Energy storage system data processing method and system

Abstract

The invention provides an energy storage system data processing method and system, and the method comprises the steps: dividing an energy storage system into a plurality of levels according to the level of the energy storage system, and building a first mapping relation table of different levels of the energy storage system and a second mapping relation table of each level of the energy storage system and index data according to the levels of the energy storage system; acquiring operation data of different energy storage systems of each level, and managing the operation data of the different energy storage systems of each level according to different types of the operation data of the different energy storage systems of each level; and according to the treated operation data of the different energy storage systems of each level, the first mapping relation table and the second mapping relation table, outputting the corresponding index data of the different energy storage systems of each level, thereby effectively solving the problems of relatively low efficiency and reliability in energy storage system index data processing in the prior art, and improving the processing efficiency of the energy storage system index data. The efficiency and the reliability of index data processing of the energy storage system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of power data processing, and in particular to a method and system for processing energy storage system data. Background Art

[0002] In the face of the growing energy storage system and the explosive growth of basic data, it is necessary to achieve automation and intelligence as much as possible to efficiently arrange various tasks. With the national "dual carbon" goal, the development record of energy storage systems has been constantly breaking records. The growing scale of data fully demonstrates the importance of efficient data processing. Traditional data processing methods can no longer meet the needs.

[0003] At present, the data processing of energy storage systems is generally carried out during the calculation of index data of different energy storage systems, based on the operating data involved in the index data. However, the energy storage systems are not classified according to their levels, nor are they effectively managed according to the types of operating data of the energy storage systems, resulting in low efficiency and reliability in the calculation and output of index data of the energy storage systems.

[0004] In view of this problem, the present invention provides a data processing method and system for an energy storage system to solve the above problem. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention innovatively proposes a method and system for processing energy storage system data, which effectively solves the problems of low efficiency and reliability in the processing of energy storage system indicator data caused by the prior art, and effectively improves the efficiency and reliability of the processing of energy storage system indicator data.

[0006] A first aspect of the present invention provides a method for processing energy storage system data, comprising:

[0007] Divide the energy storage system into several levels according to the level of the energy storage system, and establish a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and indicator data according to the energy storage system level;

[0008] Obtain the operating data of different energy storage systems at each level, and manage the operating data of different energy storage systems at each level according to the different types of operating data of different energy storage systems at each level;

[0009] According to the managed operating data of different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table, the corresponding indicator data of different energy storage systems at each level are output.

[0010] A second aspect of the present invention provides an energy storage system data processing system, comprising:

[0011] Establish a module, divide the energy storage system into several levels according to the level of the energy storage system, and establish a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and indicator data according to the energy storage system level;

[0012] The governance module obtains the operating data of different energy storage systems at each level and manages the operating data of different energy storage systems at each level according to the different types of operating data of different energy storage systems at each level.

[0013] The output module outputs the corresponding indicator data of the different energy storage systems at each level according to the managed operation data of the different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table.

[0014] The technical solution adopted by the present invention includes the following technical effects:

[0015] 1. The present invention divides the energy storage system into several levels according to the level, and establishes a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and index data according to the energy storage system level; obtains the operation data of different energy storage systems at each level, and manages the operation data of different energy storage systems at each level according to the different types of operation data of different energy storage systems at each level; outputs the corresponding index data of different energy storage systems at each level according to the managed operation data of different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table, effectively solving the problem of low efficiency and reliability in the processing of index data of energy storage systems caused by the prior art, and effectively improving the efficiency and reliability of the processing of index data of energy storage systems.

[0016] 2. In the technical solution of the present invention, according to the different types of energy storage system operation data at each level and the preset execution order relationship, the first type of energy storage system operation data is cleaned in a first execution order, and the second type of energy storage system operation data is cleaned in a second execution order; wherein, the first type of energy storage system operation data is energy storage system operation data close to a normal distribution, and the second type of energy storage system operation data is energy storage system operation data deviating from a normal distribution, the energy storage system operation data close to a normal distribution includes energy storage system voltage data, energy storage system SOC data, and energy storage system average data, and the energy storage system operation data deviating from a normal distribution includes energy storage system current data, energy storage system SOH data, and energy storage system extreme value data, so that different orders of data cleaning algorithms can be executed according to different energy storage system operation data, further improving the efficiency and reliability of energy storage system indicator data processing.

[0017] 3. The technical solution of the present invention specifically includes the following steps: using the wavelet transform method to perform data noise reduction on the cleaned operating data of the different energy storage systems at each level; using the linear interpolation method to fill in the missing values ​​for abnormal value vacancies and data with missing time points, thereby ensuring the fast and reliable processing of the energy storage system indicator data.

[0018] 4. The technical solution of the present invention specifically includes managing the operating data of different energy storage systems at each level: re-screening the operating data of a certain energy storage system at a certain level after management according to a third mapping relationship table and the sensor measuring points corresponding to the operating data of different energy storage systems at each level; the third mapping relationship table stores the sensor measuring points corresponding to the operating data of different energy storage systems at each level, and the correspondence between the theoretical minimum value and the theoretical maximum value corresponding to the sensor measuring points, which further ensures the fast and reliable processing of the energy storage system indicator data.

[0019] 5. In the technical solution of the present invention, if the index data corresponding to the level where a certain energy storage system is located is abnormal, the level where the energy storage system is located and the energy storage systems of the corresponding levels above the level where the energy storage system is located are located according to the first mapping relationship table. Not only can the abnormal state of the index data of the energy storage system be determined, but also the energy storage system corresponding to the abnormal index data and the energy storage systems of the corresponding levels above the level where the energy storage system is located can be determined according to the first mapping relationship table, which is convenient for quickly locating the abnormal energy storage system.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 This is a schematic diagram of a process of a method in Example 1 of the present invention;

[0023] Figure 2 Schematic diagram of the process of step S2 in the method of embodiment 1 of the present invention (I);

[0024] Figure 3 Schematic diagram (II) of the process of step S2 in the method of embodiment 1 in the scheme of the present invention;

[0025] Figure 4Schematic diagram (III) of the process of step S2 in the method of embodiment 1 of the present invention;

[0026] Figure 5 Schematic diagram (I) of the process of step S3 in the method of embodiment 1 of the present invention;

[0027] Figure 6 Schematic diagram (II) of the process of step S3 in the method of embodiment 1 in the scheme of the present invention;

[0028] Figure 7 It is a schematic diagram of the structure of the system of Example 2 in the solution of the present invention. DETAILED DESCRIPTION

[0029] In order to clearly illustrate the technical features of the present solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings. The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the accompanying drawings are not necessarily drawn to scale. The present invention omits the description of known components and processing techniques and processes to avoid unnecessary limitations on the present invention.

[0030] Embodiment 1

[0031] like Figure 1 As shown, the present invention provides a method for processing data of an energy storage system, comprising:

[0032] S1, dividing the energy storage system into several levels according to the level of the energy storage system, and establishing a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and indicator data according to the energy storage system levels;

[0033] S2, obtaining the operating data of different energy storage systems at each level, and managing the operating data of different energy storage systems at each level according to the different types of operating data of different energy storage systems at each level;

[0034] S3, outputting the corresponding indicator data of different energy storage systems at each level according to the managed operating data of different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table.

[0035] Among them, in step S1, the energy storage system is divided into several levels according to its level, specifically:

[0036] The divided energy storage system hierarchy includes energy storage power stations, energy storage units, battery compartments, battery clusters, and battery cells from high to low; the first mapping table stores the correspondence between energy storage power stations, energy storage units, battery compartments, battery clusters, and battery cells.

[0037] The first mapping relationship table can be a general data table or a plurality of interrelated sub-data tables. Taking a plurality of interrelated sub-data tables as an example, the first mapping relationship table can be stored in a relational database Mysql, and each table lists some fields:

[0038] Table 1- Independent energy storage power station table (Power_plant):

[0039] ID Name Code Group City ... 666 Guanjia Three Gorges GJX DTJT 0531 ... .. .. .. .. .. ..

[0040] Note: The id in Table 1 is the primary key of the database table and is unique. The following tables all have it and will not be repeated here. The Name field is the name of the energy storage power station, the Code field is the code of the energy storage power station name, the Group field indicates the group number to which the energy storage power station belongs; the city field indicates the city number to which the energy storage power station belongs.

[0041] Table 2 - Energy storage unit table (eunit), which has a one-to-many relationship with Table 1, an independent energy storage power station contains multiple energy storage units.

[0042] ID Plant_id Code Name ... 1 666 001 Energy storage unit 1 ... 2 666 002 Energy storage unit 2 ... .. .. .. .. ..

[0043] Note: The Plant_id field is the ID of the energy storage power station corresponding to the energy storage unit, the Code field is the code of the energy storage unit, and the Name field is the name of the energy storage unit.

[0044] Table 3 - Battery compartment table (compartment), which has a one-to-many relationship with Table 2, and one energy storage unit corresponds to multiple battery compartments.

[0045]

[0046] Note: The eunit_id field is the ID of the energy storage unit corresponding to the battery bin, the Code field is the code of the battery bin, and the notes field is the name of the battery bin and the corresponding energy storage unit and energy storage power station.

[0047] Table 4 - Battery cluster table (cluster), which has a one-to-many relationship with Table 3 battery compartment table, one battery compartment corresponds to multiple battery clusters

[0048]

[0049]

[0050] Note: The compartment_id field is the ID of the battery compartment corresponding to the battery cluster, the Code field is the code of the battery cluster, and the notes field is the name of the battery cluster and the corresponding battery compartment, energy storage unit, and energy storage power station.

[0051] Table 5 - Cell table (cell), which has a one-to-many relationship with Table 4 battery cluster table, one battery cluster corresponds to multiple cells

[0052]

[0053]

[0054] Note: The cluster_id field is the ID of the battery cluster corresponding to the battery cell, the Code field is the battery cell code, and the notes field is the name of the battery cell and the corresponding battery cluster, battery compartment, energy storage unit, and energy storage power station.

[0055] The second mapping relationship table stores each energy storage system level and indicator data. The second mapping relationship table can be stored in the relational database Mysql. Each table lists some fields:

[0056] Table 6-Independent energy storage index table (index)

[0057]

[0058] Note: The level field indicates the energy storage system level. 1 indicates that this indicator is an independent energy storage power station level indicator, 2 indicates that this indicator is an energy storage unit level indicator, 3 indicates that this indicator is a battery compartment level indicator, 4 indicates that this indicator is a battery cluster level indicator, and 5 indicates that this indicator is a battery cell level indicator. The Name field indicates the name of the indicator data, and the Code field indicates the abbreviation or code of the indicator data name.

[0059] Among them, Figure 2 As shown, in step S2, according to the different types of operating data of different energy storage systems at each level, the operating data of different energy storage systems at each level are managed, specifically including:

[0060] S21, obtaining the operating data types of different energy storage systems at each level;

[0061] Specifically, the operation data of different energy storage systems at each level may be acquired by using sensors corresponding to different operation data of different energy storage systems at each level, and an energy storage power station is taken as an example for explanation.

[0062] Table 7-Basic operation data table of independent energy storage power station (realtime_data)

[0063]

[0064]

[0065] Note: Tag indicates the measurement point code of a sensor. Each tag can have multiple data at different times.

[0066] Table 8 - Basic operation data and level association table (Tag_index)Tag_index

[0067] Id Tags Level Belong_id 1 gjsx_e_syzx_yggl 1 666 2 gjsx_e_cndy001_dcc001_ygggl 3 1

[0068] Note: The tag field corresponds to the tag code in Table 9; the meaning of the level field is the same as that in Table 6, indicating the energy storage system level; Belong_id has different meanings according to the level level. When level is 1, it is the id in Table 1, when it is 2, it is the id in Table 2, when it is 3, it is the id in Table 3, when it is 4, it is the id in Table 4, and when it is 5, it is the id in Table 5; this table associates Table 6 with Tables 1-5, and combines Tables 7 and 8 to obtain the operating data involved in the indicators, so as to realize the monitoring of the operating data involved in all indicators at the five levels of independent energy storage site level, energy storage unit level, battery compartment level, battery cluster level, and battery cell level.

[0069] The data collected by the sensor is at the second and minute level, that is, each sensor collects one data per second / minute and uploads it to the basic operation table 7; the collection points correspond to the measuring point table 8. For example, an independent energy storage A has 9999 collection points at all levels, so there are 9999 measuring points in Table 8; and Table 7 stores the specific values ​​of Table 8 at different time points, which is equivalent to 9999*t data. If the measuring points under the A site are all collected at the second level, then the amount of data in one day is 60*60*24*9999=800 million data. The amount of data is very huge. Here, Tables 7 and 8 only list the storage structure, which is actually stored in the time series library (a database specifically for storing time-data).

[0070] The basic operating data involved in the indicator calculation may include:

[0071] Station-level data: active power, three-phase voltage, three-phase current, etc.;

[0072] Energy storage unit data: operating status, three-phase voltage, three-phase current, frequency, etc.;

[0073] Battery compartment data: three-phase voltage, three-phase current, frequency, temperature, etc.;

[0074] Battery cluster data: voltage, current, frequency, temperature, etc.;

[0075] Battery cell data: voltage, current, frequency, temperature, etc.;

[0076] S22, according to the types of energy storage system operation data at each level and the preset execution order relationship, perform data cleaning of the first type of energy storage system operation data in a first execution order, and perform data cleaning of the second type of energy storage system operation data in a second execution order.

[0077] Among them, the first type of energy storage system operation data is the energy storage system operation data close to the normal distribution, and the second type of energy storage system operation data is the energy storage system operation data that deviates from the normal distribution. The energy storage system operation data close to the normal distribution includes the energy storage system voltage data, the energy storage system SOC data, and the energy storage system average data. The energy storage system operation data that deviates from the normal distribution includes the energy storage system current data, the energy storage system SOH data, and the energy storage system extreme value data.

[0078] Data that are close to a normal distribution usually have the following characteristics: symmetry, unimodality, data concentration near the mean, and its distribution shape conforms to a bell curve. Data that deviate from a normal distribution may be skewed (left or right), multimodal, have frequent extreme values, or have a large distribution range.

[0079] Combined with the time series operation data of the energy storage system, an example is as follows:

[0080] 1. Data close to normal distribution:

[0081] Voltage (overall): If the energy storage system operates normally, the single cell voltage distribution is usually stable, with a small fluctuation range, and may be close to a normal distribution.

[0082] SOC (State of Charge): In a normal charge and discharge cycle, the SOC distribution may be concentrated in a certain range (such as 20%-80%) and fluctuate evenly, so it may be close to a normal distribution at a specific stage.

[0083] Average cell temperature: If the cooling system is normal, the temperature is evenly distributed and the variation is small, the average cell temperature may show a normal distribution.

[0084] 2. Data that deviates from normal distribution:

[0085] Current: The current changes dramatically with the load and may show strong volatility, especially when switching between charging, discharging or standby states. The distribution often has multi-peak characteristics and deviates from the normal distribution.

[0086] SOH (state of health): SOH decays slowly over time, and the distribution trend may show obvious right skewness or left skewness, making it difficult to meet the normal distribution conditions.

[0087] Maximum / minimum voltage of a single cell: Due to the inconsistency of different battery cells, the maximum or minimum voltage is affected by the extreme points, and the distribution may be skewed or discrete, making it difficult to conform to a normal distribution.

[0088] Minimum cell temperature: When the local heat dissipation is uneven or the ambient temperature difference is large, the minimum temperature may be far from the average level, and the distribution is prone to extreme points or skewness.

[0089] The first execution order is the HANTS algorithm, the 3sigma method, and the box plot method, and the second execution order is the HANTS algorithm, the box plot method, and the 3sigma method.

[0090] The advantage of the first execution order is that the HANTS algorithm first smoothes the data to minimize the misjudgment of the latter two, and then marks the anomalies at multiple levels based on the global distribution and local distribution, which is more robust;

[0091] The applicable scenario is for data with more noise and distribution close to normal.

[0092] The second execution order has the advantage of first using a box plot to capture extreme values ​​after smoothing, and then using the 3sigma method to check for distribution anomalies. This is suitable for non-normally distributed data (data that deviates from normal distribution) and is applicable when the data distribution deviates from normality and there are a small number of extreme outliers.

[0093] Specifically, the HANTS algorithm (Harmonic Analysis for Time Series) is as follows:

[0094] Harmonic fitting is performed on the operating data of the energy storage system to be managed. The discrete Fourier transform is used to extract the periodic components of the signal and fit an ideal signal model based on harmonics. The specific fitting process is as follows:

[0095] I_{fit}(t)=a_0+sum_{k=1}^{n}(a_kcos(k omega t)+b_ksin(k omegat)),

[0096] Wherein, I_{fit}(t) represents the signal (target function or approximate waveform) after fitting on the time variable t; a_0 is a constant, which represents the DC component (average value) of the signal; if the signal is centered at 0, then a_0=0; sum_{k=1}^{n} is the summation symbol, which means that the harmonics are added from the fundamental frequency (1) to the highest harmonic frequency (n); a_kcos(k omega t) represents the cosine term in the harmonic component, a_k is the coefficient of the harmonic component, which represents the projection intensity of the signal in the cosine direction of the k-th order harmonic; b_k is the amplitude of the sine component, which represents the projection intensity of the signal in the sine direction of the k-th order harmonic; cos(k omegat) represents the cosine waveform of the k-th order harmonic; (b_ksin(k omega t)) represents the sine term in the harmonic component, sin(k omegat) represents the sinusoidal waveform of the kth order harmonic, k represents the order of the harmonic, indicating that the current harmonic is the kth multiple of the fundamental frequency; omega represents the fundamental angular frequency (unit: radians / second), which is defined as omega=2pi f, where f is the fundamental frequency (unit: Hz).

[0097] Calculate the residual (R(t)) of each data point (I(t)) in the operation data of the energy storage system to be managed and the signal after harmonic fitting (I_{fit}(t)). If the residual exceeds the set residual threshold, the corresponding data point is marked as an outlier.

[0098] In each iteration, the data points marked as abnormal are removed and the harmonic fitting is performed again until the remaining data points meet the fitting accuracy or reach the upper limit of abnormal points.

[0099] Take the timing data of a voltage measurement point at a certain level for explanation:

[0100] Assume the voltage data is as follows: V = [100, 102, 105, 200, 103, 101]

[0101] The input upper and lower limits are [95,110], and the maximum number of outliers allowed is 1.

[0102] Initial screening: 200 exceeds the upper and lower limits and is marked as a suspicious data point.

[0103] Harmonic fitting: After removing 200, a normal trend curve is fitted, for example:

[0104] I_{fit}(t)=101+2\cos(omega t)+1\sin(omegat)

[0105] Calculate the residuals: For 200, the residuals deviate significantly from the fitted values, thus confirming it as an outlier.

[0106] The 3sigma method is as follows:

[0107] Calculate the mean and standard deviation of the operating data of the energy storage system to be managed respectively.

[0108] According to the 3σ principle, the threshold range of normal values ​​is set, where the threshold range of normal values ​​is: [mu-3sigma, mu+3sigma];

[0109] Traverse the operating data of the energy storage system to be managed. If a data point exceeds the threshold range of the normal value, it is marked as an abnormal value or abnormal data point;

[0110] For example: I = {5.1, 5.2, 5.0, 4.9, 5.3, 4.8, 10.5, 4.7, 5.2},

[0111] The mean mu=rac{5.1+5.2+5.0+4.9+5.3+4.8+10.5+4.7+5.2}{9}=5.19, the standard deviation sigma=sqrt{frac{(5.1-5.19)^2+cdots+(10.5-5.19)^2}{9}}=1.76. According to the 3σ principle, the normal value range is: [5.19-3cdot 1.76, 5.19+3cdot 1.76]=[-0.09, 10.47]; 5.1, 5.2, 5.0, 4.9, 5.3, 4.8, 4.7 are within the range [-0.09, 10.47] and are normal values. 10.5 is out of range and marked as an abnormal value.

[0112] The box plot method is as follows:

[0113] Calculate the quartiles and interquartile ranges of the operating data of the energy storage system to be managed after sorting them by size;

[0114] According to the box plot rules, the upper and lower limits of the normal value are calculated, where the lower limit of the normal value is =Q1-1.5cdot{IQR}, and the upper limit is Q3+1.5cdot{IQR}, Q1 is the first quartile of the operating data of the energy storage system to be managed after being sorted by size (the 25% quantile of the data), Q3 is the third quartile of the operating data of the energy storage system to be managed after being sorted by size (the 75% quantile of the data), and IQR is the interquartile range of the operating data of the energy storage system to be managed after being sorted by size;

[0115] Traverse the operating data of the energy storage system to be managed. If a data point exceeds the threshold range of the normal value, it is marked as an outlier.

[0116] Take the time series data of current measurement points at a certain level as an example: I = {1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.5, 3.0, 5.0}, after sorting: {1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.5, 3.0, 5.0},

[0117] Q1=1.5,Q3=2.5

[0118] IQR=2.5-1.5=1.0

[0119] Lower limit = Q1-1.5cdot {IQR} = 1.5-1.5cdot 1.0 = 0

[0120] Upper limit}=Q3+1.5cdot{IQR}=2.5+1.5cdot 1.0=4.0

[0121] Data within the range [0,4.0] are normal values, and data outside the range are outliers: {5.0}.

[0122] like Figure 3 As shown, step S2 specifically also includes:

[0123] S23, performing data noise reduction on the cleaned operation data of different energy storage systems at each level;

[0124] The time series data of independent energy storage power stations usually contains noise, which may be caused by interference from various factors. In combination with the characteristics of the power industry, the wavelet transform method is used. Other noise reduction methods can also be used as long as data noise reduction can be achieved.

[0125] Data preparation, step 1: data after data cleaning;

[0126] Spectrum analysis, to obtain the main frequency components of the noise;

[0127] According to the data spectrum, select db4, db8, sym8, coif3 wavelet basis functions

[0128] The signal-to-noise ratio (SNR) is used to evaluate the denoising effect of the wavelet basis, and then the selection of the data wavelet basis function is determined;

[0129] Data decomposition: Perform wavelet decomposition on the power time series data and decompose it into multiple subsequences (approximate components and detail components) at different scales;

[0130] Threshold selection: Select an appropriate threshold based on the data to distinguish between signals and noise.

[0131] Thresholding: According to the selected threshold, the detail components are thresholded. The detail components below the threshold are considered as noise and set to zero.

[0132] Reconstruction: The processed approximate components and detail components are reconstructed by wavelet to obtain the noise-reduced power time series data.

[0133] S24, for outlier vacancies and missing time points, linear interpolation was used to fill in missing values.

[0134] For outlier vacancies and missing data at other time points, linear interpolation is used to fill in the missing values. The linear interpolation method in this embodiment is the same as the linear interpolation method in the prior art, and this embodiment will not be described in detail here.

[0135] like Figure 4 As shown, step S2 manages the operation data of different energy storage systems at each level according to the different types of operation data of different energy storage systems at each level, and specifically includes:

[0136] S25, obtaining the operating data of different energy storage systems at each level after treatment, and re-screening the operating data of a certain energy storage system at a certain level after treatment according to the third mapping relationship table and the sensor measuring points corresponding to the operating data of different energy storage systems at each level; the third mapping relationship table stores the sensor measuring points corresponding to the operating data of different energy storage systems at each level, and the correspondence between the theoretical minimum value and the theoretical maximum value corresponding to the sensor measuring points.

[0137] The third mapping relationship table may be as shown in Table 9.

[0138] Table 9-Measurement point threshold table (Tag_Threshold)

[0139]

[0140]

[0141] Note: The code field corresponds to the measurement point code in the measurement point table 10, that is, the sensor measurement point corresponding to a certain operating data of a certain energy storage system at a certain level. The max field is the theoretical maximum value of the measurement point, and the min field indicates the theoretical minimum value of the measurement point. Data exceeding the theoretical maximum value or less than the theoretical minimum value is removed so that the operating data of a certain energy storage system at a certain level after governance can be screened again.

[0142] Among them, Figure 5 As shown, in step S3, according to the managed operation data of different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table, the corresponding indicator data of different energy storage systems at each level are output, specifically including:

[0143] S31, obtaining the operation data of a certain energy storage system after treatment, and determining the index data corresponding to the level of the energy storage system according to the second mapping relationship table; the second mapping relationship table also stores the types of energy storage system operation data involved in the calculation of the index data corresponding to the level of the energy storage system, and the calculation method of calculating the index data corresponding to the level of the energy storage system from the energy storage system operation data;

[0144] S32, calculating and outputting the index data corresponding to the level of the energy storage system according to the second mapping relationship table;

[0145] The output indicator data may be in the form of that shown in Table 10.

[0146] Table 10-Index analysis results table (index_result)

[0147]

[0148]

[0149] Note: The meaning of the level field is the same as that of Table 6, i.e., the energy storage system level; Index_id corresponds to the id in Table 6; Belong_id has different meanings depending on the level. When level is 1, it is the id in Table 1, when it is 2, it is the id in Table 2, when it is 3, it is the id in Table 3, when it is 4, it is the id in Table 4, and when it is 5, it is the id in Table 5; the value field indicates the calculated result or analysis result of this indicator.

[0150] The operation logic is to obtain the indicators that need to be calculated at the five levels of station level, energy storage unit level, battery compartment level, battery cluster level, and battery cell level through Table 10 combined with Table 1-Table 5, and then add Table 7 and Table 8 to obtain all the basic operation data at the five levels of station level, energy storage unit level, battery compartment level, battery cluster level, and battery cell level. Combined with the preset business calculation formula, the indicators in Table 6 are calculated and the calculation results are written into Table 10.

[0151] In Table 10 of this embodiment, only part of the data fields are displayed, and other fields may also be set in Table 10, for example, all the operating data names involved in the indicator data, the sensor measurement points corresponding to the operating data, the calculation formula, etc., which are not elaborated in the present invention.

[0152] Calculation formula:

[0153] Here we only list the calculation methods of indicators involved in power quality monitoring; in actual applications, we can add grid adaptability monitoring, active power control performance monitoring, reactive voltage control performance monitoring, primary dispatch performance monitoring, power regulation factor monitoring, overload capacity monitoring, fault ride-through capability monitoring and other indicators according to the business. The calculation formula is as follows:

[0154] (1) Voltage fluctuation rate (UD): UD = U / U0 × 100% (U is the voltage fluctuation, U0 is the rated voltage),

[0155] (2) Voltage flicker rate (Pst): Pst = ∑{P(t) × Δt} / T × 100%, where P(t) is the instantaneous voltage, Δt is the time step, and T is the time range.

[0156] (3) Voltage harmonic content (THD): THD = (∑Un / U1) × 100%, where Un is the harmonic voltage and U1 is the fundamental voltage.

[0157] (4) Current fluctuation rate (ID): ID = I / I0 × 100% (I is the current fluctuation, I0 is the rated current),

[0158] (5) Current unbalance (Iunb): Iunb = (Ia + Ib + Ic) / In × 100%, where Ia, Ib, Ic are three-phase currents and In is the rated current.

[0159] (6) Current harmonic content (THDi): THDi = (∑In / I1) × 100%, where In is the harmonic current and I1 is the fundamental current.

[0160] (7) Total power factor (PF): PF = P / S, where P is the active power and S is the apparent power.

[0161] (8) Harmonic distortion power (THDP): THDP = ∑ (Pn) / P × 100%, where Pn is the harmonic power,

[0162] (9) Active power regulation (PDR): PDR = P / P0 × 100%, where P is the active power fluctuation and P0 is the average active power.

[0163] The relevant monitoring of energy storage units, battery compartments, battery clusters and battery cells can be calculated based on the business calculation formula and combined with the relevant indicators of energy storage units, battery compartments, battery clusters and battery cells and the base operation data (Table 7).

[0164] Query the supervision indicators of independent energy storage power stations, including power quality monitoring, energy storage unit monitoring, battery compartment monitoring, battery cluster monitoring, battery cell monitoring, grid adaptability monitoring, active power control performance monitoring, reactive voltage control performance monitoring, primary dispatch performance monitoring, power regulation factor monitoring, overload capacity monitoring, fault ride-through capability monitoring, etc.

[0165] S33, outputting the level where the energy storage system is located and energy storage systems of corresponding levels above the level where the energy storage system is located according to the first mapping relationship table.

[0166] Preferably, if Figure 6 As shown, step S3 also includes:

[0167] S34, if the indicator data corresponding to the level where a certain energy storage system is located is abnormal, locate the level where the energy storage system is located and the energy storage systems of the corresponding levels above the level where the energy storage system is located according to the first mapping relationship table.

[0168] When an indicator is abnormal, the associated relationship in the first mapping relationship table can be used to reversely investigate the associated measurement points, and then the actual collection points can be found to locate the abnormality.

[0169] For example, if the voltage of cell 001 of independent energy storage cell level A is abnormal, the system query module can be used to reversely query the measurement point information of all operating data associated with the indicator, and the energy storage system level corresponding to the measurement point information of all operating data and the energy storage systems of the corresponding levels above the level of the energy storage system are fed back to the on-site personnel, who will handle the abnormality.

[0170] The present invention divides the energy storage system into several levels according to its level, and establishes a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and index data according to the energy storage system level; obtains the operation data of different energy storage systems at each level, and manages the operation data of different energy storage systems at each level according to the different types of operation data of different energy storage systems at each level; outputs the corresponding index data of different energy storage systems at each level according to the managed operation data of different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table, effectively solving the problem of low efficiency and reliability in the processing of index data of energy storage systems caused by the prior art, and effectively improving the efficiency and reliability of the processing of index data of energy storage systems.

[0171] In the technical solution of the present invention, according to the different types of energy storage system operation data at each level and the preset execution order relationship, the first type of energy storage system operation data is cleaned in a first execution order, and the second type of energy storage system operation data is cleaned in a second execution order; wherein the first type of energy storage system operation data is energy storage system operation data close to normal distribution, and the second type of energy storage system operation data is energy storage system operation data deviating from normal distribution, the energy storage system operation data close to normal distribution includes energy storage system voltage data, energy storage system SOC data, and energy storage system average data, and the energy storage system operation data deviating from normal distribution includes energy storage system current data, energy storage system SOH data, and energy storage system extreme value data, so that different orders of data cleaning algorithms can be executed according to different energy storage system operation data, further improving the efficiency and reliability of energy storage system indicator data processing.

[0172] The technical solution of the present invention specifically includes: using the wavelet transform method to reduce the noise of the cleaned operating data of each level of different energy storage systems; for abnormal value vacancies and data with missing time points, using the linear interpolation method to fill the missing values, thereby ensuring the fast and reliable processing of the energy storage system indicator data.

[0173] The technical solution of the present invention specifically includes managing the operating data of different energy storage systems at each level: re-screening the operating data of a certain energy storage system at a certain level after management according to a third mapping relationship table and the sensor measuring points corresponding to the operating data of different energy storage systems at each level; the third mapping relationship table stores the sensor measuring points corresponding to the operating data of different energy storage systems at each level, and the corresponding relationship between the theoretical minimum value and the theoretical maximum value corresponding to the sensor measuring points, which further ensures the rapid and reliable processing of the energy storage system indicator data.

[0174] In the technical solution of the present invention, if the indicator data corresponding to the level where a certain energy storage system is located is abnormal, the level where the energy storage system is located and the energy storage systems of the corresponding levels above the level where the energy storage system is located are located according to the first mapping relationship table. Not only can the abnormal state of the indicator data of the energy storage system be determined, but also the energy storage system corresponding to the abnormal indicator data and the energy storage systems of the corresponding levels above the level where the energy storage system is located can be determined according to the first mapping relationship table, which is convenient for quickly locating the abnormal energy storage system.

[0175] Embodiment 2

[0176] like Figure 7 As shown, the technical solution of the present invention also provides an energy storage system data processing system, including:

[0177] Establishing module 101, dividing the energy storage system into several levels according to the level of the energy storage system, and establishing a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and indicator data according to the energy storage system level;

[0178] A management module 102 is used to obtain the operating data of different energy storage systems at each level, and manage the operating data of different energy storage systems at each level according to the different types of operating data of different energy storage systems at each level;

[0179] The output module 103 outputs the corresponding indicator data of the different energy storage systems at each level according to the managed operation data of the different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table.

[0180] The implementation process of the establishment module 101, the management module 102, and the output module 103 in the technical solution of the present invention is the same as the corresponding method steps in Example 1, and this embodiment will not be repeated here.

[0181] The present invention divides the energy storage system into several levels according to its level, and establishes a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and index data according to the energy storage system level; obtains the operation data of different energy storage systems at each level, and manages the operation data of different energy storage systems at each level according to the different types of operation data of different energy storage systems at each level; outputs the corresponding index data of different energy storage systems at each level according to the managed operation data of different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table, effectively solving the problem of low efficiency and reliability in the processing of index data of energy storage systems caused by the prior art, and effectively improving the efficiency and reliability of the processing of index data of energy storage systems.

[0182] In the technical solution of the present invention, according to the different types of energy storage system operation data at each level and the preset execution order relationship, the first type of energy storage system operation data is cleaned in a first execution order, and the second type of energy storage system operation data is cleaned in a second execution order; wherein the first type of energy storage system operation data is energy storage system operation data close to normal distribution, and the second type of energy storage system operation data is energy storage system operation data deviating from normal distribution, the energy storage system operation data close to normal distribution includes energy storage system voltage data, energy storage system SOC data, and energy storage system average data, and the energy storage system operation data deviating from normal distribution includes energy storage system current data, energy storage system SOH data, and energy storage system extreme value data, so that different orders of data cleaning algorithms can be executed according to different energy storage system operation data, further improving the efficiency and reliability of energy storage system indicator data processing.

[0183] The technical solution of the present invention specifically includes: using the wavelet transform method to reduce the noise of the cleaned operating data of each level of different energy storage systems; for abnormal value vacancies and data with missing time points, using the linear interpolation method to fill the missing values, thereby ensuring the fast and reliable processing of the energy storage system indicator data.

[0184] The technical solution of the present invention specifically includes managing the operating data of different energy storage systems at each level: re-screening the operating data of a certain energy storage system at a certain level after management according to a third mapping relationship table and the sensor measuring points corresponding to the operating data of different energy storage systems at each level; the third mapping relationship table stores the sensor measuring points corresponding to the operating data of different energy storage systems at each level, and the corresponding relationship between the theoretical minimum value and the theoretical maximum value corresponding to the sensor measuring points, which further ensures the rapid and reliable processing of the energy storage system indicator data.

[0185] In the technical solution of the present invention, if the indicator data corresponding to the level where a certain energy storage system is located is abnormal, the level where the energy storage system is located and the energy storage systems of the corresponding levels above the level where the energy storage system is located are located according to the first mapping relationship table. Not only can the abnormal state of the indicator data of the energy storage system be determined, but also the energy storage system corresponding to the abnormal indicator data and the energy storage systems of the corresponding levels above the level where the energy storage system is located can be determined according to the first mapping relationship table, which is convenient for quickly locating the abnormal energy storage system.

[0186] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for processing energy storage system data, characterized in that: include: Divide the energy storage system into several levels according to the level of the energy storage system, and establish a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and indicator data according to the energy storage system level; Obtain the operating data of different energy storage systems at each level, and manage the operating data of different energy storage systems at each level according to the different types of operating data of different energy storage systems at each level; According to the managed operating data of different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table, the corresponding indicator data of different energy storage systems at each level are output.

2. The energy storage system data processing method according to claim 1, characterized in that: The energy storage system is divided into several levels: The divided energy storage system hierarchy includes energy storage power stations, energy storage units, battery compartments, battery clusters, and battery cells from high to low; the first mapping table stores the correspondence between energy storage power stations, energy storage units, battery compartments, battery clusters, and battery cells.

3. The energy storage system data processing method according to claim 1, characterized in that: According to the different types of operating data of different energy storage systems at each level, the management of operating data of different energy storage systems at each level includes: Obtain the operating data types of different energy storage systems at each level; According to the different energy storage system operation data types and preset execution order relationships at each level, data cleaning of the first type of energy storage system operation data is performed in a first execution order, and data cleaning of the second type of energy storage system operation data is performed in a second execution order; wherein, the first type of energy storage system operation data is energy storage system operation data close to a normal distribution, and the second type of energy storage system operation data is energy storage system operation data deviating from a normal distribution, the energy storage system operation data close to a normal distribution includes energy storage system voltage data, energy storage system SOC data, and energy storage system average value data, and the energy storage system operation data deviating from a normal distribution includes energy storage system current data, energy storage system SOH data, and energy storage system extreme value data.

4. The energy storage system data processing method according to claim 3 is characterized in that: The first execution order is the HANTS algorithm, the 3sigma method, and the box plot method, and the second execution order is the HANTS algorithm, the box plot method, and the 3sigma method.

5. The energy storage system data processing method according to claim 4 is characterized in that: The specific HANTS algorithm is: Harmonic fitting is performed on the operating data of the energy storage system to be managed. The discrete Fourier transform is used to extract the periodic components of the signal and fit an ideal signal model based on harmonics. The specific fitting process is as follows: I_{fit}(t)=a_0+sum_{k=1}^{n}(a_kcos(k omega t)+b_ksin(komega t)), Where, I_{fit}(t) represents the signal after fitting on the time variable t; a_0 is a constant, representing the DC component of the signal; sum_{k=1}^{n} is a summation symbol, indicating that the harmonics are accumulated from the fundamental frequency to the highest harmonic frequency; a_k is the coefficient of the harmonic component, representing the projection intensity of the signal in the cosine direction of the k-th order harmonic; omega is the fundamental angular frequency; b_k is the amplitude of the sine component, representing the projection intensity of the signal in the sine direction of the k-th order harmonic; Calculate the residual of each data point in the operation data of the energy storage system to be managed and the signal after harmonic fitting. If the residual exceeds the set residual threshold, the corresponding data point is marked as an outlier. In each iteration, the data points marked as abnormal are removed and the harmonic fitting is performed again until the remaining data points meet the fitting accuracy or reach the upper limit of abnormal points; The 3sigma method is as follows: Calculate the mean and standard deviation of the operating data of the energy storage system to be managed respectively. According to the 3σ principle, the threshold range of normal values ​​is set, where the threshold range of normal values ​​is: [mu-3sigma, mu+3sigma]; Traverse the operating data of the energy storage system to be managed. If a data point exceeds the threshold range of the normal value, it is marked as an outlier; The box plot method is as follows: Calculate the quartiles and interquartile ranges of the operating data of the energy storage system to be managed after sorting them by size; According to the box plot rules, calculate the upper and lower limits of the normal value, where the lower limit of the normal value is = Q1-1.5cdot{IQR}, and the upper limit is Q3+1.5cdot{IQR}, Q1 is the first quartile of the operating data of the energy storage system to be managed after sorting by size, Q3 is the third quartile of the operating data of the energy storage system to be managed after sorting by size, and IQR is the interquartile range of the operating data of the energy storage system to be managed after sorting by size; Traverse the operating data of the energy storage system to be managed. If a data point exceeds the threshold range of the normal value, it is marked as an outlier.

6. The energy storage system data processing method according to claim 4 is characterized in that: According to the different types of operation data of different energy storage systems at each level, the management of the operation data of different energy storage systems at each level specifically includes: Perform data noise reduction on the cleaned operating data of different energy storage systems at each level; For outlier vacancies and missing time points, linear interpolation was used to fill in missing values.

7. A method for processing energy storage system data according to any one of claims 3 to 6, characterized in that: According to the different types of operation data of different energy storage systems at each level, the management of the operation data of different energy storage systems at each level specifically includes: The operating data of different energy storage systems at each level after treatment are obtained, and the operating data of a certain energy storage system at a certain level after treatment are screened again according to a third mapping relationship table and the sensor measuring points corresponding to the operating data of different energy storage systems at each level; the third mapping relationship table stores the sensor measuring points corresponding to the operating data of different energy storage systems at each level, and the correspondence between the theoretical minimum value and the theoretical maximum value corresponding to the sensor measuring points.

8. The energy storage system data processing method according to claim 2, characterized in that: According to the managed operation data of different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table, the output of the corresponding indicator data of different energy storage systems at each level specifically includes: Obtaining the operation data of a certain energy storage system after management, and determining the index data corresponding to the level of the energy storage system according to the second mapping relationship table; the second mapping relationship table also stores the types of energy storage system operation data involved in the calculation of the index data corresponding to the level of the energy storage system, and the calculation method of calculating the index data corresponding to the level of the energy storage system from the energy storage system operation data; Calculate and output the index data corresponding to the level of the energy storage system according to the second mapping relationship table; The level at which the energy storage system is located and energy storage systems at corresponding levels above the level at which the energy storage system is located are output according to the first mapping relationship table.

9. The energy storage system data processing method according to claim 8, characterized in that: include: If the indicator data corresponding to the level where a certain energy storage system is located is abnormal, the level where the energy storage system is located and the energy storage systems of the corresponding levels above the level where the energy storage system is located are located according to the first mapping relationship table.

10. An energy storage system data processing system, characterized in that: include: Establish a module, divide the energy storage system into several levels according to the level of the energy storage system, and establish a first mapping relationship table of different energy storage system levels and a second mapping relationship table of each energy storage system level and indicator data according to the energy storage system level; The governance module obtains the operating data of different energy storage systems at each level and manages the operating data of different energy storage systems at each level according to the different types of operating data of different energy storage systems at each level. The output module outputs the corresponding indicator data of the different energy storage systems at each level according to the managed operation data of the different energy storage systems at each level and the first mapping relationship table and the second mapping relationship table.

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