Running state evaluation method for direct-current charging and discharging system of power distribution network

Abstract

The invention discloses an operation state evaluation method of a power distribution network direct-current charging and discharging system, and relates to the technical field of direct-current charging and discharging, and the method comprises the following steps: in the charging and discharging process of the power distribution network direct-current charging and discharging system, collecting operation data of a state monitoring sensor, the operation data including state sensor mechanical data and electromagnetic interference data; establishing a sensor hidden danger evaluation model according to the operation data, and generating a hidden danger evaluation index; classifying the state of the state detection sensor according to the hidden danger evaluation index, evaluating the state of the state monitoring sensor, sending a repair request for the abnormal state type of the state monitoring sensor, and evaluating the state of the power distribution network DC charging and discharging system through the state monitoring sensor after the state monitoring sensor returns to normal; according to the method, the condition of the sensor can be comprehensively and accurately evaluated, so that the accuracy of the operation state of the system is evaluated, and the problem of the hidden danger of inaccurate state evaluation of the state monitoring sensor on the power grid system caused by the hidden danger of inaccurate detection of the sensor is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current charging and discharging, and more particularly to an operating status evaluation method for a direct current charging and discharging system of a distribution network. Background Art

[0002] Distribution network DC charging and discharging systems are a new type of energy storage and management system, primarily used to balance supply and demand differences in power systems, improve grid stability and flexibility, and facilitate the large-scale integration of renewable energy. These systems typically include components such as energy storage devices, charging and discharging controllers, and smart grid management systems. They can store electrical energy in DC form and release it when needed.

[0003] To ensure the proper operation of DC charging and discharging systems in distribution networks and the stability of the power grid, operational status assessment is crucial. This assessment primarily involves monitoring and evaluating the overall system operation, the status of energy storage devices, and the performance of charge and discharge controllers. Traditional methods rely primarily on monitoring the operating parameters of various system components, such as battery voltage, current, and temperature, as well as indicators such as the system's charge and discharge efficiency and charge and discharge rate, to assess system operational status. Furthermore, operational status assessments of DC charging and discharging systems in distribution networks also need to consider the impact of external environmental factors on the system, such as temperature, humidity, and electromagnetic interference.

[0004] In recent years, with the development of technologies such as artificial intelligence, big data, and the Internet of Things, new methods are being introduced for assessing the operating status of DC charging and discharging systems in distribution networks. These methods include using intelligent algorithms to analyze and predict system data, employing condition monitoring sensors to achieve real-time monitoring of system components, and integrating data mining techniques to identify system anomalies. The introduction of these new technologies enables more accurate and timely assessments of the operating status of DC charging and discharging systems in distribution networks, helping to improve system reliability, safety, and economic efficiency.

[0005] Deficiencies in existing technologies: Condition monitoring sensors play a decisive role in the system status assessment in the DC charging and discharging system of the distribution network. However, they themselves may have the hidden danger of inaccurate monitoring; these hidden dangers may seriously affect the accuracy of the status assessment of the power grid system, thereby causing the hidden danger of inaccurate assessment during system operation.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an operating status assessment method for a DC charging and discharging system of a distribution network, which can comprehensively and accurately assess the operating status of the system and improve the stability and reliability of the system.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The method for evaluating the operating status of a DC charging and discharging system of a distribution network comprises the following steps:

[0010] When the DC charging and discharging system of the distribution network is charging and discharging, the operating data of the status monitoring sensors are collected, and the operating data include the mechanical data and electromagnetic interference data of the status sensors. A sensor hidden danger assessment model is established based on the operating data to generate a hidden danger assessment index. The status of the status detection sensors is classified according to the hidden danger assessment index, and the status of the status monitoring sensors is evaluated. A repair request is issued for abnormal status types of the status monitoring sensors. When the status monitoring sensors return to normal, the status of the DC charging and discharging system of the distribution network is evaluated through the status monitoring sensors.

[0011] In a preferred embodiment, the state sensor mechanical data includes a sensor sensitivity coefficient and a sensor input-output difference anomaly coefficient, and the electromagnetic interference data includes an electromagnetic interference coefficient.

[0012] In a preferred embodiment, the status of the status detection sensor is classified according to the hidden danger assessment index, specifically: the hidden danger assessment index is compared and analyzed with a preset hidden danger assessment index threshold; when the hidden danger assessment index is greater than the preset hidden danger assessment index threshold, the status monitoring sensor is marked as an abnormal state, and a maintenance requirement report is issued; after receiving the report, the maintenance personnel inspect, diagnose and maintain the sensor; during the maintenance process, the hidden danger assessment index of the status monitoring sensor is detected in real time, and when the hidden danger assessment index is greater than the preset hidden danger assessment index threshold, a maintenance success signal is issued; when the hidden danger assessment index is less than the preset hidden danger assessment index threshold, the status monitoring sensor is marked as a healthy state, and the maintenance personnel will directly evaluate the status of the distribution network DC charging and discharging system through the status monitoring sensor.

[0013] In a preferred embodiment, the specific method for obtaining the sensor sensitivity coefficient is as follows: obtaining sensor data of an interval of L within a G time during the charging and discharging process of the DC charging and discharging system of the distribution network; standardizing the sensor data to obtain standardized sensor data, using the principal component method to analyze the standardized sensor data to obtain temperature eigenvalues, voltage eigenvalues, and current eigenvalues ​​as characteristics of the sensor data; constructing a sensor data matrix based on the characteristics of the sensor data; calculating the covariance matrix of the sensor data matrix; performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues, and analyzing and calculating the sensor sensitivity coefficient.

[0014] In a preferred embodiment, the method for obtaining the sensor input-output difference anomaly coefficient is as follows: obtaining the sensor real input data and sensor output or display data of the L interval within the G time during the charging and discharging process of the distribution network DC charging and discharging system, and fitting a linear model to represent the relationship between the sensor real input data and the sensor output or display data; calculating the sensor input-output difference coefficient by minimizing the residual sum of squares between the sensor output or display data and the sensor real input data; calculating the sensor input-output difference coefficient and the preset sensor input-output difference standard value to obtain the sensor input-output difference anomaly coefficient.

[0015] In a preferred embodiment, the method for obtaining the electromagnetic interference coefficient is as follows: obtain electromagnetic signal data in the environment and store it in the form of a time series to obtain a time domain signal; perform a discrete Fourier transform on the acquired time domain signal, and convert it into a spectral component in the frequency domain by calculation, and establish a spectral component set for the spectral components of all frequency points; calculate the modulus of the spectrum of each frequency point based on the spectral component, and calculate the electromagnetic interference coefficient.

[0016] The technical effects and advantages of the operating status evaluation method of the distribution network DC charging and discharging system of the present invention are as follows:

[0017] 1. The present invention collects mechanical data of state sensors and external environmental data, and uses the principal component analysis method to obtain the sensor's sensitivity coefficient and input-output difference coefficient, while using spectrum analysis technology to obtain the electromagnetic interference coefficient in the environment; the sensitivity coefficient is calculated by converting the sensor data into an eigenvalue matrix; the input-output difference coefficient is calculated by fitting a linear model and minimizing the residual sum of squares; the electromagnetic interference coefficient is obtained by obtaining the spectral components in the frequency domain through discrete Fourier transform and calculated; the comprehensive application of these methods enables the evaluation method to comprehensively and accurately evaluate the sensor status and thus evaluate the operating status of the system, thereby improving the stability and reliability of the system.

[0018] 2. The present invention establishes a sensor hidden danger assessment model, comprehensively considers the status sensor mechanical data and external environmental data, and generates a hidden danger assessment index to reflect the degree of inaccurate detection hidden dangers that may exist in the sensor. Subsequently, by comparing with a pre-set threshold, the sensor status is evaluated, abnormal conditions are discovered in a timely manner, and a maintenance demand report is issued. After maintenance, maintenance personnel re-evaluate the sensor status to ensure the stable operation of the system. This method integrates multiple factors and has the advantages of strong real-time performance, high maintenance efficiency, and strong system stability. It is suitable for improving the status monitoring and maintenance efficiency of the DC charging and discharging system of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1This is a structural diagram of the operating status evaluation method of the DC charging and discharging system of the distribution network of the present invention. DETAILED DESCRIPTION

[0020] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] Example 1, Figure 1 The present invention provides a method for evaluating the operating status of a DC charging and discharging system in a distribution network, comprising the following steps:

[0022] S10, when the DC charging and discharging system of the distribution network is charging and discharging, collecting operating data of the status monitoring sensor, the operating data including mechanical data and electromagnetic interference data of the status sensor;

[0023] The state sensor mechanical data includes a sensor sensitivity coefficient and a sensor input-output difference anomaly coefficient;

[0024] The sensitivity coefficient of a sensor refers to its sensitivity to changes in the physical quantity being measured. In DC charging and discharging systems in distribution networks, sensors are typically used to monitor changes in parameters such as current, voltage, and temperature. The sensitivity coefficient tells us how much the sensor output signal changes when the physical quantity being measured changes by a unit.

[0025] The sensitivity coefficient of a sensor is crucial to ensuring the accuracy and reliability of a monitoring system. A high sensitivity coefficient means that the sensor is very sensitive to changes in the physical quantity being measured, allowing it to detect subtle changes, but it may also make the sensor more susceptible to external interference. Therefore, when designing and selecting sensors, it is necessary to balance the relationship between sensitivity and stability based on the specific application scenario to ensure that the system performance meets the requirements.

[0026] The sensor sensitivity coefficient has the following effects on analyzing the status and hidden dangers of the sensor:

[0027] Accuracy assessment: The sensitivity coefficient of a sensor directly affects the accuracy and precision of its measurements. If the sensitivity coefficient of a sensor is unstable or deviates from the expected value, it may lead to increased measurement errors, thus affecting the accurate assessment of the system status;

[0028] Fault diagnosis: Changes in the sensor sensitivity coefficient may indicate a sensor failure or degradation. If the sensitivity coefficient changes abnormally, it may mean that the sensor element is damaged, the calibration is invalid, or there is external interference, which requires timely diagnosis and maintenance;

[0029] Performance monitoring: Monitoring changes in sensor sensitivity coefficients can help promptly detect signs of sensor performance degradation or failure. By regularly checking the sensor sensitivity coefficient, the performance of the sensor can be monitored in real time, and timely measures can be taken for maintenance or replacement;

[0030] System health assessment: The stability and accuracy of sensor sensitivity directly impact the reliability of system status assessment. By analyzing changes in sensor sensitivity, we can more accurately assess system health, identify potential issues promptly, and take appropriate measures to adjust or repair them.

[0031] Therefore, analyzing the sensitivity coefficient of the monitoring sensor can timely discover the errors in the operating status evaluation of the DC charging and discharging system of the distribution network caused by the hidden dangers of the sensor;

[0032] The method for obtaining the sensor sensitivity coefficient is as follows:

[0033] Acquire sensor data within an interval of L during a charging and discharging process of a DC charging and discharging system in a distribution network; standardize the sensor data to obtain standardized sensor data; analyze the standardized sensor data using a principal component method to obtain temperature characteristic values, voltage characteristic values, and current characteristic values ​​as features of the sensor data;

[0034] Construct a sensor data matrix based on the sensor data’s characteristics;

[0035] Calculate the covariance matrix of the sensor data matrix;

[0036] Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalues, and analyze and calculate the sensor sensitivity coefficient, where k is the number of eigenvalues;

[0037] The calculation formula of the covariance matrix is:

[0038]

[0039] Where XF is the covariance matrix, n is the number of eigenvalues, and CG is the sensor data matrix;

[0040] The calculation formula of the sensor sensitivity coefficient is:

[0041]

[0042] Where β is the sensor sensitivity coefficient, k is the number of eigenvalues, i is the eigenvalue number, and λ is the i is the eigenvalue, λ 总is the sum of the eigenvalues, m is the number of rows in the sensor data matrix, n is the number of columns in the sensor data matrix, CG zj is the element of the sensor data matrix, TZ i is the feature vector corresponding to the i-th sensor data.

[0043] It should be noted that each row of the sensor data matrix represents a sensor data, and each column represents a feature; principal component analysis converts the data into a set of new variables, called principal components, which are linear combinations of the original variables and can explain most of the variance in the data.

[0044] From the calculation formula of the sensor sensitivity coefficient, it can be seen that the larger the performance value of the sensor sensitivity coefficient, the smaller the quality risk of the sensor, and vice versa, the greater the quality risk of the sensor.

[0045] The sensor input-output variation coefficient refers to the error or deviation that may occur in the process of converting the input signal into the output signal. This error or deviation may be caused by many factors, including the manufacturing process of the sensor, environmental conditions, and age. The sensor input-output variation coefficient is usually used to describe the accuracy and stability of the sensor. It reflects the degree of measurement error that may occur in actual application of the sensor.

[0046] The sensor's input-output difference anomaly coefficient has the following effects on analyzing the sensor's status and hidden dangers:

[0047] Accuracy assessment: The input-output variation coefficient is one of the important indicators for evaluating sensor accuracy. By understanding the input-output variation coefficient of a sensor, we can more accurately assess whether the sensor's measurement results are reliable and thus determine whether the sensor is in good working condition.

[0048] Condition Monitoring: The input-output coefficient of variation can be used to monitor the status of the sensor. When the input-output coefficient of variation of a sensor begins to deviate from the expected range, it may indicate a problem or failure of the sensor. Therefore, regular monitoring of the input-output coefficient of variation can help to promptly detect abnormal sensor conditions and take appropriate maintenance or repair measures.

[0049] Hidden danger identification: Abnormal fluctuations or deviations in the input-output coefficient of variation may indicate potential sensor problems or hidden dangers. By analyzing the change pattern of the input-output coefficient of variation, it can help identify possible sensor failure modes or problems in the operating environment, so that preventive or corrective measures can be taken in a timely manner to prevent sensor failures or errors from affecting system performance.

[0050] Maintenance optimization: Based on the monitoring and analysis results of the input-output difference coefficient, a more effective sensor maintenance plan can be formulated; regular inspection and calibration of sensors to ensure their stable and accurate performance helps to extend the service life of the sensor and reduce unnecessary repair costs and downtime.

[0051] Therefore, analyzing the input-output difference anomaly coefficient of the monitoring sensor can timely discover the operating status assessment errors of the distribution network DC charging and discharging system caused by the hidden dangers of the sensor.

[0052] The method for obtaining the sensor input-output difference anomaly coefficient is as follows:

[0053] The actual sensor input data and sensor output or display data are obtained within the interval L during the charging and discharging process of the DC charging and discharging system of the distribution network. A linear model is fitted to represent the relationship between the actual sensor input data and the sensor output or display data. The linear model is as follows:

[0054] SC=ω0+ω*SR+σ

[0055] Where SC is the sensor output or display data, SR is the actual sensor input data, ω is the sensor input-output difference coefficient, ω0 is the parameter to be estimated, and σ is the observation error;

[0056] The sensor input-output difference coefficient is calculated by minimizing the residual sum of squares between the sensor output or displayed data and the sensor's true input data;

[0057] The calculation formula of the sensor input-output difference coefficient is as follows:

[0058]

[0059] Where q is the total number of acquired data, SR v is the real input data for the sensor, is the average value of the actual input data of the sensor, SC v Output or display data for sensors, is the average value of the sensor output or display data, ω is the sensor input and output difference coefficient;

[0060] The sensor input-output difference coefficient is calculated with the preset sensor input-output difference standard value to obtain the sensor input-output difference abnormality coefficient. The specific formula is as follows:

[0061]

[0062] Where YC is the sensor input-output difference anomaly coefficient, ω 标 It is the standard value of sensor input and output difference;

[0063] It should be noted that the residual is defined as the difference between the observed value and the model predicted value;

[0064] From the calculation formula of the sensor input-output difference anomaly coefficient, it can be seen that the larger the performance value of the sensor input-output difference anomaly coefficient, the greater the quality risk of the sensor, and vice versa.

[0065] The electromagnetic interference data includes an electromagnetic interference coefficient;

[0066] Electromagnetic interference refers to interference signals from external electromagnetic fields, which may affect the accuracy, sensitivity and stability of sensors. This interference may come from various sources, such as electromagnetic radiation, electromagnetic waves, electromagnetic induction, etc., and may cause sensor output offset, increased noise or signal distortion.

[0067] The electromagnetic interference coefficient has the following effects on the status and hidden dangers of the analysis sensor:

[0068] State accuracy assessment: Considering the electromagnetic interference coefficient can help evaluate the measurement accuracy of sensors. When the sensor is subject to significant electromagnetic interference, it may experience output offset or signal distortion, resulting in inaccurate measurement results. By analyzing the electromagnetic interference coefficient, the state accuracy of the sensor can be more accurately assessed, avoiding misjudgment of the system state.

[0069] Hidden danger identification and elimination: Considering the electromagnetic interference coefficient helps identify potential sensor hazards. When electromagnetic interference has a significant impact on sensor performance, it may cause system operation abnormalities or unreliable data, thereby posing potential safety hazards. By analyzing the electromagnetic interference coefficient, hidden dangers in sensors can be discovered in a timely manner, and appropriate measures can be taken to eliminate or repair them to ensure the safe and stable operation of the system.

[0070] Performance prediction and optimization: By analyzing the electromagnetic interference coefficient, the sensor's performance under different electromagnetic interference conditions can be predicted, allowing targeted optimization of system design or selection of more suitable sensor types. By optimizing sensor performance, the impact of electromagnetic interference on the system can be reduced, improving system reliability and stability.

[0071] Monitoring and maintenance: Considering the electromagnetic interference coefficient helps establish a sensor status monitoring and maintenance system. By regularly monitoring the electromagnetic interference coefficient, the degree of electromagnetic interference to the sensor performance can be discovered in a timely manner, so that appropriate maintenance measures can be taken to extend the service life of the sensor and ensure the reliable operation of the system.

[0072] Therefore, analyzing the electromagnetic interference coefficient can timely discover the errors in the operating status evaluation of the DC charging and discharging system of the distribution network caused by the hidden dangers of the sensors.

[0073] The specific method for obtaining the electromagnetic interference coefficient is as follows:

[0074] Acquire electromagnetic signal data in the environment and store it in the form of time series to obtain time domain signals;

[0075] Perform discrete Fourier transform on the acquired time domain signal and convert it into frequency domain spectral components through calculation, and establish a spectral component set for the spectral components of all frequency points;

[0076] Calculate the modulus of the spectrum at each frequency point based on the spectrum components, and calculate the electromagnetic interference coefficient;

[0077] The calculation formula of the spectral components in the frequency domain is:

[0078]

[0079] Where PF h is the spectral component of the frequency domain, h is the frequency, μ is the total number of acquired time domain signal data, is an imaginary unit;

[0080] The formula for calculating the modulus of the spectrum is as follows:

[0081]

[0082] The calculation formula of the electromagnetic interference coefficient is as follows:

[0083]

[0084] Where c is the total number of spectral components in the spectral component set, d is the index of the spectral component in the spectral component set, PF 总 is the mode of the total spectrum, GR is the electromagnetic interference coefficient, MO d is the modulus of the spectrum;

[0085] It should be noted that the modulus of the spectrum is the amplitude spectrum, which can represent the intensity of the electromagnetic signal at different frequencies. When performing Fourier transform, it is necessary to select an appropriate sampling rate and sampling window length to ensure that all important signal components within the frequency range can be captured.

[0086] From the calculation formula of the electromagnetic interference coefficient, it can be seen that the larger the performance value of the electromagnetic interference coefficient, the greater the quality risk of the sensor, and vice versa.

[0087] This embodiment collects mechanical data and external environmental data of the state sensor, and uses the principal component analysis method to obtain the sensitivity coefficient and input-output difference coefficient of the sensor, and adopts the spectrum analysis technology to obtain the electromagnetic interference coefficient in the environment; the sensitivity coefficient is calculated by converting the sensor data into an eigenvalue matrix; the input-output difference coefficient is calculated by fitting a linear model and minimizing the residual sum of squares; the electromagnetic interference coefficient is obtained by obtaining the spectral components of the frequency domain through discrete Fourier transform and calculated; the comprehensive application of these methods enables the evaluation method to comprehensively and accurately evaluate the situation of the sensor and thus evaluate the operating status of the system, thereby improving the stability and reliability of the system.

[0088] In Example 2, S20, a sensor hidden danger assessment model is established using the processed state sensor mechanical data and electromagnetic interference data to generate a hidden danger assessment index;

[0089] The obtained sensor sensitivity coefficient, sensor input-output difference anomaly coefficient and electromagnetic interference coefficient are comprehensively analyzed and weighted summed to generate a hidden danger assessment index;

[0090] The calculation formula of the hidden danger assessment index is as follows:

[0091]

[0092] Where, YH o is the hidden danger assessment index, α1 is the preset proportional coefficient of the sensor sensitivity coefficient, α2 is the preset proportional coefficient of the sensor input-output difference abnormality coefficient, and α3 is the preset proportional coefficient of the electromagnetic interference coefficient;

[0093] It can be seen from the calculation formula of the hidden danger assessment index that the smaller the sensor sensitivity coefficient, the larger the sensor input-output difference abnormality coefficient, and the larger the electromagnetic interference coefficient, that is, the larger the performance value of the hidden danger assessment index, indicating that the hidden danger of inaccurate detection of the sensor is greater, thereby causing the hidden danger of inaccurate status assessment of the power grid system by the status monitoring sensor to be greater; the larger the sensor sensitivity coefficient, the smaller the sensor input-output difference abnormality coefficient, and the larger the electromagnetic interference coefficient, that is, the smaller the performance value of the hidden danger assessment index, indicating that the hidden danger of inaccurate detection of the sensor is smaller, thereby causing the hidden danger of inaccurate status assessment of the power grid system by the status monitoring sensor to be smaller.

[0094] S30, compare the hidden danger assessment index with a pre-set hidden danger assessment index threshold, evaluate the status of the condition monitoring sensor, issue a repair request for the abnormal condition monitoring sensor status type, and when the condition monitoring sensor returns to normal, evaluate the status of the distribution network DC charging and discharging system through the condition monitoring sensor.

[0095] Compare and analyze the hidden danger assessment index with the pre-set hidden danger assessment index threshold; when the hidden danger assessment index is greater than the pre-set hidden danger assessment index threshold, the condition monitoring sensor is marked as abnormal and a maintenance demand report is issued; after receiving the report, the maintenance personnel inspect, diagnose and maintain the sensor;

[0096] During the maintenance process, the hidden danger assessment index of the status monitoring sensor is detected in real time. When the hidden danger assessment index is greater than the pre-set hidden danger assessment index threshold, a maintenance success signal is issued;

[0097] When the hidden danger assessment index is less than the pre-set hidden danger assessment index threshold, the status monitoring sensor will be marked as healthy, and maintenance personnel will directly evaluate the status of the distribution network DC charging and discharging system through the status monitoring sensor.

[0098] This embodiment establishes a sensor hidden danger assessment model, comprehensively considering the status sensor's mechanical data and external environmental data to generate a hidden danger assessment index that reflects the extent of the sensor's potential detection inaccuracy. Subsequently, the sensor's status is evaluated by comparison with a pre-set threshold, allowing abnormal conditions to be promptly detected and a maintenance requirement report to be issued. Maintenance personnel then reassess the sensor status after performing maintenance to ensure stable system operation. This method integrates multiple factors and offers advantages such as strong real-time performance, high maintenance efficiency, and strong system stability. It is suitable for improving the efficiency of status monitoring and maintenance of DC charging and discharging systems in distribution networks.

[0099] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0100] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0101] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0103] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0104] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the operating status of a DC charging and discharging system in a distribution network, characterized in that: The steps include: Collect operating data of the DC charging and discharging system of the distribution network during the charging and discharging process. The operating data of the condition monitoring sensor includes the mechanical data and electromagnetic interference data of the condition sensor; Establish a sensor hidden danger assessment model based on operating data and generate a hidden danger assessment index; The status of the status detection sensor is classified according to the hidden danger assessment index, the status of the status monitoring sensor is evaluated, and a repair request is issued for the abnormal status type of the status monitoring sensor. When the status monitoring sensor returns to normal, the status of the DC charging and discharging system of the distribution network is evaluated through the status monitoring sensor.

2. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 1, wherein: The state sensor mechanical data includes a sensor sensitivity coefficient and a sensor input-output difference anomaly coefficient, and the electromagnetic interference data includes an electromagnetic interference coefficient; the operation data is obtained through a state monitoring sensor.

3. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 2, wherein: The status of the status detection sensor is classified according to the hidden danger assessment index, specifically: Compare and analyze the hidden danger assessment index with the pre-set hidden danger assessment index threshold; when the hidden danger assessment index is greater than the pre-set hidden danger assessment index threshold, the condition monitoring sensor is marked as abnormal and a maintenance requirement report is issued; Real-time detection of the hidden danger assessment index of the condition monitoring sensor during maintenance. When the hidden danger assessment index exceeds the pre-set hidden danger assessment index threshold, a maintenance success signal is issued. When the hidden danger assessment index is less than the pre-set hidden danger assessment index threshold, the status monitoring sensor will be marked as healthy, and maintenance personnel will directly evaluate the status of the distribution network DC charging and discharging system through the status monitoring sensor.

4. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 3, wherein: The calculation formula of the hidden danger assessment index is as follows: Where, YH o is the hidden danger assessment index, α1 is the preset proportional coefficient of the sensor sensitivity coefficient, α2 is the preset proportional coefficient of the sensor input-output difference abnormality coefficient, α3 is the preset proportional coefficient of the electromagnetic interference coefficient, β is the sensor sensitivity coefficient, YC is the sensor input-output difference abnormality coefficient, and GR is the electromagnetic interference coefficient.

5. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 4, characterized in that: The specific method for obtaining the sensor sensitivity coefficient is as follows: Acquire sensor data of a G time interval and a L interval during a charging and discharging process of a DC charging and discharging system of a distribution network; and standardize the sensor data to obtain standardized sensor data; The standardized sensor data is analyzed using the principal component method to obtain the temperature characteristic value, voltage characteristic value, and current characteristic value as the characteristics of the sensor data; Constructing a sensor data matrix based on the characteristics of the sensor data and calculating the covariance matrix of the sensor data matrix; Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue, and analyze and calculate the sensor sensitivity coefficient.

6. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 4, characterized in that: The method for obtaining the sensor input-output difference anomaly coefficient is as follows: Obtaining the actual sensor input data and sensor output or display data for a period of time G and interval L during the charging and discharging process of the DC charging and discharging system of the distribution network; The sensor input-output difference coefficient is calculated by minimizing the residual sum of squares between the sensor output or displayed data and the sensor's true input data; The sensor input-output difference coefficient is calculated with the preset sensor input-output difference standard value to obtain the sensor input-output difference abnormality coefficient.

7. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 4, wherein: The method for obtaining the electromagnetic interference coefficient is as follows: The electromagnetic signal data in the environment is acquired and stored in the form of a time series to obtain a time domain signal; the acquired time domain signal is subjected to a discrete Fourier transform and converted into a frequency domain spectrum component by calculation, and a spectrum component set is established for the spectrum components of all frequency points; the mode of the spectrum of each frequency point is calculated based on the spectrum components, and the electromagnetic interference coefficient is calculated.

8. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 5, wherein: The calculation formula of the sensor sensitivity coefficient is as follows: Where i is the number of the eigenvalue, λ i is the eigenvalue, λ 总 is the sum of the eigenvalues, m is the number of rows in the sensor data matrix, n is the number of columns in the sensor data matrix, CG zj is the element of the sensor data matrix, TZ i is the feature vector corresponding to the i-th sensor data.

9. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 6, wherein: The calculation formula of the sensor input-output difference abnormality coefficient is as follows: Where q is the total number of acquired data, SR v is the real input data for the sensor, is the average value of the actual input data of the sensor, SC v Output or display data for sensors, is the average value of the sensor output or displayed data, ω 标 It is the standard value of sensor input and output difference.

10. The method for evaluating the operating status of a DC charging and discharging system of a distribution network according to claim 7, wherein: The calculation formula of the electromagnetic interference coefficient is as follows: Where c is the total number of spectral components in the spectral component set, d is the index of the spectral component in the spectral component set, PF 总 is the mode of the total spectrum, GR is the electromagnetic interference coefficient, MO d is the modulus of the spectrum, h is the frequency, μ is the total number of acquired time domain signal data, is the imaginary unit, and S(ρ) is the time domain signal.

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