Resonance suppression method and device for energy storage converter

Abstract

This invention relates to the field of energy storage converter technology, specifically disclosing a resonance suppression method and apparatus for energy storage converters. The method includes: monitoring the environment of the transformer substation and its actual energy access status; determining whether the actual energy access status is acceptable and whether resonance suppression is necessary; analyzing the load and electrical conditions of substations with unacceptable energy access status; obtaining the switching frequency of the energy storage converter; and combining this with the evaluation value of the actual energy access status, the characteristic values ​​of the load conditions, and the characteristic values ​​of the electrical conditions to determine the resonance suppression activation level of the energy storage converter. This invention solves the problem that traditional filters cannot provide flexible resonance suppression under different conditions and lack intelligent real-time monitoring and adaptive adjustment capabilities. It helps improve the accuracy and efficiency of system decision-making, flexibly adjusts the resonance suppression activation level, prevents excessive resonance, and avoids resource waste caused by excessive suppression.

Description

Technical Field

[0001] This invention relates to the field of energy storage converter technology, specifically to a method and apparatus for suppressing resonance in energy storage converters. Background Technology

[0002] As the proportion of renewable energy (such as solar and wind power) in the power grid continues to increase, the role of energy storage systems is becoming increasingly important. Energy storage systems can balance the output of intermittent energy sources, provide power stability, and support the reduction of peak loads. Energy storage converters are the core devices connecting energy storage devices (such as batteries) and the power grid, responsible for converting between direct current and alternating current. In energy storage converters, resonance is usually caused by the interaction between the inductor and capacitor elements of the converter and the power grid or load. With the increase of the converter switching frequency and the increase of power grid complexity, the resonance problem becomes more prominent. In particular, the nonlinear load and switching action of the converter can lead to an increase in system harmonics, which in turn can cause electrical oscillations. The development of smart grid technology has placed higher demands on energy storage systems, requiring them to have high dynamic response capabilities, flexible power control, and good stability. Especially when a large number of distributed energy sources are connected, energy storage converters, as key interfaces, need to have adaptive resonance suppression capabilities to cope with different loads and power grid fluctuations.

[0003] Currently, there are still some shortcomings in the research on resonance suppression of energy storage converters. Specifically, traditional passive filters are usually designed for fixed harmonic frequencies, while the conditions of the power grid and load are dynamically changing. This results in the traditional filters not performing well under different conditions, failing to provide flexible resonance suppression, and lacking intelligent real-time monitoring and adaptive adjustment capabilities. They are also unable to cope with the complex and rapidly changing operating environment of the power grid. With the large-scale integration of distributed renewable energy (such as wind and solar power), their intermittency and instability lead to higher fluctuation frequencies of power grid frequency and voltage. Traditional resonance suppression methods are difficult to effectively cope with this highly dynamic power grid environment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a resonance suppression method and apparatus for energy storage converters, which can effectively solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a resonance suppression method for an energy storage converter, comprising the following steps: monitoring the environment of the transformer substation, determining the seasonal scenarios of the substation and the threshold range of the energy access status for those seasonal scenarios; monitoring the actual energy access status of the substation, determining the actual energy access status assessment value, and, in conjunction with the threshold range of the energy access status for those seasonal scenarios, determining whether the actual energy access status of the substation is qualified; if the actual energy access status of the substation is qualified, continuing to monitor the environment of the substation; if the actual energy access status of the substation is unqualified, then resonance suppression of the substation is required; analyzing the load conditions of the substation with unqualified actual energy access status to obtain characteristic values ​​of the load conditions; analyzing the electrical conditions of the substation with unqualified actual energy access status to obtain characteristic values ​​of the electrical conditions; obtaining the switching frequency of the energy storage converter and, in conjunction with the actual energy access status assessment value, the load condition characteristic value, and the electrical condition characteristic value, determining the resonance suppression activation level of the energy storage converter.

[0006] As a further method, the environment of the transformer substation is monitored to determine the seasonal scenarios of the substation and the threshold range of energy access status for those seasonal scenarios. The specific analysis process is as follows: A dataset of the environment of the transformer substation is acquired, including ambient temperature, humidity, wind speed, and precipitation. Based on the acquired dataset, environmental characteristic values ​​are comprehensively analyzed to obtain the environmental feature values ​​of the substation, which serve as the basis for determining the seasonal scenarios of the substation. The environmental feature values ​​are stored as specified labels, and these labels are compared with the seasonal scenarios corresponding to each specified label stored in the database to obtain the seasonal scenarios corresponding to each specified label. The seasonal scenarios are stored as specified codes, and these codes are compared with the threshold ranges of energy access status for each specified code stored in the database to obtain the threshold ranges of energy access status for each seasonal scenario corresponding to each specified code.

[0007] As a further method, to determine whether the actual energy access status of the transformer substation is qualified, the specific analysis process is as follows: Obtain a dataset of the actual energy access status of the transformer substation, which specifically includes the deviation of the proportion of new energy access, the deviation of the proportion of solar power generation, and the deviation of wind power generation. Based on the obtained dataset, a comprehensive analysis is performed to obtain an evaluation value of the actual energy access status of the transformer substation. This evaluation value serves as the basis for determining whether the actual energy access status of the transformer substation is qualified. The evaluation value is compared with the threshold range of the seasonal energy access status of the transformer substation. If the evaluation value falls within the threshold range, the actual energy access status of the transformer substation is qualified, and the environment of the transformer substation continues to be monitored. If the evaluation value does not fall within the threshold range, the actual energy access status of the transformer substation is unqualified, and resonance suppression of the transformer substation is required.

[0008] As a further method, the load conditions of transformer substations with unqualified actual energy access status are analyzed. The specific analysis process is as follows: obtain the transformer substation load condition dataset, and based on the obtained transformer substation load condition dataset, comprehensively analyze to obtain the characteristic values ​​of transformer substation load conditions. The characteristic values ​​of transformer substation load conditions are used as the basis for determining the resonance suppression activation level of the energy storage converter.

[0009] As a further method, the distribution area load condition dataset specifically includes the ratio of average load to peak load of the distribution area, the total load deviation of the main transmission lines of the distribution area, and the total load deviation of the distribution lines of the distribution area.

[0010] As a further method, the electrical conditions of transformer substations with unqualified actual energy access status are analyzed. The specific analysis process is as follows: obtain the electrical condition dataset of the transformer substations; based on the obtained electrical condition dataset, comprehensively analyze to obtain the characteristic values ​​of the electrical conditions of the transformer substations; the characteristic values ​​of the electrical conditions of the transformer substations serve as the basis for determining the resonance suppression activation level of the energy storage converter.

[0011] As a further method, the transformer area electrical condition dataset specifically includes the transformer area voltage fluctuation frequency, transformer area power factor, and transformer area total harmonic distortion.

[0012] As a further method, the resonance suppression activation level of the energy storage converter is determined. The specific analysis process is as follows: the switching frequency of the energy storage converter is obtained, and the switching frequency deviation of the energy storage converter is determined; the switching frequency deviation of the energy storage converter, the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area are imported into the resonance suppression matching model, and the resonance suppression matching characteristic value is obtained through comprehensive analysis. The resonance suppression matching characteristic value is used as the basis for determining the resonance suppression activation level of the energy storage converter; if the resonance suppression matching characteristic value is not lower than the first resonance suppression matching threshold stored in the database, the resonance suppression activation level of the energy storage converter is level three; if the resonance suppression matching characteristic value is lower than the first resonance suppression matching threshold stored in the database, but not lower than the second resonance suppression matching threshold stored in the database, the resonance suppression activation level of the energy storage converter is level two; if the resonance suppression matching characteristic value is lower than the second resonance suppression matching threshold stored in the database, the resonance suppression activation level of the energy storage converter is level one.

[0013] As a further method, the resonance suppression matching model is analyzed as follows:

[0014]

[0015] In the formula, ω is the resonance suppression matching characteristic value, δ is the switching frequency deviation of the energy storage converter, γ is the actual energy access status assessment value of the distribution area, α is the load condition characteristic value of the distribution area, and β is the electrical condition characteristic value of the distribution area.

[0016] The second aspect of this invention provides a resonance suppression device for an energy storage converter, comprising a transformer area seasonal scenario determination module, an actual energy access status qualification judgment module, a load condition characteristic value acquisition module, an electrical condition characteristic value acquisition module, and a resonance suppression activation level determination module, wherein: the transformer area seasonal scenario determination module is used to monitor the environment of the transformer area and determine the seasonal scenario of the transformer area and the threshold range of the energy access status of the seasonal scenario of the transformer area; the actual energy access status qualification judgment module is used to monitor the actual energy access status of the transformer area, determine the evaluation value of the actual energy access status of the transformer area, and, in combination with the threshold range of the energy access status of the seasonal scenario of the transformer area, determine whether the actual energy access status of the transformer area is qualified: if the transformer area If the actual energy access status is qualified, the environment of the transformer substation will continue to be monitored; if the actual energy access status of the transformer substation is unqualified, resonance suppression of the transformer substation is required. The load condition characteristic value acquisition module is used to analyze the load conditions of the transformer substation with unqualified actual energy access status and obtain the load condition characteristic value of the transformer substation. The electrical condition characteristic value acquisition module is used to analyze the electrical conditions of the transformer substation with unqualified actual energy access status and obtain the electrical condition characteristic value of the transformer substation. The resonance suppression activation level determination module is used to obtain the switching frequency of the energy storage converter and, in combination with the actual energy access status evaluation value of the transformer substation, the load condition characteristic value of the transformer substation, and the electrical condition characteristic value of the transformer substation, determine the resonance suppression activation level of the energy storage converter.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0018] (1) This invention provides a resonance suppression method and device for energy storage converters, which monitors the actual energy access status of the distribution area and compares it with seasonal scenario thresholds. Potential anomalies can be detected before the problem becomes serious, and preventive maintenance and adjustments can be made in advance to prevent more serious resonance phenomena or system instability. By comprehensively analyzing the actual energy access status, load conditions and electrical conditions of the distribution area, the resonance suppression is avoided blindly, which improves the accuracy and efficiency of system decision-making. By obtaining the switching frequency of the energy storage converter and combining multiple evaluation parameters, the activation level of resonance suppression can be flexibly adjusted. According to different resonance degrees, the system can select different levels of suppression measures, which can prevent excessive resonance and avoid resource waste caused by excessive suppression.

[0019] (2) This invention monitors the environment of the transformer substation to determine the seasonal scenarios of the substation and the energy access threshold range of the seasonal scenarios. Different seasons have a significant impact on the power demand of the substation. By monitoring the seasonal scenarios, energy allocation can be adjusted to ensure a more balanced load distribution in the power grid. When the seasonal load changes significantly, a reasonable energy access threshold can ensure that the voltage and frequency of the power grid remain within a stable range, reducing voltage fluctuations and frequency deviations. A reasonable load allocation that adapts to the seasonal scenarios can reduce the generation of harmonics, improve the harmonic distortion problem in the power grid, and improve the overall power quality. By continuously monitoring the environment of the transformer substation and the seasonal scenarios, the power system can dynamically adjust the energy allocation and control strategies according to the actual situation to achieve intelligent energy management. This can not only improve the flexibility of power grid operation, but also respond to unexpected load fluctuations and environmental changes in a timely manner.

[0020] (3) This invention determines the resonance suppression activation level of the energy storage converter by obtaining the switching frequency of the energy storage converter and combining it with the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area. By combining the switching frequency of the energy storage converter with multiple parameters such as load and electrical conditions, it can more accurately determine whether there is a resonance risk, avoid blind or excessive suppression, and adaptively adjust according to different operating states and environmental conditions to ensure that the suppression measures are effective and not excessive. The load conditions, electrical parameters and switching frequency of the energy storage converter in the power grid may fluctuate with time, environment, season, etc. By obtaining and combining these parameters for analysis, it can ensure that the resonance suppression measures can respond quickly to dynamic changes and maintain the flexibility and adaptability of the power grid operation. Attached Figure Description

[0021] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0023] Figure 2 This is a schematic diagram of the device module connection of the present invention.

[0024] Figure 3 This is a flowchart for determining whether the actual energy access status of a transformer substation is qualified according to the present invention. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] Reference Figure 1 As shown, the first aspect of the present invention provides a resonance suppression method for an energy storage converter, comprising: monitoring the environment of the transformer substation, determining the seasonal scenarios of the substation and the threshold range of the energy access status of the seasonal scenarios of the substation.

[0027] The specific analysis process is as follows: First, acquire the environmental dataset of the transformer substation, which includes ambient temperature, humidity, wind speed, and precipitation. Second, based on the acquired dataset, comprehensively analyze and obtain the environmental characteristic values ​​of the substation. These characteristic values ​​serve as the basis for determining the seasonal scenarios of the substation. Third, store the environmental characteristic values ​​as specified labels. Compare these specified labels with the seasonal scenarios corresponding to each specified label stored in the database to obtain the seasonal scenario corresponding to the specified label. Fourth, store the seasonal scenario as a specified code. Compare this code with the energy access status threshold range of each specified code corresponding to the seasonal scenario stored in the database to obtain the energy access status threshold range of the seasonal scenario corresponding to the specified code.

[0028] In a specific embodiment, the ambient temperature refers to the air temperature of the area where the transformer station is located, usually expressed in degrees Celsius (°C). Temperature sensors (such as electronic thermometers, infrared temperature sensors, etc.) are typically used to acquire ambient temperature data and can be installed at key locations in the transformer station to monitor the air temperature in real time. The ambient humidity of the transformer station refers to the moisture content in the air of the area where the transformer station is located, usually expressed in relative humidity (RH), with the unit being percentage (%). Humidity sensors (such as relative humidity sensors, dew point sensors) are used to detect air humidity. The ambient wind speed of the transformer station refers to the air flow speed of the area where the transformer station is located, usually expressed in meters per second (m / s). Wind speed sensors (such as ultrasonic anemometers, mechanical anemometers) are used to monitor ambient wind speed. The ambient precipitation of the transformer station refers to the amount of rainfall in the area where the transformer station is located within a day, usually expressed in millimeters (mm). Rainfall sensors (such as tipping bucket rain gauges, photoelectric rain sensors) are used to measure precipitation.

[0029] It should be explained that the aforementioned high temperatures may exacerbate impedance changes in power equipment, especially power capacitors and inductors, whose impedance characteristics change at high temperatures, potentially making system resonance more pronounced. If excessively high temperatures lead to a mismatch between the energy access status and the actual environmental conditions, the system may need to activate resonance suppression mechanisms to avoid frequency fluctuations or power instability. Increased humidity may degrade the insulation performance of cables and transformers in the power system, thereby affecting electrical parameters, altering system impedance characteristics, and increasing the likelihood of resonance. High wind speeds may increase the output power fluctuations of wind power generation equipment, leading to voltage or frequency fluctuations in the power system. Wind speed regulates the distribution of temperature and humidity, playing a crucial role in cooling and heat dissipation, while also affecting the distribution of precipitation. Precipitation may degrade the insulation performance of cables and equipment, thus triggering impedance changes in the power system and consequently causing resonance.

[0030] It should be explained that the seasonal scene labels generated from the above environmental data (temperature, humidity, wind speed, precipitation) can more accurately predict the energy demand and distribution of the transformer substation. Under different seasons or climatic conditions, the energy consumption pattern of the transformer substation may change significantly. The threshold range after comparison helps to optimize energy supply. The comparison between the seasonal scene of the transformer substation and its energy access status threshold range can ensure that the system always maintains the optimal energy access status under different seasonal environments, thereby improving the overall efficiency of energy access, ensuring the reliability and stability of energy, and adjusting the energy access status according to the changes in seasonal scenes can avoid the equipment from operating under unsuitable conditions, extend the equipment life, reduce the frequency of maintenance and replacement, improve the overall system reliability and operating economy, and help to determine whether the energy access status is qualified and whether resonance suppression of the transformer substation is required.

[0031] It should be explained that the specific analysis process for the environmental characteristic values ​​of the above-mentioned transformer area is as follows:

[0032]

[0033] In the formula, λ is the environmental characteristic value of the area where the transformer station is located, wd is the ambient temperature of the area where the transformer station is located, sd is the ambient humidity of the area where the transformer station is located, fs is the ambient wind speed of the area where the transformer station is located, js is the ambient precipitation of the area where the transformer station is located, μ1 is the set compensation factor for wd, μ2 is the set compensation factor for sd, μ3 is the set compensation factor for fs, μ4 is the set compensation factor for js, and e is the natural constant.

[0034] It should be explained that the environmental characteristic values ​​of the above-mentioned transformer substations are calculated based on the ambient temperature, humidity, wind speed, and precipitation of the substations. Normalization of wd, sd, fs, and js is applied to comprehensively reflect the current environmental conditions of the substations. Calculating these environmental characteristic values ​​allows for accurate assessment of the impact of external climate on the operation of the power system and equipment. For example, high temperatures may cause equipment overheating, and humidity may affect the insulation performance of equipment. Based on these environmental characteristic values, the system can automatically adjust operating parameters according to real-time environmental changes. For instance, under extreme weather conditions, energy storage converters or other power equipment can adjust their operating modes in a timely manner based on the environmental characteristic values ​​to ensure safe operation and optimize performance. Dynamic optimization helps improve the adaptability and reliability of the power system. The calculation of environmental characteristic values ​​provides an important basis for system decision-making, especially for the scheduling and operation management of energy storage systems, power generation equipment, or distribution systems.

[0035] It should be explained that the compensation factors for wd, sd, fs, and js set above are obtained from the database. A mapping set is established based on historical data, which maps the historical measured ambient temperature, humidity, wind speed, and precipitation of the transformer area to the compensation factors for wd, sd, fs, and js. This yields the compensation factors for wd, sd, fs, and js corresponding to the current wd, sd, fs, and js.

[0036] It should be noted that ε1, ε2, ε3, σ1, σ2, σ3, τ1, τ2, and τ3 in the following text are all obtained through the mapping set of historical data and compensation factors established in the database, that is, the corresponding compensation factors are obtained based on the current data.

[0037] like Figure 3 As shown, the actual energy access status of the transformer substation is monitored to determine the evaluation value of the actual energy access status. Combined with the threshold range of the energy access status in the seasonal scenarios of the transformer substation, it is determined whether the actual energy access status of the transformer substation is qualified. If the actual energy access status of the transformer substation is qualified, the environment in which the transformer substation is located will continue to be monitored. If the actual energy access status of the transformer substation is unqualified, resonance suppression of the transformer substation is required.

[0038] The specific analysis process is as follows: First, obtain the actual energy access status dataset for the transformer substation. This dataset includes the following: deviation of the renewable energy access ratio (the absolute value of the difference between the actual renewable energy access ratio and the reference renewable energy access ratio), deviation of the solar power generation ratio (the absolute value of the difference between the actual solar power generation ratio and the reference solar power generation ratio), and deviation of the wind power generation (the absolute value of the difference between the actual wind power generation ratio and the reference wind power generation ratio). Based on the obtained dataset, a comprehensive analysis is performed to obtain the evaluation value of the actual energy access status of the transformer substation. This evaluation value serves as the basis for... The analysis criteria for determining whether the actual energy access status of a transformer substation is qualified include: comparing the assessed value of the actual energy access status of the substation with the threshold range of the seasonal energy access status of the substation; if the assessed value of the actual energy access status of the substation falls within the threshold range of the seasonal energy access status of the substation, then the actual energy access status of the substation corresponding to the assessed value is qualified, and the environment of the substation continues to be monitored; if the assessed value of the actual energy access status of the substation does not fall within the threshold range of the seasonal energy access status of the substation, then the actual energy access status of the substation corresponding to the assessed value is unqualified, and resonance suppression of the substation is required.

[0039] It should be explained that the above-mentioned deviation in the proportion of renewable energy access in the transformer area represents the difference between the actual proportion of renewable energy access and the reference value, whether the difference is positive or negative. It measures the degree of deviation between the actual renewable energy access and the set reference value. The absolute value is used to focus on the magnitude of the deviation, without considering the direction of the deviation. Smart meters can record the renewable energy input and the traditional energy input. The deviation in the proportion of solar power generation in the transformer area represents the magnitude of the deviation between the actual proportion of solar energy access in the transformer area and the reference proportion. It measures whether the solar energy access situation meets expectations and is used to judge whether the current solar energy access is within a reasonable range. Based on the monitoring of photovoltaic inverters, the deviation in the proportion of wind power generation in the transformer area represents the deviation between the actual wind power generation and the reference value. It is used to assess whether the current wind power generation meets expectations. The smaller the deviation, the closer the power generation is to the ideal state. Based on the energy management system (EMS), the actual power generation data from each wind farm or wind turbine is summarized, the actual wind energy access is calculated, and then compared with the reference value.

[0040] It should be explained that the absolute value of the above-mentioned difference in the proportion of new energy access is a general measure, while the absolute values ​​of the difference in the proportion of solar power generation and the difference in the proportion of wind power generation assess the deviation between the power generation and access of different types of new energy. The larger the difference of each individual item, the more serious the deviation between the actual access of that type of new energy and the target. This will affect the difference in the total proportion of new energy access, and can more comprehensively judge the status of new energy access in the entire distribution area. When the difference between the actual new energy access ratio and the reference ratio is large, especially when the access of solar or wind power deviates significantly from the expectation, it may cause power imbalance in the power system. This imbalance may lead to grid voltage fluctuations, frequency changes or other resonance phenomena. Frequent fluctuations in power generation can easily trigger resonance problems in the system, leading to a decline in power quality or equipment malfunctions. After discovering that the new energy access status is unqualified and resonance suppression measures are taken, environmental monitoring of the distribution area is still very important. Continuous monitoring of these external conditions helps to further adjust the energy access strategy and prevent the recurrence of resonance phenomena.

[0041] It should be explained that obtaining the difference between the actual renewable energy access ratio of the distribution area and the reference value allows for more precise management of renewable energy access in different seasons or environments. If the access ratio is too high or too low, it may lead to power system imbalance and affect overall stability. Real-time monitoring and evaluation help to accurately adjust the access volume and ensure that the system is in a qualified state. The changes in solar and wind power generation are greatly affected by environmental factors. Therefore, by comparing the difference between the actual power generation ratio and the reference ratio, it is possible to understand in a timely manner whether the contribution of solar and wind power to the energy in the distribution area meets expectations. Dynamic adjustment helps to better allocate energy resources, avoid over-reliance on a single energy source, and prevent energy shortages or overloads. If the actual energy access status assessment value of the distribution area is not within the threshold range of the seasonal scenario energy access status, it indicates that the current energy access status may have shown signs of instability. This helps to prevent resonance phenomena in the power system, reduce voltage fluctuations and frequency anomalies, and ensure the stability of the power system. A qualified access status can prevent system overload or equipment damage and ensure the safety and reliability of energy supply.

[0042] It should be explained that the specific analysis process for the above-mentioned actual energy access status assessment values ​​for the transformer substations is as follows:

[0043]

[0044] In the formula, γ is the actual energy access status assessment value of the transformer area, bl is the deviation of the new energy access ratio of the transformer area, fd is the deviation of the solar power generation ratio of the transformer area, dl is the deviation of the wind power generation of the transformer area, ε1 is the set compensation factor of bl, ε2 is the set compensation factor of fd, and ε3 is the set compensation factor of dl.

[0045] It should be explained that the above-mentioned actual energy access status assessment value for the transformer area is calculated based on the deviation of the renewable energy access ratio, the deviation of the solar power generation ratio, and the deviation of the wind power generation. Normalization is applied to bl, fd, and dl, and the difference between the actual access ratio of solar and wind power and the reference value is calculated separately. This allows for a more comprehensive assessment of the renewable energy access deviation in the transformer area, helping to understand the difference between the actual contribution and expected contribution of different types of renewable energy (such as solar and wind power) to the power system. This helps identify problems in the access process, accurately assess renewable energy access deviation, and allows the system to better balance load and power supply, preventing oversupply or undersupply. The actual access status assessment value helps the system anticipate potential power supply fluctuations and flexibly adjust energy dispatch strategies to ensure system stability and efficiency. When there is a significant difference between the actual renewable energy access ratio and the reference value, the power system may experience power fluctuations, which can affect power quality. Timely assessment of these differences allows the system to take measures to smooth power fluctuations, ensuring stable power access, thereby improving power quality and reducing the impact of voltage fluctuations or frequency anomalies on equipment.

[0046] The load conditions of transformer substations with substandard actual energy access status are analyzed to obtain characteristic values ​​of the load conditions of the substations.

[0047] The specific analysis process is as follows: obtain the load condition dataset of the transformer area, and based on the obtained load condition dataset, conduct a comprehensive analysis to obtain the characteristic values ​​of the load condition of the transformer area. The characteristic values ​​of the load condition of the transformer area serve as the basis for determining the resonance suppression activation level of the energy storage converter.

[0048] It should be explained that the above analysis of the transformer area load condition data can extract load condition characteristic values, which can reflect the operating status of the transformer area load and its potential impact on the energy storage converter. This helps to determine under what load conditions resonance suppression is more necessary. Based on the load condition characteristic values, the system can accurately determine whether resonance suppression needs to be activated and determine the activation level, avoiding excessive or insufficient suppression measures and ensuring the efficiency and accuracy of the suppression strategy. Determining the activation level of resonance suppression based on the load condition characteristic values ​​can optimize the operating efficiency of the energy storage system. Activating higher-level resonance suppression measures only when necessary can avoid energy waste and maximize the output efficiency of the energy storage system. When the energy storage converter over-activates resonance suppression under unnecessary conditions, it may increase the operating load of the equipment, leading to premature wear. Activating an appropriate level of resonance suppression through load condition characteristic values ​​can reduce the operating pressure of the energy storage converter and reduce the equipment wear rate. Automatically determining the resonance suppression activation level of the energy storage converter based on load condition characteristic values ​​can achieve a high degree of automated management and reduce the risk of manual intervention and misoperation.

[0049] Furthermore, the distribution area load condition dataset specifically includes the ratio of average load to peak load of the distribution area, the total load deviation of the main transmission lines of the distribution area (the absolute value of the difference between the total load of the main transmission lines of the distribution area and the total load of the reference main transmission lines), and the total load deviation of the distribution lines of the distribution area (the absolute value of the difference between the total load of the distribution lines of the distribution area and the total load of the reference distribution lines).

[0050] It should be explained that the ratio of average load to peak load of the above-mentioned transformer area reflects the fluctuation of the transformer area load, which is obtained by using smart meters and load monitoring systems. The total load deviation of the main transmission lines of the transformer area measures the difference between the actual load and the ideal load of the main transmission lines, which is obtained by using line load monitoring equipment and smart grid systems. The total load deviation of the distribution lines of the transformer area assesses the deviation between the actual load and the target load of the distribution lines, which is obtained by using distribution line monitoring equipment and power dispatching systems.

[0051] It should be explained that the load fluctuation of the above-mentioned transformer area is reflected by the peak to average load ratio, which directly affects the load pressure of transmission lines and distribution lines. The greater the load fluctuation, the greater the load difference between transmission lines and distribution lines, and the more difficult it is to guarantee the stability of the system. If the difference between the actual load and the reference load of transmission lines and distribution lines is small, it means that the system load distribution is relatively balanced, and the power network operates more stably and efficiently. When the difference is large, it may lead to unbalanced power transmission and distribution, and the operating efficiency and safety of the system will be affected.

[0052] It should be explained that the specific analysis process for the above-mentioned characteristic values ​​of the transformer area load conditions is as follows:

[0053]

[0054] In the formula, α is the characteristic value of the load condition of the transformer area, bi is the ratio of the average load to the peak load of the transformer area, zx is the total load deviation of the main transmission lines of the transformer area, px is the total load deviation of the distribution lines of the transformer area, σ1 is the set compensation factor of bi, σ2 is the set compensation factor of zx, and σ3 is the set compensation factor of px.

[0055] It should be explained that the aforementioned characteristic values ​​of transformer area load conditions are calculated using the ratio of average load to peak load of the transformer area, the total load deviation of the main transmission lines of the transformer area, and the total load deviation of the distribution lines of the transformer area. Normalization is applied to bi, zx, and px. By calculating the ratio of average load to peak load, the system can quantify the volatility of the transformer area load and identify the difference between peak and average demand during load operation. The larger the ratio, the more stable the load; the smaller the ratio, the greater the load volatility. This provides an important reference for the system's load stability. Assessing load volatility helps identify unbalanced or drastically fluctuating areas in the system, thereby optimizing load management and preventing frequent load changes from affecting system stability and equipment lifespan. Through the calculation of these differences, uneven load distribution can be detected in a timely manner, potential risks in the transmission and distribution network can be identified, and damage to the system caused by excessive or insufficient load can be prevented, thus improving system security.

[0056] The electrical conditions of transformer substations with substandard actual energy access status are analyzed to obtain characteristic values ​​of the electrical conditions of the substations.

[0057] The specific analysis process is as follows: obtain the electrical condition dataset of the transformer area, and based on the obtained electrical condition dataset, conduct a comprehensive analysis to obtain the characteristic values ​​of the electrical conditions of the transformer area. The characteristic values ​​of the electrical conditions of the transformer area serve as the basis for determining the resonance suppression activation level of the energy storage converter.

[0058] It should be explained that the above electrical condition characteristic values ​​reflect the electrical operating environment of the distribution area. Analyzing these characteristic values ​​can determine whether the current power grid state is prone to resonance, thereby accurately determining whether resonance suppression needs to be activated, and the specific level of activation. Electrical conditions in the distribution area (such as voltage fluctuations, frequency anomalies, etc.) may cause instability in the power grid, leading to resonance. If the electrical condition characteristic values ​​exceed the safe range, the system can activate an appropriate level of resonance suppression in a timely manner to avoid system oscillation and instability caused by abnormal electrical conditions. By comprehensively analyzing the electrical conditions of the distribution area, the operating mode of the energy storage converter can be dynamically adjusted according to actual needs. If the electrical conditions indicate that the system is in a relatively stable state, the energy storage converter can operate in a low-power mode to reduce unnecessary energy consumption.

[0059] Furthermore, the electrical condition dataset for the transformer substation specifically includes the voltage fluctuation frequency, power factor, and total harmonic distortion of the substation.

[0060] It should be explained that the voltage fluctuation frequency mentioned above refers to the frequency of voltage change in the power grid over time. Voltage fluctuations are usually caused by load changes, switching of power factor compensation equipment, etc. Frequent voltage fluctuations can affect the stable operation of equipment and even lead to power system failures. Based on power quality monitoring instruments, the power factor is the ratio of active power to apparent power in the power grid, representing the proportion of actually effectively utilized power in the total input power. The power factor ranges from 0 to 1. A value close to 1 indicates that the power grid is more efficient and less wasteful of electricity. A power factor that is too low means that there is a lot of reactive power in the system, which may lead to a decrease in power transmission efficiency. Based on power factor tables, the total harmonic distortion refers to the magnitude of harmonic content in the power grid. It is the ratio of the sum of all harmonics to the fundamental component, usually expressed as a percentage. Based on harmonic analyzers.

[0061] It should be explained that high harmonic content leads to a lower power factor, while a low power factor exacerbates the distortion of the system's current waveform, further increasing the harmonic content. The presence of harmonics also causes voltage fluctuations, making the system more unstable. The increase in voltage fluctuation frequency affects the control of the power factor and may amplify the impact of harmonics in the system, thereby further deteriorating the system's power quality. When the voltage fluctuation frequency increases, the reactive power demand in the system changes significantly, making it difficult to maintain the power factor at an ideal level, and the impact of harmonics becomes more significant. Low power factor and high harmonic content lead to more frequent voltage fluctuations, forming a vicious cycle that further reduces the system's stability and power quality.

[0062] It should be explained that the specific analysis process for the above-mentioned electrical condition characteristic values ​​of the transformer substation is as follows:

[0063]

[0064] In the formula, β is the characteristic value of the electrical conditions of the transformer area, bdp is the voltage fluctuation frequency of the transformer area, ys is the power factor of the transformer area, jb is the total harmonic distortion of the transformer area, τ1 is the set compensation factor of bdp, τ2 is the set compensation factor of ys, and τ3 is the set compensation factor of jb.

[0065] It should be explained that the aforementioned electrical condition characteristic values ​​of the transformer substation are calculated using the voltage fluctuation frequency, power factor, and total harmonic distortion (THD) of the substation. Normalization of bdp, ys, and jb is performed. Analyzing the voltage fluctuation frequency, power factor, and THD provides a more comprehensive reflection of the operating status of the power system in the substation, covering multiple aspects of voltage stability, system energy efficiency, and power quality. This helps to understand the overall health of the power system. The voltage fluctuation frequency reflects voltage stability; frequent voltage fluctuations may affect the operating efficiency and lifespan of power equipment. By calculating the voltage fluctuation frequency, the system can adopt more effective voltage regulation strategies to maintain voltage stability and ensure the safe and reliable operation of equipment. THD and power factor variations may cause resonance phenomena in the system, affecting the stability of the power system. Through the electrical condition characteristic values, the system can predict resonance risks in advance and take corresponding suppression measures to avoid grid faults or equipment damage caused by resonance.

[0066] The switching frequency of the energy storage converter is obtained, and combined with the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area, the resonance suppression activation level of the energy storage converter is determined.

[0067] The specific analysis process is as follows: The switching frequency of the energy storage converter is obtained, and the switching frequency deviation of the energy storage converter (the absolute value of the difference between the switching frequency of the energy storage converter and the reference switching frequency) is determined. The switching frequency deviation of the energy storage converter, the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area are imported into the resonance suppression matching model. A comprehensive analysis is performed to obtain the resonance suppression matching characteristic value, which serves as the basis for determining the resonance suppression activation level of the energy storage converter. If the resonance suppression matching characteristic value is not lower than the first resonance suppression matching threshold stored in the database, the resonance suppression activation level of the energy storage converter is level three. If the resonance suppression matching characteristic value is lower than the first resonance suppression matching threshold stored in the database, but not lower than the second resonance suppression matching threshold stored in the database, the resonance suppression activation level of the energy storage converter is level two. If the resonance suppression matching characteristic value is lower than the second resonance suppression matching threshold stored in the database, the resonance suppression activation level of the energy storage converter is level one.

[0068] It should be explained that the resonance suppression matching characteristic value derived from the above comprehensive analysis based on the difference between the switching frequency and the reference frequency of the energy storage converter, the actual operating conditions and load of the distribution area, and electrical characteristics can better reflect the current operating status and resonance risk of the system. The dynamic matching model can adjust the resonance suppression activation level according to the real-time situation, enabling the system to adapt to different load changes and grid environments, effectively cope with resonance phenomena in the system, and avoid excessive or insufficient suppression operations. Using the resonance suppression matching characteristic value as the basis for determining the resonance suppression activation level of the energy storage converter, different suppression levels can be accurately set according to the actual needs of the system. Based on hierarchical management, the system can activate resonance suppression measures of different intensities under different operating conditions, ensuring stronger suppression when the resonance risk is high and reducing energy consumption when the risk is low. Resonance phenomena affect the power quality of the system and reduce the efficiency and stability of the power system. By acquiring data such as the switching frequency of the energy storage converter and the energy access status of the distribution area in real time and importing it into the resonance suppression matching model, the system can accurately predict and suppress resonance phenomena, preventing problems such as voltage fluctuations and frequency anomalies in the power grid. Intelligent resonance suppression management improves the automation level of the system, reduces manual intervention, and enhances the system response speed and flexibility.

[0069] Furthermore, the resonance suppression matching model is analyzed in detail as follows:

[0070]

[0071] In the formula, ω is the resonance suppression matching characteristic value, δ is the switching frequency deviation of the energy storage converter, γ is the actual energy access status assessment value of the distribution area, α is the load condition characteristic value of the distribution area, and β is the electrical condition characteristic value of the distribution area.

[0072] It should be explained that the aforementioned resonance suppression matching characteristic value is calculated based on the switching frequency deviation of the energy storage converter, the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area. Through comprehensive analysis of the switching frequency of the energy storage converter, the energy access status, the load conditions, and the electrical conditions, resonance risks can be identified more accurately. This comprehensive analysis results in the resonance suppression matching characteristic value, which helps to accurately determine whether resonance suppression needs to be activated and to determine the appropriate suppression level (Level 1, Level 2, or Level 3). The resonance suppression matching characteristic value is dynamically adjusted based on real-time data changes of the energy storage converter, load, energy access, and electrical conditions. This ensures that a suitable suppression strategy can be selected under different system states (such as load fluctuations and power quality changes), enabling the system to dynamically adjust according to the actual situation. This ensures the efficiency and flexibility of resonance suppression measures. The resonance suppression matching characteristic value provides the basis for automatic system decision-making, enabling intelligent adjustment based on real-time conditions without human intervention. This intelligent resonance suppression management reduces errors in human judgment and improves the system's adaptability.

[0073] In a specific embodiment, the ambient temperature of the transformer area is 30℃, the ambient humidity is 70%, the ambient wind speed is 5m / s, the ambient precipitation is 10mm, μ1=0.005, μ2=0.02, μ3=0.1, μ4=0.01, e=2.718, and the calculated environmental characteristic value of the transformer area is 3.3006. The seasonal scenario of this transformer area is spring with a small amount of precipitation, and the energy access status threshold range is [-10, 10].

[0074] The monitoring showed that the difference between the actual wind power generation and the reference value in this transformer area was 50kW, the difference between the actual solar power generation ratio and the reference value was 6%, and the difference between the actual new energy access ratio and the reference value was 10%. ε1=0.5, ε2=0.3, ε3=0.4. The calculated energy access status assessment value was -19.249, which did not fall within the seasonal scenario energy access status threshold range for the transformer area. Therefore, the actual energy access status of the transformer area corresponding to the actual energy access status assessment value was unqualified, and resonance suppression of the transformer area was required.

[0075] The ratio of average load to peak load in the distribution area is 0.6. The absolute value of the difference between the total load of the main transmission lines in the distribution area and the reference value is 200kW. The absolute value of the difference between the total load of the distribution lines in the distribution area and the reference value is 150kW. σ1=0.1, σ2=0.05, σ3=0.07. The calculated characteristic value of the load condition is 0.06238.

[0076] The voltage fluctuation frequency of the transformer area is 0.05Hz, the power factor of the transformer area is 0.92, the total harmonic distortion of the transformer area is 3%, τ1=0.1, τ2=0.02, τ3=0.05, and the calculated characteristic value of the electrical conditions is 0.15005.

[0077] The calculated resonance suppression matching characteristic value is 0.0452, the first threshold for resonance suppression matching is 0.3, and the second threshold for resonance suppression matching is 0.03. Since 0.0452 is greater than 0.03 but less than 0.3, the resonance suppression activation level of the energy storage converter is level two. This avoids the equipment from over-activating resonance suppression when it is not necessary, reduces equipment wear, and extends service life.

[0078] Reference Figure 2 As shown, the second aspect of the present invention provides a resonance suppression device for an energy storage converter, a transformer area seasonal scenario determination module, an actual energy access status qualification judgment module, a load condition characteristic value acquisition module, an electrical condition characteristic value acquisition module, and a resonance suppression activation level determination module.

[0079] The seasonal scenario determination module for transformer substations is used to monitor the environment in which the transformer substation is located, determine the seasonal scenario of the transformer substation and the threshold range of energy access status for the seasonal scenario of the transformer substation.

[0080] The actual energy access status qualification judgment module is used to monitor the actual energy access status of the transformer area, determine the evaluation value of the actual energy access status of the transformer area, and judge whether the actual energy access status of the transformer area is qualified by combining the seasonal scenario energy access status threshold range of the transformer area: if the actual energy access status of the transformer area is qualified, the environment in which the transformer area is located will continue to be monitored; if the actual energy access status of the transformer area is unqualified, resonance suppression of the transformer area is required.

[0081] The load condition characteristic value acquisition module is used to analyze the load conditions of transformer areas with unqualified actual energy access status and obtain the load condition characteristic values ​​of the transformer area.

[0082] The electrical condition characteristic value acquisition module is used to analyze the electrical conditions of transformer substations that are not up to standard in terms of actual energy access status, and to obtain the electrical condition characteristic values ​​of the transformer substations.

[0083] The resonance suppression activation level determination module is used to obtain the switching frequency of the energy storage converter and, in conjunction with the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area, determine the resonance suppression activation level of the energy storage converter.

[0084] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A resonance suppression method for energy storage converters, characterized in that, Includes the following steps: Monitor the environment of the transformer substation to determine the seasonal scenarios of the substation and the threshold range of energy access status for the seasonal scenarios of the substation. Monitor the actual energy access status of the transformer substation, determine the assessment value of the actual energy access status of the substation, and combine it with the threshold range of energy access status for seasonal scenarios in the substation to determine whether the actual energy access status of the substation is qualified: If the actual energy access status of the transformer area is qualified, the environment in which the transformer area is located will continue to be monitored. If the actual energy access status of the transformer area is not up to standard, resonance suppression of the transformer area is required. The load conditions of transformer substations with substandard actual energy access status are analyzed to obtain characteristic values ​​of the load conditions of the transformer substations. The electrical conditions of transformer substations with substandard actual energy access status are analyzed to obtain characteristic values ​​of the electrical conditions of the substations. The switching frequency of the energy storage converter is obtained, and combined with the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area, the resonance suppression activation level of the energy storage converter is determined.

2. The resonance suppression method for the energy storage converter according to claim 1, characterized in that: The monitoring of the environment of the transformer substation, determining the seasonal scenarios of the substation and the threshold range of energy access status for the seasonal scenarios, is specifically analyzed as follows: Obtain the environmental dataset of the transformer area, which specifically includes the environmental temperature, humidity, wind speed, and precipitation of the transformer area. Based on the acquired environmental dataset of the transformer substation, the environmental characteristic values ​​of the transformer substation are obtained through comprehensive analysis. These environmental characteristic values ​​serve as the basis for determining the seasonal scenarios of the transformer substation. The environmental characteristic value of the transformer area is stored as a specified label. The specified label is compared with the seasonal scene of the transformer area corresponding to each specified label stored in the database to obtain the seasonal scene of the transformer area corresponding to the specified label. The seasonal scenario of the transformer area is stored as a specified code. The specified code is then compared with the energy access status threshold range of the seasonal scenario of the transformer area corresponding to each specified code stored in the database to obtain the energy access status threshold range of the seasonal scenario of the transformer area corresponding to the specified code.

3. The resonance suppression method for the energy storage converter according to claim 1, characterized in that: The specific analysis process for determining whether the actual energy access status of the transformer area is qualified is as follows: Obtain the actual energy access status dataset of the transformer area. The actual energy access status dataset of the transformer area includes the deviation of the proportion of new energy access in the transformer area, the deviation of the proportion of solar power generation in the transformer area, and the deviation of wind power generation in the transformer area. Based on the acquired dataset of actual energy access status of the transformer substations, a comprehensive analysis is conducted to obtain the evaluation value of the actual energy access status of the transformer substations. The evaluation value of the actual energy access status of the transformer substations serves as the basis for determining whether the actual energy access status of the transformer substations is qualified. Compare the actual energy access status assessment value of the transformer area with the threshold range of energy access status in seasonal scenarios of the transformer area; If the actual energy access status assessment value of the transformer substation falls within the threshold range of the seasonal scenario energy access status of the transformer substation, then the actual energy access status of the transformer substation corresponding to the actual energy access status assessment value is qualified, and the environment of the transformer substation will continue to be monitored. If the actual energy access status assessment value of the transformer substation does not fall within the seasonal scenario energy access status threshold range of the transformer substation, then the actual energy access status of the transformer substation corresponding to the actual energy access status assessment value is unqualified, and resonance suppression of the transformer substation is required.

4. The resonance suppression method for the energy storage converter according to claim 1, characterized in that: The analysis of load conditions in transformer substations with substandard actual energy access status is as follows: A dataset of transformer area load conditions is obtained. Based on the obtained dataset, the characteristic values ​​of transformer area load conditions are obtained through comprehensive analysis. These characteristic values ​​serve as the basis for determining the resonance suppression activation level of the energy storage converter.

5. The resonance suppression method for the energy storage converter according to claim 4, characterized in that: The data set of load conditions for the transformer area specifically includes the ratio of average load to peak load of the transformer area, the total load deviation of the main transmission lines of the transformer area, and the total load deviation of the distribution lines of the transformer area.

6. The resonance suppression method for the energy storage converter according to claim 1, characterized in that: The analysis of the electrical conditions of transformer substations with substandard actual energy access status is as follows: A dataset of electrical conditions for the transformer substations is obtained. Based on this dataset, a comprehensive analysis is conducted to obtain characteristic values ​​of the electrical conditions for the transformer substations. These characteristic values ​​serve as the basis for determining the resonance suppression activation level of the energy storage converter.

7. The resonance suppression method for the energy storage converter according to claim 6, characterized in that: The electrical condition dataset for the transformer substation specifically includes the voltage fluctuation frequency, power factor, and total harmonic distortion of the substation.

8. The resonance suppression method for the energy storage converter according to claim 1, characterized in that: The specific analysis process for determining the resonance suppression activation level of the energy storage converter is as follows: Obtain the switching frequency of the energy storage converter and determine the switching frequency deviation of the energy storage converter; The switching frequency deviation of the energy storage converter, the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area are imported into the resonance suppression matching model. The resonance suppression matching characteristic value is obtained through comprehensive analysis and serves as the basis for determining the resonance suppression activation level of the energy storage converter. If the resonance suppression matching characteristic value is not lower than the resonance suppression matching first threshold stored in the database, then the resonance suppression activation level of the energy storage converter is level three; If the resonance suppression matching characteristic value is lower than the first threshold of resonance suppression matching stored in the database, but not lower than the second threshold of resonance suppression matching stored in the database, then the resonance suppression activation level of the energy storage converter is level two. If the resonance suppression matching characteristic value is lower than the second threshold for resonance suppression matching stored in the database, the resonance suppression activation level of the energy storage converter is level one.

9. The resonance suppression method for the energy storage converter according to claim 8, characterized in that: The specific analysis process of the resonance suppression matching model is as follows: In the formula, ω is the resonance suppression matching characteristic value, δ is the switching frequency deviation of the energy storage converter, γ is the actual energy access status assessment value of the distribution area, α is the load condition characteristic value of the distribution area, and β is the electrical condition characteristic value of the distribution area.

10. A resonance suppression device for an energy storage converter, applied to the resonance suppression method for an energy storage converter as described in any one of claims 1-9, characterized in that, This includes a module for determining seasonal scenarios in the transformer area, a module for judging the qualification of actual energy access status, a module for obtaining load condition characteristic values, a module for obtaining electrical condition characteristic values, and a module for determining the activation level of resonance suppression. The transformer area seasonal scenario determination module is used to monitor the environment where the transformer area is located, determine the seasonal scenario of the transformer area and the threshold range of the energy access status of the seasonal scenario of the transformer area. The actual energy access status qualification judgment module is used to monitor the actual energy access status of the transformer substation, determine the evaluation value of the actual energy access status of the transformer substation, and, in conjunction with the seasonal scenario energy access status threshold range of the transformer substation, determine whether the actual energy access status of the transformer substation is qualified. If the actual energy access status of the transformer area is qualified, the environment in which the transformer area is located will continue to be monitored. If the actual energy access status of the transformer area is not up to standard, resonance suppression of the transformer area is required. The load condition characteristic value acquisition module is used to analyze the load conditions of transformer areas with unqualified actual energy access status and obtain the load condition characteristic values ​​of the transformer areas. The electrical condition characteristic value acquisition module is used to analyze the electrical conditions of transformer substations with unqualified actual energy access status and obtain the electrical condition characteristic values ​​of the transformer substations. The resonance suppression activation level determination module is used to obtain the switching frequency of the energy storage converter and, in conjunction with the actual energy access status assessment value of the distribution area, the load condition characteristic value of the distribution area, and the electrical condition characteristic value of the distribution area, determine the resonance suppression activation level of the energy storage converter.

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