutions of each generator unit are as follows: ， in, The respective Time of the first Information entropy of each performance indicator of a generator unit and its positive and negative ideal solutions; 、 The respective Positive and negative ideal solutions for each performance indicator.

6. The method for dynamic analysis of generator peak capacity based on improved information entropy according to claim 5, characterized in that: The formula for calculating the improved information entropy between the offset performance index of each generator set and the positive and negative ideal solutions of the offset performance index is as follows: ， ， in, The respective Time of the first Improved information entropy of each generator set's offset performance index and the positive and negative ideal solutions of offset; 、 The respective The positive and negative ideal solutions for each deviation performance indicator.

7. The method for dynamic analysis of generator peak capacity based on improved information entropy according to claim 1, characterized in that: In step 5, the formula for calculating the combined peak capacity of the generator set is as follows: ， in, ， These are the weighting coefficients for effectiveness and deviation effectiveness, respectively. ， Indicates the first Time of the first The combined peak capacity of individual generator sets ， , respectively representing the first Time of the first Peak capabilities of each generator set's performance indicators and offset performance indicators.

8. A dynamic analysis system for generator peak capacity based on improved information entropy using the method described in any one of claims 1-7, comprising a data acquisition module, a data processing module, a static and dynamic peak capacity calculation module, and a fused peak capacity calculation module, characterized in that: The data acquisition module is used to collect the performance index data of each generator group at each time in the region, and form a performance index data matrix. The data processing module is used to normalize the performance indicator data at each time point and the dynamic changes of the performance indicator data between different time points, and obtain normalization matrices respectively. The static and dynamic peak capacity calculation module is used to calculate the static and dynamic peak capacity of each performance indicator of the generator set. The fusion peak capacity calculation module is used to calculate the fusion peak capacity of each performance indicator of the generator set.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-7.

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