Intelligent park energy data management and control system based on big data

Abstract

The application discloses an intelligent park energy data management and control system based on big data, relates to the technical field of park energy management, and comprises a first acquisition module used for acquiring first power data and first temperature data of a power supply device; a model processing module used for inputting the first power data and the first temperature data into a first model for processing to obtain temperature sequence data and first integral data of the power supply device; an upper limit determination module used for determining first capacity upper limit data of an electric energy quality compensation device connected with the power supply device; a second acquisition module used for calculating first change rate data; a dynamic adjustment module used for obtaining second capacity upper limit data of the electric energy quality compensation device; and a control execution module used for controlling the electric energy quality compensation device to perform electric energy compensation according to the second capacity upper limit data.

Description

Technical Field

[0001] This invention relates to the technical field of smart park energy management, specifically a smart park energy data management and control system based on big data. Background Technology

[0002] In a smart park containing multiple industrial plants, the large-scale connection of nonlinear loads such as variable frequency speed control equipment, uninterruptible power supplies and charging piles causes the total harmonic distortion rate of the current at the transformer input line to continuously exceed the standard. Existing solutions typically deploy active power filters (APFs) to detect harmonic currents in real time and inject reverse compensation currents to cancel them out. The operating logic of this solution takes THDi compliance as its sole objective and does not acquire the current temperature status of the transformer.

[0003] During the high-temperature period in summer or under continuous high-load operation, the temperature of the transformer windings is close to the heat resistance limit of the insulation material. At this time, the high-frequency switching ripple superimposed in the compensation current injected by the APF to suppress harmonics will induce skin effect and hysteresis loss in the transformer core, causing the top oil temperature to rise further on the basis of the existing heat accumulation. When this heat accumulation process is superimposed on the increase in fundamental copper loss caused by the rapid rise in load, the winding temperature may exceed the gas relay tripping threshold within a few minutes, triggering a trip of the entire plant. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A smart park energy data management and control system based on big data includes: The first acquisition module is used to acquire the first power data and the first temperature data of the power supply equipment; The model processing module is used to input the first power data and the first temperature data into the first model for processing, so as to obtain the temperature sequence data and the first integral data of the power supply equipment. The upper limit determination module is used to determine the first capacity upper limit data of the power quality compensation equipment connected to the power supply equipment based on the temperature sequence data and the first integral data. The second acquisition module is used to acquire the second power data of the power supply equipment and calculate the first rate of change data based on the second power data; The dynamic adjustment module is used to adjust the first capacity upper limit data according to the first rate of change data to obtain the second capacity upper limit data of the power quality compensation device. The control execution module is used to control the power quality compensation device to perform power compensation based on the second capacity upper limit data.

[0005] As a preferred embodiment of the present invention, the upper limit determination module includes: The temperature comparison submodule is used to acquire each sub-temperature data in the temperature sequence data, and compare each sub-temperature data with a first temperature threshold and a second temperature threshold, wherein the second temperature threshold is greater than the first temperature threshold. The first capacity determination submodule is used to determine the rated capacity data of the power quality compensation device as the first capacity upper limit data when the sub-temperature data is less than the first temperature threshold. The second capacity determination submodule is used to adjust the rated capacity data according to the first ratio data when the sub-temperature data is greater than or equal to the first temperature threshold and less than the second temperature threshold, so as to obtain the first capacity upper limit data. The third capacity determination submodule is used to adjust the rated capacity data according to the second ratio data when the sub-temperature data is greater than or equal to the second temperature threshold, so as to obtain the first capacity upper limit data.

[0006] As a preferred embodiment of the present invention, the upper limit determination module further includes: The integral increment determination submodule is used to determine, based on the first integral data, the first integral increment data corresponding to the target time period and the second integral data corresponding to the target period when the sub-temperature data is less than the first temperature threshold. The ratio determination submodule is used to determine the third ratio data based on the first integral increment data and the second integral data; The fourth capacity determination submodule is used to adjust the rated capacity data according to the fourth ratio data when the third ratio data is greater than the preset ratio threshold, so as to obtain the first capacity upper limit data.

[0007] As a preferred embodiment of the present invention, the model processing module is specifically used for: Based on the first power data and the first temperature data, the sub-temperature data corresponding to each time step data is determined by the first model to obtain the temperature sequence data; The first difference data is determined based on the sub-temperature data and the preset starting temperature data; If the first difference data is greater than zero, the single-step integral data is obtained based on the first difference data and the corresponding time step data. The first integral data is obtained by summing the single-step integral data corresponding to the multiple time step data.

[0008] As a preferred embodiment of the present invention, the dynamic adjustment module is specifically used for: When the first rate of change data is positive and the first rate of change data is greater than the first rate of change threshold, the first step length data is determined based on the second difference data between the first rate of change data and the first rate of change threshold. The first capacity limit data is adjusted downward based on the first step length data to obtain the second capacity limit data.

[0009] As a preferred embodiment of the present invention, the dynamic adjustment module is further specifically used for: When the first rate of change data is negative and the first rate of change data is less than the second rate of change threshold, the second step size data is determined based on the third difference data between the first rate of change data and the second rate of change threshold. The first capacity limit data is adjusted upward based on the second step size data to obtain the second capacity limit data. Wherein, the second upper limit of capacity data is not greater than the rated capacity data of the power quality compensation equipment.

[0010] As a preferred embodiment of the present invention, the system further includes a model update module, which is used for: Obtain the second temperature data of the power supply equipment; Based on the first power data and the first temperature data, the third temperature data is determined using the first model; Determine temperature deviation sequence data based on the second temperature data and the third temperature data; The first parameter data and the second parameter data in the first model are updated based on the temperature deviation sequence data.

[0011] As a preferred embodiment of the present invention, the system further includes a frequency control module, which is used for: Acquire the first current data and the second current data of the power supply equipment; The coefficient data of the power supply equipment are determined based on the first current data, the second current data, and the corresponding frequency data. If the coefficient data is greater than the preset coefficient threshold and the second capacity upper limit data is less than the rated capacity data of the power quality compensation device, the third step size data of the power quality compensation device is determined. The frequency data of the power quality compensation device is adjusted downward based on the third step length data, wherein the adjusted frequency data is not lower than the preset lower limit frequency data.

[0012] As a preferred embodiment of the present invention, when the frequency control module determines the coefficient data of the power supply equipment based on the first current data, the second current data, and the corresponding frequency count data, it is specifically used for: The percentage data is obtained by calculating based on the first current data and the second current data; The weighted coefficient data is obtained by processing the percentage data and the corresponding frequency data. The weighted coefficient data corresponding to each frequency count data is summed to obtain the coefficient data.

[0013] As a preferred embodiment of the present invention, the system further includes a data preprocessing module, which is used for: Obtain historical power data and feature data of the power supply equipment; Based on the historical power data, the feature data, and the first temperature data, the first power data is obtained by processing the data using a second model. The first power data acquired by the first acquisition module is the first power data obtained by the data preprocessing module through the second model.

[0014] The beneficial effects of this invention are: 1. This application obtains the first power data and first temperature data of the power supply equipment through the first acquisition module, and the model processing module processes the data based on the first model to obtain temperature sequence data and first integral data. This provides a reference condition for the transformer thermal accumulation state for the decision on APF compensation capacity, thereby playing the role of judging the transformer thermal safety margin before APF compensation. This solves the problem that the existing scheme continues to output compensation current under a high thermal accumulation state without considering the current temperature of the transformer, which leads to a conflict between the governance behavior and thermal safety requirements. This achieves the effect of keeping the APF compensation intensity within the allowable range of the current thermal capacity of the transformer.

[0015] 2. This application uses the upper limit determination module to determine the first capacity upper limit data based on the temperature sequence data and the first integral data. The dynamic adjustment module then adjusts the first capacity upper limit data downward when the first rate of change data is positive and exceeds the first rate of change threshold. This effectively reduces the compensation intensity before the load increases, solving the problem in existing solutions where the APF compensation behavior is unrelated to load changes, and the superposition of fundamental wave heating and additional compensation heating during a sudden load increase causes the winding temperature to exceed the tripping threshold in a short time. This achieves the effect of keeping the winding temperature below the gas relay action threshold during a sudden load increase. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall structure of the intelligent park energy data management and control system of the present invention; Figure 2 This is a schematic diagram of the transient response coordinated control of power supply equipment and power quality compensation equipment. Detailed Implementation

[0017] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0018] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0019] like Figure 1 As shown, the smart park energy data management and control system based on big data in this application includes a comprehensive industrial park power distribution network with multiple industrial plants and supporting facilities. Nonlinear loads such as frequency conversion speed control equipment, uninterruptible power supply, and electric vehicle charging piles are connected to the power distribution network, which causes the total harmonic distortion rate of the current at the transformer inlet to continuously exceed the standard.

[0020] This system involves three types of hardware devices: power quality monitoring terminals deployed on the low-voltage side of the transformer, temperature sensing units and park ambient temperature sensors deployed on the transformer body, and active power filter controllers connected to the low-voltage side busbar of the transformer.

[0021] The aforementioned equipment forms a bidirectional connection with the big data server deployed in the park's monitoring center via industrial Ethernet or fieldbus. The server runs various functional modules of this system, generates control commands based on monitoring data, and sends them to the APF controller via a downlink.

[0022] The first acquisition module is used to acquire the first power data and the first temperature data of the power supply equipment. The power supply equipment is the main step-down transformer or distribution transformer in the park. The first power data is the active power sampling value of the low-voltage side of the transformer, and the first temperature data is the ambient temperature of the transformer installation location. Both are collected by the power quality monitoring terminal and the ambient temperature sensor, respectively, and the sampling time is kept synchronized.

[0023] The model processing module inputs the first power data and the first temperature data into the first model for processing, and obtains the temperature sequence data and the first integral data of the power supply equipment. The first model is a transformer thermal circuit model constructed based on thermoelectric analogy theory. It includes a sub-model describing the thermal dynamic change of the top oil temperature relative to the ambient temperature and a sub-model describing the thermal dynamic change of the winding hot spot temperature relative to the top oil temperature. It is pre-stored in the server and configured with adjustable thermal resistance and thermal capacity parameters.

[0024] The temperature sequence data is the sequence of winding hot spot temperature estimates calculated hourly by the first model within a preset time range. The first integral data is the total heat accumulation integral calculated based on the temperature sequence. Its function is to quantify the amount of heat damage accumulated by the transformer insulation system within the time range.

[0025] The upper limit determination module determines the first capacity upper limit data of the power quality compensation device connected to the power supply equipment based on the temperature sequence data and the first integral data. The power quality compensation device is the APF connected to the low-voltage side bus of the transformer. The first capacity upper limit data represents the maximum compensation output capacity allowed by the APF in a specific period within a preset time range, based on the rated capacity.

[0026] The upper limit determination module compares the estimated hot spot temperature values ​​for each time period with the preset temperature threshold to assess the thermal safety margin. It sets a higher capacity upper limit for time periods with sufficient margin, and sets a reduced capacity upper limit for time periods with narrow or insufficient margin. It can also reduce the capacity limit in advance for time periods where the total heat accumulation of the whole day has been largely consumed. The generated capacity upper limits for each time period are stored in the server cache in the form of a daily scheduling plan table.

[0027] The second acquisition module acquires the second power data of the power supply equipment and calculates the first rate of change data based on the second power data. The second power data and the first power data have the same source but are sampled values ​​at the current moment acquired during the real-time operation phase. The first rate of change data is the derivative of the second power data with respect to time, representing the current rate and direction of change of the load power.

[0028] The dynamic adjustment module adjusts the first capacity upper limit data based on the first rate of change data to obtain the second capacity upper limit data for the power quality compensation equipment. When the first rate of change data is positive and its absolute value exceeds the preset threshold, it indicates that the load is increasing rapidly. The dynamic adjustment module further temporarily lowers the compensation capacity upper limit based on the first capacity upper limit data to release heat capacity buffers for the upcoming load temperature rise. When the first rate of change data is negative and its absolute value exceeds the preset threshold, it indicates that the load is decreasing rapidly. The dynamic adjustment module temporarily raises the compensation capacity upper limit to utilize the heat capacity surplus window to increase the treatment depth, but the raised limit does not exceed the rated capacity. When the absolute value of the rate of change is lower than the threshold, the original baseline remains unchanged.

[0029] The control execution module controls the power quality compensation equipment to perform power compensation based on the second capacity upper limit data. This module converts the second capacity upper limit data into parameter commands that the APF controller can recognize and sends them out through the downlink communication link. When the capacity upper limit is lower than the rated capacity, the output of the APF inverter is limited, and the compensation behavior is restricted to the heat dissipation capacity boundary allowed by the current thermal state of the transformer. At the same time, this module receives the actual operating status data returned by the APF through the uplink to realize the monitoring of the operating status and the identification of anomalies.

[0030] Furthermore, the upper limit determination module includes a temperature comparison submodule, a first capacity determination submodule, a second capacity determination submodule, and a third capacity determination submodule.

[0031] The temperature comparison submodule acquires each sub-temperature data in the temperature sequence data and compares each sub-temperature data with a first temperature threshold and a second temperature threshold, wherein the second temperature threshold is greater than the first temperature threshold.

[0032] The temperature sequence data is a time series of hot spot temperature values ​​of the power supply equipment windings, which are calculated hourly by the model processing module through the first model. Each sub-temperature data refers to the calculated temperature value of a single hot spot in the sequence that corresponds one-to-one with each time step.

[0033] Both the first temperature threshold and the second temperature threshold are preset temperature limits. In a preferred embodiment of this application, for an oil-immersed transformer using Class B insulation material, the first temperature threshold is set to 90°C to 100°C, and the second temperature threshold is set to 110°C to 120°C.

[0034] The first temperature threshold is the first temperature boundary value. When the temperature is below this value, the transformer's heat capacity is sufficient and there is no need to limit the compensation intensity of the APF. The second temperature threshold is higher than the first temperature threshold and is the second temperature boundary value. When the temperature is above this value, the transformer's heat capacity is insufficient and the compensation intensity of the APF needs to be limited.

[0035] The above thresholds are selected based on the following: When the winding hot spot temperature is below 90℃ to 100℃, the aging rate of the insulation material is at a low level, and the transformer has sufficient heat capacity to withstand the additional heat generated by the power quality compensation equipment. When the temperature enters the 110℃ to 120℃ range, the thermal aging rate of the insulation material has accelerated, and the additional heat source needs to be restricted.

[0036] The temperature comparison submodule performs the above comparison operation on each sub-temperature data one by one, and marks the position state of each sub-temperature data relative to the first temperature threshold and the second temperature threshold. The state marking result serves as the trigger condition for the subsequent three capacity determination submodules.

[0037] When the sub-temperature data is less than the first temperature threshold, the first capacity determination submodule determines the rated capacity data of the power quality compensation equipment as the first capacity upper limit data. When the temperature comparison submodule determines that the sub-temperature data of a certain time step is less than the first temperature threshold, it indicates that the winding hot spot temperature of the power supply equipment is in a safe range and the thermal safety margin is sufficient. At this time, the first capacity determination submodule directly determines the rated capacity data of the power quality compensation equipment as the first capacity upper limit data for that time period, that is, without imposing any derating restrictions, the power quality compensation equipment is allowed to perform harmonic compensation at its maximum design capacity.

[0038] When the sub-temperature data is greater than or equal to the first temperature threshold and less than the second temperature threshold, the second capacity determination submodule adjusts the rated capacity data according to the first ratio data to obtain the first capacity upper limit data.

[0039] When the temperature comparison submodule determines that the sub-temperature data of a certain time step is between the first temperature threshold and the second temperature threshold, it indicates that the winding hot spot temperature of the power supply equipment has entered the warning range during that period, and the thermal safety margin tends to narrow. If the power quality compensation equipment still compensates at full capacity at this time, the high-frequency switching ripple superimposed by its compensation current will cause the winding hot spot temperature to rise further toward the second temperature threshold.

[0040] The first percentage data is a percentage value that is less than 100% and greater than 0. In a preferred embodiment of this application, it is set to 70% to 80%. The determination of this range is based on the fact that when the compensation capacity of the power quality compensation equipment is limited to 70% to 80% of the rated capacity, the high-frequency switching ripple component in its compensation current is reduced proportionally, the high-frequency additional loss of the transformer core is reduced, and the residual compensation capacity can still suppress most of the characteristic subharmonics. The first capacity upper limit data is the product of the rated capacity data and the first percentage data.

[0041] When the sub-temperature data is greater than or equal to the second temperature threshold, the third capacity determination submodule adjusts the rated capacity data according to the second proportional data to obtain the first capacity upper limit data.

[0042] When the temperature comparison submodule determines that the sub-temperature data of a certain time step is greater than or equal to the second temperature threshold, it indicates that the winding hot spot temperature of the power supply equipment has entered the critical range during that period, and the thermal aging rate of the insulation material has been accelerated. If the power quality compensation equipment continues to compensate at a high capacity, its additional heat generation will directly threaten the winding insulation life and may trigger the gas relay to operate.

[0043] The second proportional data is a percentage value that is less than the first proportional data. In a preferred embodiment of this application, it is set to 50% to 60%. The determination of this range is based on the fact that when the compensation capacity depth of the power quality compensation equipment is limited to 50% to 60% of the rated capacity, the additional core loss caused by the high-frequency switching ripple is significantly reduced, and the rising trend of the winding hot spot temperature is curbed. Although the THDi may exceed the national standard allowable value during this period, the safety of the transformer insulation system is given priority. The first capacity upper limit data is the product of the rated capacity data and the second proportional data.

[0044] The three capacity determination submodules mentioned above are mutually exclusive triggering conditions. After the sub-temperature data of each time step is compared by the temperature comparison submodule, it must satisfy one and only one of the three conditions. The three capacity determination submodules will select one to execute based on the output result of the temperature comparison submodule.

[0045] Furthermore, the integral increment determination submodule starts working when the sub-temperature data is less than the first temperature threshold. That is, the submodule is only triggered when the sub-temperature data of a certain time step is determined by the temperature comparison submodule to be within the safe temperature range.

[0046] At this point, the integral increment determination submodule determines two values ​​based on the first integral data: The first item is the first integral increment data corresponding to the target time period, which is the heat accumulation integral sum value extracted from the first integral data for the target time period currently being evaluated, representing the heat damage increment suffered by the insulation system of the power supply equipment during the target time period. The second item is the second integral data corresponding to the target period, which is the cumulative value of heat accumulation integral within the complete evaluation period extracted from the first integral data. In this application, the target period is the complete period in one day, which represents the total heat damage dose borne by the insulation system of the power supply equipment within the complete period.

[0047] The two sets of data mentioned above have the same dimensions, both being the dimensions of temperature-time integrals.

[0048] The proportion determination submodule calculates based on the first integral increment data and the second integral data, and divides the first integral increment data by the second integral data to obtain the third proportion data. The third proportion data represents the percentage of the heat accumulation integral contribution of the target time period to the total heat accumulation integral of the target period.

[0049] In a preferred embodiment of this application, if the proportion of the first integral increment data to the second integral data in a certain period exceeds 20%, then the period is determined to be a time window for rapid increase in the thermal accumulation integral throughout the day, and the system needs to perform an advance reduction in the amount for that period. The 20% threshold is only an illustrative example. In actual applications, it can be adjusted within the range of 15% to 30% according to the temperature-aging characteristics of the transformer insulation material and the load characteristics of the park.

[0050] If the third ratio data is greater than the preset ratio threshold, the fourth capacity determination submodule adjusts the rated capacity data according to the fourth ratio data to obtain the first capacity upper limit data.

[0051] When the third ratio data is greater than the preset ratio threshold, it indicates that although the current hot spot temperature calculation value has not yet exceeded the first temperature threshold, this period accounts for a significant share of the total heat accumulation throughout the day. If the compensation intensity of the power quality compensation equipment is not limited, the heat accumulation will accumulate rapidly during this period and approach the starting temperature of the accelerated aging of the insulation material, increasing the risk of insufficient heat margin in subsequent periods.

[0052] The fourth percentage data is a percentage value less than 100% and greater than 0. In a preferred embodiment of this application, it is set to 70% to 80%, that is, the compensation capacity of the power quality compensation equipment is derated to the same extent as the warning temperature range. The basis for this is that although the current temperature has not yet entered the warning range, the rapid increase in heat accumulation indicates that the temperature may cross the warning boundary in the later period. Executing the same level of deration as the warning range in advance can intervene in the early stage of accelerated heat accumulation, preventing the system from being passive when the heat accumulation has already accumulated to a high level. The first capacity upper limit data is the product of the rated capacity data and the fourth percentage data.

[0053] The temperature comparison submodule first calibrates the temperature range of the sub-temperature data at the current time step. If the calibration result is that the sub-temperature data is greater than or equal to the first temperature threshold, then the derating is executed according to the logic of the second or third capacity determination submodule. If the calibration result shows that the sub-temperature data is less than the first temperature threshold, the integral increment determination submodule is triggered. The system further calculates the first integral increment data corresponding to the target time period and the second integral data corresponding to the target period, and calculates the third proportional data through the proportional determination submodule. If the third proportional data is greater than the preset proportional threshold, the fourth capacity determination submodule performs early throttling. If the third proportional data is not greater than the preset proportional threshold, the system determines that there is neither current temperature risk nor heat accumulation trend risk in this time period, and directly determines the rated capacity as the first capacity upper limit data according to the logic of the first capacity determination submodule.

[0054] Furthermore, the model processing module determines the sub-temperature data corresponding to each time step based on the first power data and the first temperature data using the first model, thereby obtaining temperature sequence data.

[0055] The first model is a transformer thermal circuit model constructed based on thermoelectric analogy theory. It includes a thermal dynamic change sub-model of top oil temperature relative to ambient temperature and a thermal dynamic change sub-model of winding hot spot temperature relative to top oil temperature. The inputs are the first power data (the active power of the load on the low-voltage side of the power supply equipment, in kW) and the first temperature data (the ambient temperature of the transformer installation location, in °C). The output is the calculated value of the winding hot spot temperature (in °C).

[0056] The time step data is the time division interval of the first model in the calculation process. In a preferred embodiment of this application, it is set to 1 hour. Its value is less than the thermal time constant of the power supply equipment so as to ensure the numerical stability and accuracy of the simulation results.

[0057] The model processing module inputs the first power data and the first temperature data into the first model point by point in chronological order. At each time step, it outputs a corresponding sub-temperature data at the discrete time node. After traversing all time steps, all sub-temperature data are arranged in chronological order to form a temperature sequence data.

[0058] Subsequently, the model processing module determines the first difference data based on the sub-temperature data and the preset starting temperature data. The preset starting temperature data is the aging acceleration starting temperature of the insulation material of the power supply equipment. That is, when the winding hot spot temperature is lower than this value, the thermal aging rate of the insulation material is at a low level, and when the temperature exceeds this value, the thermal aging rate begins to accelerate.

[0059] For oil-immersed transformers with Class B insulation, the preset starting temperature is set to 80°C to 90°C. This is because the thermal aging rate of Class B insulation material is in the transition zone from slow to accelerated within this temperature range. Below this range, the aging rate is extremely low, while above this range, the aging rate increases with increasing temperature.

[0060] The first difference data is the difference between the sub-temperature data and the preset starting temperature data, which indicates the extent to which the winding hot spot temperature at the current time step exceeds the aging acceleration starting temperature.

[0061] When the first difference data is greater than zero, the model processing module obtains the single-step integral data based on the first difference data and the corresponding time step data. The conditional truncation logic of this step is that the thermal accumulation integral increment of the time step is calculated only when the first difference data is positive. When the first difference data is not greater than zero, the single-step integral data of the time step is set to zero and is not included in the accumulation.

[0062] The basis for this conditional cutoff is that there is a non-linear relationship between the thermal aging rate of insulating materials and temperature. When the temperature is below the aging acceleration initiation temperature, the rate of consumption of insulation life is extremely low. If the heat below this temperature is also included in the cumulative damage, the calculation result of the thermal accumulation integral will be too high, which will lead to the upper limit determination module being overly conservative in reducing the derating without unnecessarily limiting the compensation capability of the power quality compensation equipment.

[0063] The single-step integral data is the product of the first difference data (unit: °C) and the time step data (unit: h or min), and its dimension is temperature-time integral (°C·h or °C·min).

[0064] The model processing module sums the single-step integral data corresponding to multiple time steps to obtain the first integral data. That is, it accumulates all the single-step integral data that meet the condition that the first difference data is greater than zero in all time steps covered by the temperature sequence data to obtain the first integral data representing the total amount of thermal damage accumulated by the insulation system of the power supply equipment during the complete evaluation period.

[0065] Based on the above steps, the logic for the model processing module to calculate the first integral data can be represented by the following expression: ; Among them, Q acc The first integral data (unit: °C·h or °C·min) is given, where n is the total number of time steps in the temperature series data, and T is the total number of time steps. i For the sub-temperature data at the i-th time step, T base The preset starting temperature data, Δt i This represents the data at the i-th time step.

[0066] The `max` function represents conditional truncation logic; its output value is the larger of the expression within the parentheses and 0. When T... i -T base Output 0 when T ≤ 0, and output 0 when T ≤ 0. i -T base When >0, output T i -Tbase .

[0067] The summation of single-step integral data represents the irreversible cumulative characteristic of thermal aging of insulation materials. The overall damage is not determined by the highest temperature at a single moment, but by the combined effect of temperature and time over various time periods. By summing the integrals of thermal accumulation at each time step, the first integral data can comprehensively represent the total amount of thermal damage accumulated by the power supply equipment insulation system over the entire evaluation period.

[0068] Furthermore, the dynamic adjustment module is activated when the first rate of change data is positive and the first rate of change data is greater than the first rate of change threshold. The first rate of change data is calculated by the second acquisition module based on the second power data of the power supply equipment. Its positive sign indicates that the load power of the power supply equipment is currently increasing, and its absolute value indicates the rate of power increase.

[0069] The first rate of change threshold is a preset power change rate limit value, which is set to 5% / min of the rated load of the power supply equipment in a preferred embodiment of this application. The basis for this value is that when the load change rate is lower than the threshold, the rate of temperature rise of the transformer winding is within the range that the thermal inertia of the transformer can withstand, and no additional compensation intervention is required; only when the load change rate exceeds the threshold can the rapid increase of the load current produce a significant temperature rise superposition effect in a short period of time, requiring system intervention.

[0070] When the above two conditions are met simultaneously, the dynamic adjustment module determines the first step length data based on the second difference data between the first rate of change data and the first rate of change threshold. The second difference data is the difference obtained by subtracting the first rate of change threshold from the absolute value of the first rate of change data. It means the amount by which the load ramp-up rate exceeds the safety boundary. The first step length data is positively correlated with this amount of excess, that is, the more the load ramp-up rate exceeds the safety boundary, the larger the step length will be.

[0071] The dynamic adjustment module lowers the first capacity limit data based on the first step length data to obtain the second capacity limit data. The basis for this downward adjustment is: When the load current increases rapidly, the copper loss of the fundamental current in the transformer winding increases at a rate equal to the square of the load current. This fundamental heating effect overlaps with the additional heating effect introduced by the compensation current of the power quality compensation equipment in time. If both of them act on the transformer at a high intensity at the same time, the temperature of the winding hot spot will rise rapidly in a short time, which can easily exceed the short-time withstand limit of the insulation material and trigger the gas relay to operate.

[0072] In the early stages of load ramp-up, the system proactively reduces the compensation capacity based on the amount by which the load ramp-up rate exceeds the safety boundary. This is equivalent to releasing a portion of the transformer's thermal capacity buffer before the peak of the thermal shock arrives, in order to absorb the upcoming fundamental current temperature rise, thereby preventing the fundamental current heating and the heat peak of the compensation additional heating from overlapping in time.

[0073] The mapping relationship between the second difference data and the first step length data can be implemented in various ways. For example, a mapping relationship table with difference data as input and step length data as output can be preset in the dynamic adjustment module, or the difference data can be multiplied by a preset proportional coefficient to obtain the step length data, or a segmented step mapping can be used, that is, when the difference data falls into different intervals, different step length values ​​are corresponding to them. The logic is that the larger the difference, the larger the step length is adjusted.

[0074] The second capacity limit data obtained after the above-mentioned downward adjustment is used as a compensation constraint instruction issued by the control execution module to the power quality compensation equipment controller, so that the winding hot spot temperature of the power supply equipment is controlled within the safe boundary that does not trigger the accelerated aging of the insulation material and the action of the protection device during the load impact process.

[0075] Furthermore, the dynamic adjustment module is activated when the first rate of change data is negative and less than the second rate of change threshold. The first rate of change data is calculated by the second acquisition module based on the second power data of the power supply equipment. Its negative sign indicates that the load power of the power supply equipment is currently decreasing, and its absolute value indicates the rate of power reduction.

[0076] The second rate of change threshold is a preset power change rate limit value. In a preferred embodiment of this application, it is the same as the first rate of change threshold (for example, both are set to 5% / min of the rated load). However, the two can also be set to different values ​​independently. Their meaning is to define whether the load change has reached the level that requires system intervention.

[0077] When the load change rate is negative and its absolute value exceeds the second change rate threshold, it indicates that the load power of the power supply equipment is rapidly decreasing. The system determines that it has entered the heat capacity surplus window period and then performs a delayed replenishment action.

[0078] In the delayed supplementation action, the dynamic adjustment module determines the second step size data based on the third difference data between the first rate of change data and the second rate of change threshold.

[0079] The third difference data is the difference obtained by subtracting the second change rate threshold from the absolute value of the first change rate data, and it means the amount by which the load drop rate exceeds the safety boundary.

[0080] The second step size data is positively correlated with this excess amount, that is, the more the load drop rate exceeds the safety boundary, the larger the step size will be.

[0081] The dynamic adjustment module adjusts the first capacity limit data upward based on the second step size data to obtain the second capacity limit data.

[0082] The basis for this upward adjustment is: The rapid decrease in load current causes the fundamental current copper loss in the transformer winding to decrease at a rate equal to the square of the load current. The winding temperature will enter a period of natural decrease or slowed-down heat capacity surplus. During this period, the transformer has additional heat capacity margin that can be utilized. If the system still operates at the upper limit of the baseline capacity set in the daily dispatch schedule, it is equivalent to giving up a period of available governance window, resulting in the idle harmonic governance capacity.

[0083] The system proactively increases the upper limit of compensation capacity based on the amount by which the load drop rate exceeds the safety boundary in the early stages of load drop, and uses the thermal capacity surplus window brought about by load drop to centrally process some harmonic mitigation tasks that were previously restricted or shelved due to thermal constraints.

[0084] It should be noted that the dynamic adjustment module must follow a physical safety constraint when performing the upward adjustment process: the second capacity upper limit data must not exceed the rated capacity data of the power quality compensation equipment.

[0085] The basis for this constraint is that the power quality compensation equipment itself has a rated capacity limit determined by the thermal cycling capability of power semiconductor devices (such as IGBT modules) and the design of the output filter. Regardless of whether the current thermal capacity of the transformer is sufficient, the output capacity of the power quality compensation equipment should not exceed its factory-specified maximum continuous operating capacity, otherwise it may trigger the equipment's own overcurrent or overheat protection.

[0086] Therefore, delayed supplementary actions can only be implemented within the difference between the first capacity limit set in the day-ahead scheduling schedule and the equipment's rated capacity. If the current day-ahead scheduling schedule has set the capacity limit to 100% of the rated capacity, the dynamic adjustment module cannot perform an upward adjustment even if the load drops rapidly. If the day-ahead scheduling schedule reduces the capacity limit to 70% to 80% of the rated capacity due to a narrowing thermal safety margin, the system can temporarily replenish the capacity limit to a higher level or even restore it to the rated capacity when the load drops, but it must not exceed the rated capacity boundary.

[0087] When the load rises rapidly, the system compresses the compensation capacity in advance to buffer the upcoming fundamental temperature rise (advanced decoupling). When the load drops rapidly, the system utilizes the released heat capacity surplus window to increase the treatment depth (lagging supplementation). The two systems perform opposite capacity adjustment actions before and after the peak of the thermal shock, respectively. This allows the compensation capacity of the power quality compensation equipment to adjust with the instantaneous changes in the transformer's heat capacity throughout the day. During periods of tight heat capacity, compensation is reduced to protect equipment safety, while during periods of ample heat capacity, compensation is increased to improve the treatment effect.

[0088] The second capacity limit data obtained after the above adjustment is used as a compensation constraint instruction issued by the control execution module to the power quality compensation equipment controller, so that the power supply equipment can perform harmonic control at a feasible compensation intensity not exceeding the rated capacity of the equipment during the heat capacity surplus window period after the load drop.

[0089] Furthermore, although the first model was initially set based on design data such as the thermal time constant and rated temperature rise limit in the transformer nameplate parameters, there are deviations between these factory parameters and the actual thermal dynamic characteristics of the transformer in operation.

[0090] On the one hand, the differences in manufacturing processes of different transformers result in their actual thermal resistance and thermal capacity values ​​not being entirely consistent with the design specifications; on the other hand, during long-term operation, the heat dissipation conditions of transformers will gradually change due to factors such as dust accumulation clogging the heat sink, aging of the cooling fan leading to a decrease in airflow, and oil viscosity increasing with the number of years of operation.

[0091] If the parameters of the first model remain unchanged from their initial factory settings, the thermal accumulation integral simulation results during the daytime scheduling phase will gradually deviate from the actual state, causing the accuracy of thermal margin identification to decrease over time and weakening the effect of capacity pre-allocation of the upper limit determination module.

[0092] Based on the above considerations, in addition to the dual-scale framework of daytime scheduling and real-time control, this system is further configured with a model update module to correct the parameters of the first model on a daily basis.

[0093] The model update module first obtains the second temperature data of the power supply equipment. The second temperature data is not the ambient temperature obtained by the first acquisition module, but the measured value of the top oil temperature collected and recorded by the temperature sensing unit deployed on the transformer body during the actual operation of the day. Its sampling time granularity corresponds to the simulation time step of the previous day's scheduling phase.

[0094] The second method for obtaining temperature data is as follows: at the correction time after the end of the daily operation cycle, the model update module retrieves all the top-level oil temperature measurement records stored on that day from the historical database of the data acquisition layer, forming a temperature measurement sequence indexed by time.

[0095] The model update module determines the third temperature data based on the first power data and the first temperature data through the first model. The first power data and the first temperature data are the input data used by the model processing module in the day-ahead scheduling phase, namely the hourly load forecast value and the ambient temperature forecast value of the next day generated based on historical load forecasts.

[0096] The third temperature data is a sequence of winding hot spot temperatures calculated by the model update module after substituting the above input data back into the first model for simulation. The difference between this and the temperature sequence data is: Temperature sequence data is the prediction result output by the model processing module during the day-ahead scheduling phase when generating the next day's scheduling plan, while the third temperature data is the result output by the model update module after rerunning the simulation with the same input data during the calibration phase. The two should be consistent in value, both representing the prediction value based on the current model parameters.

[0097] The model update module determines the temperature deviation sequence data based on the second and third temperature data. The temperature deviation sequence data is generated as follows: The measured value of the top oil temperature at each time step is subtracted from the predicted value of the top oil temperature at each time step to obtain the deviation value at each time step.

[0098] The positive and negative distribution and magnitude of the deviation sequence reflect the direction of overestimation or underestimation of the transformer's thermal dynamics by the first model under the current parameter configuration.

[0099] If the deviation is generally positive, it indicates that the model's predicted value is lower than the measured value, meaning that the model is biased towards believing that the temperature is low, and the thermal resistance parameter needs to be reduced or the heat capacity parameter increased. If the bias is generally negative, it indicates that the model's predicted values ​​are higher than the measured values, meaning the model is biased towards believing that the temperature is higher, and needs to be corrected in the opposite direction.

[0100] The model update module updates the first parameter data and the second parameter data in the first model based on the temperature deviation sequence data.

[0101] The first and second parameter data are the equivalent thermal resistance and equivalent heat capacity parameters in the first model.

[0102] The update process is as follows: Based on the deviation sequence data, regression analysis or Kalman filtering is used to minimize the deviation sequence. The first and second parameter data are adjusted in reverse iteratively until the deviation between the predicted and measured values ​​of the corrected model under the same input conditions converges to a preset tolerance range.

[0103] The corrected first and second parameter data are saved to the server's storage medium and used as the reference parameters for the first model when the day-ahead scheduling module starts the simulation the following day.

[0104] The correction process of the above model update module is executed once after each running cycle, so that "day-ahead forecast → real-time execution → measured feedback → parameter correction → next day-ahead forecast" constitutes an iterative process with a daily cycle.

[0105] Furthermore, a frequency control module is configured to identify operating conditions dominated by high-frequency harmonics and implement carrier frequency downsampling.

[0106] The frequency control module acquires the first current data and the second current data from the power supply equipment. The first current data is the effective value of the fundamental current (I1), and the second current data is the effective value of each harmonic current (I2). h (h=2,3,4,...).

[0107] The above data is obtained by using a power quality monitoring terminal deployed on the low-voltage side outgoing circuit of the power supply equipment to separate the fundamental component and each harmonic component from the collected total current waveform and record them in the form of effective values.

[0108] The frequency control module determines the coefficient data of the power supply equipment based on the first current data, the second current data, and the corresponding frequency count data.

[0109] The coefficient data is the K coefficient, which is a comprehensive evaluation index defined based on the ANSI / IEEE C57.110 standard to quantify the impact of harmonic current on the additional heating of transformers.

[0110] The meaning of the K coefficient is to assign different weights to harmonic currents of different frequencies. The higher the frequency, the greater the weight. This is because high-frequency harmonic currents will induce a more significant skin effect in transformer windings, causing the current to concentrate on the conductor surface and increase the equivalent resistance, thereby generating higher additional eddy current losses.

[0111] The corresponding frequency number data is the number h of each harmonic. For example, the frequency number data corresponding to the 5th harmonic is 5, and the frequency number data corresponding to the 7th harmonic is 7.

[0112] The frequency control module obtains coefficient data by multiplying the square of the ratio of each harmonic current amplitude to the fundamental current amplitude by the square of the corresponding frequency number h, and then summing all the harmonic numbers of interest.

[0113] When the frequency control module determines the third step length data of the power quality compensation device if the coefficient data is greater than the preset coefficient threshold and the second capacity upper limit data is less than the rated capacity data of the power quality compensation device.

[0114] The preset coefficient threshold is a pre-set limit value of the K coefficient, which means whether the current harmonic spectrum has posed a threat of high-frequency eddy current loss to the transformer core. In a preferred embodiment of this application, the preset coefficient threshold is set to 70% to 80% of the maximum allowable K coefficient marked on the nameplate of the power supply equipment.

[0115] When the coefficient data K exceeds the threshold, it indicates that higher harmonics (such as the 11th, 13th and above) have become dominant in the harmonic spectrum of the power supply equipment, and the high-frequency eddy current loss of the iron core has become one of the main contributing factors to the heat accumulation of the transformer.

[0116] At the same time, the condition that the second capacity limit data is less than the rated capacity ensures that the APF is currently constrained by the capacity derating logic, that is, the system has identified that the transformer thermal safety margin is insufficient.

[0117] The combination of these two conditions indicates that the system has entered a capacity-limited and high-frequency loss-dominated operating condition, requiring optimization of the thermal efficiency per unit compensation capacity based on capacity derating.

[0118] The third step length data represents the downward adjustment range of the carrier frequency, which is positively correlated with the degree to which the coefficient data exceeds the preset coefficient threshold.

[0119] The frequency control module adjusts the frequency data of the power quality compensation device based on the third step length data, wherein the adjusted frequency data is not lower than the preset lower limit frequency data.

[0120] The frequency data refers to the switching carrier frequency of the IGBT module in the APF inverter.

[0121] In a preferred embodiment of this application, the rated carrier frequency is set to 20kHz, the lower limit frequency data is set to 4kHz, and the down-adjustment step size is 2kHz, that is, the frequency decreases by 2kHz each time a down-adjustment action is triggered, but not lower than 4kHz.

[0122] The lower limit frequency is set based on the fact that when the carrier frequency is below 4kHz, the output compensation accuracy and dynamic response speed of the APF will decrease, and the switching frequency may fall into the audible frequency band, generating perceptible noise.

[0123] The effect of downsampling the carrier frequency is to shift the energy spectrum distribution of the switching ripple in the compensation current to a lower frequency direction, weakening its excitation intensity on the high-frequency skin effect of the transformer core, thereby reducing the additional eddy current heat generation of the core under the same compensation capacity output conditions.

[0124] It should be noted that the trigger condition for carrier frequency reduction is that the second capacity upper limit data is less than the rated capacity data of the power quality compensation equipment. That is, the scheme is a thermal efficiency optimization measure that is already in a derating state, rather than a carrier frequency adjustment for full capacity operation. When the transformer has sufficient thermal safety margin and the APF can output at full rated capacity, the system does not need to reduce the carrier frequency, because at this time the transformer has sufficient thermal capacity to withstand the high-frequency component in the APF compensation current, and maintaining a high-frequency carrier is beneficial to maintaining the compensation accuracy and dynamic response speed of the APF.

[0125] With the combined effect of capacity derating and carrier frequency reduction, the APF operates at a lower capacity and lower heating efficiency during periods of insufficient thermal safety margin, thus ensuring the thermal safety of the transformer.

[0126] Furthermore, the frequency control module first calculates the proportion data based on the first current data and the second current data. The first current data is the effective value of the fundamental current at the power supply equipment's inlet line, which means the amplitude of the 50Hz power frequency component in the load current.

[0127] The second current data is the effective value of the h-th harmonic current, denoted as I. h , where h is the harmonic order, h=2,3,4,..., corresponding to the current components at frequencies of 100Hz, 150Hz, 200Hz and higher, respectively, and the unit is ampere (A).

[0128] The aforementioned fundamental and harmonic current data were obtained by power quality monitoring terminals deployed on the low-voltage side of the power supply equipment from the collected total current waveform through Fourier decomposition and recorded in the form of effective values.

[0129] The percentage data is the square of the ratio of the second current data to the first current data, i.e. (I h / I1) 2 This value reflects the relative intensity of each harmonic current relative to the fundamental current. The percentage data indicates the proportion of energy of each harmonic component in the total current. The larger the value, the greater the contribution of that harmonic to the transformer loss.

[0130] The frequency control module processes the percentage data and the corresponding frequency count data to obtain the weighted coefficient data.

[0131] The frequency order data is the harmonic order h. The processing method is to multiply the percentage data by the square of the corresponding frequency order data, i.e., (I... h / I1) 2 ×h 2 We obtain the weighted coefficient data.

[0132] The meaning of this weighting coefficient data is to represent the contribution weight of the h-th harmonic current to the additional heating of the transformer winding.

[0133] The basis for introducing the square term of frequency h in the weighted coefficient data is the high-frequency skin effect in the transformer winding. When the harmonic current flows through the transformer winding, the current tends to concentrate on the conductor surface. The equivalent AC resistance increases with the square root of the frequency, resulting in the additional eddy current loss in the winding copper loss being proportional to the square of the frequency.

[0134] Therefore, the weighted coefficient data can reflect the different thermal effects of harmonic currents of different frequencies on transformers at the same amplitude.

[0135] The frequency control module sums the weighted coefficient data corresponding to each frequency order to obtain coefficient data. The summation operation involves accumulating the weighted coefficient data corresponding to all harmonic orders of interest to obtain the final coefficient data K, i.e.: ; Where n is the highest harmonic order of interest. The larger the value of the coefficient data, the more serious the additional heating caused by the current harmonic spectrum to the transformer core and windings. The actual heat accumulation rate of the transformer under the same load conditions is higher than the theoretical value when only the fundamental current is considered.

[0136] Frequency reduction is only triggered when the coefficient data K exceeds the preset threshold and the capacity has been reduced, in order to prevent the compensation accuracy and dynamic response performance of the power quality compensation equipment from being sacrificed during unnecessary periods.

[0137] Furthermore, the data preprocessing module acquires the historical power data and first feature data of the power supply equipment. The historical power data is the actual recorded value of the hourly active power of the power supply equipment over a period of time, and its time span usually covers the past 30 to 90 days, and is stored in the historical database of the big data server.

[0138] The first feature data is auxiliary information related to load change patterns, including at least calendar features, i.e., attribute labels for the dates corresponding to historical power data, such as weekdays, rest days, or statutory holidays.

[0139] The basis for calendar feature information lies in the regular differences in the electricity load of industrial plants and office buildings in the park between weekdays and rest days. Using calendar feature information as an auxiliary input dimension for load forecasting can improve forecast accuracy.

[0140] The data preprocessing module processes the historical power data, the first feature data, and the first temperature data using the second model to obtain the first power data.

[0141] The second model is a load forecasting model, which is used to predict the load for future periods based on the trend characteristics, calendar characteristics and meteorological characteristics of historical power data. In a preferred embodiment of this application, the second model can be implemented using time series regression or neural network methods. The specific algorithm type is not particularly limited in this application. Its function is to map multidimensional input features into hourly load forecast values.

[0142] The first power data obtained through the above processing is the first power data acquired by the first acquisition module.

[0143] The first power data acquired by the first acquisition module is not the real-time power value obtained directly from the field measurement, but the predicted power value generated by the data preprocessing module after comprehensively processing historical data, calendar features and ambient temperature through the second model.

[0144] This predicted power value is used for heat accumulation integral simulation during the subsequent day-ahead scheduling phase, representing the expected load level of the power supply equipment in each time period of the following day.

[0145] like Figure 2 As shown, the figure contains the dynamic evolution of key data in four dimensions on the same time axis (0 to 30 minutes): from top to bottom, the second power data curve of the power supply equipment, the first rate of change data curve, the sub-temperature data curve, and the capacity limit data curve of the power quality compensation equipment.

[0146] During the initial stable phase (0 to 10 minutes), the second power data of the power supply equipment remained stable at around 400kW, and the corresponding first rate of change data was close to 0% / min, far below the first rate of change threshold of 5.0% / min. At this time, the sub-temperature data determined by the model processing module was stable at around 85℃, without reaching the first temperature threshold of 95℃. During this phase, the upper limit determination module determined the rated capacity data (300kvar) of the power quality compensation equipment as the first capacity upper limit data, and the system performed compensation at full load.

[0147] During the sudden increase phase (occurring at the 10th minute), the second power data rapidly increased from 400kW to around 700kW in a short period due to the sudden surge of high-power load in the park. At this time, due to the physical constraints of the thermal inertia of the transformer's large oil body, the sub-temperature data curve in the figure showed significant physical hysteresis characteristics in the interval between the 10th and 15th minutes. The temperature only slowly increased from 85℃ to about 88℃, which was still far below the first temperature threshold of 95℃. If the system only relies on the traditional static temperature threshold for judgment at this time, the upper limit of capacity data will remain at 300kvar full load. The high-frequency switching heat generated by the APF will be dangerously superimposed on the fundamental copper loss caused by the sudden increase in load in the subsequent period.

[0148] During the pre-decoupling intervention phase (between the 10th and 12th minutes), according to the operating logic of the system's dynamic adjustment module, at the instant the second power data climbs, the first rate of change data curve in the graph rapidly spikes, and around the 10.5th minute, it breaks through the first rate of change threshold of 5.0% / min. With the first rate of change data positive and greater than the first rate of change threshold, the dynamic adjustment module calculates and determines the first step length data in real time based on the second difference between the first rate of change data and the first rate of change threshold. As shown by the bottom curve in the graph, the dynamic adjustment module immediately lowers the first capacity upper limit data, rapidly reducing the capacity upper limit from 300kvar to around 150kvar before the temperature exceeds the limit, thus obtaining the second capacity upper limit data.

[0149] As can be clearly seen from the timeline comparison in the figure, before the sub-temperature data showed a substantial temperature rise exceeding the limit (approaching 95℃), the upper limit of the capacity of the power quality compensation equipment had been significantly reduced by the system in advance. The control execution module limited the output of the compensation current based on the reduced second upper limit data, successfully giving way to the rapidly increasing fundamental copper loss heating generated by the compensation, thereby preventing the final winding hot spot temperature from exceeding the insulation critical value at the physical level.

[0150] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart park energy data management and control system based on big data, characterized in that: include: The first acquisition module is used to acquire the first power data and the first temperature data of the power supply equipment; The model processing module is used to input the first power data and the first temperature data into the first model for processing, so as to obtain the temperature sequence data and the first integral data of the power supply equipment. The upper limit determination module is used to determine the first capacity upper limit data of the power quality compensation equipment connected to the power supply equipment based on the temperature sequence data and the first integral data. The second acquisition module is used to acquire the second power data of the power supply equipment and calculate the first rate of change data based on the second power data; The dynamic adjustment module is used to adjust the first capacity upper limit data according to the first rate of change data to obtain the second capacity upper limit data of the power quality compensation device. The control execution module is used to control the power quality compensation device to perform power compensation based on the second capacity upper limit data.

2. The intelligent park energy data management and control system based on big data according to claim 1, characterized in that, The upper limit determination module includes: The temperature comparison submodule is used to acquire each sub-temperature data in the temperature sequence data, and compare each sub-temperature data with a first temperature threshold and a second temperature threshold, wherein the second temperature threshold is greater than the first temperature threshold. The first capacity determination submodule is used to determine the rated capacity data of the power quality compensation device as the first capacity upper limit data when the sub-temperature data is less than the first temperature threshold. The second capacity determination submodule is used to adjust the rated capacity data according to the first ratio data when the sub-temperature data is greater than or equal to the first temperature threshold and less than the second temperature threshold, so as to obtain the first capacity upper limit data. The third capacity determination submodule is used to adjust the rated capacity data according to the second ratio data when the sub-temperature data is greater than or equal to the second temperature threshold, so as to obtain the first capacity upper limit data.

3. The intelligent park energy data management and control system based on big data according to claim 2, characterized in that, The upper limit determination module also includes: The integral increment determination submodule is used to determine, based on the first integral data, the first integral increment data corresponding to the target time period and the second integral data corresponding to the target period when the sub-temperature data is less than the first temperature threshold. The ratio determination submodule is used to determine the third ratio data based on the first integral increment data and the second integral data; The fourth capacity determination submodule is used to adjust the rated capacity data according to the fourth ratio data when the third ratio data is greater than the preset ratio threshold, so as to obtain the first capacity upper limit data.

4. The intelligent park energy data management and control system based on big data according to claim 1, characterized in that, The model processing module is specifically used for: Based on the first power data and the first temperature data, the sub-temperature data corresponding to each time step data is determined by the first model to obtain the temperature sequence data; The first difference data is determined based on the sub-temperature data and the preset starting temperature data; If the first difference data is greater than zero, the single-step integral data is obtained based on the first difference data and the corresponding time step data. The first integral data is obtained by summing the single-step integral data corresponding to the multiple time step data.

5. The intelligent park energy data management and control system based on big data according to claim 1, characterized in that, The dynamic adjustment module is specifically used for: When the first rate of change data is positive and the first rate of change data is greater than the first rate of change threshold, the first step length data is determined based on the second difference data between the first rate of change data and the first rate of change threshold. The first capacity limit data is adjusted downward based on the first step length data to obtain the second capacity limit data.

6. The intelligent park energy data management and control system based on big data according to claim 5, characterized in that, The dynamic adjustment module is also specifically used for: When the first rate of change data is negative and the first rate of change data is less than the second rate of change threshold, the second step size data is determined based on the third difference data between the first rate of change data and the second rate of change threshold. The first capacity limit data is adjusted upward based on the second step size data to obtain the second capacity limit data. Wherein, the second upper limit of capacity data is not greater than the rated capacity data of the power quality compensation equipment.

7. The intelligent park energy data management and control system based on big data according to claim 1, characterized in that, The system also includes a model update module, which is used for: Obtain the second temperature data of the power supply equipment; Based on the first power data and the first temperature data, the third temperature data is determined using the first model; Determine temperature deviation sequence data based on the second temperature data and the third temperature data; The first parameter data and the second parameter data in the first model are updated based on the temperature deviation sequence data.

8. The intelligent park energy data management and control system based on big data according to claim 1, characterized in that, The system further includes a frequency control module, which is used for: Acquire the first current data and the second current data of the power supply equipment; The coefficient data of the power supply equipment are determined based on the first current data, the second current data, and the corresponding frequency data. When the coefficient data is greater than the preset coefficient threshold and the second capacity upper limit data is less than the rated capacity data of the power quality compensation device, the third step size data of the power quality compensation device is determined. The frequency data of the power quality compensation device is adjusted downward based on the third step length data, wherein the adjusted frequency data is not lower than the preset lower limit frequency data.

9. The intelligent park energy data management and control system based on big data according to claim 8, characterized in that, When the frequency control module determines the coefficient data of the power supply equipment based on the first current data, the second current data, and the corresponding frequency count data, it is specifically used for: The percentage data is obtained by calculating based on the first current data and the second current data; The weighted coefficient data is obtained by processing the percentage data and the corresponding frequency data. The weighted coefficient data corresponding to each frequency count data is summed to obtain the coefficient data.

10. The intelligent park energy data management and control system based on big data according to claim 1, characterized in that, The system further includes a data preprocessing module, which is used for: Obtain historical power data and feature data of the power supply equipment; Based on the historical power data, the feature data, and the first temperature data, the first power data is obtained by processing the data using a second model. The first power data acquired by the first acquisition module is the first power data obtained by the data preprocessing module through the second model.

Images