Energy storage power station management system based on big data analysis

Abstract

The present invention relates to the technical field of energy storage power station management, and discloses an energy storage power station management system based on big data analysis, including a data acquisition module, a preprocessing module, a performance analysis module, a comprehensive evaluation module, and an early warning display unit. The present invention can clean abnormal values ​​and normalize data through preprocessing to improve data accuracy and comparability, laying a foundation for analysis; in terms of energy storage unit performance monitoring, it can accurately evaluate performance, warn of failures in advance, and reasonably guide replacement to ensure its stable operation; for the overall performance evaluation of the energy storage power station, it can fully grasp the status and discover potential problems in time, which is conducive to the formulation of operation and maintenance strategies; at the operation optimization level, it can optimize the charging and discharging process, extend the life of equipment, and improve energy utilization efficiency; in terms of information display and decision support, it can transmit information in real time and assist scientific decision-making, so that operation and maintenance personnel can make reasonable operations in time, and comprehensively ensure the efficient, stable and safe operation of the energy storage power station.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage power station management, and in particular to an energy storage power station management system based on big data analysis. Background Art

[0002] As a key energy storage facility, energy storage power stations are increasingly used in power systems. However, energy storage power stations are usually composed of a large number of energy storage units, and their operating environment is complex and changeable, and they face many challenges in long-term operation.

[0003] On the one hand, the performance of the energy storage unit itself is prone to fluctuations and decline due to frequent charge and discharge cycles, aging effects, and interference from external environmental factors. Traditional operation and maintenance methods often rely on regular manual inspections or simple monitoring equipment, making it difficult to accurately grasp the actual operating status of each energy storage unit in real time. For example, it is impossible to detect subtle changes in the internal resistance of the energy storage unit in time, and the change in internal resistance is precisely one of the important indicators reflecting the health of the battery; at the same time, it is difficult to detect abnormal increases in power loss early, which may cause the energy storage unit to deteriorate faster or even fail prematurely if it is not maintained in time, seriously affecting the overall performance and reliability of the energy storage power station.

[0004] On the other hand, from the overall perspective of energy storage power stations, there is currently a lack of a complete and intelligent system to comprehensively evaluate their operating efficiency. In the process of energy conversion and transmission, if the energy efficiency cannot be accurately calculated, it is impossible to know whether the power station is in an efficient operating state, and it is difficult to optimize the equipment operating parameters or troubleshoot potential energy loss links. In addition, the power interaction between the energy storage power station and the power grid is complex. Once there is a problem with the power balance, it will not only weaken the stability of the power station itself, but may also cause an impact on the power grid, causing power grid fluctuations or even failures, threatening the safe and stable operation of the power system.

[0005] Furthermore, in the face of massive amounts of real-time operating data, if it is not effectively pre-processed, the noise and outliers in the data will interfere with subsequent analysis and judgment, making data-driven operation and maintenance decisions lose their accuracy foundation. At the same time, the differences in the magnitude and characteristic distribution of data from different energy storage units also make it difficult to conduct unified performance evaluation and optimization. There is an urgent need for a method that can standardize and normalize data. Summary of the invention

[0006] The purpose of the present invention is to provide an energy storage power station management system based on big data analysis, which solves the technical problems raised in the background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] Energy storage power station management system based on big data analysis, including:

[0009] The data acquisition module is used to collect the real-time operation data of the energy storage power station within a specified period; the real-time operation data includes the voltage value, current value, and charge and discharge power of each energy storage unit in the energy storage power station;

[0010] A preprocessing module is used to preprocess the real-time operation data, and the preprocessing includes data cleaning and data normalization processing on the real-time operation data;

[0011] The performance analysis module is used to perform performance evaluation and analysis on each energy storage unit, and determine the internal resistance deviation, power loss and operating status of each energy storage unit at each acquisition time node based on the performance evaluation and analysis results;

[0012] A comprehensive evaluation module is used to conduct a comprehensive performance evaluation of the energy storage power station, and determine the energy efficiency and power balance status of the energy storage power station based on the comprehensive performance evaluation results;

[0013] The early warning display unit is used to display the relevant signals obtained by the performance analysis module and the comprehensive evaluation module to the relevant operation and maintenance personnel.

[0014] As a further solution of the present invention: the voltage value, current value, and charge / discharge power of each energy storage unit in the energy storage power station are marked as V i，j , L i，j , P i，j , i = 1, 2, ... n, j = 1, 2, ... m, n represents the total number of energy storage units in the energy storage power station, m represents the total number of acquisition time nodes in a specified period, where the time intervals of adjacent acquisition time nodes are consistent and are recorded as t 0 .

[0015] As a further solution of the present invention: the pretreatment method is as follows:

[0016] StepS1, data cleaning:

[0017] StepS1.1, select the voltage value V i，j ;

[0018] pass Calculate the V in each energy storage unit i，j The average value P V，i ;

[0019] StepS1.2, through Calculate the V in each energy storage unit i，j The standard deviation of B V，i ;

[0020] StepS1.3, set the voltage value Vi，j Combined with P V，i and B V，i , analyze all voltage values ​​V i，j Outliers in ;

[0021] Specifically:

[0022] If V i，j -P V，i >3×B V，i , then the corresponding voltage value V i，j Mark as outlier;

[0023] Step S1.4, in the corresponding energy storage unit, extract all voltage values ​​V except abnormal values i，j , and calculate its average value, and then use the average value as the replacement value of the abnormal value in the corresponding energy storage unit;

[0024] Step S1.5, clean the current value, charge and discharge power, and ambient temperature according to the method from Step S1.1 to Step S1.4;

[0025] StepS2, data normalization processing:

[0026] StepS2.1, select the voltage value V i，j ;

[0027] Extract the maximum voltage value and the minimum voltage value from each energy storage unit and mark them as V i，max and V i，min ;

[0028] StepS2.2, then pass: Calculate the normalized value V1 of the corresponding voltage value in each energy storage unit i，j ;

[0029] Step S2.3, perform data normalization on the current value, charge and discharge power, and ambient temperature according to the method from Step S2.1 to Step S2.2.

[0030] As a further solution of the present invention: the performance evaluation analysis method is as follows:

[0031] StepA1, using Ohm's law to calculate the internal resistance of each energy storage unit at each acquisition time node;

[0032] The formula is:

[0033] In the formula, R i，j is the internal resistance value of each energy storage unit at each acquisition time node;

[0034] Then, the preset internal resistance threshold is extracted and marked as RY;

[0035] At the same time, the initial internal resistance value of each energy storage unit is extracted and recorded as R0 i ;

[0036] Then through:

[0037] Calculate the internal resistance deviation DR of each energy storage unit i，j ;

[0038] Then according to the internal resistance deviation DR of the energy storage unit i，j Determine the performance status of the energy storage unit;

[0039] When DR i，j When it is greater than 0, it indicates that the internal resistance increases, indicating that the performance of the relevant energy storage unit is abnormal, and a performance abnormality signal 1 is generated; otherwise, no signal is generated;

[0040] StepA2, through: SP i，j =L i，j 2 ×R i，j ;

[0041] Calculate the power loss SP of each energy storage unit i，j ;

[0042] Then the power loss SP of each energy storage unit is i，j Compare with the pre-set power loss threshold SPY:

[0043] If SP i，j >SPY, it is determined that the relevant energy storage unit has a risk of performance degradation, and the performance abnormality signal 2 is generated; otherwise, no signal is generated;

[0044] Step A3, calculate the voltage value V of each energy storage unit i，j , current value L i，j , internal resistance R i，j The absolute value of the difference between adjacent acquisition time nodes is compared with the preset voltage warning threshold V1y, current warning threshold L1y and internal resistance warning threshold R1y:

[0045] When |V i，j -V i，j-1 |>V1yor|L i，j -L i，j-1 |>L1y or |R i，j -R i，j-1|>When at least one item in R1y is true, it means that the operation status of the energy storage unit at the corresponding acquisition time node is abnormal, and a fault warning signal is generated at the jth acquisition time node;

[0046] When |V i，j -V i，j-1 |>V1yor|L i，j -L i，j-1 |>L1y or |R i，j -R i，j-1 |>When all R1y are not true, it means that the energy storage unit is operating normally at the corresponding acquisition time node and no fault warning signal is generated;

[0047] Step A4, when the fault warning signal is generated, obtain the internal resistance deviation and power loss at the jth acquisition time node and the g acquisition time nodes thereafter;

[0048] Wherein, g is the preset quantity value;

[0049] When DR i，j >0、DR i，j+1 >0、DR i，j+2 >0, ...DR i，j+g >0, if all are true, then the unit replacement signal 1 is generated; otherwise, it is not generated;

[0050] When SP i，j >SPY、SP i，j+1 >SPY、SP i，j+2 >SPY, …SP i，j+g >When all SPY0s are established, the unit replacement signal 2 is generated; otherwise, it is not generated.

[0051] As a further solution of the present invention: unit replacement signal 1 indicates that the internal resistance deviation of the relevant energy storage unit continues to increase, and unit replacement signal 2 indicates that the power loss of the relevant energy storage unit continues to be higher than the corresponding threshold, thereby reminding the relevant operation and maintenance personnel to replace the energy storage unit.

[0052] As a further solution of the present invention: the comprehensive performance evaluation method is as follows:

[0053] Step B1, Energy efficiency evaluation:

[0054] Step B1.1: In a charging cycle, extract the charging power of all time nodes in the charging and discharging cycle and mark it as CP j1 , where j1=1, 2…m1, m1 represents the total number of all time nodes in the charge and discharge cycle, CP 1 Indicates the charging start time, CP m1 Indicates the end time of charging;

[0055] Then through:

[0056] Calculate the input energy EC of the energy storage power station;

[0057] StepB1.2: In a discharge cycle, extract the discharge power of all time nodes in the discharge cycle and mark it as FP j2 , where j2 = 1, 2, ..., m2, m1 represents the total number of all time nodes in the discharge cycle, FP 1 Indicates the discharge start time, FP m2 Indicates the end time of discharge;

[0058] Then through:

[0059] Calculate the output energy EF of the energy storage power station;

[0060] Step B1.3, through:

[0061] Calculate the energy efficiency CFX of the energy storage power station;

[0062] Step B1.4, compare the energy efficiency CFX of the energy storage power station during the discharge cycle with the preset energy efficiency threshold CFXy:

[0063] If CFX>CFXy, it is determined that the energy efficiency of the energy storage power station is high during the discharge cycle;

[0064] If CFX≤CFXy, the energy efficiency is judged to be low, indicating that equipment inspection or optimization operation is required, and the power plant abnormality signal 1 is generated; otherwise, it is not generated;

[0065] Step B2, power balance assessment:

[0066] Step B2.1. Extract the charging and discharging power P of each energy storage unit in the energy storage power station i，j ;

[0067] Then through:

[0068] Calculate the total charging and discharging power ZP of the energy storage station j ;

[0069] Step B2.2: The total charging and discharging power ZP of the energy storage station j Compare with the pre-set power balance threshold PPy:

[0070] If | ZP j|>PPy, it is determined that the power balance state of the energy storage power station is abnormal, which indicates that the grid stability is abnormal, and the power station abnormality signal 2 is generated. Otherwise, it is not generated.

[0071] As a further solution of the present invention, it also includes:

[0072] The operation optimization module is used to optimize the charge and discharge according to the analysis results of the performance analysis module, and the method is as follows:

[0073] When DR i，j >0 and SP i，j > If any one of the following conditions holds in SPY, then: Calculate the adjusted power allocation ratio FL b ;

[0074] Where FLa is the power allocation ratio before adjustment.

[0075] As a further solution of the present invention, the performance analysis module and the comprehensive evaluation module perform performance evaluation analysis and comprehensive performance evaluation based on the real-time operation data preprocessed by the preprocessing module.

[0076] Beneficial effects of the present invention:

[0077] Improve data accuracy: The preprocessing module cleans the real-time operation data and can judge abnormal values ​​based on scientific calculation methods, such as combining the average value and standard deviation, to identify and process abnormal values ​​in data such as voltage, current, charge and discharge power, and ambient temperature. More accurate data is used as the basis for subsequent analysis to avoid erroneous judgments caused by interference from abnormal data, thereby improving the reliability of the analysis results of the entire management system.

[0078] Enhanced data comparability: Data normalization processing makes various operating data of different energy storage units in the same dimensional range, which is convenient for more intuitive and scientific measurement of the relative situation of each part of the data when comparing and analyzing data of multiple energy storage units and different acquisition time nodes, and helps to fully and accurately grasp the overall operating status of the energy storage power station.

[0079] Accurate performance evaluation: The performance analysis module uses scientific methods such as Ohm's law to evaluate and analyze the performance of each energy storage unit from the perspective of internal resistance deviation, power loss, and data difference between adjacent acquisition time nodes. It can accurately determine the performance status of each energy storage unit at different acquisition time nodes, and promptly discover problems such as increased internal resistance, risk of performance degradation, and abnormal operating status, providing an accurate basis for subsequent targeted operation and maintenance.

[0080] Early warning of faults: Based on the set internal resistance threshold, power loss threshold, voltage warning threshold, current warning threshold, etc., corresponding performance abnormality signals and fault warning signals can be generated at an early stage when the energy storage unit has performance abnormalities or abnormal operating status, so that operation and maintenance personnel can intervene in advance and take corresponding measures to prevent the fault from further deteriorating, ensure the stable operation of the energy storage unit, and reduce the losses caused by energy storage unit failures.

[0081] Reasonable guidance for replacement operations: By judging the internal resistance deviation and power loss persistence within a certain time range after the fault warning, a unit replacement signal is generated to reasonably prompt operation and maintenance personnel to replace energy storage units with continuously poor performance, avoiding the long-term impact of problematic units on the overall performance of the energy storage power station and improving the overall health level of energy storage units in the energy storage power station.

[0082] Comprehensively understand the status of the power station: The comprehensive evaluation module conducts a comprehensive performance evaluation of the energy storage power station from two key perspectives: energy efficiency and power balance. This allows operation and maintenance personnel to understand the overall operating efficiency of the energy storage power station from a macro perspective, and fully understand whether the power station has problems such as low energy efficiency and abnormal power balance that affect its stable operation and grid stability. This helps to formulate operation and maintenance and optimization strategies that are more in line with the actual situation of the power station.

[0083] Timely detection of potential problems: By comparing with the preset energy efficiency threshold and power balance threshold, abnormal power station signals are generated in time to remind operation and maintenance personnel to conduct corresponding inspections and optimize operations for possible equipment problems or grid stability problems, prevent possible major failures in advance, ensure long-term and stable power supply to the grid by the energy storage power station, and maintain the normal operation order of the grid.

[0084] Optimize the charging and discharging process: Based on the analysis results of the performance analysis module, the operation optimization module can optimize the charging and discharging process by calculating and adjusting the power allocation ratio when performance-related problems occur in the energy storage unit. This helps to improve the charging and discharging efficiency of the energy storage power station, enable the energy storage power station to better play the role of energy storage under the existing energy storage unit status, and improve the overall energy utilization efficiency.

[0085] Extending equipment service life: Reasonable charge and discharge optimization can avoid excessive or unreasonable use due to poor performance of some energy storage units, reduce unreasonable pressure on energy storage units, and thus extend the service life of energy storage units to a certain extent, reduce the operating costs of energy storage power stations, and improve economic benefits.

[0086] Real-time information transmission: The early warning display unit displays various relevant signals obtained by the performance analysis module and the comprehensive evaluation module to the operation and maintenance personnel in a timely manner, realizing real-time information transmission, so that the operation and maintenance personnel can immediately grasp the operation status of the energy storage power station from a single energy storage unit to the overall power station level, making it easier to make reasonable operation and maintenance decisions quickly and improving the timeliness and effectiveness of the operation and maintenance work.

[0087] Assist scientific decision-making: By displaying multi-dimensional analysis results and early warning signals, it provides comprehensive and accurate decision-making basis for operation and maintenance personnel, so that they can formulate scientific and reasonable maintenance, inspection, optimization and replacement operation plans based on specific signal conditions to ensure efficient, stable and safe operation of energy storage power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The present invention will be further described below in conjunction with the accompanying drawings.

[0089] Figure 1 It is a system block diagram of the energy storage power station management system based on big data analysis of the present invention.

[0090] Figure 2 It is a flow chart of the comprehensive evaluation module in the energy storage power station management system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0091] 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.

[0092] Embodiment 1

[0093] See also Figure 1 and Figure 2 As shown, the present invention is an energy storage power station management system based on big data analysis, including:

[0094] Data acquisition module, used to collect real-time operation data of the energy storage power station within a specified period;

[0095] The real-time operation data includes the voltage value, current value, charge and discharge power of each energy storage unit in the energy storage power station, which are marked as V i，j , L i，j , P i，j , i = 1, 2, ... n, j = 1, 2, ... m, n represents the total number of energy storage units in the energy storage power station, m represents the total number of acquisition time nodes in a specified period, where the time intervals of adjacent acquisition time nodes are consistent and are recorded as t 0 ;

[0096] The performance analysis module is used to evaluate and analyze the performance of each energy storage unit. The performance evaluation and analysis method is as follows:

[0097] StepA1, using Ohm's law to calculate the internal resistance of each energy storage unit at each acquisition time node;

[0098] The formula is:

[0099] In the formula, R i，j is the internal resistance value of each energy storage unit at each acquisition time node;

[0100] Then, the preset internal resistance threshold is extracted and marked as RY;

[0101] At the same time, the initial internal resistance value of each energy storage unit is extracted and recorded as R0 i ;

[0102] Then through:

[0103] Calculate the internal resistance deviation DR of each energy storage unit i，j ;

[0104] Then according to the internal resistance deviation DR of the energy storage unit i，j Determine the performance status of the energy storage unit;

[0105] When DR i，j When it is greater than 0, it indicates that the internal resistance increases, indicating that the performance of the relevant energy storage unit is abnormal, and a performance abnormality signal 1 is generated; otherwise, no signal is generated;

[0106] StepA2, through: SP i，j =L i，j 2 ×R i，j ;

[0107] Calculate the power loss SP of each energy storage unit i，j ;

[0108] Then the power loss SP of each energy storage unit is i，j Compare with the pre-set power loss threshold SPY:

[0109] If SP i，j >SPY, it is determined that the relevant energy storage unit has a risk of performance degradation, and the performance abnormality signal 2 is generated; otherwise, no signal is generated;

[0110] Step A3, calculate the voltage value V of each energy storage unit i，j , current value L i，j , internal resistance R i，jThe absolute value of the difference between adjacent acquisition time nodes is compared with the preset voltage warning threshold V1y, current warning threshold L1y and internal resistance warning threshold R1y:

[0111] When |V i，j -V i，j-1 |>V1yor|L i，j -L i，j-1 |>L1y or |R i，j -R i，j-1 |>When at least one item in R1y is true, a fault warning signal is generated at the jth acquisition time node;

[0112] When |V i，j -V i，j-1 |>V1yor|L i，j -L i，j-1 |>L1y or |R i，j -R i，j-1 |>When all R1y are not true, it will not be generated;

[0113] Step A4, when the fault warning signal is generated, obtain the internal resistance deviation and power loss at the jth acquisition time node and the g acquisition time nodes thereafter;

[0114] Wherein, g is the preset quantity value;

[0115] When DR i，j >0、DR i，j+1 >0、DR i，j+2 >0, ...DR i，j+g >0, if all are true, then the unit replacement signal 1 is generated; otherwise, it is not generated;

[0116] When SP i，j >SPY、SP i，j+1 >SPY、SP i，j+2 >SPY, …SP i，j+g >When all SPY0s are established, the unit change signal 2 is generated; otherwise, it is not generated;

[0117] Unit replacement signal 1 indicates that the internal resistance deviation of the relevant energy storage unit continues to increase, and unit replacement signal 2 indicates that the power loss of the relevant energy storage unit continues to be higher than the corresponding threshold, which reminds the relevant operation and maintenance personnel to replace the energy storage unit;

[0118] The early warning display unit is used to display the relevant signals obtained by the performance analysis module to the relevant operation and maintenance personnel;

[0119] In this embodiment, the data acquisition module collects real-time operation data of the energy storage power station. The performance analysis module calculates the internal resistance, internal resistance deviation, power loss and other indicators by using Ohm's law and other methods, and compares them with the corresponding thresholds, so as to accurately judge the performance status of the energy storage unit, such as timely discovering performance abnormalities, performance degradation risks and fault warnings, etc., which helps the operation and maintenance personnel to know in advance and take corresponding measures to ensure the normal operation of each energy storage unit in the energy storage power station; based on the generated performance abnormality signals, fault warning signals and unit replacement signals, etc., the operation and maintenance personnel can be clearly prompted to perform targeted maintenance, inspection or replacement operations on the energy storage units with problems, so as to improve the efficiency and accuracy of the operation and maintenance work and reduce the impact of energy storage unit failures on the overall operation of the energy storage power station; with the help of the early warning display unit, the relevant signals are displayed to the operation and maintenance personnel, so as to realize real-time information transmission, so that the operation and maintenance personnel can grasp the actual operation status of each energy storage unit in the energy storage power station at the first time, so as to make reasonable operation and maintenance decisions in time.

[0120] Embodiment 2

[0121] See also Figure 1 and Figure 2 As shown, as the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of this embodiment is different from that of the first embodiment only in that the second embodiment further includes:

[0122] The comprehensive evaluation module is used to conduct a comprehensive performance evaluation of the energy storage power station. The comprehensive performance evaluation method is as follows:

[0123] Step B1, Energy efficiency evaluation:

[0124] Step B1.1: In a charging cycle, extract the charging power of all time nodes in the charging and discharging cycle and mark it as CP j1 , where j1=1, 2…m1, m1 represents the total number of all time nodes in the charge and discharge cycle, CP 1 Indicates the charging start time, CP m1 Indicates the end time of charging;

[0125] Then through:

[0126] Calculate the input energy EC of the energy storage power station;

[0127] StepB1.2: In a discharge cycle, extract the discharge power of all time nodes in the discharge cycle and mark it as FP j2 , where j2 = 1, 2, ..., m2, m1 represents the total number of all time nodes in the discharge cycle, FP 1 Indicates the discharge start time, FP m2 Indicates the end time of discharge;

[0128] Then through:

[0129] Calculate the output energy EF of the energy storage power station;

[0130] Step B1.3, through:

[0131] Calculate the energy efficiency CFX of the energy storage power station;

[0132] Step B1.4, compare the energy efficiency CFX of the energy storage power station during the discharge cycle with the preset energy efficiency threshold CFXy:

[0133] If CFX>CFXy, it is determined that the energy efficiency of the energy storage power station is high during the discharge cycle;

[0134] If CFX≤CFXy, the energy efficiency is judged to be low, indicating that equipment inspection or optimization operation is required, and the power plant abnormality signal 1 is generated; otherwise, it is not generated;

[0135] Step B2, power balance assessment:

[0136] Step B2.1. Extract the charging and discharging power P of each energy storage unit in the energy storage power station i，j ;

[0137] Then through:

[0138] Calculate the total charging and discharging power ZP of the energy storage station j ;

[0139] Step B2.2: The total charging and discharging power ZP of the energy storage station j Compare with the pre-set power balance threshold PPy:

[0140] If | ZP j |>PPy, it is determined that the power balance state of the energy storage power station is abnormal, which indicates that the grid stability is abnormal, and the power station abnormality signal 2 is generated; otherwise, it is not generated;

[0141] The early warning display unit is also used to display the relevant signals obtained by the comprehensive evaluation module to relevant operation and maintenance personnel;

[0142] In addition to the performance analysis of a single energy storage unit, the newly added comprehensive evaluation module of this embodiment can conduct a comprehensive performance evaluation of the entire energy storage power station from two important aspects: energy efficiency and power balance, so as to grasp the overall operating efficiency of the energy storage power station from a macro perspective and have a more comprehensive understanding of the operating status of the power station. By comparing the energy efficiency with the preset threshold and the total charge and discharge power with the power balance threshold, it can timely determine whether the energy storage power station has low energy efficiency, abnormal power balance, etc., and then generate corresponding power station abnormal signals to remind operation and maintenance personnel to conduct equipment inspections, optimize operations, or take measures to maintain grid stability in advance, which is helpful to prevent possible major operating failures and ensure that the energy storage power station supplies power to the grid stably. The early warning display unit displays the relevant signals obtained by the comprehensive evaluation module, which can provide operation and maintenance personnel with a more dimensional decision-making basis, so that they can reasonably arrange maintenance, optimization, etc. based on the overall performance of the power station to ensure efficient and stable operation of the energy storage power station.

[0143] Embodiment 3

[0144] See also Figure 1 and Figure 2 As shown, as the third embodiment of the present invention, when the present application is implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments, and the difference between the technical solution of this embodiment and the first and second embodiments is that in this embodiment, it also includes:

[0145] The preprocessing module is used to preprocess the real-time running data. The preprocessing method is as follows:

[0146] StepS1, data cleaning:

[0147] StepS1.1, select the voltage value V i，j For example;

[0148] pass Calculate the V in each energy storage unit i，j The average value P V，i ;

[0149] StepS1.2, through Calculate the V in each energy storage unit i，j The standard deviation of B V，i ;

[0150] StepS1.3, set the voltage value V i，j Combined with P V，i and B V，i , analyze all voltage values ​​V i，j Outliers in ;

[0151] Specifically:

[0152] If V i，j -P V，i >3×B V，i , then the corresponding voltage value V i，j Mark as outlier;

[0153] Step S1.4, in the corresponding energy storage unit, extract all voltage values ​​V except abnormal values i，j , and calculate its average value, and then use the average value as the replacement value of the abnormal value in the corresponding energy storage unit;

[0154] Step S1.5, clean the current value, charge and discharge power, and ambient temperature according to the method from Step S1.1 to Step S1.4;

[0155] StepS2, data normalization processing:

[0156] StepS2.1, select the voltage value V i，j For example;

[0157] Extract the maximum voltage value and the minimum voltage value from each energy storage unit and mark them as V i，max and V i，min ;

[0158] StepS2.2, then pass: Calculate the normalized value V1 of the corresponding voltage value in each energy storage unit i，j ;

[0159] Step S2.3, perform data normalization processing on the current value, charge and discharge power, and ambient temperature according to the method from Step S2.1 to Step S2.2;

[0160] In this embodiment, the performance analysis module and the comprehensive evaluation module perform performance evaluation analysis and comprehensive performance evaluation based on the real-time operation data preprocessed by the preprocessing module;

[0161] In this embodiment, the preprocessing module performs data cleaning and normalization processing on the real-time operation data, which can effectively remove abnormal values ​​in the data, make the data more accurate and reliable, avoid abnormal data from misleading subsequent performance analysis, comprehensive evaluation and other tasks, and improve the accuracy of the entire system's analysis and judgment based on data; after data normalization processing, various types of operation data of different energy storage units, such as voltage values, current values, charging and discharging powers, etc., are in the same dimensional range, which is more convenient for horizontal comparison and comprehensive analysis in the subsequent analysis process, and helps to more scientifically and accurately evaluate the operating status of various parts of the energy storage power station and the overall performance. The preprocessing improves the data quality input to other modules, such as the performance analysis module, the comprehensive evaluation module, etc., so that the entire energy storage power station management system based on big data analysis can play a more effective role and provide a more reliable basis for operation and maintenance and other tasks.

[0162] Embodiment 4

[0163] See also Figure 1 and Figure 2 As shown, as the fourth embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the second embodiment and the third embodiment, the difference between the present embodiment and the first embodiment, the second embodiment and the third embodiment is that the present embodiment further includes:

[0164] The operation optimization module is used to optimize the charge and discharge according to the analysis results of the performance analysis module, and the method is as follows:

[0165] When DR i，j >0 and SP i，j > If any one of the following conditions holds in SPY, then: Calculate the adjusted power allocation ratio FL b ;

[0166] Where FLa is the power allocation ratio before adjustment.

[0167] The operation optimization module of this embodiment is based on the analysis results of the performance analysis module. When it is found that the energy storage unit has performance problems, such as increased internal resistance deviation or excessive power loss, the charging and discharging can be optimized by calculating the adjusted power allocation ratio, which helps to improve the charging and discharging efficiency of the energy storage power station and extend the service life of the energy storage unit. At the same time, it can also ensure the overall stable operation of the energy storage power station to a certain extent. Through reasonable adjustment of the power allocation ratio, the energy storage power station can better adapt to the actual status of different energy storage units during operation, avoid energy waste due to poor performance of some energy storage units, thereby improving the economy of the overall operation of the energy storage power station and achieving better economic benefits.

[0168] Embodiment 5

[0169] See also Figure 1 and Figure 2 As shown, as the fifth embodiment of the present invention, when the present application is implemented specifically, compared with the first, second, third and fourth embodiments, the technical solution of this embodiment is to combine the solutions of the above-mentioned first, second, third and fourth embodiments for implementation.

[0170] This embodiment combines the solutions of Embodiments 1, 2, 3, and 4 for implementation, integrating functions such as energy storage unit performance monitoring, overall power station performance evaluation, data preprocessing, and charge and discharge optimization, forming a comprehensive and coordinated energy storage power station management system, which can comprehensively guarantee the efficient implementation of various links of the energy storage power station from data collection and analysis to operation optimization, maximize the operational stability, safety, and economy of the energy storage power station, and provide strong support for the long-term reliable operation of the energy storage power station; through the synergy of various modules, operation and maintenance personnel can grasp the power station situation from multiple angles, and problems at both the single energy storage unit and the entire power station level can be discovered and handled in a timely manner, and at the same time, the power station operation effect can be continuously improved based on optimization measures, realizing all-round and full-process operation and maintenance guarantees for the energy storage power station.

[0171] 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 and thresholds in the formula are set by technicians in this field according to actual conditions.

[0172] 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. The energy storage power station management system based on big data analysis is characterized by: include: Data acquisition module, used to collect real-time operation data of the energy storage power station within a specified period; Real-time operation data includes the voltage value, current value, and charge and discharge power of each energy storage unit in the energy storage power station; The preprocessing module is used to preprocess the real-time operation data. The preprocessing includes data cleaning and data normalization. The data cleaning method is as follows: the voltage value, current value, and charge and discharge power of each energy storage unit in the energy storage power station are marked as V i，j , L i，j , P i，j , i = 1, 2, ... n, j = 1, 2, ... m, n represents the total number of energy storage units in the energy storage power station, and m represents the total number of acquisition time nodes in a specified period; StepS1.1, select the voltage value V i，j ; pass Calculate the V in each energy storage unit i，j The average value P V，i ; StepS1.2, through Calculate the V in each energy storage unit i，j The standard deviation of B V，i ; StepS1.3, set the voltage value V i，j Combined with P V，i and B V，i , analyze all voltage values ​​V i，j Outliers in ; Step S1.4, in the corresponding energy storage unit, extract all voltage values ​​V except abnormal values i，j , and calculate its average value, and then use the average value as the replacement value of the abnormal value in the corresponding energy storage unit; Step S1.5, clean the current value, charge and discharge power, and ambient temperature according to the method from Step S1.1 to Step S1.4; The performance analysis module is used to perform performance evaluation and analysis on each energy storage unit, and determine the internal resistance deviation, power loss and operating status of each energy storage unit at each acquisition time node based on the performance evaluation and analysis results; A comprehensive evaluation module is used to conduct a comprehensive performance evaluation of the energy storage power station, and determine the energy efficiency and power balance status of the energy storage power station based on the comprehensive performance evaluation results; The early warning display unit is used to display the results obtained by the performance analysis module and the comprehensive evaluation module to the operation and maintenance personnel.

2. The energy storage power station management system based on big data analysis according to claim 1 is characterized in that: The performance evaluation analysis method is as follows: StepA1, using Ohm's law to calculate the internal resistance of each energy storage unit at each acquisition time node; The formula is: In the formula, R i，j is the internal resistance value of each energy storage unit at each acquisition time node; Then, the preset internal resistance threshold is extracted and marked as RY; At the same time, the initial internal resistance value of each energy storage unit is extracted and recorded as R0 i ; Then through: Calculate the internal resistance deviation DR of each energy storage unit i，j ; StepA2, through: SP i，j =L i，j 2 ×R i，j ; Calculate the power loss SP of each energy storage unit i，j ; Step A3, calculate the voltage value V of each energy storage unit i，j , current value L i，j , internal resistance R i，j The absolute value of the difference between adjacent acquisition time nodes is compared with the preset voltage warning threshold V1y, current warning threshold L1y and internal resistance warning threshold R1y: When |V i，j -V i，j-1 |>V1yor|L i，j -L i，j-1 |>L1y or |R i，j -R i，j-1 |>When at least one item in R1y is true, it means that the operation status of the energy storage unit at the corresponding acquisition time node is abnormal, and a fault warning signal is generated at the jth acquisition time node; When |V i，j -V i，j-1 |>V1yor|L i，j -L i，j-1 |>L1y or |R i，j -R i，j-1 |>When all R1y are not true, it means that the energy storage unit is operating normally at the corresponding acquisition time node and no fault warning signal is generated.

3. The energy storage power station management system based on big data analysis according to claim 2 is characterized in that: in, The time interval between each adjacent acquisition time node is consistent and is recorded as t0.

4. The energy storage power station management system based on big data analysis according to claim 2 is characterized in that: In StepA1, the internal resistance deviation DR of the energy storage unit is also i，j Determine the performance status of the energy storage unit; When DR i，j >0, a performance abnormality signal 1 is generated; Otherwise, it will not be generated; In Step A2, the power loss SP of each energy storage unit is also i，j Compare with the pre-set power loss threshold SPY: If SP i，j >SPY, performance abnormality signal 2 will be generated; otherwise, it will not be generated.

5. The energy storage power station management system based on big data analysis according to claim 2 is characterized in that: When the fault warning signal is generated, the internal resistance deviation and power loss at the jth acquisition time node and the subsequent g acquisition time nodes are obtained; Wherein, g is the preset quantity value; When DR i，j >0、DR i，j+1 >0、DR i，j+2 >0, ...DR i，j+g >0, if all are true, then the unit replacement signal 1 is generated; otherwise, it is not generated; When SP i，j >SPY、SP i，j+1 >SPY、SP i，j+2 >SPY, …SP i，j+g >When all SPY0s are established, the unit replacement signal 2 is generated; otherwise, it is not generated.

6. The energy storage power station management system based on big data analysis according to claim 3 is characterized in that: The comprehensive performance evaluation method is as follows: Step B1, Energy efficiency evaluation: Step B1.1: In a charging cycle, extract the charging power of all time nodes in the charging and discharging cycle and mark it as CP j1 , where j1=1, 2…m1, m1 represents the total number of all time nodes in the charge and discharge cycle, CP1 represents the start time of charging, CP m1 Indicates the end time of charging; Then through: Calculate the input energy EC of the energy storage power station; StepB1.2: In a discharge cycle, extract the discharge power of all time nodes in the discharge cycle and mark it as FP j2 , where j2 = 1, 2, ..., m2, m1 represents the total number of all time nodes in the discharge cycle, FP1 represents the start time of discharge, FP m2 Indicates the end time of discharge; Then through: Calculate the output energy EF of the energy storage power station; Step B1.3, through: Calculate the energy efficiency CFX of the energy storage power station; Step B1.4, compare the energy efficiency CFX of the energy storage power station during the discharge cycle with the preset energy efficiency threshold CFXy: If CFX>CFXy, it is determined that the energy efficiency of the energy storage power station is high during the discharge cycle; If CFX≤CFXy, the energy efficiency is judged to be low, and the power plant abnormality signal 1 is generated; otherwise, no signal is generated; Step B2, power balance assessment: Step B2.

1. Extract the charging and discharging power P of each energy storage unit in the energy storage power station i，j ; Then through: Calculate the total charging and discharging power ZP of the energy storage station j ; Step B2.2: The total charging and discharging power ZP of the energy storage station j Compare with the pre-set power balance threshold PPy: If | ZP j |>PPy, it is determined that the power balance state of the energy storage power station is abnormal, and the power station abnormality signal 2 is generated. Otherwise, it is not generated.

7. The energy storage power station management system based on big data analysis according to claim 2 is characterized in that: The data normalization process is as follows: StepS2.1, select the voltage value V i，j ; Extract the maximum voltage value and the minimum voltage value from each energy storage unit and mark them as V i，max and V i，min ; StepS2.2, then pass: Calculate the normalized value V1 of the corresponding voltage value in each energy storage unit i，j ; Step S2.3, perform data normalization on the current value, charge and discharge power, and ambient temperature according to the method from Step S2.1 to Step S2.

2.

8. The energy storage power station management system based on big data analysis according to claim 1 is characterized in that: In Step S1.3, the outlier is determined as follows: If V i，j -P V，i >3×B V，i , then the corresponding voltage value V i，j Mark as an outlier.

9. The energy storage power station management system based on big data analysis according to claim 4 is characterized in that: Also includes: The operation optimization module is used to optimize the charge and discharge according to the analysis results of the performance analysis module, and the method is as follows: When DR i，j >0 and SP i，j > If any one of the following conditions holds in SPY, then: Calculate the adjusted power allocation ratio FL b ; Where FLa is the power allocation ratio before adjustment.

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