An intelligent fault positioning method and system for energy storage power stations

Abstract

The application discloses a kind of energy storage power station fault intelligent positioning method and system, the method includes: obtaining the fault data of energy storage power station equipment, wherein the fault data includes open-in quantity, analog quantity and state quantity data;The fault data of the equipment is filtered, and useless fault data is removed;The real state of the corresponding equipment is inquired to the filtered fault data, and the fault data consistent with the real state of the corresponding equipment is obtained;The fault data consistent with the real state of the corresponding equipment is hierarchically positioned, and the fault reason is obtained;Different levels of fault tree model are established in advance, and the fault reason positioned is input into the fault tree model to judge the specific fault equipment.Can realize the logical processing and correlation verification of the fault signal reported on energy storage power station, and after logical processing and correlation verification, the real running condition of energy storage power station can be more accurately judged.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a method and system for intelligent fault location in energy storage power stations. Background Technology

[0002] Currently, energy storage power stations primarily upload fault data from their individual devices to an EMS (Energy Management System) for further alerts. However, this existing technology has several drawbacks: 1. The EMS acquires fault signals from critical locations of single devices, which lack hierarchy and correlation, making it impossible to accurately pinpoint the specific source of the fault within the energy storage power station. 2. The EMS itself cannot verify the accuracy of the reported signals. 3. The EMS cannot filter out interference signals from fault alarms of the same type of equipment or from the same fault source in different types of equipment. 4. The EMS cannot analyze fault alarms to determine causes beyond the reported fault information. Summary of the Invention

[0003] One of the objectives of this invention is to provide a method and system for intelligent fault location in energy storage power stations. The method and system can perform logical processing and correlation verification on the fault signals reported by the energy storage power station. After logical processing and correlation verification, the actual operating status of the energy storage power station can be more accurately determined, and irrelevant fault data can be filtered out to avoid interference with the judgment of the operating status of the energy storage power station.

[0004] Another objective of this invention is to provide a method and system for intelligent fault location in energy storage power stations. The method and system can analyze the fault signals reported by the energy storage power station to achieve hierarchical fault location, and can determine whether the fault is caused by battery or non-battery factors, thereby improving the accuracy of fault diagnosis.

[0005] Another objective of this invention is to provide a method and system for intelligent fault location in energy storage power stations. The method and system can determine the faulty equipment based on fault data using a tree model, and combine the switching and analog signals of the power station equipment to analyze the causes of battery faults, thereby improving the accuracy of fault diagnosis.

[0006] To achieve at least one of the above-mentioned objectives, the present invention further provides a method for intelligent fault location in an energy storage power station, the method comprising:

[0007] Acquire fault data of energy storage power station equipment, wherein the fault data includes input quantity, analog quantity and status quantity data;

[0008] The fault data of the device is filtered to remove useless fault data;

[0009] The filtered fault data is queried against the real state of the corresponding device to obtain fault data consistent with the real state of the corresponding device;

[0010] The fault data consistent with the real state of the corresponding device is subjected to hierarchical positioning to obtain the fault cause;

[0011] A fault tree model of different levels is established in advance, and the located fault cause is input into the fault tree model to determine the specific fault device.

[0012] According to one preferred embodiment of the present application, the method for filtering the fault data of the device comprises: establishing a fault database according to the obtained fault data, wherein the fault data includes device type, fault type and device mark; comparing the newly obtained fault data with the fault database; removing the same fault of the same device; and removing the fault data marked as useless fault.

[0013] According to another preferred embodiment of the present application, after obtaining and filtering the fault data, it is further determined whether the state of the device corresponding to the fault data is the same as the fault data according to the on-off quantity and analog quantity of the upstream and downstream devices corresponding to the fault data, and if not, the fault data is determined as false alarm data.

[0014] According to another preferred embodiment of the present application, the device state data includes alarm, early warning, fault, charging, discharging and standby, and when the device state is inconsistent with the fault data, the inconsistent data is saved and the alarm formed by the fault data is closed.

[0015] According to another preferred embodiment of the present application, when the device state data is consistent with the fault data, the fault data is further subjected to hierarchical positioning, and the hierarchical positioning method comprises: obtaining battery fault characteristic data and non-battery characteristic parameters according to the fault data, wherein the battery characteristic parameters include: upper and lower limits of battery voltage, upper and lower limits of temperature, upper and lower limits of SOC, voltage consistency, gas detection (carbon monoxide, hydrogen); and the non-battery fault characteristic parameters include: PCS three-phase alternating current, voltage deviation, three-phase unbalance degree, transformer temperature and circuit breaker state.

[0016] According to another preferred embodiment of the present application, the hierarchical positioning method further comprises: layering the current fault according to the battery fault characteristic parameters and the non-battery fault characteristic parameters in the fault data, and if at least one characteristic parameter meets the battery fault characteristic parameter condition, the fault is determined as battery fault; or if at least one characteristic parameter meets the battery fault characteristic parameter condition, the fault is determined as non-battery fault.

[0017] According to another preferable embodiment of the present application, a battery fault tree model and a non-battery fault tree model are established in advance, wherein the two tree models respectively comprise node devices having a connection relationship or a communication relationship, and a corresponding superior-inferior relationship is configured according to the connection relationship or the communication relationship, and a trigger condition is configured for each node of the tree model, when there is input fault data, whether the corresponding device of the current level is faulty is judged by the trigger condition of the tree model, if yes, the faulty device is output, otherwise, whether the corresponding device of the next level is a faulty device is sequentially found until the lowest level device is found.

[0018] According to another preferable embodiment of the present application, when the corresponding faulty device is found according to the tree model, a state quantity and an analog quantity of the faulty device are further acquired, a fault reason is generated according to an abnormal condition of the state quantity and the analog quantity, and the fault reason is output in a list.

[0019] In order to achieve at least one of the above-mentioned purposes, the present application further provides a storage power station fault intelligent positioning system, which executes the above-mentioned storage power station fault intelligent positioning method.

[0020] The present application further provides a computer readable storage medium, which stores a computer program, and the computer program can execute the above-mentioned storage power station fault intelligent positioning method by a processor. BRIEF DESCRIPTION OF DRAWINGS

[0021] Fig. 1 A flowchart of a storage power station fault intelligent positioning method is shown.

[0022] Fig. 2 A schematic diagram of fault positioning in a storage power station fault intelligent positioning method is shown.

[0023] Fig. 3 A screening flowchart of a fault database in a storage power station fault intelligent positioning method is shown. DETAILED DESCRIPTION

[0024] The following description is provided to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only examples, and other obvious modifications can be made by those skilled in the art. The basic principles of the present application defined in the following description can be applied to other embodiments, modifications, improvements, equivalents and other technical solutions without departing from the spirit and scope of the present application.

[0025] It can be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of one element can be one, and in another embodiment, the number of the element can be multiple, and the term "one" cannot be understood as a limitation on the number.

[0026] In conjunction with Figs. 1-3 The application discloses a kind of energy storage power station fault intelligent positioning method and system, wherein the method includes the following steps: first, the communication connection between each device of energy storage power station needs to be established, the fault data of each device is collected, and fault data is reported, wherein the power station equipment fault data includes the input quantity of equipment, analog quantity and state quantity.Further according to fault data, establish fault database, and filter out the same fault of the same equipment and the data marked as non-fault through the fault database.Further, by establishing fault tree model of different levels, the fault type and fault level are judged through the fault tree model, and the accurate positioning of fault equipment is executed.After completing the positioning of fault equipment, further obtain the analog quantity and state quantity of fault data to analyze the fault cause, and output the fault cause list of fault equipment.

[0027] Specifically, the input quantity in the fault data is the information of the contact between each device, which is represented by 0 or 1 to indicate whether there is an input, wherein the input quantity includes but is not limited to: the opening and closing of device circuit breaker and switch and the input and output of protection, and the input quantity of the device can determine whether there is a clear connection state between each device; The analog quantity in the fault data includes but is not limited to: the operating condition and real-time data (including current and voltage sampling values) of the battery, battery voltage consistency, energy storage converter (PCS) and transformer in energy storage power station, the analog quantity is the physical quantity of the device, which can reflect the actual physical parameter size of the device in the running process. The state quantity in the fault data includes but is not limited to the charging, discharging and standby state of energy storage converter (PCS) and the communication state between each device, wherein the state quantity is the state of the whole device, and the state can be formed by one or more devices reaching the trigger condition. The state quantity of the device can reflect the current state of the device, and whether there is an abnormality can be determined according to the state of the device.

[0028] After the fault data is acquired, the fault data is saved and a fault database is generated, wherein the fault database is used to record the device type corresponding to the fault data, the device identification, the analog quantity of the device, the state quantity and the input quantity, etc. It should be noted that the device identification is a fuzzy identification, which can represent that a large device or system has a certain fault, and therefore the device identification is not the result of accurate positioning. In one preferred embodiment of the present application, after the state quantity of the fault data is acquired, the device identification corresponding to the fault data and the state quantity are compared with the fault database respectively. If the corresponding fault data is found, it indicates that the fault data is repeated data, and the fault data is discarded. Otherwise, the fault data is saved. In another preferred embodiment of the present application, in the fault database, the useless fault signals including but not limited to maintenance and maintenance are marked. When the acquired fault data has the above-mentioned fault signals, the useless fault data is screened out.

[0029] After the filtering of the fault data is completed, it is further judged whether the filtered fault data is real. The input quantity and the analog quantity of the upstream and downstream devices connected with the device corresponding to the current fault data are acquired, and it is further judged whether the upstream and downstream devices have fault data or are in an open state according to the input quantity and the analog quantity. For example, if the input quantity between the upstream and downstream devices is in a state of 0 or the analog quantity between the upstream and downstream devices is in a state of 0, it indicates that the upstream and downstream devices connected with the device are in a disconnected or inoperative state. Therefore, the current device having a connection relationship is also in a disconnected state, so that it can be judged that the fault data detected by the current device is not real fault data. The unreal fault data needs to be screened out.

[0030] After the filtering of the fault data and the judgment that there is real fault data, the device identification corresponding to the fault data is further acquired, and the real state quantity of the device is found according to the device identification. If the device state quantity in the fault data and the real state quantity of the device are inconsistent, the fault data of the inconsistent state quantity is marked as false alarm. For example, the device state quantity corresponding to the fault data is standby state, and the real state quantity of the device found according to the fault data is normal state. It can be judged that the fault data is false alarm information. The false alarm information is further saved and the corresponding false alarm information is closed.

[0031] When the state variable of the corresponding device in the fault data and the real state variable of the device are consistent, the hierarchical positioning of the fault data can be further performed, wherein the hierarchical positioning method of the fault data comprises: acquiring a battery fault characteristic variable according to the fault data, wherein the battery fault characteristic variable includes but is not limited to: upper and lower limits of battery voltage, upper and lower limits of temperature, upper and lower limits of SOC, voltage consistency, gas detection (carbon monoxide, hydrogen). When the detected battery voltage data is not within the range of the upper and lower limits of the battery voltage, it indicates that the battery fault characteristic variable is satisfied. When, for example, the voltage consistency condition is not satisfied, it indicates that there is a battery fault characteristic variable that satisfies the voltage consistency condition. If carbon monoxide, hydrogen and other gases are detected, it indicates that the battery fault characteristic variable that satisfies the gas detection is satisfied. The selection of the above characteristic variables is for illustration, and the present application will not be described in detail. The non-battery fault characteristic variable includes but is not limited to: PCS three-phase alternating current, voltage deviation, three-phase unbalance degree, transformer temperature and circuit breaker state, etc. When at least one of the above non-battery fault characteristic variables is satisfied, the fault data is determined as non-battery fault data. If at least one of the above battery fault characteristic variables is satisfied, the fault data is determined as battery fault data. If both the battery fault characteristic variable and the non-battery fault characteristic variable are satisfied, the fault data is classified as mixed fault data. Therefore, the basic classification of different fault data can be realized.

[0032] Further, the application pre-establishes a fault tree model with hierarchical relationship, wherein the fault tree model can be different according to different fault types, wherein the corresponding fault tree model can be constructed according to the battery fault parameters, and the corresponding fault tree model can be constructed according to the non-battery fault parameters, the fault tree model has different node relationships, there is a connection relationship or a communication relationship between different nodes, and the fault tree model is sequentially provided with different hierarchical relationships according to the transmission relationship of the equipment, such as the upper and lower hierarchical relationships between the total control equipment and each sub-control equipment, and the upper and lower hierarchical relationships between the total control equipment and its own component equipment, the fault tree model with upper and lower hierarchies is established according to the pre-established connection relationship or communication relationship of the equipment of the energy storage power station, and each node of the fault tree model represents a corresponding equipment saving a corresponding equipment identifier. After the basic classification of the fault data is completed, the trigger condition of each fault tree node is further set, and it should be noted that the trigger condition will be respectively set according to the fault type, such as the trigger condition of the fault tree model of the battery fault data, which can be set to include but not limited to the upper and lower limits of battery voltage, temperature, SOC, voltage consistency, gas detection (carbon monoxide, hydrogen), and the trigger condition of the fault tree model of the non-battery fault data, which can be set to include but not limited to the non-battery fault characteristic parameters such as PCS three-phase alternating current, voltage deviation, three-phase unbalance degree, transformer temperature and circuit breaker state.

[0033] The filtered fault data is respectively input into the battery fault tree model and the non-battery fault tree model for sequential matching, and the matching result is obtained, if the input fault equipment has a hierarchical equipment satisfying the trigger condition of the model, the fault data is positioned to the equipment of the hierarchical equipment, and further according to the tree model, whether the lower hierarchical equipment of the positioned equipment has an associated fault data trigger condition is found, if the lower hierarchical equipment satisfies the trigger condition of the model, the lower hierarchical equipment is further marked as a fault equipment. In another preferred embodiment of the application, after the fault data is input, whether each node of the highest hierarchical level of the fault tree model satisfies the fault trigger condition is sequentially queried, if the current hierarchical level does not have an equipment satisfying the fault trigger condition, whether the next hierarchical level has an equipment satisfying the fault trigger condition is further found, until the lowest hierarchical equipment is found, and the found fault equipment is output.

[0034] It should be noted that in another preferred embodiment of the present application, since different fault triggering conditions exist because of the fault correlation between different devices, the fault triggering conditions need to meet the preset values of the input quantity, analog quantity and state quantity of multiple devices with superior-inferior relationship at the same time. That is, the system finds that the input quantity, analog quantity and state quantity of the fault data of multiple devices with superior-inferior relationship meet the preset values at the same time, and then the system automatically determines that the fault triggering conditions of the devices with correlation are met, and the devices with correlation are marked as fault devices at the same time.

[0035] After the device of the fault data is located according to the tree model, the fault cause of the device is further judged according to the input quantity, analog quantity and state quantity of the fault data, wherein the fault cause is a logical processing process. The present application only illustrates the following examples: 1. When the energy storage converter (PCS) appears a fault alarm, it is analyzed whether the superior-inferior devices of the energy storage converter exist a fault alarm. If the superior-inferior devices do not exist a fault alarm, it is analyzed whether the fault alarm is caused by the self reason such as component or temperature exceeding, etc. If the superior-inferior devices of the energy storage converter exist a fault alarm, it is judged whether the fault alarm information exists correlation according to the fault tree, and if the fault alarm information exists correlation, the fault cause is analyzed downward or upward. 2. When the BMS generates a battery voltage lower limit fault alarm, the battery with voltage lower limit is screened out, the single battery section number is determined, and then it is judged whether the battery is insufficient charging combined with the charging power of the energy storage converter where the battery is located. Then it is analyzed whether the voltage consistency of the battery pack where the battery is located causes the battery to be over-discharged or over-charged because of inconsistent voltage, and if not, it is analyzed whether the battery exists a falling behind reason combined with the relationship between the capacity and voltage of the battery. The above logical processing process can be realized by a computer program, and the present application will not be described in detail. The device of the fault location and the fault cause list are further generated in the form of output to the control end or the display.

[0036] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments disclosed herein. For example, embodiments of the disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication section, and / or installed from a detachable medium. When the computer program is executed by a central processing unit (CPU), the above-described functions defined in the methods of the present application are performed. It should be noted that the computer readable medium described above in the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, in which a computer readable program code is carried. Such a propagated data signal can take many forms, including but not limited to, an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium that can send, propagate or transfer a program for use by or in connection with an instruction execution system, apparatus or device. The program code contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to, wireless, wire, optical cable, RF or the like, or any suitable combination of the above.

[0037] The computer program product of the present application can be a computer program product comprising a computer-readable medium bearing computer program code embodied therein for use with a computer. The computer program code can be code defining and / or implementing the present application. The computer program code can be written in any suitable computer readable programming language. The computer program code can be stored in a computer- readable storage medium, such as, but not limited to, any type of disk including an optical disk, a CD-ROM, a CD-R, a CD-RW, a DVD, a flash memory, a ROM, a RAM, a magnetic disk or hard drive, or any other suitable type of medium including a medium that holds the software for a particular or specialized computing purpose, or any suitable combination of media. The computer program product can be a computer program product distributed to end users, whether as a stand-alone program, as part of a physical system, or as a software download. The computer program product can be distributed on a physical medium, such as, but not limited to, a floppy disk, a CD-ROM, a CD-R, a CD-RW, a DVD, a flash memory, a ROM, a RAM, a magnetic disk or hard drive, or any other suitable type of medium, or any suitable combination of media. The computer program product can be distributed from a program distribution center, either as a tangible medium or via electronic delivery, such as from a Web site via the Internet, or from one computer to another via electronic transfer, such as by e-mail. The computer program product can be distributed in an encrypted manner, such as via encryption or via password protection.

[0038] Those skilled in the art will understand that the application described above and illustrated in the accompanying drawings is presented by way of example only and is not intended to limit the application. The present application thus extends to any and all embodiments within the scope of the following claims.

Claims

1. A method for intelligent positioning of faults in energy storage power stations, characterized in that, The method comprises: acquiring fault data of energy storage power station equipment, wherein the fault data comprises input quantity, analog quantity and state quantity data; filtering the fault data of the equipment to remove useless fault data; querying the filtered fault data against the real state of the corresponding equipment to obtain fault data consistent with the real state of the corresponding equipment; hierarchically positioning the fault data consistent with the real state of the corresponding equipment; pre-establishing fault tree models of different levels and inputting the located fault causes into the fault tree models to determine the specific fault equipment and obtain the fault causes; when the corresponding fault equipment is found according to the tree model, further obtaining the state quantity and analog quantity of the fault equipment, generating fault causes according to the abnormal conditions of the state quantity and analog quantity, and outputting the fault causes in a list; wherein the hierarchical positioning comprises: obtaining battery fault characteristic data and non-battery characteristic parameters from the fault data, wherein the battery fault characteristic data comprises: upper and lower limits of battery voltage, upper and lower limits of temperature, upper and lower limits of SOC, voltage consistency, carbon monoxide gas detection, hydrogen gas detection; and the non-battery characteristic parameters comprise: PCS three-phase alternating current, voltage deviation, three-phase unbalance degree, transformer temperature and circuit breaker state; the hierarchical positioning further comprises: layering the current fault according to the battery fault characteristic data and non-battery fault characteristic parameters in the fault data, if at least one characteristic parameter meets the battery fault characteristic parameter condition, the fault is determined to be a battery fault; or if at least one characteristic parameter meets the non-battery fault characteristic parameter condition, the fault is determined to be a non-battery fault; pre-establishing a battery fault tree model and a non-battery fault tree model, wherein the two tree models respectively contain node equipment with connection relationship or communication relationship, and the corresponding seniority relationship is configured according to the connection relationship or communication relationship, and the trigger condition of each node of the tree model is configured, when there is input fault data, whether the corresponding equipment of the current level has a fault is determined through the trigger condition of the tree model, if yes, the fault equipment is output, otherwise, whether the corresponding equipment is a fault equipment is sequentially found in the next level until the lowest level equipment is found.

2. The intelligent fault locating method for energy storage power station according to claim 1, characterized in that, The method for filtering the fault data of the equipment comprises: establishing a fault database according to the acquired fault data, wherein the fault data includes equipment type, fault type and equipment mark, comparing the newly acquired fault data with the fault database, removing the same fault of the same equipment, and removing the fault data marked as useless fault.

3. The intelligent fault locating method for energy storage power station according to claim 1, characterized in that, After acquiring and filtering the fault data, further judging whether the equipment state data corresponding to the fault data is the same as the fault data according to the input quantity and analog quantity of the upstream and downstream equipment corresponding to the fault data, if not, the fault data is determined to be false alarm data.

4. The intelligent fault locating method for energy storage power station according to claim 3, characterized in that, wherein the equipment state data comprises alarm, early warning, fault, charging, discharging and standby, when the equipment state data and the fault data are inconsistent, the inconsistent data is saved and the alarm information formed by the fault data is closed.

5. An intelligent fault locating system for energy storage power stations, characterized in that, The system executes the intelligent fault positioning method of the energy storage power station in any one of claims 1-4.

6. A computer readable storage medium storing a computer program, wherein the computer program is executable by a processor to execute the intelligent fault positioning method of the energy storage power station in any one of claims 1-4.

Images