On-line monitoring system for storage battery of transformer substation

Abstract

An on-line monitoring system for a storage battery of a transformer substation comprises a storage battery monitoring host, a detection module, a battery equalization module, an emergency processing module and an alarm module, and the storage battery monitoring host judges whether the storage battery breaks down or not; when the storage battery monitoring host monitors that the charge states of the single batteries in the storage battery pack are unbalanced, the corresponding single batteries are controlled to charge and discharge, so that the charge states of the single batteries are balanced; when the storage battery pack breaks down, emergency treatment is carried out on the storage battery pack; the alarm module is used for giving an alarm according to the alarm instruction transmitted by the storage battery monitoring host. The on-line monitoring system for the storage battery of the transformer substation integrates on-line monitoring, fault intelligent processing and alarming of the storage battery, can provide health diagnosis and efficiency guarantee of the battery, and can quickly and intelligently take corresponding measures to solve corresponding battery faults when the storage battery breaks down, so that the safety of the storage battery is improved. Therefore, the safety and reliability of a power supply system are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of battery monitoring, in particular to an online monitoring system for batteries in a transformer substation. Background Art

[0002] With the rapid development of coal-to-liquid projects, the safe and stable operation of the power system has become the key. As the core of the power system, the monitoring and management of batteries plays a vital role in ensuring the stability of power supply and the normal operation of key facilities. However, the monitoring of batteries in the existing technology is mostly limited to offline monitoring, and it is not possible to handle the battery failure in a timely manner when it is detected.

[0003] In order to ensure that the health status of batteries (including UPS, EPS, DC panels and other equipment) in substations is effectively monitored and managed and to improve the safety management level of substations, the present invention aims to establish a battery online monitoring system and integrate it into the existing auxiliary equipment monitoring platform to realize the intelligent and refined operation and maintenance of substations. Summary of the invention

[0004] In view of the problems in the prior art, the present invention provides an online monitoring system for batteries in a substation.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] The online monitoring system of the battery used in the substation includes: a battery monitoring host, a detection module, a battery balancing module, an emergency processing module and an alarm module. The battery monitoring host is installed in the communication room of the substation and is responsible for data collection, processing, storage and judging whether the battery fails. The detection module is connected to the battery monitoring host in communication, installed near the battery group and electrically connected to the battery, and performs real-time monitoring of various battery parameters. The battery balancing module is connected to the battery monitoring host and the battery group respectively. When the battery monitoring host detects that the charge state is unbalanced between the single cells in the battery group, the corresponding battery cells are controlled to charge and discharge according to the instructions of the battery monitoring host to achieve the balance of the charge state between the battery cells. The emergency processing module is connected to the battery monitoring host and the battery group respectively. When the battery group fails, the battery group is emergency processed according to the instructions of the battery monitoring host. The alarm module is connected to the battery monitoring host in communication, and is used to alarm according to the alarm instructions transmitted by the battery monitoring host.

[0007] Specifically, the battery includes a box body, and sensors, fire extinguishing devices, circuit breakers and cooling systems arranged in the box body, wherein the detection module is connected to the sensor and is used to receive detection data of the sensor; the sensor includes: a voltage sensor, a current sensor, a temperature sensor and a smoke sensor; the battery parameters include battery temperature, voltage, internal resistance, SOC, charge and discharge current and smoke concentration; the circuit breaker, cooling system and fire extinguishing device are respectively connected to the emergency processing module and are used to cut off power, cool and extinguish fire according to control instructions.

[0008] Specifically, the battery monitoring host includes: a data storage module, which is used to store data collected and processed by the battery monitoring host; a statistical analysis module, which is used to perform systematic statistical analysis on the monitoring data, automatically generate reports, and mine and analyze historical data to predict the remaining life of the battery and evaluate its performance status; a fault diagnosis module, which is used to perform fault diagnosis using an intelligent algorithm based on the real-time monitoring data and provide fault type and location information; an early warning module, when the fault diagnosis module diagnoses that the battery status is abnormal, the corresponding abnormal data is sent to the storage module for storage and an alarm instruction is issued to the alarm device, and at the same time, corresponding instructions are sent to the emergency processing module or the battery balancing module according to the corresponding fault type.

[0009] Specifically, it also includes a remote control module, which is electrically connected to the battery monitoring host and is used for remote control of the charging and discharging process of the battery pack, and remote isolation or restart of faulty equipment.

[0010] Specifically, the fault types include short circuit, overcharge, over discharge, open circuit, insufficient remaining battery life, excessive temperature, unbalanced charge state between battery cells, and excessive smoke concentration.

[0011] Specifically, the voltage sensor acquires the battery voltage of each battery cell in the battery pack; for any battery cell, determines whether the battery pack is overcharged or over-discharged according to the battery voltage of the battery cell and a preset battery voltage threshold; in response to the battery pack not being overcharged or over-discharged, traverses each battery cell, and for the mth battery cell currently traversed, acquires a first voltage average value according to the battery voltage of each battery cell, and acquires a second voltage average value according to the battery voltage of the mth battery cell within a preset time, where m is a positive integer; determines a correction coefficient according to the magnitude relationship between the first voltage average value and the cell voltage of the battery cell, wherein, in response to the cell voltage of the battery cell being less than the first voltage average value, determines the correction coefficient to be a first preset value, and in response to the battery The cell voltage of the cell is equal to the first voltage average value, and the correction coefficient is determined to be a second preset value. In response to the cell voltage of the battery cell being greater than the first voltage average value, the correction coefficient is determined to be a third preset value, and the third preset value is greater than the first preset value and less than the second preset value, the first preset value is a negative number, and the second preset value is a positive number; a first variance is determined according to the first voltage average value, the second voltage average value and the cell voltage of the mth battery cell; a second variance is obtained according to the correction coefficient and the first variance; a difference between the second variance at the previous moment and the second variance at the current moment is obtained; in response to the difference being greater than a preset first difference threshold, it is determined that a short circuit exists in the battery pack; in response to the difference being less than a preset second difference threshold, it is determined that an open circuit exists in the battery pack.

[0012] Specifically, the first variance is determined using the following formula:

[0013]

[0014] Among them, the D m ' represents the first variance, N represents the number of battery cells in the battery pack, V m (i) represents the cell voltage of the mth battery cell, and V avg (i) represents the first voltage average value, represents the second voltage average value.

[0015] Specifically, the battery voltage threshold includes a first battery voltage threshold and a second battery voltage threshold, the first battery voltage threshold is greater than the second battery voltage threshold, and determining whether the battery pack is overcharged or over-discharged based on the battery voltage of the battery cell and the preset battery voltage threshold includes: in response to the battery voltage of the battery cell being greater than the first battery voltage threshold, determining that the battery pack is overcharged; or in response to the battery voltage of the battery cell being less than the second battery voltage threshold, determining that the battery pack is over-discharged.

[0016] Specifically, the historical data mining and analysis to predict the remaining life of the battery includes: obtaining the historical charging and discharging data of the battery as raw data; sorting and cleaning the obtained raw data, removing invalid and abnormal data points, retaining valid data points, and selecting characteristic parameters based on the cleaned data by analyzing the correlation between the raw data, the characteristic parameters including the charging start voltage, the voltage at the end of charging, the SOC extreme difference, the charging rate and the cumulative number of charging times; performing EMD decomposition on the data sequence of the obtained characteristic parameters in turn, and obtaining n intrinsic mode function IMF components of the data sequence; using the selection principle based on the correlation coefficient between the IMF component and the original signal to judge the authenticity of the IMF component to remove the IMF pseudo-component; reconstructing the denoised IMF component to obtain an optimized data sequence; inputting the optimized data sequence into a trained ILPSO-BP neural network model for iterative prediction, and setting the output as the capacity of the battery, thereby predicting the remaining life of the battery.

[0017] Specifically, the battery monitoring host also includes: a data transmission module, which includes a data transmission protocol, data compression and encryption; the data transmission protocol specifically adopts the TCP / IP protocol; the data compression and encryption specifically compresses and encrypts the transmitted data to improve transmission efficiency and data security.

[0018] Beneficial effects of the present invention:

[0019] The online monitoring system for batteries in substations of the present invention integrates online monitoring of batteries, intelligent fault processing and alarm, can provide battery health diagnosis and performance guarantee, and can quickly and intelligently take corresponding measures to solve the corresponding battery failure when the battery fails, so as to ensure the safety and reliability of the power supply system. In addition, the system can also perform statistical analysis and storage on the real-time data of the battery, so that the staff can retrieve relevant data to more comprehensively understand the working status of the connected battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0021] Figure 1 The overall structural diagram of the online monitoring system for batteries in a substation provided by the present invention;

[0022] Figure 2 It is a structural schematic diagram of the storage battery of the present invention;

[0023] Figure 3 The figure is a flow chart of fault detection of a battery pack according to the present invention. DETAILED DESCRIPTION

[0024] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0025] like Figure 1-2 As shown, the online monitoring system for batteries in a substation of the present invention comprises: a battery monitoring host, a detection module, a battery balancing module, an emergency processing module and an alarm module. The battery monitoring host is installed in the communication room of the substation and is responsible for data collection, processing, storage and judging whether the battery fails. The detection module is connected to the battery monitoring host in communication, installed near the battery pack and electrically connected to the battery, and performs real-time monitoring of various battery parameters. The battery balancing module is connected to the battery monitoring host and the battery pack respectively. When the battery monitoring host detects that the charge state is unbalanced between the single cells in the battery pack, the corresponding battery cells are controlled to charge and discharge according to the instructions of the battery monitoring host to achieve the balance of the charge state between the battery cells. The emergency processing module is connected to the battery monitoring host and the battery pack respectively. When the battery pack fails, the battery pack is emergency processed according to the instructions of the battery monitoring host. The alarm module is connected to the battery monitoring host in communication, and is used to alarm according to the alarm instructions transmitted by the battery monitoring host. In the present invention, by online monitoring of the data of the battery pack, the health status of the battery pack can be understood in real time. When the battery fails such as imbalance, overcharge, over discharge, over temperature, etc., the system can automatically handle the corresponding fault and notify the staff through an alarm to prevent the battery failure problem from further deteriorating and causing greater economic losses. It can provide a good monitoring and management environment to ensure the stability of the power supply system. In addition, the battery monitoring host also has a data storage module that can store various items of the battery so that the staff can retrieve the historical data of the battery, so as to understand the working status of the battery more intuitively.

[0026] Furthermore, the battery includes a box, and sensors, fire extinguishing devices, circuit breakers and cooling systems arranged in the box, wherein the detection module is connected to the sensor to receive the detection data of the sensor; the sensor includes: a voltage sensor, a current sensor, a temperature sensor and a smoke sensor; the battery parameters include battery temperature, voltage, internal resistance, SOC, charge and discharge current and smoke concentration. The circuit breaker, cooling system and fire extinguishing device are respectively connected to the emergency processing module to perform power-off, cooling, fire extinguishing and other operations according to control instructions. The state data of the battery during operation is obtained in real time through sensors such as voltage sensors, current sensors, and temperature sensors, and the corresponding data is analyzed and stored. When problems such as overcharging and over-discharging occur, the system can cut off the power supply circuit of the corresponding battery at the first moment. In addition, the temperature sensor and the smoke sensor can monitor in real time whether the temperature of the battery is too high and whether a fire occurs. When the temperature is found to be too high, the corresponding cooling system can be started to cool it. When the smoke concentration is detected to be too high and the battery is on fire, the battery can be automatically extinguished by controlling the corresponding fire extinguishing device.

[0027] Furthermore, the battery monitoring host includes: a data storage module for storing data collected and processed by the battery monitoring host; a statistical analysis module for performing systematic statistical analysis on the monitoring data, automatically generating reports, and mining and analyzing historical data to predict the remaining life of the battery and evaluate its performance status; a fault diagnosis module for performing fault diagnosis using intelligent algorithms based on the real-time monitoring data, and providing fault type and location information; and an early warning module. When the fault diagnosis module diagnoses that the battery state is abnormal, the corresponding abnormal data is sent to the storage module for storage and an alarm instruction is issued to the alarm device, and at the same time, a corresponding instruction is sent to the emergency processing module or the battery balancing module according to the corresponding fault type. The battery monitoring host can analyze and store the battery state data, and calculate the remaining service life of the battery according to the historical data. When the remaining service life is too low, the staff can be prompted to replace the corresponding battery by means of an alarm to ensure the good operation of the power supply system. In addition, the battery monitoring host can also diagnose whether the battery has a fault based on the real-time data of the battery, and provide the fault type and fault location, and issue corresponding control instructions according to the corresponding fault type, thereby realizing intelligent solutions to various battery fault problems.

[0028] Furthermore, it also includes a remote control module, which is electrically connected to the battery monitoring host and is used for remote control of the charging and discharging process of the battery pack, as well as remote isolation or restart of faulty equipment. When certain battery faults cannot be solved by instructions issued by the battery monitoring host, the remote control module can be used to remotely operate the corresponding faults. In addition, the staff can remotely retrieve the historical data of the battery through the remote control module to more intuitively understand the working status of the battery.

[0029] Furthermore, the fault types include short circuit, overcharge, over discharge, open circuit, insufficient remaining battery life, overtemperature, unbalanced charge state between battery cells, and excessive smoke concentration. The battery monitoring host can analyze and process the real-time working data of the battery, monitor whether the battery has a fault in real time and determine the specific fault type of the battery, and send corresponding control instructions to the corresponding control module according to the fault type for intelligent fault processing.

[0030] further, Figure 3 is a flow chart of a method for detecting a fault of a battery pack according to an embodiment of the present application. Figure 3 As shown, the method comprises the following steps:

[0031] S101, obtaining the battery voltage of each battery cell in the battery pack.

[0032] In the embodiment of the present application, the battery pack is composed of hundreds or thousands of battery cells connected in series and / or in parallel. In some implementations, the measured voltage of each battery cell can be obtained based on a sensor as the battery voltage of the battery cell.

[0033] S102 , for any battery cell, determining whether the battery pack is overcharged or over-discharged according to the battery voltage of the battery cell and a preset battery voltage threshold.

[0034] In the embodiment of the present application, the battery voltage threshold includes a first battery voltage threshold and a second battery voltage threshold, the first battery voltage threshold is greater than the second battery voltage threshold, and in response to the battery voltage of the battery cell being greater than the first battery voltage threshold, it is determined that the battery pack is overcharged. In response to the battery voltage of the battery cell being less than the second battery voltage threshold, it is determined that the battery pack is over-discharged.

[0035] That is, in the embodiment of the present application, the first battery voltage threshold is the maximum battery voltage limit, and the second battery voltage threshold is the minimum battery voltage threshold, that is, if the battery cell voltage exceeds the maximum battery voltage limit, it is determined that the battery pack is overcharged. If the battery cell voltage is lower than the minimum battery voltage limit, it is determined that the battery pack is over-discharged.

[0036] S103 , in response to the battery pack not being overcharged or over-discharged, traverse each battery cell, and for the mth battery cell currently traversed, obtain a correction coefficient and a first variance according to the battery voltage of each battery cell.

[0037] In some implementations, in response to the battery pack not being overcharged or over-discharged, it is continued to be determined whether the battery pack is open-circuited or short-circuited.

[0038] In the embodiment of the present application, the first variance is obtained according to the battery voltage of each battery cell. The larger the variance, the greater the dispersion of the battery voltage sequence, that is, there may be a small fault in the battery. On the contrary, the smaller the variance, the lower the dispersion of the battery voltage sequence, that is, the smaller the possibility of a fault in the battery cell. Therefore, even if the battery voltage is within the cut-off voltage range, the fault in the battery pack can be diagnosed.

[0039] In some implementations, in order to further identify whether the fault type of the battery pack is open circuit or short circuit, the correction coefficient of the discrete information of the battery voltage sequence can be obtained according to the battery voltage of each battery cell, so as to subsequently reflect the abnormal dispersion degree of the battery voltage and identify the fault type of the battery pack.

[0040] For the mth battery cell currently traversed, a first voltage average value is obtained according to the battery voltage of each battery cell, and a second voltage average value is obtained according to the battery voltage of the mth battery cell within a preset time.

[0041] Taking the i-th moment of the m-th battery cell as an example, optionally, the following formula can be used to obtain the first voltage average value V avg (i)

[0042]

[0043] Taking the mth battery cell as an example, optionally, the second voltage average value may be obtained by using the following formula:

[0044]

[0045] in, represents the second voltage average value.

[0046] As one of the fault diagnosis strategies, it is crucial to ensure the accuracy and robustness of the diagnosis strategy online and in real time. Therefore, a sliding window algorithm is used to quantitatively update battery data in real time. In other words, N in the above formula can be understood as the size of the sliding window, that is, the preset time is N moments. The sliding window size N essentially determines the accuracy of the subsequent variance algorithm. Increasing the sliding window size N will increase the instability of the variance curve and reduce the accuracy and robustness of battery fault diagnosis. Reducing the sliding window size N can adaptively improve the accuracy of the variance value of the abnormal voltage and suppress the fluctuation amplitude of the variance curve, thereby having a strong robustness for the diagnosis of battery faults. However, when the window size is greater than 2, although the fault voltage changes, the variance value will be identically equal to zero. Therefore, no fault can be diagnosed when the sliding window is too small. In the embodiment of the present application, the size of the sliding window can be determined according to the offline experiment, and the fault diagnosis effect of the variance algorithm can be optimized, the amount of calculation can be reduced, and the diagnostic accuracy and robustness can be improved.

[0047] Regardless of how the dispersion of the battery voltage sequence changes, the corresponding voltage improvement variance value is always non-negative. Therefore, the variance algorithm can detect abnormal faults in the battery module, but the traditional variance algorithm and the first variance cannot diagnose the fault type of the battery. Therefore, the present invention designs a correction coefficient θ of the discrete information of the battery voltage sequence to accurately diagnose the minor fault type and fault time of the early battery.

[0048] In the embodiment of the present application, in response to the cell voltage of the battery cell being less than the first voltage average value, the correction coefficient is determined to be the first preset value. In response to the cell voltage of the battery cell being equal to the first voltage average value, the correction coefficient is determined to be the second preset value. In response to the cell voltage of the battery cell being greater than the first voltage average value, the correction coefficient is determined to be the third preset value, wherein the third preset value is greater than the first preset value and less than the second preset value, the first preset value is a negative number, and the second preset value is a positive number.

[0049] Among them, θ is the correction coefficient, which can be obtained by the following judgment:

[0050] That is to say, at the i-th moment, when the cell voltage of the battery cell is less than the first voltage average value, the correction coefficient θ is determined to be -1; when the cell voltage of the battery cell is equal to the first voltage average value, the correction coefficient θ is determined to be 1; when the cell voltage of the battery cell is equal to the first voltage average value, the correction coefficient θ is determined to be 0.

[0051] The first variance is determined based on the first voltage average, the second voltage average, and the cell voltage of the mth battery cell. In practical applications, the battery is very sensitive to interference from changes in the external environment, there are serious inconsistencies between battery packs (i.e., there are differences in voltage, internal resistance, and capacity between battery packs of the same type and specification), and the occurrence of battery failures is also random. In particular, the variance algorithm is very sensitive to the normal fluctuations of the battery voltage sequence, so directly using the battery voltage as the input of the variance algorithm will increase the possibility of misdiagnosis and reduce the accuracy and stability of the method.

[0052] Therefore, it is very necessary to pre-process the input data of the variance algorithm, that is, to use the difference between the battery voltage and the average voltage of all batteries as the output of the variance algorithm, which can significantly reduce the possibility of normal voltage being misdiagnosed as abnormal voltage. This pre-processing procedure can not only eliminate the influence of voltage inconsistency, but also improve the fault diagnosis accuracy of the variance algorithm, thereby highlighting the potential early small fault information in the series battery module.

[0053] Optionally, in the embodiment of the present application, the first variance of the mth battery cell may be determined by the following formula:

[0054]

[0055] Among them, the D m ' represents the first variance, N represents the number of battery cells in the battery pack, V m (i) represents the cell voltage of the mth battery cell, and V avg (i) represents the first voltage average value, represents the second voltage average value.

[0056] S104, obtaining a second variance according to the correction coefficient and the first variance, and determining whether the battery pack has an open circuit or a short circuit according to the second variance.

[0057] In some implementations, the product of the correction coefficient and the first variance may be determined as the second variance.

[0058] In some implementations, the second variance is compared to a preset variance threshold to determine whether the battery pack has an open circuit or a short circuit.

[0059] In some implementations, when a random sudden increase in the battery voltage of a single cell causes the real-time voltage to be greater than the average voltage, the second variance will also suddenly increase; when a random sudden decrease in the battery voltage of a single cell causes the real-time voltage to be less than the average voltage, the second variance will also suddenly decrease. Therefore, in the embodiment of the present application, if there is a sudden increase in the second variance of the battery cell at adjacent moments, it is determined that an open circuit has occurred; if there is a sudden decrease in the second variance of the battery cell at adjacent moments, it is determined that a short circuit has occurred.

[0060] In the embodiment of the present application, for any battery cell, it is determined whether the battery pack is overcharged or over-discharged according to the battery voltage of the battery cell and the preset battery voltage threshold; in response to the battery pack not being overcharged or over-discharged, each battery cell is traversed, and for the mth battery cell currently traversed, a correction coefficient and a first variance are obtained according to the battery voltage of each battery cell; a second variance is obtained according to the correction coefficient and the first variance, and it is determined whether the battery pack is open or short-circuited according to the second variance. The embodiment of the present application can effectively diagnose the initial micro-fault of the battery, prevent the occurrence of battery safety accidents, and improve the robustness of fault detection of the battery pack.

[0061] Furthermore, the mining and analysis of historical data to predict the remaining life of the battery includes: obtaining the historical charging and discharging data of the battery as raw data; sorting and cleaning the obtained raw data, removing invalid and abnormal data points, retaining valid data points, and selecting characteristic parameters based on the obtained cleaned data by analyzing the correlation between the raw data, the characteristic parameters including the charging start voltage, the voltage at the end of charging, the SOC extreme difference, the charging rate and the cumulative number of charging times; performing EMD decomposition on the data sequence of the obtained characteristic parameters in turn, and obtaining n intrinsic mode function imf components of the data sequence; using the selection principle based on the correlation coefficient between the imf component and the original signal to judge the authenticity of the imf component to remove the imf pseudo component; reconstructing the denoised imf component to obtain an optimized data sequence; inputting the optimized data sequence into the trained ILPSO-BP neural network model for iterative prediction, and setting the output as the capacity of the battery, thereby predicting the remaining life of the battery. Wherein, the ILPSO-BP neural network model is trained by the historical driving data sequence of several power batteries with the same battery model to be predicted. Perform EMD decomposition on the characteristic parameter data and decompose the noisy signal x(i) into:

[0062] In the formula, x(i) is the observed value of the noisy signal at time i; imf is the intrinsic mode function, and the frequency is arranged from high to low; r(i) is the residual component; j is the number of intrinsic mode functions. It also includes the construction of a battery life prediction model. The battery life prediction model is constructed by introducing the Levy flight algorithm on the traditional PSO algorithm; an ILPSO algorithm is proposed to change the particle position update formula and accelerate the algorithm iteration. Through this method, the BP neural network is trained using the training set data to obtain a battery life prediction model that meets the conditions, thereby completing the battery life prediction. The Levy flight algorithm is introduced on the traditional PSO algorithm to change the position of the particle through the following formula:

[0063] Among them, α = α0 (xid-pgd) is the step size information, which is used to control the range of Levy flight random search, where α0 = 0.01; is the dot product symbol; Levy(β)=s; is the particle position after Levy flight; xid and vid are the position and velocity of the particle of this generation respectively; k is the current iteration number. In order to ensure that the particle can escape from the local extreme value and increase the optimization ability of the PSO algorithm, a method for guiding the Levy flight direction is proposed. The specific position update formula is shown in the following formula:

[0064] In the formula, and are the positions and velocities of the particles of this generation respectively; ||·|| represents modulus; It means that the particle is flying in the direction of the optimal particle; S is a random variable that obeys the Bernoulli distribution, S~B(p), the value of S is 0 or 1, and the probability of taking 1 is P; the value of the probability p is related to the position of the particle.

[0065] Furthermore, the battery monitoring host also includes: a data transmission module, which includes a data transmission protocol, data compression and encryption; the data transmission protocol specifically adopts the TCP / IP protocol; the data compression and encryption specifically compresses and encrypts the transmitted data to improve transmission efficiency and data security.

[0066] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of the present invention is defined by the attached claims and their equivalents.

Claims

1. An online monitoring system for batteries in a substation, characterized in that: include: A battery monitoring host, a detection module, a battery balancing module, an emergency processing module and an alarm module. The battery monitoring host is installed in the communication room of the substation and is responsible for data collection, processing, storage and judging whether the battery fails. The detection module is connected to the battery monitoring host in communication, installed near the battery pack and electrically connected to the battery, and performs real-time monitoring of various battery parameters. The battery balancing module is connected to the battery monitoring host and the battery pack respectively. When the battery monitoring host detects that the charge state is unbalanced between the single cells in the battery pack, it controls the corresponding battery cells to charge and discharge according to the instructions of the battery monitoring host to achieve a balance in the charge state between the battery cells. The emergency processing module is connected to the battery monitoring host and the battery pack respectively. When the battery pack fails, the battery pack is emergency processed according to the instructions of the battery monitoring host. The alarm module is connected to the battery monitoring host in communication, and is used to alarm according to the alarm instructions transmitted by the battery monitoring host.

2. The battery online monitoring system according to claim 1, wherein the battery comprises a box, and a sensor, a fire extinguishing device, a circuit breaker and a cooling system arranged in the box, wherein the detection module is connected to the sensor to receive detection data of the sensor; the sensor comprises: Voltage sensor, current sensor, temperature sensor and smoke sensor; the battery parameters include battery temperature, voltage, internal resistance, SOC, charge and discharge current and smoke concentration; the circuit breaker, cooling system and fire extinguishing device are respectively connected to the emergency processing module for power off, cooling and fire extinguishing according to control instructions.

3. The battery online monitoring system according to claim 2, wherein the battery monitoring host comprises: A data storage module, used to store data collected and processed by the battery monitoring host; Statistical analysis module, used to conduct systematic statistical analysis on monitoring data, automatically generate reports, and mine and analyze historical data to predict the remaining life of the battery and evaluate its performance status; The fault diagnosis module is used to perform fault diagnosis using intelligent algorithms based on the data monitored in real time, and provide fault type and location information; the early warning module, when the fault diagnosis module diagnoses that the battery status is abnormal, sends the corresponding abnormal data to the storage module for storage and issues an alarm instruction to the alarm device, and at the same time sends corresponding instructions to the emergency processing module or the battery balancing module according to the corresponding fault type.

4. The battery online monitoring system according to claim 3 further includes a remote control module, which is electrically connected to the battery monitoring host and is used for remote control of the charging and discharging process of the battery pack, and remote isolation or restart of faulty equipment.

5. According to the battery online monitoring system of claim 4, the fault types include short circuit, overcharge, over discharge, open circuit, insufficient remaining battery life, excessive temperature, unbalanced charge state between battery cells, and excessive smoke concentration.

6. The battery online monitoring system according to claim 5, wherein the voltage sensor obtains the battery voltage of each battery cell in the battery pack; for any battery cell, determines whether the battery pack is overcharged or over-discharged according to the battery voltage of the battery cell and a preset battery voltage threshold; in response to the battery pack not being overcharged or over-discharged, traverses each battery cell, and for the mth battery cell currently traversed, obtains a first voltage average value according to the battery voltage of each battery cell, and obtains a second voltage average value according to the battery voltage of the mth battery cell within a preset time, wherein m is a positive integer; determines a correction coefficient according to the relationship between the first voltage average value and the battery cell voltage of the battery cell, wherein, In response to the cell voltage of the battery cell being less than the first voltage average value, the correction coefficient is determined to be a first preset value; in response to the cell voltage of the battery cell being equal to the first voltage average value, the correction coefficient is determined to be a second preset value; in response to the cell voltage of the battery cell being greater than the first voltage average value, the correction coefficient is determined to be a third preset value, the third preset value is greater than the first preset value and less than the second preset value, the first preset value is a negative number, and the second preset value is a positive number; a first variance is determined according to the first voltage average value, the second voltage average value, and the cell voltage of the mth battery cell; a second variance is obtained according to the correction coefficient and the first variance; and a difference between the second variance at the previous moment and the second variance at the current moment is obtained; In response to the difference being greater than a preset first difference threshold, determining that a short circuit exists in the battery pack; In response to the difference being less than a preset second difference threshold, it is determined that a short circuit exists in the battery pack.

7. The battery online monitoring system according to claim 6, wherein the first variance is determined by the following formula: in, The D m ' represents the first variance, N represents the number of battery cells in the battery pack, V m (i) represents the cell voltage of the mth battery cell, and V avg (i) represents the first voltage average value, represents the second voltage average value.

8. The battery online monitoring system according to claim 7, wherein the battery voltage threshold comprises a first battery voltage threshold and a second battery voltage threshold, the first battery voltage threshold is greater than the second battery voltage threshold, and the determining whether the battery pack is overcharged or over-discharged according to the battery voltage of the battery cell and the preset battery voltage threshold comprises: In response to a battery voltage of a battery cell being greater than the first battery voltage threshold, determining that the battery pack is overcharged; Or in response to a battery voltage of a battery cell being lower than the second battery voltage threshold, it is determined that the battery pack is over-discharged.

9. The battery online monitoring system according to claim 8, wherein the mining and analysis of historical data to predict the remaining life of the battery comprises: Obtaining historical charging and discharging data of the battery as raw data; The acquired raw data are sorted and cleaned, invalid and abnormal data points are removed, and valid data points are retained. According to the cleaned data, the correlation between the raw data is analyzed to select characteristic parameters, and the characteristic parameters include the charging start voltage, the voltage at the end of charging, the SOC extreme difference, the charging rate and the cumulative number of charging times; the data sequence of the acquired characteristic parameters is sequentially subjected to EMD decomposition, and n intrinsic mode function imf components are obtained from the data sequence; the authenticity of the imf components is judged based on the selection principle based on the correlation coefficient between the imf components and the original signals to remove the imf pseudo components; the denoised imf components are reconstructed to obtain the optimized data sequence; the optimized data sequence is input into the trained ILPSO-BP neural network model for iterative prediction, and the output is set as the capacity of the battery, so as to predict the remaining life of the battery.

10. The battery online monitoring system according to claim 9, wherein the battery monitoring host further comprises: The data transmission module includes a data transmission protocol, data compression and encryption; the data transmission protocol specifically adopts the TCP / IP protocol; the data compression and encryption specifically compresses and encrypts the transmitted data to improve transmission efficiency and data security.

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