CN118378179B - Optimized regulation and control method for distributed new energy storage network - Google Patents

Abstract

The invention discloses an optimized regulation method of a distributed new energy storage network, which particularly relates to the technical field of new energy storage, and comprises the steps of comprehensively and accurately monitoring the running state of an inverter by acquiring external environment information, electric fluctuation information, control firmware information and control software information when the inverter runs, acquiring multidimensional information, ensuring accurate perception of potential fault hidden danger, constructing an inverter running hidden danger monitoring model, generating an inverter running hidden danger monitoring index, comprehensively analyzing the potential fault hidden danger existing when the inverter runs, timely finding the potential fault hidden danger, comparing the inverter running hidden danger monitoring index with a preset inverter running hidden danger monitoring index threshold, classifying the fault hidden danger, carrying out secondary analysis and grading according to classification results, judging the severity of the fault hidden danger, grading according to the influence depth of the fault hidden danger, and effectively dispatching most suitable maintenance personnel for accurate maintenance.

Description

Optimization and control method of distributed new energy storage network

Technical Field

The present invention relates to the field of new energy storage technology, and more specifically, to an optimization and control method for a distributed new energy storage network.

Background Art

With the continuous growth of global energy demand and the increasing pressure of environmental protection, the development and utilization of new energy has become the focus of attention of countries around the world. Distributed energy systems occupy an important position in modern power systems due to their flexibility, reliability and environmental friendliness. Among them, distributed new energy storage networks are an important part of distributed energy systems and can effectively solve the intermittent and instability problems of new energy power generation.

As a converter that can convert DC power (battery, storage bottle) into constant frequency and voltage or frequency and voltage regulated AC power, the inverter plays an important role in the distributed new energy storage network. Usually, energy storage devices such as batteries store DC power, which can be converted into AC power by inverter before it can be supplied to the load or connected to the power grid. There are a large number of inverters in the distributed new energy storage network. In addition to regular maintenance of the inverter, maintenance personnel are usually dispatched for temporary repairs only when the inverter shows obvious fault symptoms. However, before the inverter shows obvious fault symptoms, its operating state may have potential fault hazards. The existing maintenance strategy lacks timely perception of the potential fault hazards in the operating state of the inverter and in-depth analysis of its potential fault hazards. It is impossible to give a fault severity rating based on the potential fault hazards and effectively dispatch the most suitable maintenance personnel for precise maintenance based on the fault severity rating.

In order to solve the above defects, a technical solution is now provided.

Summary of the invention

In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an optimization and control method for a distributed new energy storage network to solve the problems raised in the above-mentioned background technology.

To achieve the above object, the present invention provides the following technical solutions:

The optimization and control method of the distributed new energy storage network includes the following steps:

Step S1, obtaining external environment information, electrical fluctuation information, control firmware information and control software information when the inverter is running;

Step S2, constructing an inverter operation hidden danger monitoring model based on external environment information, electrical fluctuation information, control firmware information, and control software information, generating an inverter operation hidden danger monitoring index, and comprehensively analyzing potential fault hidden dangers existing during inverter operation;

Step S3, comparing the inverter operation hidden danger monitoring index with a preset inverter operation hidden danger monitoring index threshold, and classifying potential fault hidden dangers existing during the operation of the inverter;

Step S4, performing a secondary analysis on the impact depth of the potential fault hidden dangers according to the classification results of the potential fault hidden dangers, and rating the impact depth of the potential fault hidden dangers according to the analysis results.

In a preferred embodiment, the external environment information includes the temperature rise rate abnormality coefficient, the electrical fluctuation information includes the output voltage fluctuation coefficient, the control firmware information includes the IGBT aging coefficient, and the control software information includes the control response delay coefficient, and the temperature rise rate abnormality coefficient, the output voltage fluctuation coefficient, the IGBT aging coefficient, and the control response delay coefficient are marked as .

In a preferred embodiment, the acquisition logic of the temperature rise rate anomaly coefficient is as follows:

The temperature sensor collects the temperature data of the inverter's surrounding environment within the T time period, records the temperature values at different times, and calculates the temperature rise rate at time t based on the temperature values at adjacent times. The expression is as follows , where represents the temperature rise rate at time t, Interval time, represents the temperature value at time t, Indicates the temperature value at time t-1;

Obtain the temperature rise rate at adjacent moments during the inverter's historical normal operation period and calculate the normal temperature rise rate range , the expression is as follows , where It represents the average value of the temperature rise rate at adjacent moments during the inverter's historical normal operation period. It represents the standard deviation of the temperature rise rate at adjacent moments during the normal operation period of the inverter, and k is the adjustment coefficient;

Calculate the temperature rise rate anomaly coefficient, the expression is as follows , t={2,3...,n}, n is a positive integer.

In a preferred embodiment, the logic for obtaining the output voltage fluctuation coefficient is as follows:

Obtain the inverter output voltage data within the T time period, and mark the time series of the inverter output voltage data within the T time period as , where w represents different moments in the time period T;

Perform wavelet decomposition on the output voltage data, and select Daubechies wavelet as the wavelet basis function and the number of decomposition scale layers ;

Will Decomposed into approximate coefficients and detail coefficients, the decomposition formula is as follows , where Indicates The approximation coefficient of the layer, Indicates The detail factor of the layer;

The energy value is calculated according to the detail coefficient of each layer scale. The expression is as follows , where Indicates The energy value of the layer scale, w={1,2...,m}, m is a positive integer;

Compare the energy value of each layer scale with the preset energy fluctuation threshold. When the energy value is greater than the energy fluctuation threshold, mark the energy value of the corresponding layer scale as a fluctuating energy value.

Calculate the output voltage fluctuation coefficient, the expression is as follows , where Represents the total number of fluctuating energy values.

In a preferred embodiment, the logic for obtaining the IGBT aging coefficient is as follows:

Get the temperature data of the IGBT during operation in the T time period and calculate the temperature stress factor. The expression is as follows: , where represents the temperature stress factor, represents the empirical constant, It represents the average temperature of IGBT during operation in the T time period. Indicates the reference temperature;

Obtain the voltage data of the IGBT during operation in the T time period and calculate the voltage stress factor. The expression is as follows: , where represents the voltage stress factor, and represents the empirical constant, It indicates the average voltage when the IGBT is working during the T time period;

Get the current data of the IGBT during operation in the T time period and calculate the current stress factor. The expression is as follows: , where represents the voltage stress factor, and represents the empirical constant, It indicates the average current when the IGBT is working during the T time period;

Get the number of switching times when the IGBT is working within the T time period , calculate the switching frequency, the expression is as follows , where Represents the switching frequency, calculates the switching frequency stress factor, and the expression is as follows , where represents the switching frequency stress factor, and represents an empirical constant;

Calculate the IGBT aging coefficient, the expression is as follows .

In a preferred embodiment, the logic for obtaining the control response delay coefficient is as follows:

The time when the inverter receives the control signal each time and the actual response time are obtained within the T time period, and the time when the control signal is received is marked as , marking the actual response time as , calculate the response delay time , the expression is as follows ;

Set the delay reference threshold for the response delay time ;

The response delay time calculated within the T time period is compared with the delay reference threshold. When the response delay time is greater than the delay reference threshold, the response delay time greater than the delay reference threshold is marked as the response delay overload time, and the response delay overload time is marked as ,in Indicates the number of times when the response delay time is greater than the delay reference threshold. , is a positive integer;

Calculate the control response delay coefficient, the expression is as follows .

In a preferred embodiment, the acquired temperature rise rate abnormal coefficient, output voltage fluctuation coefficient, IGBT aging coefficient, and control response delay coefficient are normalized to construct an inverter operation hidden danger monitoring model and generate an inverter operation hidden danger monitoring index YHL, which is based on the following formula: , They represent the proportional coefficients of the temperature rise rate abnormality coefficient, output voltage fluctuation coefficient, IGBT aging coefficient, and control response delay coefficient, respectively. Both are greater than 0.

In a preferred embodiment, the inverter operation hidden danger monitoring index is compared with a preset inverter operation hidden danger monitoring index threshold, and the potential fault hidden dangers existing in the inverter operation are classified, and the specific situation is as follows:

If the inverter operation hidden danger monitoring index is greater than the inverter operation hidden danger monitoring index threshold, a hidden danger risk signal is generated;

If the inverter operation hidden danger monitoring index is less than or equal to the inverter operation hidden danger monitoring index threshold, there is no need to generate a hidden danger risk signal.

In a preferred embodiment, when a hidden danger risk signal is generated, the inverter operation hidden danger monitoring index generated in multiple T time periods is obtained, and an inverter operation hidden danger monitoring index data set is established, and the inverter operation hidden danger monitoring index data set is marked as ,in Indicates the sequence number of multiple T time periods, , e is a positive integer;

Calculate the standard deviation of the inverter operation hidden danger monitoring index in the inverter operation hidden danger monitoring index data set , the expression is as follows , where It represents the average value of the inverter operation hidden danger monitoring index in the inverter operation hidden danger monitoring index data set. The expression is as follows .

In a preferred embodiment, the standard deviation of the inverter operation hidden danger monitoring index is compared with a preset standard deviation threshold, a secondary analysis is performed on the impact depth of the fault hidden danger, and the impact depth of the fault hidden danger is rated according to the analysis result;

If the standard deviation of the inverter operation hidden danger monitoring index is greater than the standard deviation threshold, a first-level fault hidden danger is generated;

If the standard deviation of the inverter operation hidden danger monitoring index is less than or equal to the standard deviation threshold, a second-level fault hidden danger is generated.

Technical effects and advantages of the present invention:

1. The present invention comprehensively and accurately monitors the operating status of the inverter by acquiring external environment information, electrical fluctuation information, control firmware information and control software information when the inverter is running. Multi-dimensional information collection ensures accurate perception of potential fault hazards, and constructs an inverter operation hidden danger monitoring model to generate an inverter operation hidden danger monitoring index. A comprehensive analysis is performed on potential fault hazards existing during the operation of the inverter, and potential fault hazards are discovered in time to avoid major faults caused by the accumulation of hidden dangers. The inverter operation hidden danger monitoring index is compared with a preset inverter operation hidden danger monitoring index threshold, and the fault hazards are classified. A secondary analysis and rating are performed based on the classification results to determine the severity of the fault hidden dangers. According to the depth of the impact of the fault hidden dangers, the most suitable maintenance personnel are effectively dispatched for precise maintenance, unnecessary temporary repairs are avoided, maintenance efficiency is improved, and maintenance costs are reduced.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

FIG1 is a schematic diagram of the structure of a method according to an embodiment of the present invention.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Example

FIG1 shows an optimization control method of a distributed new energy storage network of the present invention, comprising the following steps:

Step S1, obtaining external environment information, electrical fluctuation information, control firmware information and control software information when the inverter is running;

The external environment information includes the temperature rise rate abnormality coefficient, the electrical fluctuation information includes the output voltage fluctuation coefficient, the control firmware information includes the IGBT aging coefficient, and the control software information includes the control response delay coefficient. The temperature rise rate abnormality coefficient, the output voltage fluctuation coefficient, the IGBT aging coefficient, and the control response delay coefficient are marked as ;

The temperature rise rate abnormality coefficient is used to measure the abnormal degree of the temperature rise rate of the surrounding environment where the inverter is located. The greater the temperature rise rate, the more abnormal the ambient temperature change, which may lead to an increase in the risk of inverter overheating or other related failures. When the temperature rise rate is in an abnormal state, it may have the following effects on the operating state of the inverter:

Frequent activation of overheat protection: The temperature protection mechanism inside the inverter may be activated frequently, causing the inverter to automatically shut down or operate at a reduced rating to prevent overheating from damaging internal components;

Accelerated aging of components: When the inverter is in a high temperature environment for a long time, the electronic components inside the inverter (such as capacitors, IGBT modules, etc.) will age faster, shorten their service life, and increase the probability of failure.

Efficiency reduction: The conversion efficiency of the inverter will decrease as the temperature rises, resulting in lower power conversion efficiency and affecting the performance of the entire energy storage system;

Increased risk of failure: Excessive temperature can cause thermal runaway, especially for key components such as IGBT, which may cause local overheating and cause short circuit or burning, leading to inverter failure;

Increased maintenance requirements: Frequent overheating protection and accelerated component aging will increase the maintenance requirements of the inverter, increase operation and maintenance costs and manpower investment;

System safety hazards: Long-term temperature anomalies will have a negative impact on the insulation performance of the inverter, increase the risk of leakage and electric shock, and bring system safety hazards;

The logic for obtaining the temperature rise rate anomaly coefficient is as follows:

The temperature sensor collects the temperature data of the inverter's surrounding environment within the T time period, records the temperature values at different times, and calculates the temperature rise rate at time t based on the temperature values at adjacent times. The expression is as follows , where represents the temperature rise rate at time t, Interval time, represents the temperature value at time t, Indicates the temperature value at time t-1;

Obtain the temperature rise rate at adjacent moments during the inverter's historical normal operation period and calculate the normal temperature rise rate range , the expression is as follows , where It represents the average value of the temperature rise rate at adjacent moments during the inverter's historical normal operation period. It represents the standard deviation of the temperature rise rate at adjacent moments during the normal operation period of the inverter, and k is the adjustment coefficient;

It should be noted that the adjustment coefficient k determines the boundary between the temperature rise rate abnormal coefficient and the normal range. When k takes a smaller value, the sensitivity to temperature changes is higher, and a smaller temperature change deviation will also be judged as abnormal, which is suitable for inverters and environments that are very sensitive to temperature changes; on the contrary, when k takes a larger value, the sensitivity to temperature changes is reduced, and only a larger temperature change deviation will be judged as abnormal, which is suitable for environments with relatively stable temperature changes to avoid frequent false alarms;

Calculate the temperature rise rate anomaly coefficient, the expression is as follows , t={2,3...,n}, n is a positive integer;

The output voltage fluctuation coefficient is used to measure the stability and fluctuation of the inverter output voltage within the T time period. The greater the fluctuation of the inverter output voltage, the greater the change of the inverter output voltage, the poorer the output stability of the voltage, and the inverter may have potential internal faults or control system problems, which cannot effectively adjust the output voltage and affect its normal operation. Continuous voltage fluctuations may indicate potential hidden dangers such as aging of internal components of the inverter, control circuit failure or external environmental interference, which require timely detection and maintenance;

The logic for obtaining the output voltage fluctuation coefficient is as follows:

Obtain the inverter output voltage data within the T time period, and mark the time series of the inverter output voltage data within the T time period as , where w represents different moments in the time period T;

Perform wavelet decomposition on the output voltage data, and select Daubechies wavelet as the wavelet basis function and the number of decomposition scale layers ;

Will Decomposed into approximate coefficients and detail coefficients, the decomposition formula is as follows , where Indicates The approximation coefficient of the layer, Indicates The detail factor of the layer;

The energy value is calculated according to the detail coefficient of each layer scale. The expression is as follows , where Indicates The energy value of the layer scale, w={1,2...,m}, m is a positive integer;

Compare the energy value of each layer scale with the preset energy fluctuation threshold. When the energy value is greater than the energy fluctuation threshold, mark the energy value of the corresponding layer scale as a fluctuating energy value.

Calculate the output voltage fluctuation coefficient, the expression is as follows , where Represents the total number of fluctuating energy values;

IGBT is an insulated gate bipolar transistor and a key power semiconductor device in the inverter. In the inverter, IGBT is mainly used for power conversion and control. The aging of IGBT refers to the degradation of its performance over time and changes in usage conditions, which usually leads to reduced efficiency and increased failure rate of the inverter. The IGBT aging coefficient is an indicator to measure the aging degree of IGBT devices. When the IGBT ages, it may have the following effects on the operating status of the inverter:

Increased on-state voltage drop: IGBT aging will cause its on-state voltage drop to increase, thereby increasing conduction losses and reducing the overall efficiency of the inverter;

Increased switching losses: Aging IGBTs switch more slowly, increasing switching losses and further reducing efficiency;

Increased heat loss: Due to the increase in on-state voltage drop and switching loss, the heat generated by the IGBT increases, resulting in an increase in the operating temperature of the inverter;

Decreased current carrying capacity: IGBT aging will cause its current carrying capacity to decrease, which may not meet the load requirements, resulting in unstable inverter output power;

Reduced response speed: The switching speed of aging IGBTs slows down, and the inverter's response speed to load changes decreases, affecting the power quality;

Short circuit and open circuit faults: Deterioration of the internal structure of the IGBT may cause short circuit or open circuit faults, seriously affecting the reliability of the inverter;

Inverter life is limited: Since IGBT is a key component of the inverter, the aging of IGBT will directly affect the overall life of the inverter;

The larger the IGBT aging coefficient is, the deeper the aging degree of the IGBT device is, and the greater the probability of potential hidden dangers in the inverter is. On the contrary, the smaller the IGBT aging coefficient is, the shallower the aging degree of the IGBT device is, and the smaller the probability of potential hidden dangers in the inverter is.

The logic for obtaining the IGBT aging coefficient is as follows:

Get the temperature data of the IGBT during operation in the T time period and calculate the temperature stress factor. The expression is as follows: , where represents the temperature stress factor, represents the empirical constant, It represents the average temperature of IGBT during operation in the T time period. Indicates the reference temperature;

Obtain the voltage data of the IGBT during operation in the T time period and calculate the voltage stress factor. The expression is as follows: , where represents the voltage stress factor, and represents the empirical constant, It indicates the average voltage when the IGBT is working during the T time period;

Get the current data of the IGBT during operation in the T time period and calculate the current stress factor. The expression is as follows: , where represents the voltage stress factor, and represents the empirical constant, It indicates the average current when the IGBT is working during the T time period;

Get the number of switching times when the IGBT is working within the T time period , calculate the switching frequency, the expression is as follows , where Represents the switching frequency, calculates the switching frequency stress factor, and the expression is as follows , where represents the switching frequency stress factor, and represents an empirical constant;

Calculate the IGBT aging coefficient, the expression is as follows ;

The control response delay coefficient is used to measure the delay time of the inverter's actual response after receiving the control signal. This coefficient can reflect the performance and health of the inverter's control software. If the control response delay is too large, it may mean that the control software may have potential hidden dangers and needs timely maintenance;

When the control response of the inverter is delayed, the following effects may be caused on the inverter's operating status:

Reduced efficiency: Control response delays can cause the inverter to be unable to adjust output in a timely manner, thus affecting its operating efficiency. For example, when the load changes, the inverter needs to quickly adjust the output voltage and current. If the response delay is too large, the inverter cannot quickly adapt to the new load requirements, resulting in reduced efficiency;

Power quality degradation: The response delay of the inverter will lead to the degradation of the output power quality. Specifically, the distortion of the output voltage and current waveforms increases, and the harmonic content increases, which may cause unstable operation or damage to the load equipment;

Thermal management issues: Long control response delays may cause certain components of the inverter to operate in a non-optimal state for a long time, increasing heat accumulation. Excessive heat may accelerate component aging, shorten the life of the inverter, and may cause thermal failures;

System instability: When multiple inverters work in parallel or interact with the grid, response delays can cause oscillations and instability, affecting the stability and reliability of the entire power system;

The logic for obtaining the control response delay coefficient is as follows:

The time when the inverter receives the control signal each time and the actual response time are obtained within the T time period, and the time when the control signal is received is marked as , marking the actual response time as , calculate the response delay time , the expression is as follows ;

Set the delay reference threshold for the response delay time ;

The response delay time calculated within the T time period is compared with the delay reference threshold. When the response delay time is greater than the delay reference threshold, it means that the response time of the inverter after receiving the control signal has an overload delay. The response delay time greater than the delay reference threshold is marked as the response delay overload time, and the response delay overload time is marked as ,in Indicates the number of times when the response delay time is greater than the delay reference threshold. , is a positive integer;

Calculate the control response delay coefficient, the expression is as follows ;

Step S2, constructing an inverter operation hidden danger monitoring model based on external environment information, electrical fluctuation information, control firmware information, and control software information, generating an inverter operation hidden danger monitoring index, and comprehensively analyzing potential fault hidden dangers existing during inverter operation;

The acquired temperature rise rate abnormal coefficient, output voltage fluctuation coefficient, IGBT aging coefficient, and control response delay coefficient are normalized to construct an inverter operation hidden danger monitoring model and generate the inverter operation hidden danger monitoring index YHL. The formula is as follows: , They represent the proportional coefficients of the temperature rise rate abnormality coefficient, output voltage fluctuation coefficient, IGBT aging coefficient, and control response delay coefficient, respectively. All are greater than 0;

It can be seen from the above calculation formula that the greater the temperature rise rate abnormal coefficient, the greater the output voltage fluctuation coefficient, the greater the IGBT aging coefficient, and the greater the control response delay coefficient, the greater the inverter operation hidden danger monitoring index, indicating that the stability of the inverter's operating state is poor, and the greater the probability of its potential fault hidden dangers. On the contrary, the smaller the temperature rise rate abnormal coefficient, the smaller the output voltage fluctuation coefficient, the smaller the IGBT aging coefficient, and the smaller the control response delay coefficient, the smaller the inverter operation hidden danger monitoring index, indicating that the stability of the inverter's operating state is higher, and the probability of its potential fault hidden dangers is smaller;

Step S3, compare the inverter operation hidden danger monitoring index with the preset inverter operation hidden danger monitoring index threshold, and classify the potential fault hidden dangers existing in the inverter operation, the specific situation is as follows:

If the inverter operation hidden danger monitoring index is greater than the inverter operation hidden danger monitoring index threshold, it indicates that the stability of the inverter operation state is poor, there may be potential fault hidden dangers, and a hidden danger risk signal is generated;

If the inverter operation hidden danger monitoring index is less than or equal to the inverter operation hidden danger monitoring index threshold, it indicates that the inverter operation state is relatively stable, and the probability of potential fault hidden dangers is extremely small, and there is no need to generate a hidden danger risk signal;

Step S4, performing a secondary analysis on the impact depth of the potential fault hidden dangers according to the classification results of the potential fault hidden dangers, and rating the impact depth of the potential fault hidden dangers according to the analysis results;

When a hidden danger risk signal is generated, the inverter operation hidden danger monitoring index generated in multiple T time periods is obtained, and the inverter operation hidden danger monitoring index data set is established, and the inverter operation hidden danger monitoring index data set is marked as ,in Indicates the sequence number of multiple T time periods, , e is a positive integer;

Calculate the standard deviation of the inverter operation hidden danger monitoring index in the inverter operation hidden danger monitoring index data set , the expression is as follows , where It represents the average value of the inverter operation hidden danger monitoring index in the inverter operation hidden danger monitoring index data set. The expression is as follows ;

Compare the standard deviation of the inverter operation hidden danger monitoring index with the preset standard deviation threshold, conduct a secondary analysis on the impact depth of the fault hidden danger, and rate the impact depth of the fault hidden danger based on the analysis results;

If the standard deviation of the inverter operation hidden danger monitoring index is greater than the standard deviation threshold, it means that the inverter operation status is continuously declining, and its potential hidden dangers are continuously increasing, generating a first-level fault hidden danger;

If the standard deviation of the inverter operation hidden danger monitoring index is less than or equal to the standard deviation threshold, it means that the inverter operation status is relatively normal, and its potential hidden dangers do not show an obvious upward trend, generating a secondary fault hidden danger;

It should be noted that both the first-level fault hidden danger and the second-level fault hidden danger indicate the impact depth of the fault hidden danger, and the impact depth of the first-level fault hidden danger is greater than the impact depth of the second-level fault hidden danger;

When a level 1 fault risk is generated, it indicates that the inverter's operating status is continuously declining, potential risks are increasing, and the impact on the new energy storage network is significant. Emergency treatment is required, for example, including the following measures:

Dispatch professional technicians: Immediately dispatch experienced maintenance personnel for on-site inspection;

Detailed inspection and repair: Conduct a comprehensive inspection of the key components of the inverter (such as IGBT, control system, circuit board, etc.) to identify and repair potential fault hazards;

Replace aged or damaged components: If the IGBT or other key components are found to be severely aged or damaged, replace them immediately to ensure the normal operation of the inverter;

Upgrade firmware and software: Check and upgrade the inverter's control firmware and software to optimize its performance and response speed;

Improve the cooling system: If the temperature rise rate is abnormal, the cooling system may need to be improved or replaced to ensure that the inverter operates within a reasonable temperature range;

When a secondary fault potential is generated, it indicates that the inverter is operating normally and the potential potential risk has not shown an obvious upward trend. However, it still needs to be monitored and handled in a timely manner, for example, including the following steps:

Regular maintenance: According to the established maintenance plan, the inverter is regularly inspected and maintained to ensure its continuous and stable operation;

Detect key parameters: Regularly detect and record the key parameters of the inverter, such as voltage, current, temperature, etc., and analyze their changing trends;

Technical training: Regularly provide technical training to operation and maintenance personnel to improve their troubleshooting and handling capabilities;

The present invention comprehensively and accurately monitors the operating status of the inverter by acquiring external environment information, electrical fluctuation information, control firmware information and control software information when the inverter is running. Multi-dimensional information collection ensures accurate perception of potential fault hazards, and constructs an inverter operation hidden danger monitoring model to generate an inverter operation hidden danger monitoring index. A comprehensive analysis is performed on potential fault hazards existing during inverter operation, and potential fault hazards are discovered in time to avoid major faults caused by accumulation of hidden dangers. The inverter operation hidden danger monitoring index is compared with a preset inverter operation hidden danger monitoring index threshold, and the fault hazards are classified. A secondary analysis and rating are performed based on the classification results to determine the severity of the fault hidden dangers. According to the depth of the impact of the fault hidden dangers, the most suitable maintenance personnel are effectively dispatched for precise maintenance, unnecessary temporary repairs are avoided, maintenance efficiency is improved, and maintenance costs are reduced.

The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (4)

1. The optimizing and regulating method of the distributed new energy storage network is characterized by comprising the following steps of: the method comprises the following steps:

Step S1, external environment information, electric fluctuation information, control firmware information and control software information during the operation of an inverter are obtained;

step S2, an inverter operation hidden danger monitoring model is constructed according to external environment information, electric fluctuation information, control firmware information and control software information, an inverter operation hidden danger monitoring index is generated, and potential fault hidden danger existing in the operation of the inverter is comprehensively analyzed;

Step S3, comparing the inverter operation hidden danger monitoring index with a preset inverter operation hidden danger monitoring index threshold value, and classifying potential fault hidden danger existing in the operation of the inverter;

s4, performing secondary analysis on the influence depth of the potential fault hazards according to the classification result of the potential fault hazards, and grading the influence depth of the potential fault hazards according to the analysis result;

The external environment information comprises a temperature rise rate abnormal coefficient, the electric fluctuation information comprises an output voltage fluctuation coefficient, the control firmware information comprises an IGBT aging coefficient, the control software information comprises a control response delay coefficient, and the temperature rise rate abnormal coefficient, the output voltage fluctuation coefficient, the IGBT aging coefficient and the control response delay coefficient are respectively marked as ；

The acquisition logic of the temperature rise rate abnormality coefficient is as follows:

Temperature data of the surrounding environment of the inverter are acquired in a T time period through a temperature sensor, temperature values at different moments are recorded, and the temperature rising rate at the T moment is calculated according to the temperature values at adjacent moments, wherein the expression is as follows In which, in the process,The temperature rise rate at time t is indicated,The time interval is set to be longer than the time interval,The temperature value at time t is indicated,A temperature value at time t-1;

Acquiring the temperature rising rate of the inverter at adjacent moments of the historical normal operation period, and calculating the normal temperature rising rate range The expression is as followsIn which, in the process,An average value representing the temperature rise rate at adjacent times of the inverter history normal operation period,A standard deviation of the temperature rise rate at adjacent moments representing the historical normal operation period of the inverter, k being an adjustment coefficient;

the temperature rise rate abnormality coefficient is calculated, expressed as follows T= {2,3., n }, n is a positive integer;

The output voltage fluctuation coefficient acquisition logic is as follows:

acquiring inverter output voltage data in a T time period, and marking a time sequence of the inverter output voltage data in the T time period as Wherein w represents different moments in the T time period;

wavelet decomposition is carried out on the output voltage data, daubechies wavelet is selected as wavelet basis function and the decomposed scale layer number ；

Will beIs decomposed into approximate coefficients and detail coefficients, and the decomposition formula is as followsIn the followingRepresent the firstThe approximation coefficients of the layers are such that,Represent the firstThe detail coefficients of the layers;

the energy value is calculated according to the detail coefficient of each layer of scale, and the expression is as follows In the followingRepresent the firstThe energy value of the layer scale, w= {1, 2..m }, m is a positive integer;

Comparing the energy value of each layer of scale with a preset energy fluctuation threshold, and marking the energy value of the corresponding layer of scale as a fluctuation energy value when the energy value is larger than the energy fluctuation threshold;

calculating the fluctuation coefficient of the output voltage, wherein the expression is as follows In the followingRepresenting the total number of fluctuation energy values;

the logic for acquiring the IGBT aging coefficient is as follows:

Temperature data of IGBT in T time period during operation is obtained, temperature stress factor is calculated, and the expression is as follows In the followingRepresenting the temperature stress factor of the wafer,The empirical constants are represented by a set of values,Representing the average temperature at which the IGBT is operating during the T time period,Representing a reference temperature;

voltage data of IGBT in T time period during operation is obtained, voltage stress factor is calculated, and the expression is as follows In the followingRepresenting the voltage stress factor of the voltage,AndThe empirical constants are represented by a set of values,Representing the average voltage of the IGBT in the T time period when the IGBT works;

Current data of IGBT in T time period is obtained, current stress factor is calculated, and the expression is as follows In the followingRepresenting the voltage stress factor of the voltage,AndThe empirical constants are represented by a set of values,Representing the average current of the IGBT in the T time period when in operation;

acquiring the switching times of IGBT in T time period during working The switching frequency is calculated as followsIn the followingThe switching frequency is represented, the stress factor of the switching frequency is calculated, and the expression is as followsIn the followingRepresenting the stress factor of the switching frequency,AndRepresenting an empirical constant;

The IGBT aging coefficient is calculated, and the expression is as follows ；

The acquisition logic for controlling the response delay coefficient is as follows:

Acquiring the time of each control signal received by the inverter and the actual response time in the T time period, and marking the time of the control signal received as The actual response time is marked asCalculating response delay timeThe expression is as follows；

Setting a delay reference threshold for response delay time；

Comparing the calculated response delay time within the T time period with a delay reference threshold, and when the response delay time is larger than the delay reference threshold, marking the response delay time with the response delay time larger than the delay reference threshold as the response delay overload time, and marking the response delay overload time asWhereinA number of times indicating when the response delay time is greater than the delay reference threshold,，Is a positive integer;

calculating control response delay coefficient, the expression is as follows ；

Normalizing the obtained abnormal temperature rise rate coefficient, output voltage fluctuation coefficient, IGBT aging coefficient and control response delay coefficient, constructing an inverter operation hidden danger monitoring model, and generating an inverter operation hidden danger monitoring index YHL according to the following formula，Respectively represent the temperature rise rate abnormality coefficient, the output voltage fluctuation coefficient, the IGBT aging coefficient and the proportional coefficient of the control response delay coefficient, andAre all greater than 0.

2. The method for optimizing and controlling the distributed new energy storage network according to claim 1, wherein the method comprises the following steps: comparing the inverter operation hidden danger monitoring index with a preset inverter operation hidden danger monitoring index threshold value, and classifying potential fault hidden dangers existing in the operation of the inverter, wherein the specific conditions are as follows:

if the inverter operation hidden danger monitoring index is larger than the inverter operation hidden danger monitoring index threshold value, generating a hidden danger risk signal;

if the inverter operation hidden danger monitoring index is smaller than or equal to the inverter operation hidden danger monitoring index threshold, a hidden danger risk signal does not need to be generated.

3. The method for optimizing and controlling the distributed new energy storage network according to claim 2, wherein the method is characterized in that: when a hidden danger risk signal is generated, acquiring inverter operation hidden danger monitoring indexes generated in a plurality of T time periods, establishing an inverter operation hidden danger monitoring index data set, and marking the inverter operation hidden danger monitoring index data set asWhereinThe sequence number representing a plurality of T time periods,E is a positive integer;

Calculating standard deviation of inverter operation hidden danger monitoring indexes in inverter operation hidden danger monitoring index data set The expression is as followsIn which, in the process,Average value of inverter operation hidden trouble monitoring indexes in the inverter operation hidden trouble monitoring index data set is represented, and the expression is as follows。

4. The method for optimizing and controlling the distributed new energy storage network according to claim 3, wherein the method comprises the following steps: comparing the standard deviation of the inverter operation hidden danger monitoring index with a preset standard deviation threshold value, performing secondary analysis on the influence depth of the fault hidden danger, and grading the influence depth of the fault hidden danger according to an analysis result;

If the standard deviation of the inverter operation hidden danger monitoring index is larger than the standard deviation threshold value, generating a first-level fault hidden danger;

and if the standard deviation of the inverter operation hidden danger monitoring index is smaller than or equal to the standard deviation threshold value, generating a secondary fault hidden danger.