Low-voltage capacitor dynamic adjustment compensation control method and device

Abstract

The invention provides a low-voltage capacitor dynamic adjustment compensation control method and device, and relates to the technical field of low-voltage capacitor compensation control, and the method comprises the steps: carrying out the historical operation data collection of a low-voltage power distribution network, and obtaining the historical load sample data; feature index extraction is carried out, a load fluctuation feature library is constructed, and a target power factor interval is determined; arranging an electric energy quality monitoring device to obtain electric energy quality monitoring data; performing power quality dynamic evaluation to generate a dynamic compensation demand index; dividing the target capacitor bank into a plurality of switching units; establishing a switching state optimization model; and carrying out dynamic switching analysis by taking minimization of a reactive power gap and reduction of switching loss as targets, generating capacitance control parameters, and carrying out switching control. The technical problems that in the prior art, a fixed-capacity capacitor bank is generally adopted for reactive compensation, the compensation amount cannot be dynamically adjusted according to the change of load requirements, overcompensation or undercompensation is easily caused, and the compensation effect is poor are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage capacitor compensation control, and in particular to a low-voltage capacitor dynamic adjustment compensation control method and device. Background Art

[0002] Large industrial equipment, such as motors and inverters, generate large fluctuations in reactive power demand during operation, affecting the power factor and power quality. Traditional methods mostly use a fixed switching method, that is, a fixed switching capacity or time period of the preset capacitor group, which cannot adjust the compensation amount in real time according to load fluctuations, and is prone to over-compensation or under-compensation problems, which in turn leads to voltage fluctuations and increased reactive power loss in the operation of the distribution network; in addition, traditional reactive power compensation is mostly based on simple current or power factor threshold judgments, which makes it difficult to achieve real-time monitoring and dynamic evaluation, making the compensation strategy fixed and unable to quickly respond to dynamic fluctuations in the load, resulting in compensation lags and affecting power quality. Summary of the invention

[0003] The present application provides a low-voltage capacitor dynamic adjustment compensation control method and device, aiming to solve the technical problem that the prior art usually uses a fixed-capacity capacitor group for reactive power compensation, cannot dynamically adjust the compensation amount according to changes in load demand, and easily leads to over-compensation or under-compensation, resulting in poor compensation effect.

[0004] The first aspect disclosed in the present application provides a method for dynamic adjustment and compensation control of low-voltage capacitors, the method comprising: collecting operation data of a low-voltage power distribution network within a preset historical time range to obtain historical load sample data; extracting characteristic indicators based on the historical load sample data, constructing a load fluctuation characteristic library, and determining a target power factor interval based on the load fluctuation characteristic library; deploying a power quality monitoring device in the low-voltage power distribution network to obtain power quality monitoring data; based on the target power factor interval, dynamically evaluating the power quality of the power quality monitoring data, and generating a dynamic power factor according to the evaluation result. A compensation demand indicator, wherein the dynamic compensation demand indicator includes reactive power demand and power factor adjustment priority; according to the reactive power demand, the target capacitor group is divided into multiple switching units, each switching unit having a compensation capacity label and a priority label; a switching state optimization model is established, wherein the switching state optimization model includes compensation capacity, switching threshold, and action delay time; based on the switching state optimization model, with the goal of minimizing reactive power gap and reducing switching loss, dynamic switching analysis of the multiple switching units is performed, capacitor control parameters are generated, and switching control of the target capacitor group is performed.

[0005] In a second aspect disclosed in the present application, a low-voltage capacitor dynamic adjustment compensation control device is provided, and the device is used for the above-mentioned low-voltage capacitor dynamic adjustment compensation control method, and the device includes: an operation data acquisition module, and the operation data acquisition module is used to collect operation data of the low-voltage power distribution network within a preset historical time range, and obtain historical load sample data; a characteristic index extraction module, and the characteristic index extraction module is used to extract characteristic indicators based on the historical load sample data, construct a load fluctuation feature library, and determine a target power factor interval based on the load fluctuation feature library; a monitoring data acquisition module, and the monitoring data acquisition module is used to deploy a power quality monitoring device in the low-voltage power distribution network to obtain power quality monitoring data; a dynamic evaluation module, and the dynamic evaluation module is used to evaluate the power quality monitoring data based on the target power factor interval. A dynamic evaluation of power quality is performed according to the evaluation result, and a dynamic compensation demand index is generated according to the evaluation result, wherein the dynamic compensation demand index includes reactive power demand and power factor adjustment priority; a capacitor group division module, the capacitor group division module is used to divide the target capacitor group into multiple switching units according to the reactive power demand, and each switching unit has a compensation capacity label and a priority label; an optimization model establishment module, the optimization model establishment module is used to establish a switching state optimization model, wherein the switching state optimization model includes compensation capacity, switching threshold, and action delay time; a dynamic switching analysis module, the dynamic switching analysis module is used to perform dynamic switching analysis of the multiple switching units based on the switching state optimization model, with the goal of minimizing reactive power gap and reducing switching loss, generate capacitor control parameters, and perform switching control of the target capacitor group.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By collecting historical operating data of the low-voltage distribution network, we can accurately grasp the power consumption patterns, fluctuation characteristics and changes in reactive power demand of the load, laying a data foundation for the subsequent construction of a load fluctuation feature library and optimized compensation strategy; by extracting historical power factor fluctuation ranges, typical load distribution periods and reactive power demand change trends, we can dynamically optimize the target power factor range to ensure that it adapts to actual operating conditions. The optimized target power factor range avoids insufficient or excessive reactive power compensation caused by fixed range settings, thereby improving compensation efficiency and grid stability; through real-time collection of power quality monitoring data by monitoring devices, we can dynamically reflect the real-time operating status of the distribution network and dynamically evaluate the current The degree to which the power factor deviates from the target range is measured, and the reactive power gap is accurately calculated to ensure that the compensation amount matches the actual demand; by dividing the capacitor group into multiple switching units and setting different compensation capacities and priority labels, refined reactive power compensation is achieved to ensure the flexibility and dynamism of the compensation strategy; a switching state optimization model is established, and the compensation effect and equipment life are balanced by setting the compensation capacity, switching threshold and action delay time; based on real-time evaluation results and optimization models, the switching state of the capacitor group is dynamically adjusted to ensure that the reactive compensation effect matches the load demand in real time, and the compensation strategy is automatically optimized according to the operating status, thereby improving control accuracy and system stability.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a low-voltage capacitor dynamic adjustment compensation control method provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the structure of a low-voltage capacitor dynamic adjustment compensation control device provided in an embodiment of the present application.

[0011] Explanation of reference numerals: operation data acquisition module 10 , characteristic index extraction module 20 , monitoring data acquisition module 30 , dynamic evaluation module 40 , capacitor group division module 50 , optimization model establishment module 60 , dynamic switching analysis module 70 . DETAILED DESCRIPTION

[0012] The embodiment of the present application provides a low-voltage capacitor dynamic adjustment compensation control method to solve the technical problem that the prior art usually uses a fixed-capacity capacitor group for reactive power compensation, cannot dynamically adjust the compensation amount according to changes in load demand, and easily leads to over-compensation or under-compensation, thereby resulting in poor compensation effect.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] like Figure 1 As shown, an embodiment of the present application provides a low-voltage capacitor dynamic adjustment compensation control method, the method comprising:

[0015] Collect operating data of the low-voltage power distribution network within a preset historical time range to obtain historical load sample data.

[0016] Comprehensive data collection is carried out on the historical operating status of the low-voltage distribution network. The collection scope covers the key nodes of the entire low-voltage distribution network, including the low-voltage output end of the transformer, main load access points (such as industrial equipment, large commercial loads, etc.), access points of compensation devices, remote user nodes, etc. The specific collected data includes voltage data, current data, active power data, reactive power data, power factor data, harmonic data, etc. The collected raw data is denoised and integrated to obtain historical load sample data, laying the foundation for subsequent feature extraction and compensation optimization.

[0017] Based on the historical load sample data, characteristic indexes are extracted to construct a load fluctuation characteristic library, and a target power factor range is determined based on the load fluctuation characteristic library.

[0018] Based on historical load sample data, characteristic indicators are extracted from multiple dimensions such as power factor, reactive power demand and power load distribution to construct a load fluctuation feature library. Specifically, a time series analysis is performed on the historical power factor data to identify its fluctuation pattern and form a power factor fluctuation range. A time series prediction model is used to fit the reactive power data to identify its changing trend and output the reactive power demand trend curve and typical time period characteristics. Time period clustering technology is used to cluster the power load data to form typical load distribution periods and typical power consumption patterns.

[0019] The extracted characteristic indicators are integrated into the characteristic library, and the target power factor range is determined based on the load fluctuation characteristic library as the basis for subsequent compensation control.

[0020] A power quality monitoring device is deployed in the low-voltage power distribution network to obtain power quality monitoring data.

[0021] Power quality monitoring devices are deployed at key nodes of the low-voltage distribution network. The deployment scope covers the key nodes of the entire low-voltage distribution network, including the low-voltage output end of the transformer, main load access points (such as industrial equipment, large commercial loads, etc.), access points of compensation devices, remote user nodes, etc. The specific collected data includes voltage data, current data, active power data, reactive power data, power factor data, harmonic data, etc., and power quality monitoring data is obtained through data collection.

[0022] Based on the target power factor interval, a dynamic power quality assessment is performed on the power quality monitoring data, and a dynamic compensation demand index is generated according to the assessment result, wherein the dynamic compensation demand index includes reactive power demand and power factor adjustment priority.

[0023] Compare the real-time power quality monitoring data with the target power factor range, analyze the system's reactive power status and the degree of power factor deviation, and when the power factor deviates from the target range, calculate the system's reactive power gap value, obtain the reactive power demand, and calculate the power factor adjustment priority based on the degree of power factor deviation.

[0024] According to the reactive power demand, the target capacitor group is divided into a plurality of switching units, each of which has a compensation capacity tag and a priority tag.

[0025] The target capacitor group is divided into multiple switching units according to the reactive power demand. The capacity label of each unit represents its rated reactive compensation capacity. The specific capacity ratio is allocated according to the actual design, such as 20%, 30%, 50%, etc. Among them, the specific priority allocation strategy is as follows: the smaller the capacity of the unit, the higher the priority, that is, the small capacity unit is switched first to facilitate precise adjustment; according to the load characteristics of peak, flat and valley periods, the priority of the switching unit is dynamically adjusted. In this way, the flexibility and accuracy of capacitor switching are ensured to achieve the optimal compensation effect.

[0026] A switching state optimization model is established, wherein the switching state optimization model includes compensation capacity, switching threshold, and action delay time.

[0027] The switching state optimization model is used to guide the dynamic switching decision of the capacitor group, ensure the accuracy and efficiency of the compensation action, and reduce the switching loss of the capacitor group. The core of the model includes compensation capacity, switching threshold, and action delay time. Among them, the compensation capacity of each switching unit is set to its rated reactive power value, and the total switching capacity meets the real-time reactive power demand; the switching threshold defines the triggering condition of the capacitor switching, which is determined according to the power factor deviation and the reactive power gap. When the reactive power deviation exceeds the switching threshold, the switching action is triggered. By setting the switching threshold, frequent switching caused by slight fluctuations can be avoided; the action delay time is used to reduce the loss of equipment caused by frequent switching. The delay time is determined according to the following rules, that is, when the load fluctuation is small, the delay time is longer, such as 10 to 15 seconds; when the load fluctuation is large, the delay time is shorter, such as 3 to 5 seconds.

[0028] Based on the switching state optimization model, with the goal of minimizing reactive power gap and reducing switching loss, dynamic switching analysis of the multiple switching units is performed, capacitor control parameters are generated, and switching control of the target capacitor group is performed.

[0029] Based on the switching state optimization model, the operating status of multiple switching units is analyzed to ensure that reactive power compensation meets real-time needs while reducing the switching times and losses of the capacitor group. Specifically, when the power factor deviates from the target range, the reactive power gap value is calculated, and the optimal switching unit combination is selected according to the switching state optimization model. After the switching analysis is completed, the capacitor control parameters of the capacitor group are generated, including: the switching state, which is used to determine whether each unit is switched; the switching time sequence, which defines the execution order and time of each unit; and the action delay time.

[0030] According to the generated capacitor control parameters, the switching operation of the target capacitor group is executed, and finally accurate and efficient reactive power compensation is achieved.

[0031] Furthermore, the method comprises:

[0032] Based on the historical load sample data, historical power factor data, historical reactive power demand data, and historical electricity load data are extracted; the historical power factor data, historical reactive power demand data, and historical electricity load data are respectively standardized, and the standardized data are subjected to time series feature analysis to construct a load fluctuation feature library, wherein the load fluctuation feature library includes power factor fluctuation ranges, typical load distribution time periods and typical load modes, and reactive power demand change trends; an initial power factor range is obtained based on the network characteristics of the low-voltage power distribution network, and based on the load fluctuation feature library, the initial power factor range is dynamically adjusted to obtain the target power factor range.

[0033] Based on the historical load sample data of the low-voltage distribution network, historical power factor data, historical reactive power demand data and historical power load data are extracted. The historical power factor data reflects the changes in the power factor of the system in different time periods. The historical reactive power demand data is used to characterize the reactive power demand of the system at each moment. The historical power load data is used to reflect the power consumption change characteristics of the load. These data will provide key inputs for subsequent feature analysis and dynamic adjustment of the target power factor range.

[0034] In order to eliminate the influence of different data dimensions and ranges, the maximum and minimum normalization method is used to standardize the historical power factor data, historical reactive power demand data, and historical power load data. The standardized data range is [0,1], which is convenient for subsequent analysis.

[0035] Standardized data is used to perform time series feature analysis to extract power factor fluctuations, load distribution patterns, and reactive power demand trends. The extracted features are organized and stored in a load fluctuation feature library, which includes power factor fluctuation ranges, typical load distribution periods and typical load patterns, and reactive power demand change trends.

[0036] According to the power quality standards and distribution network design parameters, the initial power factor range is set, and based on the load fluctuation feature library, the initial power factor range is dynamically adjusted to adapt to different load characteristics. For example, during peak load periods, the lower limit of the target power factor range is appropriately increased to improve power utilization efficiency; during flat or off-peak periods, the power factor range is appropriately relaxed to reduce the risk of over-compensation. Through dynamic adjustment, the target power factor range that adapts to the actual operating conditions is output for subsequent compensation control.

[0037] Furthermore, the method for constructing a load fluctuation feature library includes:

[0038] The historical power factor data is arranged in time series to obtain a historical power factor sequence, and a sliding window analysis is performed on the historical power factor sequence using a preset window to extract the upper and lower limits of the fluctuation range to obtain the power factor fluctuation range; the historical power load data is clustered into time periods according to a preset time scale, and the peak period, off-peak period and valley period are extracted according to the clustering results to obtain the typical load distribution period and the corresponding typical load mode; the historical reactive power demand data is fitted and analyzed using a time series prediction model, and the reactive power demand change trend is obtained based on the reactive power demand time characteristic curve obtained by fitting.

[0039] The collected historical power factor data are sorted by timestamp to form a historical power factor time series. The sliding window method is used to perform local statistical analysis on the power factor series. The preset window is adjusted according to demand. For example, a short time window, such as 1 hour, is set to capture rapid fluctuations; a long time window, such as 1 day, is set to analyze periodic changes.

[0040] The upper and lower limits of the data in each sliding window are calculated, and the upper and lower limits of all windows are integrated to obtain the overall power factor fluctuation range. This range provides the fluctuation range of the historical power factor and is used to guide the dynamic adjustment of the target power factor and the design of the compensation strategy.

[0041] According to the pattern of electricity consumption, appropriate time scales are preset, such as daily scale, weekly scale, and seasonal scale. Clustering algorithms, such as K-Means, are used to classify historical electricity load data by time period. The load characteristics of each time period, such as load mean and fluctuation range, are used as clustering features. Different time periods are grouped, and peak, flat and valley periods are extracted as typical load distribution periods. The load curve of each time period is statistically analyzed, and typical features are extracted, including peak load and fluctuation range in peak period, stability and electricity consumption in flat and valley periods, as typical load patterns.

[0042] Use time series prediction models to fit and analyze reactive power demand data, such as ARIMA model and LSTM neural network model, divide historical data into training set and verification set, train the model to fit the reactive power demand curve, further verify the fitting effect, ensure that the error between the predicted curve and the actual data is within an acceptable range, and the fitted reactive power demand time characteristic curve reflects the periodic characteristics of the system's reactive power demand, and obtain the reactive power demand change trend based on the curve.

[0043] Furthermore, the method further comprises:

[0044] The peak period, the flat period and the valley period are compensated in stages in turn to obtain reactive power compensation priority; and auxiliary control of capacitor switching in corresponding periods is performed according to the reactive power compensation priority.

[0045] According to the load characteristics of different periods of peak, off-peak and off-peak, the reactive power demand is compensated in stages, the compensation resources are reasonably allocated, and the switching order of the capacitor groups is optimized to avoid over-compensation or under-compensation. Specifically, during peak hours, the power load is high and the reactive power demand is large, so the compensation priority is set to the highest priority, and the reactive power demand must be met first to avoid the decline in power quality; during off-peak hours, the power load is relatively stable and the reactive power demand is moderate, so the compensation priority is set to a medium priority to ensure stable system operation; during off-peak hours, the power load is low and the reactive power demand is small, so the compensation priority is set to the lowest priority to avoid over-compensation.

[0046] According to the compensation priority of time periods, the switching strategy of the capacitor group is dynamically adjusted to ensure that the reactive power demand is met in different time periods while reducing the impact of the switching action on the equipment. Specifically, during peak hours, capacitor units with larger capacity are switched first to quickly meet the higher reactive power demand; during off-peak hours, capacitor units with moderate capacity are switched to keep the power factor within the target range to prevent over-compensation; during off-peak hours, small-capacity units are switched first to reduce unnecessary compensation actions.

[0047] Based on the reactive power compensation priority, the auxiliary control logic of the capacitor group is adjusted to achieve accurate time-sharing compensation, optimize the equipment utilization efficiency and reduce operating losses.

[0048] Furthermore, the method comprises:

[0049] The power factor is obtained according to the power quality monitoring data; when the power factor deviates from the target power factor interval, the reactive power gap value is calculated, and the reactive power demand is obtained according to the reactive power gap value; the deviation coefficient of the power factor from the target power factor interval is calculated, and the compensation priority is set according to the deviation coefficient to obtain the power factor adjustment priority.

[0050] Power factor is an important indicator to measure the efficiency of electric energy utilization. The calculation formula is as follows: Among them, cosφ t is the power factor, P(t) is the active power, and Q(t) is the reactive power.

[0051] Real-time active power and reactive power are extracted from the power quality monitoring data, and the power factor is continuously calculated according to the timestamp to form a power factor time series, and the real-time power factor under the system operation status is obtained.

[0052] The target power factor range includes a maximum target power factor and a minimum target power factor. If the power factor is less than the minimum target power factor, it means that the power factor is too low, that is, the reactive power is too much; if the power factor is less than or greater than the maximum target power factor, it means that the power factor is too high, that is, the reactive power is insufficient.

[0053] When the power factor deviates from the target range, the reactive power gap value is calculated. A reactive power gap value greater than 0 indicates an increase in reactive power demand and needs to be compensated. A reactive power gap value less than 0 indicates an excess of reactive power and needs to be reduced. The reactive power demand is obtained based on the reactive power gap value.

[0054] When the power factor is less than the minimum target power factor, the minimum target power factor is used to calculate the power factor deviation; when the power factor is greater than the maximum target power factor, the maximum target power factor is used to calculate the power factor deviation. After calculation, the deviation coefficient is obtained to indicate the degree of power factor deviation.

[0055] The compensation priority is set according to the deviation coefficient. For example, when the deviation coefficient is greater than 0.1, it is judged as high deviation and the highest priority is set; when the deviation coefficient is greater than or equal to 0.05 and less than or equal to 0.1, it is judged as medium deviation and the medium priority is set; when the deviation coefficient is less than 0.05, it is judged as low deviation and the lowest priority is set. The power factor adjustment priority is obtained after the priority setting, which is used to dynamically adjust the capacitor switching strategy.

[0056] Furthermore, the reactive power gap value calculation formula is as follows:

[0057] Q comp =P(t)*(tanφ act -tanφ tar );

[0058] Among them, Q comp is the reactive power gap value, P(t) is the active power, φ act is the current power factor angle, defined as the phase difference between voltage and current, tanφ act is the reactive power coefficient corresponding to the power factor, φ tar is the target power factor angle, tanφ tar is the reactive power coefficient corresponding to the target power factor.

[0059] Specifically, the reactive power gap value calculation formula is as follows:

[0060] Q comp =P(t)*(tanφ act -tanφ tar );

[0061] Among them, Q compis the reactive power gap value, that is, the amount of reactive power that needs to be compensated. Its purpose is to provide this compensation through the capacitor so that the power factor of the system can be adjusted from the current value to the target power factor; P(t) is the active power, which reflects the actual electric energy demand of the load for the power grid transmission. The active power is the effective part of the load power and does not need to be compensated.

[0062] φ act is the current power factor angle, defined as the phase difference between voltage and current, tanφ act is the reactive power coefficient corresponding to the power factor, which is reflected as the ratio of the current reactive power to the active power; φ tar is the target power factor angle, corresponding to the set target power factor, tanφ tar It indicates the ideal ratio of reactive power to active power at the target power factor.

[0063] In general, the current reactive power gap value may be too high or too low. The ideal reactive power gap value can be calculated through the target power factor. In simple terms, if the current power factor is lower than the target value, that is, the reactive power is too much, it means that the reactive power needs to be reduced through compensation; if the current power factor is higher than the target value, that is, the reactive power is too little, it means that there is no need to increase compensation. In this way, the switching state of the capacitor group can be dynamically adjusted according to the gap value to achieve the target power factor.

[0064] Furthermore, the method further comprises:

[0065] The harmonic content is obtained according to the power quality monitoring data, the harmonic content is analyzed, and the total harmonic distortion rate is calculated; when the total harmonic distortion rate is greater than a preset distortion rate threshold, the main harmonic source is identified, and a harmonic excess reminder is generated to impose capacitor switching constraints.

[0066] Harmonics refer to the high-order frequency components generated when the current or voltage waveform in the power system deviates from the sinusoidal waveform. They are usually represented by harmonic current and fundamental current. The harmonic data of each key node is obtained in real time from the power quality monitoring device, including the fundamental current and each harmonic current. The total harmonic distortion rate is defined as the ratio of the sum of the squares of all harmonic currents to the square of the fundamental current. The total harmonic distortion rate is calculated based on the fundamental current and each harmonic current.

[0067] The distortion rate threshold is set according to industry standards, such as 5% (grid public connection point) or 3% (sensitive equipment power supply point). When the total harmonic distortion rate is greater than the preset distortion rate threshold, the harmonic is judged to be exceeded, and the spectrum analysis is performed on the harmonic components of the exceeding point to identify the dominant harmonic, that is, calculate the proportion of each harmonic, and identify the nth harmonic with the highest proportion as the main harmonic source. Common dominant harmonic sources include 3rd harmonic, 5th harmonic and 7th harmonic. Generate reminder information based on the analysis results, and the reminder information includes the location of the exceeding node, the type of main harmonic source and the frequency information.

[0068] Adjust the capacitor bank switching plan according to the reminder information to avoid harmonic amplification, such as limiting the switching of high-capacity units, prioritizing the switching of small-capacity units, and suspending capacitor switching at nodes near the main harmonic source, so as to reduce the risk of harmonic amplification and ensure the stability of system operation.

[0069] In summary, the low-voltage capacitor dynamic adjustment compensation control method provided by the embodiment of the present application has the following technical effects:

[0070] By collecting historical operating data of the low-voltage distribution network, we can accurately grasp the power consumption patterns, fluctuation characteristics and changes in reactive power demand of the load, laying a data foundation for the subsequent construction of a load fluctuation feature library and optimized compensation strategy; by extracting historical power factor fluctuation ranges, typical load distribution periods and reactive power demand change trends, we can dynamically optimize the target power factor range to ensure that it adapts to actual operating conditions. The optimized target power factor range avoids insufficient or excessive reactive power compensation caused by fixed range settings, thereby improving compensation efficiency and grid stability; through real-time collection of power quality monitoring data by monitoring devices, we can dynamically reflect the real-time operating status of the distribution network and dynamically evaluate the current The degree to which the power factor deviates from the target range is measured, and the reactive power gap is accurately calculated to ensure that the compensation amount matches the actual demand; by dividing the capacitor group into multiple switching units and setting different compensation capacities and priority labels, refined reactive power compensation is achieved to ensure the flexibility and dynamism of the compensation strategy; a switching state optimization model is established, and the compensation effect and equipment life are balanced by setting the compensation capacity, switching threshold and action delay time; based on real-time evaluation results and optimization models, the switching state of the capacitor group is dynamically adjusted to ensure that the reactive compensation effect matches the load demand in real time, and the compensation strategy is automatically optimized according to the operating status, thereby improving control accuracy and system stability.

[0071] Based on the same inventive concept as the low-voltage capacitor dynamic adjustment compensation control method in the above-mentioned embodiment, Figure 2 As shown, an embodiment of the present application provides a low-voltage capacitor dynamic adjustment compensation control device, the device comprising:

[0072] An operation data acquisition module 10, the operation data acquisition module 10 is used to collect operation data of the low-voltage power distribution network within a preset historical time range, and obtain historical load sample data; a characteristic index extraction module 20, the characteristic index extraction module 20 is used to extract characteristic indicators based on the historical load sample data, build a load fluctuation feature library, and determine the target power factor interval based on the load fluctuation feature library; a monitoring data acquisition module 30, the monitoring data acquisition module 30 is used to deploy a power quality monitoring device in the low-voltage power distribution network and obtain power quality monitoring data; a dynamic evaluation module 40, the dynamic evaluation module 40 is used to perform a dynamic power quality evaluation on the power quality monitoring data based on the target power factor interval, and generate a dynamic compensation demand indicator according to the evaluation result, which In the embodiment, the dynamic compensation demand index includes reactive power demand and power factor adjustment priority; a capacitor group division module 50, the capacitor group division module 50 is used to divide the target capacitor group into multiple switching units according to the reactive power demand, and each switching unit has a compensation capacity label and a priority label; an optimization model establishment module 60, the optimization model establishment module 60 is used to establish a switching state optimization model, wherein the switching state optimization model includes compensation capacity, switching threshold, and action delay time; a dynamic switching analysis module 70, the dynamic switching analysis module 70 is used to perform dynamic switching analysis of the multiple switching units based on the switching state optimization model, with the goal of minimizing reactive power gap and reducing switching loss, generate capacitor control parameters, and perform switching control of the target capacitor group.

[0073] Furthermore, the characteristic index extraction module 20 also includes the following operation steps:

[0074] Based on the historical load sample data, historical power factor data, historical reactive power demand data, and historical electricity load data are extracted; the historical power factor data, historical reactive power demand data, and historical electricity load data are respectively standardized, and the standardized data are subjected to time series feature analysis to construct a load fluctuation feature library, wherein the load fluctuation feature library includes power factor fluctuation ranges, typical load distribution time periods and typical load modes, and reactive power demand change trends; an initial power factor range is obtained based on the network characteristics of the low-voltage power distribution network, and based on the load fluctuation feature library, the initial power factor range is dynamically adjusted to obtain the target power factor range.

[0075] Furthermore, the characteristic index extraction module 20 also includes the following operation steps:

[0076] The historical power factor data is arranged in time series to obtain a historical power factor sequence, and a sliding window analysis is performed on the historical power factor sequence using a preset window to extract the upper and lower limits of the fluctuation range to obtain the power factor fluctuation range; the historical power load data is clustered into time periods according to a preset time scale, and the peak period, off-peak period and valley period are extracted according to the clustering results to obtain the typical load distribution period and the corresponding typical load mode; the historical reactive power demand data is fitted and analyzed using a time series prediction model, and the reactive power demand change trend is obtained based on the reactive power demand time characteristic curve obtained by fitting.

[0077] Furthermore, the characteristic index extraction module 20 also includes the following operation steps:

[0078] The peak period, the flat period and the valley period are compensated in stages in turn to obtain reactive power compensation priority; and auxiliary control of capacitor switching in corresponding periods is performed according to the reactive power compensation priority.

[0079] Furthermore, the dynamic evaluation module 40 further includes the following operation steps:

[0080] The power factor is obtained according to the power quality monitoring data; when the power factor deviates from the target power factor interval, the reactive power gap value is calculated, and the reactive power demand is obtained according to the reactive power gap value; the deviation coefficient of the power factor from the target power factor interval is calculated, and the compensation priority is set according to the deviation coefficient to obtain the power factor adjustment priority.

[0081] Furthermore, the reactive power gap value calculation formula is as follows:

[0082] Q comp =P(t)*(tanφ act -tanφ tar );

[0083] Among them, Q comp is the reactive power gap value, P(t) is the active power, φ act is the current power factor angle, defined as the phase difference between voltage and current, tanφ act is the reactive power coefficient corresponding to the power factor, φ tar is the target power factor angle, tanφ tar is the reactive power coefficient corresponding to the target power factor.

[0084] Furthermore, the dynamic evaluation module 40 further includes the following operation steps:

[0085] The harmonic content is obtained according to the power quality monitoring data, the harmonic content is analyzed, and the total harmonic distortion rate is calculated; when the total harmonic distortion rate is greater than a preset distortion rate threshold, the main harmonic source is identified, and a harmonic excess reminder is generated to impose capacitor switching constraints.

[0086] Through the above-mentioned detailed description of a low-voltage capacitor dynamic adjustment compensation control method in this specification, technical personnel in this field can clearly understand a low-voltage capacitor dynamic adjustment compensation control device in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0087] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A low voltage capacitor dynamic adjustment compensation control method, characterized in that: The method comprises: Collect operation data of the low-voltage power distribution network within a preset historical time range to obtain historical load sample data; Based on the historical load sample data, feature index extraction is performed to construct a load fluctuation feature library, and a target power factor range is determined based on the load fluctuation feature library; Deploying a power quality monitoring device in the low-voltage power distribution network to obtain power quality monitoring data; Based on the target power factor interval, dynamically evaluate the power quality of the power quality monitoring data, and generate a dynamic compensation demand index according to the evaluation result, wherein the dynamic compensation demand index includes reactive power demand and power factor adjustment priority; According to the reactive power demand, the target capacitor group is divided into a plurality of switching units, each switching unit having a compensation capacity label and a priority label; Establishing a switching state optimization model, wherein the switching state optimization model includes compensation capacity, switching threshold, and action delay time; Based on the switching state optimization model, with the goal of minimizing reactive power gap and reducing switching loss, dynamic switching analysis of the multiple switching units is performed, capacitor control parameters are generated, and switching control of the target capacitor group is performed.

2. A low voltage capacitor dynamic adjustment compensation control method as claimed in claim 1, characterized in that: The method comprises: Based on the historical load sample data, extract historical power factor data, historical reactive power demand data, and historical power load data; The historical power factor data, the historical reactive power demand data, and the historical power load data are respectively standardized, and the time series feature analysis is performed on the standardized data to construct a load fluctuation feature library, wherein the load fluctuation feature library includes the power factor fluctuation range, the typical load distribution period and the typical load mode, and the reactive power demand change trend; An initial power factor interval is acquired based on the network characteristics of the low-voltage power distribution network, and the initial power factor interval is dynamically adjusted based on the load fluctuation feature library to obtain the target power factor interval.

3. A low voltage capacitor dynamic adjustment compensation control method as claimed in claim 2, characterized in that: The method for constructing a load fluctuation feature library includes: Arranging the historical power factor data in time series to obtain a historical power factor sequence, performing sliding window analysis on the historical power factor sequence using a preset window, extracting upper and lower limits of the fluctuation range, and obtaining the power factor fluctuation range; Clustering the historical power load data into time periods according to a preset time scale, extracting peak time periods, off-peak time periods and valley time periods according to the clustering results, and obtaining the typical load distribution time periods and the corresponding typical load patterns; A time series prediction model is used to perform fitting analysis on the historical reactive power demand data, and the reactive power demand change trend is obtained based on the reactive power demand time characteristic curve obtained by fitting.

4. A low voltage capacitor dynamic adjustment compensation control method as claimed in claim 3, characterized in that: The method further comprises: Performing graded compensation for the peak period, the flat period and the off-peak period in turn to obtain reactive power compensation priority; The capacitor bank switching auxiliary control is performed in the corresponding time period according to the reactive power compensation priority.

5. A low voltage capacitor dynamic adjustment compensation control method as claimed in claim 1, characterized in that: The method comprises: Obtaining a power factor according to the power quality monitoring data; When the power factor deviates from the target power factor interval, a reactive power gap value is calculated, and the reactive power demand is obtained according to the reactive power gap value; The deviation coefficient of the power factor from the target power factor interval is calculated, and the compensation priority is set according to the deviation coefficient to obtain the power factor adjustment priority.

6. A low voltage capacitor dynamic adjustment compensation control method as claimed in claim 5, characterized in that: The reactive power gap value calculation formula is as follows: Q comp =P(t)*(tanφ act -tanφ tar ); Among them, Q comp is the reactive power gap value, P(t) is the active power, φ act is the current power factor angle, defined as the phase difference between voltage and current, tanφ act is the reactive power coefficient corresponding to the power factor, φ tar is the target power factor angle, tanφ tar is the reactive power coefficient corresponding to the target power factor.

7. A low voltage capacitor dynamic adjustment compensation control method as claimed in claim 5, characterized in that: The method further comprises: Obtaining harmonic content according to the power quality monitoring data, analyzing the harmonic content, and calculating and obtaining the total harmonic distortion rate; When the total harmonic distortion rate is greater than a preset distortion rate threshold, the main harmonic source is identified, and a harmonic excess reminder is generated to constrain capacitor switching.

8. A low voltage capacitor dynamic adjustment compensation control device, characterized in that: The device is used to implement a low-voltage capacitor dynamic adjustment compensation control method according to any one of claims 1 to 7, comprising: An operation data acquisition module, which is used to collect operation data of the low-voltage power distribution network within a preset historical time range to obtain historical load sample data; A characteristic index extraction module, the characteristic index extraction module is used to extract characteristic indicators based on the historical load sample data, build a load fluctuation characteristic library, and determine a target power factor range based on the load fluctuation characteristic library; A monitoring data acquisition module, the monitoring data acquisition module is used to deploy a power quality monitoring device in the low-voltage power distribution network to obtain power quality monitoring data; A dynamic evaluation module, the dynamic evaluation module is used to perform a dynamic evaluation of power quality on the power quality monitoring data based on the target power factor interval, and generate a dynamic compensation demand index according to the evaluation result, wherein the dynamic compensation demand index includes reactive power demand and power factor adjustment priority; A capacitor group division module, the capacitor group division module is used to divide the target capacitor group into a plurality of switching units according to the reactive power demand, each switching unit having a compensation capacity label and a priority label; An optimization model building module, wherein the optimization model building module is used to build a switching state optimization model, wherein the switching state optimization model includes a compensation capacity, a switching threshold, and an action delay time; A dynamic switching analysis module is used to perform dynamic switching analysis of the multiple switching units based on the switching state optimization model, generate capacitor control parameters, and perform switching control of the target capacitor group with the goal of minimizing reactive power gap and reducing switching losses.

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