Optimal configuration method for energy storage system in power distribution area

Abstract

The invention discloses an optimal configuration method for an energy storage system in a power distribution area, and relates to the technical field of energy storage optimization, and the method comprises the steps: constructing an intelligent fault diagnosis strategy, carrying out the real-time monitoring of key parameters of the energy storage system, introducing a fault prediction algorithm, carrying out the system state analysis, recognizing a potential fault, and predicting a possible risk. The predicted risks are classified according to fault types and severity; an automatic fault handling and self-healing judgment mechanism is adopted, a handling strategy is automatically generated, operation parameters are adjusted or a standby system is switched to according to the diagnosed fault, and it is ensured that stable operation can still be maintained under the fault condition. According to the method, an intelligent fault diagnosis strategy is constructed, key parameters of the energy storage system are monitored in real time, and a fault prediction algorithm is introduced, so that potential faults can be effectively identified, and possible risks can be predicted. Compared with traditional manual monitoring, problems can be found earlier and more accurately, and further deterioration of faults is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization, and in particular to an optimization configuration method for an energy storage system in a distribution station area. Background Art

[0002] With the development of distribution networks, the power supply quality and reliability of distribution areas have received increasing attention. As an important part of distribution areas, energy storage systems can effectively alleviate the contradiction between power supply and demand, improve power quality, and ensure the safe operation of power grids. However, as the complexity of energy storage systems increases, the risk of failures they face during actual operation is also gradually increasing. Once an energy storage system fails, it may lead to a decline in power quality, damage to grid stability, and even cause large-scale power outages. Therefore, how to effectively monitor the key parameters of energy storage systems and timely predict and handle possible failures has become a key issue that needs to be urgently addressed in the power industry.

[0003] At present, traditional energy storage system monitoring and fault handling methods mostly rely on manual experience and simple early warning mechanisms. These methods not only rely on human judgment, but also have a slow response speed and lack the ability to comprehensively deal with complex faults. With the expansion of the scale of power grids and energy storage systems, traditional fault diagnosis and handling methods have been unable to meet the needs of efficient and reliable operation. In addition, most of the existing fault handling methods focus on passive response after the fault occurs, lack a system self-healing judgment mechanism, and cannot quickly restore the system to normal operating status after the fault occurs. Therefore, there is an urgent need for an intelligent and automated energy storage system optimization configuration method to improve the system's fault prediction ability, automatic response and self-healing capabilities, thereby ensuring the continuous and stable operation of the distribution station area. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing method for optimizing the configuration of an energy storage system in a distribution station area, the present invention is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing the configuration of a distribution station area energy storage system, comprising:

[0007] Build intelligent fault diagnosis strategies, monitor key parameters (voltage, current, temperature) of energy storage systems in real time, introduce fault prediction algorithms, analyze system status, identify potential faults and predict possible risks, and classify predicted risks by fault type and severity;

[0008] An automatic fault response and self-healing judgment mechanism is adopted to automatically generate response strategies for diagnosed faults, adjust operating parameters, or switch to backup systems to ensure stable operation in the event of a fault.

[0009] As a preferred solution of the method for optimizing the configuration of a distribution substation energy storage system described in the present invention, the method further includes: establishing a visual management platform to display the system's operating data, fault diagnosis information, and configuration of the multi-function module in real time. Users can view the system's real-time status, historical records, and fault handling process through the platform.

[0010] As a preferred solution of the method for optimizing the configuration of a distribution station area energy storage system described in the present invention, the introduced fault prediction algorithm includes:

[0011] Capture the complex relationship between voltage and temperature over time, and focus on the system's stress response when voltage and temperature change rapidly:

[0012] Voltage fault value

[0013] Among them, V(t) represents the voltage value at time t; a(t) represents the real-time state adjustment coefficient, which is used to adjust the system's sensitivity to temperature changes; T(t) represents the temperature value at time t, which is used to detect the rate of change of voltage over time by calculating the derivative of the voltage and the exponential function, and combined with the exponential change of temperature to reflect the emergency response capability of the system.

[0014] As a preferred solution of the method for optimizing the configuration of a distribution substation energy storage system according to the present invention, the introduced fault prediction algorithm further includes:

[0015] Analyze the comprehensive relationship between the current current, power and system status, and adjust its impact through logarithmic functions to make it more consistent with the actual operating status of the system;

[0016] Current fault value

[0017] Among them, the state function It is used to reflect the stability or dynamic changes of the system at time point t. This function can be obtained by fitting historical data. I(t) represents the current value at time t, and p(t) represents the power at time t, which is the product of voltage and current.

[0018] As a preferred solution of the method for optimizing the configuration of a distribution substation energy storage system according to the present invention, the introduced fault prediction algorithm further includes:

[0019] Reflects the nonlinear effect of temperature on system failure risk. Under high temperature conditions, the increase in failure risk will be more significant.

[0020] Temperature fault value

[0021] The fractional structure makes the impact of this term smaller at low temperatures, but as the temperature T(t) increases, the value of this term gradually approaches 1, reflecting the greater risk of high temperature to the system.

[0022] As a preferred solution of the optimization configuration method of the distribution substation energy storage system described in the present invention, the voltage fault value A, the current fault value B and the temperature fault value C are comprehensively considered to form a complete fault risk probability model.

[0023] Among them, λ 1 ,λ 2 ,λ 3 :These are weight parameters used to balance the influence of each part in the formula. Under different energy storage systems and application scenarios, these weights can be adjusted to optimize the effect of fault prediction. The value range of F(t) is [0, 1]. The closer to 1, the higher the risk of fault.

[0024] As a preferred solution of the method for optimizing the configuration of a distribution station area energy storage system described in the present invention, the predicted risk is classified according to the fault type and severity according to the value of F(t), wherein the value range of F(t) is set with a low threshold and a high threshold;

[0025] If F(t) is less than the low threshold, the system operation is relatively stable with only slight fluctuations. Such risks usually have little impact on the overall operation of the system, i.e., slight voltage or temperature fluctuations.

[0026] If the low threshold value ≤ F(t) < the high threshold value, there is a significant fluctuation in the system, which indicates a functional failure, including: current overload or temperature rise;

[0027] If F(t) ≥ high threshold, the system is on the verge of serious failure, that is, system crash or equipment damage, including: system crash or equipment damage caused by severe fluctuations in voltage, temperature and current.

[0028] As a preferred solution of the method for optimizing the configuration of the energy storage system in a distribution station area described in the present invention, the coping strategy includes: the following strategies are classified according to the fault type and severity:

[0029] Voltage fluctuation fault: Response strategies include adjusting the transformer tap and adjusting the charging and discharging power of the energy storage system;

[0030] Current overload fault: Response strategies include reducing or switching loads, starting backup power, or adjusting the charge and discharge rates of energy storage devices;

[0031] Abnormal temperature failure: Strategies include increasing cooling power, reducing system workload, or switching to a backup cooling system;

[0032] The self-healing judgment mechanism is implemented by constructing a self-healing function G(t), that is, by introducing an exponential decay function to describe the decay process of F(t);

[0033]

[0034] Among them, λ is the self-healing speed factor, which determines the speed of system recovery;

[0035] The exponential decay function is expressed as:

[0036] F(t)=F(t0)·exp(-α(t-t0)·G(t));

[0037] Among them, α is the attenuation coefficient, which controls the decay speed of the risk value with the progress of self-healing;

[0038] The self-healing judgment mechanism includes the following processes:

[0039] Self-healing start, when it is detected that the system starts self-healing, the initial condition G(t)=0 is set, and the fault risk value F(t0) is the initially calculated risk value;

[0040] Self-healing process, the self-healing function G(t) represents the system recovery progress. As time t goes by, G(t) gradually increases from 0 to 1;.

[0041] In the formula, the integral term This shows that the self-healing speed is related to the fault risk value F(t). The higher the risk value, the faster the recovery.

[0042] Risk value decay, the risk value F(t) gradually decreases as G(t) increases;

[0043] Self-healing is completed. When G(t)≈1, it means that the system has basically completed the self-healing process, and F(t) is also close to 0, indicating that the fault has been completely eliminated and the system has returned to normal operation.

[0044] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned method for optimizing the configuration of an energy storage system in a distribution station area are implemented.

[0045] A computer-readable storage medium having a computer program stored thereon, characterized in that: when the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the configuration of an energy storage system in a distribution station area are implemented.

[0046] Beneficial effects of the present invention:

[0047] 1. The present invention constructs an intelligent fault diagnosis strategy, which can effectively identify potential faults and predict possible risks by real-time monitoring of key parameters of the energy storage system (such as voltage, current, and temperature) and introducing a fault prediction algorithm. Compared with traditional manual monitoring, the present invention can detect problems earlier and more accurately, thereby avoiding further deterioration of faults;

[0048] 2. The present invention designs an automatic fault response and self-healing judgment mechanism for the detected faults, which can automatically generate a response strategy according to the fault type and severity, adjust the system operating parameters, or switch to the backup system to ensure stable operation in the event of a fault. In addition, after implementing the response strategy, the system can automatically enter the self-healing state and gradually return to normal operation, thereby greatly shortening the fault recovery time and improving the reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0050] Figure 1 The present invention provides a schematic diagram of the overall process of optimizing the configuration of a distribution station area energy storage system. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] Reference Figure 1 , is an embodiment of the present invention, and provides a method for optimizing the configuration of a distribution station area energy storage system, the method specifically comprising the following steps:

[0055] Step 1: Build an intelligent fault diagnosis strategy, monitor the key parameters (voltage, current, temperature) of the energy storage system in real time, and introduce a fault prediction algorithm.

[0056] The fault prediction algorithms introduced include:

[0057] Capture the complex relationship between voltage and temperature over time, and focus on the system's stress response when voltage and temperature change rapidly:

[0058] Voltage fault value

[0059] Among them, V(t) represents the voltage value at time t; a(t) represents the real-time state adjustment coefficient, which is used to adjust the system's sensitivity to temperature changes; T(t) represents the temperature value at time t, which is used to detect the rate of change of voltage over time by calculating the derivative of the voltage and the exponential function, and combined with the exponential change of temperature to reflect the emergency response capability of the system.

[0060] Analyze the comprehensive relationship between the current current, power and system status, and adjust its impact through logarithmic functions to make it more consistent with the actual operating status of the system;

[0061] Current fault value

[0062] Among them, the state function Used to reflect the stability or dynamic changes of the system at time point t. This function can be obtained by fitting historical data;

[0063] After fitting, the state function can be expressed as: S(t) = f(X(t), θ);

[0064] S(t) represents the system state at time point t, X(t) is the feature vector extracted at time point t, and θ is the model parameter obtained by fitting.

[0065] I(t) represents the current value at time t, and p(t) represents the power at time t, which is the product of voltage and current.

[0066] The fault prediction algorithms introduced also include:

[0067] Reflects the nonlinear effect of temperature on system failure risk. Under high temperature conditions, the increase in failure risk will be more significant.

[0068] Temperature fault value

[0069] The fractional structure makes the impact of this term smaller at low temperatures, but as the temperature T(t) increases, the value of this term gradually approaches 1, reflecting the greater risk of high temperature to the system.

[0070] System status analysis identifies potential failures and predicts possible risks, and classifies the predicted risks by failure type and severity.

[0071] That is, comprehensively considering the voltage fault value A, current fault value B and temperature fault value C, a complete fault risk probability model is formed.

[0072] Among them, λ 1 ,λ 2 ,λ 3 : These are weight parameters used to balance the influence of each part in the formula. These weights can be adjusted for different energy storage systems and application scenarios to optimize the effect of fault prediction;

[0073] The value range of F(t) is [0, 1], and the closer it is to 1, the higher the risk of failure.

[0074] Then, the predicted risks are classified according to the fault type and severity based on the value of F(t), where the value range of F(t) is set with a low threshold and a high threshold;

[0075] If F(t) is less than the low threshold, the system operation is relatively stable with only slight fluctuations. Such risks usually have little impact on the overall operation of the system, i.e., slight voltage or temperature fluctuations.

[0076] If the low threshold value ≤ F(t) < the high threshold value, there is a significant fluctuation in the system, which indicates a functional failure, including: current overload or temperature rise;

[0077] If F(t) ≥ high threshold, the system is on the verge of serious failure, that is, system crash or equipment damage, including: system crash or equipment damage caused by severe fluctuations in voltage, temperature and current.

[0078] Step 2: Use an automatic fault response and self-healing judgment mechanism to automatically generate response strategies for diagnosed faults, adjust operating parameters, or switch to a backup system to ensure stable operation in the event of a fault.

[0079] The response strategies include: classified into the following strategies according to the fault type and severity:

[0080] Voltage fluctuation fault: Response strategies include adjusting the transformer tap and adjusting the charging and discharging power of the energy storage system;

[0081] Current overload fault: Response strategies include reducing or switching loads, starting backup power, or adjusting the charge and discharge rates of energy storage devices;

[0082] Abnormal temperature failure: Strategies include increasing cooling power, reducing system workload, or switching to a backup cooling system;

[0083] The self-healing judgment mechanism is implemented by constructing a self-healing function G(t), that is, by introducing an exponential decay function to describe the decay process of F(t);

[0084]

[0085] Among them, λ is the self-healing speed factor, which determines the speed of system recovery;

[0086] The exponential decay function is expressed as:

[0087] F(t)=F(t0)·exp(-α(t-t0)·G(t));

[0088] Among them, α is the attenuation coefficient, which controls the decay speed of the risk value with the progress of self-healing;

[0089] The self-healing judgment mechanism includes the following processes:

[0090] Self-healing start, when it is detected that the system starts self-healing, the initial condition G(t)=0 is set, and the fault risk value F(t0) is the initially calculated risk value;

[0091] Self-healing process, the self-healing function G(t) represents the system recovery progress. As time t goes by, G(t) gradually increases from 0 to 1;.

[0092] In the formula, the integral term This shows that the self-healing speed is related to the fault risk value F(t). The higher the risk value, the faster the recovery.

[0093] Risk value decay, the risk value F(t) gradually decreases as G(t) increases;

[0094] Self-healing is completed. When G(t)≈1, it means that the system has basically completed the self-healing process, and F(t) is also close to 0, indicating that the fault has been completely eliminated and the system has returned to normal operation.

[0095] Step 3: Establish a visual management platform to display the system's operating data, fault diagnosis information, and configuration of multi-function modules in real time. Users can view the system's real-time status, historical records, and fault handling process through the platform.

[0096] In summary, the present invention constructs an intelligent fault diagnosis strategy, which can effectively identify potential faults and predict possible risks by real-time monitoring of key parameters of the energy storage system (such as voltage, current, and temperature) and introducing a fault prediction algorithm. Compared with traditional manual monitoring, the present invention can detect problems earlier and more accurately, thereby avoiding further deterioration of the fault. For the detected fault, the present invention designs an automatic fault response and self-healing judgment mechanism, which can automatically generate a response strategy according to the type and severity of the fault, adjust the system operating parameters, or switch to a backup system to ensure that stable operation can be maintained in the event of a fault. In addition, after implementing the response strategy, the system can automatically enter a self-healing state and gradually return to normal operation, thereby greatly shortening the fault recovery time and improving the reliability of the system.

[0097] This embodiment also provides a computer device, which is applicable to a method for optimizing the configuration of an energy storage system in a distribution station area, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a method for optimizing the configuration of an energy storage system in a distribution station area as proposed in the above embodiment.

[0098] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0099] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an optimization configuration method for a distribution station area energy storage system proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the configuration of a distribution station area energy storage system, characterized in that: include: Build intelligent fault diagnosis strategies, monitor key parameters of energy storage systems in real time, introduce fault prediction algorithms, analyze system status, identify potential faults and predict possible risks, and classify predicted risks by fault type and severity; An automatic fault response and self-healing judgment mechanism is adopted to automatically generate response strategies for diagnosed faults, adjust operating parameters, or switch to backup systems to ensure stable operation in the event of a fault.

2. The method for optimizing the configuration of a distribution station area energy storage system according to claim 1, characterized in that: The method also includes: establishing a visual management platform to display the system's operating data, fault diagnosis information, and configuration of multi-function modules in real time; users can view the system's real-time status, historical records, and fault handling process through the platform.

3. The method for optimizing the configuration of a distribution station area energy storage system according to claim 1, characterized in that: The fault prediction algorithms introduced include: Capture the complex relationship between voltage and temperature over time, and focus on the system's stress response when voltage and temperature change rapidly: Voltage fault value Among them, V(t) represents the voltage value at time t; a(t) represents the real-time state adjustment coefficient, which is used to adjust the system's sensitivity to temperature changes; T(t) represents the temperature value at time t, which is used to detect the rate of change of voltage over time by calculating the derivative of the voltage and the exponential function, and combined with the exponential change of temperature to reflect the emergency response capability of the system.

4. The method for optimizing the configuration of a distribution station area energy storage system according to claim 3, characterized in that: The fault prediction algorithms introduced also include: Analyze the comprehensive relationship between the current current, power and system status, and adjust its impact through logarithmic functions to make it more consistent with the actual operating status of the system; Current fault value Among them, the state function It is used to reflect the stability or dynamic changes of the system at time point t. This function can be obtained by fitting historical data. I(t) represents the current value at time t, and p(t) represents the power at time t, which is the product of voltage and current.

5. The method for optimizing the configuration of a distribution station area energy storage system according to claim 4, characterized in that: The fault prediction algorithms introduced also include: Reflects the nonlinear effect of temperature on system failure risk. Under high temperature conditions, the increase in failure risk will be more significant. Temperature fault value The fractional structure makes the impact of this term smaller at low temperatures, but as the temperature T(t) increases, the value of this term gradually approaches 1, reflecting the greater risk of high temperature to the system.

6. The method for optimizing the configuration of a distribution station area energy storage system according to claim 5, characterized in that: Comprehensively consider the voltage fault value A, current fault value B and temperature fault value C to form a complete fault risk probability model Among them, λ1, λ2, λ3: These are weight parameters used to balance the influence of each part in the formula. The value range of F(t) is [0, 1]. The closer it is to 1, the higher the risk of failure.

7. The method for optimizing the configuration of a distribution station area energy storage system according to claim 6, characterized in that: According to the value of F(t), the predicted risk is classified according to the fault type and severity, wherein the value range of F(t) is set with a low threshold and a high threshold; If F(t) is less than the low threshold, the system operation is relatively stable with only slight fluctuations. Such risks usually have little impact on the overall operation of the system, i.e., slight voltage or temperature fluctuations. If the low threshold value ≤ F(t) < the high threshold value, there is a significant fluctuation in the system, which indicates a functional failure, including: current overload or temperature rise; If F(t) ≥ high threshold, the system is on the verge of serious failure, that is, system crash or equipment damage, including: system crash or equipment damage caused by severe fluctuations in voltage, temperature and current.

8. The method for optimizing the configuration of a distribution station area energy storage system according to claim 2, characterized in that: The response strategies include: classified into the following strategies according to the fault type and severity: Voltage fluctuation fault: Response strategies include adjusting the transformer tap and adjusting the charging and discharging power of the energy storage system; Current overload fault: Response strategies include reducing or switching loads, starting backup power, or adjusting the charge and discharge rates of energy storage devices; Abnormal temperature failure: Strategies include increasing cooling power, reducing system workload, or switching to a backup cooling system; The self-healing judgment mechanism is implemented by constructing a self-healing function G(t), that is, by introducing an exponential decay function to describe the decay process of F(t); Among them, λ is the self-healing speed factor, which determines the speed of system recovery; The exponential decay function is expressed as: F(t)=F(t0)·exp(-α(t-t0)·G(t)); Among them, α is the attenuation coefficient, which controls the decay speed of the risk value with the progress of self-healing; The self-healing judgment mechanism includes the following processes: Self-healing start, when it is detected that the system starts self-healing, the initial condition G(t)=0 is set, and the fault risk value F(t0) is the initially calculated risk value; Self-healing process, the self-healing function G(t) represents the system recovery progress. As time t goes by, G(t) gradually increases from 0 to 1; Risk value decay, the risk value F(t) gradually decreases as G(t) increases; Self-healing is completed. When G(t)≈1, it means that the system has basically completed the self-healing process, and F(t) is also close to 0, indicating that the fault has been completely eliminated and the system has returned to normal operation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of the method for optimizing the configuration of a distribution station area energy storage system according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing configuration of a distribution station area energy storage system according to any one of claims 1 to 8 are implemented.

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