A digital distribution network voltage active management method and system

Abstract

The present invention provides a method and system for active voltage management of a digital distribution network, the method comprising evaluating the voltage state of the distribution network based on real-time voltage monitoring data and an intelligent prediction algorithm, and then adjusting the voltage-reactive coordinated control strategy to obtain a stable voltage value of each node; obtaining a key node of voltage fluctuation in the stable voltage value of each node, constructing a control area according to the key node, extending the control range to the surrounding associated nodes respectively, and obtaining control path data; setting a control parameter range according to the control path data to limit the adjustment range of the voltage-reactive coordinated control strategy; searching for the optimal reactive compensation point and compensation capacity based on the distribution network equivalent circuit model and the voltage loss calculation model under the criterion of the minimum sum of squares of voltage deviation, and correcting the voltage deviation of each node. The present invention can improve the voltage stability and power quality of the distribution network and ensure the safe and reliable operation of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage management of distribution networks, and more specifically, to a method and system for active voltage management of digital distribution networks. Background Art

[0002] In the power system, the voltage stability of the distribution network is crucial to the quality and reliability of power supply. With the continuous growth of power demand and the increasing complexity of the power grid structure, the traditional distribution network voltage management method faces many challenges. Traditional management methods often rely on manual experience and simple monitoring methods, making it difficult to control the distribution network voltage in real time and accurately.

[0003] Existing voltage management technologies lack effective prediction capabilities and coordinated control strategies when facing complex power grid environments and variable load conditions. For example, in terms of voltage status assessment, it may not be possible to fully utilize historical data and real-time monitoring information to accurately predict voltage change trends; in terms of reactive power compensation control, it is difficult to determine the optimal compensation point and compensation capacity, resulting in inaccurate voltage adjustment and inability to effectively deal with voltage fluctuations.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: lack of accurate voltage status assessment means, unable to timely and accurately detect voltage abnormalities; the identification of key nodes of voltage fluctuations is not accurate enough, affecting the reasonable construction of the control area; the voltage and reactive power coordinated control strategy is not optimized enough, and the adjustment range is unreasonable; in terms of voltage deviation correction, there is a lack of systematic and effective methods to determine the optimal reactive power compensation point and compensation capacity, making it difficult to achieve efficient voltage deviation correction, affecting the overall voltage stability and power quality of the distribution network. Summary of the invention

[0005] The present invention provides a method and system for active voltage management of a digital power distribution network.

[0006] In a first aspect of the present invention, a method for active voltage management of a digital power distribution network is provided, comprising:

[0007] Based on real-time voltage monitoring data and intelligent prediction algorithms, the voltage status of the distribution network is evaluated, and then the voltage and reactive power coordinated control strategy is used to adjust the voltage to obtain the stable voltage value of each node;

[0008] Obtain the key nodes of voltage fluctuation from the stable voltage values ​​of each node, construct a control area based on the key nodes, extend the control range to the surrounding related nodes, and obtain the control path data;

[0009] Set the control parameter range according to the control path data to limit the adjustment range of the voltage and reactive power coordinated control strategy;

[0010] Under the principle of minimizing the sum of squares of voltage deviation, based on the distribution network equivalent circuit model and voltage loss calculation model, the optimal reactive power compensation point and compensation capacity are searched, and the voltage deviation of each node is corrected.

[0011] The coordinate range of the control area is:

[0012]

[0013] in, and The minimum and maximum coordinate values ​​of the control area in the direction of the node number;

[0014] and The minimum and maximum coordinate values ​​of the control area in the direction of the voltage level number;

[0015] The coordinates of the key nodes of voltage fluctuation are , is the coordinate value of the key node of voltage fluctuation in the direction of node number;

[0016] It is the coordinate value of the key node of voltage fluctuation in the direction of voltage level number;

[0017] is the control range width in the direction of node number, It is the control range height in the direction of voltage level number.

[0018] Furthermore, the intelligent prediction algorithm is a prediction algorithm based on time series analysis;

[0019] The mathematical model of the voltage-reactive power coordinated control strategy is:

[0020]

[0021] in, is the reactive power compensation amount, For Node The actual voltage, is the node voltage target value, is the equivalent reactance between the node and the power supply.

[0022] Further, the evaluating the voltage state of the distribution network includes:

[0023] The voltage data collected by high-precision voltage sensors installed at key nodes of the distribution network are used as basic data for analysis. The current voltage change trend is estimated based on historical voltage data and current load conditions, and the optimal prediction algorithm parameters are selected based on the voltage change trend and grid topology.

[0024] A corresponding prediction model is obtained according to the prediction algorithm parameters, and a voltage prediction curve is obtained according to the model characteristics;

[0025] According to the voltage prediction curve, the threshold judgment method is used to design the parameters of the voltage abnormality warning mechanism;

[0026] The early warning mechanism is used to evaluate the voltage data of different nodes respectively, and then the control coefficient of the corresponding prediction algorithm parameter is used to adjust the voltage state of the distribution network.

[0027] Furthermore, the optimal prediction algorithm parameters are determined as follows:

[0028] For each node, by comparing the error between the predicted voltage value and the actual voltage value under different parameters, the mean square error corresponding to each parameter is calculated;

[0029] The mean square error calculation formula is:

[0030]

[0031] is the predicted voltage value, is the actual voltage value, is the number of data points.

[0032] Furthermore, obtaining the voltage fluctuation key node from the stable voltage value of each node includes:

[0033] The data collected by the voltage sensor are sequentially processed through data preprocessing, feature extraction and voltage fluctuation quantification to obtain the voltage fluctuation degree results of each node;

[0034] Search for the location of the maximum voltage fluctuation degree of each node as a potential key node, perform correlation analysis on the voltage fluctuation degrees of multiple adjacent nodes in segments, and statistically analyze the relative fluctuation coefficient of each node and the average fluctuation coefficient of nodes in the segment;

[0035] The node segment with the largest average fluctuation coefficient is taken as the key node segment, and the node position is selected according to the relative fluctuation coefficient threshold, the voltage amplitude information of each selected node position is extracted, and the node with the largest voltage amplitude change is selected as the key node of voltage fluctuation.

[0036] Furthermore, the voltage deviation correction for each node includes:

[0037] According to the voltage level range, the data of each power supply area is divided into multiple sections along the feeder. First, the voltage value of the central node before and after voltage adjustment is obtained based on the voltage difference between the edge of the area and the adjacent area, and then the voltage value and power factor of other nodes before and after voltage adjustment are obtained.

[0038] Furthermore, the equivalent circuit model of the distribution network is:

[0039]

[0040] in, is the voltage phasor at the sending end, is the receiving terminal voltage phasor, is the line equivalent impedance, is the line current phasor.

[0041] Furthermore, the voltage loss calculation model is:

[0042]

[0043] in, is the voltage loss, ´ is the active power, is the reactive power, is the line resistance, is the line reactance, is the line voltage.

[0044] In a second aspect of the present invention, a digital distribution network voltage active management system is provided, comprising:

[0045] Voltage status assessment module: used to assess the voltage status of the distribution network based on real-time voltage monitoring data and intelligent prediction algorithms. This module includes a data acquisition submodule, a trend estimation submodule, a parameter selection submodule, a model generation submodule, an early warning design submodule, and an assessment and adjustment submodule;

[0046] Key node and control area determination module: used to obtain the key nodes of voltage fluctuation in the stable voltage values ​​of each node, and construct the control area according to the key nodes. This module includes a data processing submodule, a potential key node search submodule, a correlation analysis submodule, a key node determination submodule and a control area construction submodule;

[0047] Voltage deviation correction module: It is used to search for the optimal reactive compensation point and compensation capacity under the criterion of minimizing the sum of squared voltage deviations, based on the distribution network equivalent circuit model and voltage loss calculation model, and perform voltage deviation correction on each node. This module includes a segmentation submodule, a voltage value acquisition submodule, and a deviation correction submodule.

[0048] According to the above-mentioned embodiments of the present invention, at least the following beneficial effects are achieved: the digital distribution network voltage active management method and system described in the present invention can effectively evaluate the voltage state of the distribution network based on real-time voltage monitoring data and intelligent prediction algorithms, and obtain the stable voltage value of each node in combination with the voltage-reactive coordinated control strategy, thereby improving the accuracy and timeliness of voltage management. At the same time, the key nodes of voltage fluctuation can be accurately obtained from the stable voltage values ​​of each node and the control area can be constructed. By setting the control parameter range to limit the adjustment range, the effectiveness and stability of the control strategy can be ensured.

[0049] Based on the distribution network equivalent circuit model and voltage loss calculation model, the optimal reactive compensation point and compensation capacity are searched for under the criterion of minimizing the sum of squares of voltage deviations to correct the voltage deviation of each node. This can further optimize the voltage quality, reduce the impact of voltage deviations on power equipment and user electricity consumption, and improve the power quality, operational stability and reliability of the distribution network as a whole. This ensures the safe and stable operation of the power system, reduces various risks and losses caused by voltage problems, and adapts to the needs of modern power systems for efficient and stable power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0051] Figure 1 A schematic diagram of a flow chart of a method for active voltage management in a digital distribution network provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0052] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0053] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0054] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0055] Reference below Figure 1 , Figure 1 The figure is a flow chart of a method for active voltage management of a digital distribution network provided by an embodiment of the present invention. Figure 1 As shown, a method 100 for active voltage management of a digital distribution network includes:

[0056] Step 101, based on real-time voltage monitoring data and intelligent prediction algorithm, the voltage state of the distribution network is evaluated, and then the voltage and reactive power coordinated control strategy is adopted to adjust the voltage state to obtain the stable voltage value of each node;

[0057] Step 102, obtaining the voltage fluctuation key nodes from the stable voltage values ​​of each node, constructing a control area according to the key nodes, extending the control range to the surrounding related nodes respectively, and obtaining control path data;

[0058] Step 103, setting a control parameter range according to the control path data to limit the adjustment range of the voltage-reactive power coordinated control strategy;

[0059] Step 104 , under the criterion of minimizing the sum of squares of voltage deviations, based on the distribution network equivalent circuit model and the voltage loss calculation model, search for the optimal reactive power compensation point and compensation capacity, and perform voltage deviation correction on each node.

[0060] It should be noted that this embodiment relates to a method for active voltage management of a digital distribution network. The core of this method is to evaluate the voltage state of the distribution network using real-time voltage monitoring data and intelligent prediction algorithms, and to adjust it using a voltage-reactive coordinated control strategy to achieve the acquisition of stable voltage values ​​at each node. Here, the distribution network refers to the network in the power system that is responsible for distributing electric energy from the high-voltage transmission network to the user end, and the voltage state evaluation refers to the real-time monitoring and prediction of the voltage level of the distribution network.

[0061] Specifically, the method first needs to collect real-time voltage monitoring data in the distribution network, which can be obtained by high-precision voltage sensors installed at key nodes. Then, the data is analyzed using an intelligent prediction algorithm based on time series analysis to predict the voltage change trend. Time series analysis is a statistical technique used to analyze data points arranged in chronological order to identify trends, seasonality and other patterns. In this embodiment, by comparing the error between the predicted voltage value and the actual voltage value under different parameters, the mean square error corresponding to each parameter is calculated to determine the optimal prediction algorithm parameters.

[0062] More specifically, we can use the ARIMA (Autoregressive Integrated Moving Average) model as a prediction algorithm for time series analysis, which can handle non-stationary time series data and is suitable for voltage prediction. In terms of parameter setting, we can determine the parameters of the ARIMA model through cross-validation method based on historical data and current load conditions to minimize the prediction error.

[0063] Furthermore, in order to improve the accuracy of the prediction, it is also possible to consider introducing external factors, such as weather conditions, temperature changes, etc., as input parameters of the model. In the voltage-reactive power coordinated control strategy, a dynamically adjusted parameter range can be set to limit the adjustment range and ensure that the voltage adjustment is carried out within a safe and effective range.

[0064] In some embodiments, the intelligent prediction algorithm is a prediction algorithm based on time series analysis;

[0065] The mathematical model of the voltage-reactive power coordinated control strategy is:

[0066]

[0067] in, is the reactive power compensation amount, For Node The actual voltage, is the node voltage target value, is the equivalent reactance between the node and the power supply.

[0068] It should be noted that this embodiment describes a prediction algorithm based on time series analysis for evaluating the voltage state of the distribution network and using a specific mathematical model to adjust the voltage-reactive power coordinated control strategy. Time series analysis here is a statistical method for analyzing data points arranged in time order to identify trends, seasonality and other patterns. The voltage-reactive power coordinated control strategy refers to a power system control method that aims to maintain the voltage level by adjusting reactive power.

[0069] Specifically, the mathematical model is used to calculate the reactive power compensation amount, that is,

[0070]

[0071] in Indicates the reactive power compensation amount, represents the actual voltage of the node, is the node voltage target value, is the equivalent reactance between the node and the power source. In this model, the reactive compensation amount is calculated based on the difference between the actual voltage and the target voltage and the equivalent reactance of the line. Parameter settings include determining the target voltage for each node and equivalent reactance ,These parameters can be set according to the historical data and design standards of the power grid.

[0072] Preferably, we can further refine the parameter settings of the model. For example, the equivalent reactance This can be determined by measuring the resistance and reactance of the line, or estimated using historical data and grid models.

[0073] Furthermore, in order to improve the adaptability and accuracy of the model, an adaptive mechanism can be introduced to make and Ability to dynamically adjust based on real-time load changes and voltage monitoring data. Alternatives may include using other types of predictive algorithms,

[0074] In some embodiments, the evaluating the voltage status of the power distribution network includes:

[0075] The voltage data collected by high-precision voltage sensors installed at key nodes of the distribution network are used as basic data for analysis. The current voltage change trend is estimated based on historical voltage data and current load conditions, and the optimal prediction algorithm parameters are selected based on the voltage change trend and grid topology.

[0076] A corresponding prediction model is obtained according to the prediction algorithm parameters, and a voltage prediction curve is obtained according to the model characteristics;

[0077] According to the voltage prediction curve, the threshold judgment method is used to design the parameters of the voltage abnormality warning mechanism;

[0078] The early warning mechanism is used to evaluate the voltage data of different nodes respectively, and then the control coefficient of the corresponding prediction algorithm parameter is used to adjust the voltage state of the distribution network.

[0079] It should be noted that this implementation method describes in detail how to evaluate the voltage state of the distribution network, including using the data collected by high-precision voltage sensors as a basis, combining historical voltage data and current load conditions to estimate the voltage change trend, and selecting the optimal prediction algorithm parameters based on these trends and the grid structure. Here, the distribution network refers to the network responsible for distributing electric energy from the high-voltage transmission network to the user end, and the voltage state assessment refers to the real-time monitoring and prediction of the voltage level of the distribution network.

[0080] Specifically, the evaluation process first involves collecting and analyzing voltage data collected by high-precision voltage sensors installed at key nodes of the distribution network. These data will serve as the basic data for the evaluation. Then, based on the historical voltage data and the current load situation, the current voltage change trend can be estimated. The historical voltage data refers to the voltage values ​​recorded over a period of time in the past, while the current load situation refers to the current power demand of the power grid.

[0081] More specifically, based on this information, the most suitable forecasting algorithm parameters can be selected, which include model parameters in time series analysis, such as the number of autoregressive terms, difference terms, and moving average terms of the ARIMA model.

[0082] Preferably, the evaluation process can be further refined. For example, machine learning techniques can be used to automatically identify and adjust the prediction algorithm parameters to adapt to the dynamic changes of the power grid. In addition, more external factors, such as weather conditions, temperature changes, etc., can be introduced as inputs to the prediction model to improve the accuracy of the prediction.

[0083] Further alternatives include the use of other types of prediction models, such as deep learning models, which are able to handle more complex data patterns and may provide better prediction performance. At the same time, it is also possible to explore the use of multi-model fusion methods to combine the advantages of different prediction models to improve the overall accuracy and robustness of voltage state assessment.

[0084] In some embodiments, the optimal prediction algorithm parameters are determined as follows:

[0085] For each node, by comparing the error between the predicted voltage value and the actual voltage value under different parameters, the mean square error corresponding to each parameter is calculated;

[0086] The mean square error calculation formula is:

[0087]

[0088] is the predicted voltage value, is the actual voltage value, is the number of data points.

[0089] It should be noted that this embodiment describes how to determine the optimal prediction algorithm parameters by comparing the error between the predicted voltage value and the actual voltage value under different parameters and calculating the mean square error corresponding to each parameter. Here, the mean square error (MSE) is a commonly used statistic used to measure the difference between the predicted value and the actual value. It is the average of the sum of the squares of the prediction errors and can provide a quantitative indicator of the accuracy of the model prediction.

[0090] Specifically, the calculation of the mean square error involves comparing the predicted voltage value with the actual voltage value. In this process, for each parameter setting, the predicted voltage value is calculated by the model based on the input data, while the actual voltage value is directly measured from the voltage sensor. The number of data points refers to the total number of voltage data points considered when calculating the mean square error.

[0091] Furthermore, specific parameter settings include selecting different time series analysis model parameters, such as the values ​​of p (autoregressive term), d (difference term) and q (moving average term) in the ARIMA model. The settings of these parameters will directly affect the prediction ability of the model, and the optimal parameter combination needs to be determined through cross-validation of historical data.

[0092] Preferably, the calculation of the mean squared error can be further refined, such as introducing a weighted mean squared error, where different data points can have different weights to reflect that some data points may be more important or reliable than others. In addition, different error metrics can be explored, such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE), which may be less sensitive to outliers.

[0093] Going a step further, alternatives might include using machine learning methods, such as random forests or neural networks, which can automatically adjust parameters to minimize prediction errors. At the same time, you might also consider using ensemble learning methods, such as bagging or boosting, which improve the overall prediction accuracy by combining the predictions of multiple models.

[0094] In some embodiments, obtaining a voltage fluctuation key node from the stable voltage value of each node includes:

[0095] The data collected by the voltage sensor are sequentially processed through data preprocessing, feature extraction and voltage fluctuation quantification to obtain the voltage fluctuation degree results of each node;

[0096] Search for the location of the maximum voltage fluctuation degree of each node as a potential key node, perform correlation analysis on the voltage fluctuation degrees of multiple adjacent nodes in segments, and statistically analyze the relative fluctuation coefficient of each node and the average fluctuation coefficient of nodes in the segment;

[0097] The node segment with the largest average fluctuation coefficient is taken as the key node segment, and the node position is selected according to the relative fluctuation coefficient threshold, the voltage amplitude information of each selected node position is extracted, and the node with the largest voltage amplitude change is selected as the key node of voltage fluctuation.

[0098] It should be noted that the voltage fluctuation key nodes refer to the nodes where the voltage changes most significantly in the distribution network. The voltage stability of these nodes has an important impact on the stability of the entire power grid. Data preprocessing refers to the process of cleaning and formatting the raw voltage data for further analysis. Feature extraction refers to identifying key information that helps in voltage fluctuation analysis from the preprocessed data. Voltage fluctuation quantification refers to expressing the degree of voltage fluctuation in numerical values ​​for easy comparison and analysis.

[0099] Specifically, the data collected by the voltage sensor is first preprocessed, which may include removing outliers, filling missing values, smoothing, etc. Then feature extraction is performed, which may involve identifying key features such as peaks, valleys, and trend changes in the voltage data. Then, through the operation of voltage fluctuation quantification, the voltage fluctuation degree results of each node can be obtained.

[0100] More specifically, this involves calculating statistics such as the standard deviation, maximum and minimum values ​​of the voltage. Next, by searching for the location of the maximum voltage fluctuation degree at each node, potential key nodes can be determined. Correlation analysis is performed on these nodes, and the relative fluctuation coefficient of each node in the statistical analysis results and the average fluctuation coefficient of the nodes in the segment are used to determine the key node segment.

[0101] Preferably, the operation method for quantifying voltage fluctuations can be further refined, for example, by introducing advanced signal processing techniques such as wavelet transform to more accurately capture the characteristics of voltage fluctuations. When determining key nodes, machine learning methods such as cluster analysis can be used to automatically identify node groups with similar voltage fluctuation patterns.

[0102] Furthermore, dynamic thresholds can be set to automatically adjust the threshold of the relative fluctuation coefficient according to the changes in real-time data and historical data to adapt to changes in the operating status of the power grid. Alternative solutions may include using fuzzy logic or neural networks to process voltage fluctuation data. These methods can handle uncertainty and nonlinear problems and provide more flexible voltage fluctuation analysis.

[0103] In some embodiments, the coordinate range of the control area is:

[0104]

[0105] in, and The minimum and maximum coordinate values ​​of the control area in the direction of the node number;

[0106] and The minimum and maximum coordinate values ​​of the control area in the direction of the voltage level number;

[0107] The coordinates of the key nodes of voltage fluctuation are , is the coordinate value of the key node of voltage fluctuation in the direction of node number;

[0108] It is the coordinate value of the key node of voltage fluctuation in the direction of voltage level number;

[0109] is the control range width in the direction of node number, It is the control range height in the direction of voltage level number.

[0110] It should be noted that this embodiment describes in detail how to determine the coordinate range of the control area, which is constructed based on the location of the key nodes of voltage fluctuations. The control area refers to a set of nodes within a certain range extending around the key nodes in the distribution network, which is used to implement the voltage and reactive power coordinated control strategy. The node number direction and the voltage level number direction refer to the arrangement order of nodes and voltage levels in the topological structure of the distribution network. The control range width and control range height refer to the degree of expansion of the control area in these two directions.

[0111] Specifically, the coordinate range of the control area is determined by determining the coordinates of the key nodes of voltage fluctuation ( ),in is the coordinate value in the direction of the node number, is the coordinate value in the direction of the voltage level number. Then, according to the coordinates of this key node, a certain width is extended in both directions. and height , to form a control area.

[0112] More specifically, the width and height here can be set according to the actual situation of the power grid and the control requirements. For example, these parameters can be determined based on the historical data of the power grid and the characteristics of voltage fluctuations. The coordinate range expression of the control area is: .

[0113] Preferably, the determination of the control area can be further refined. For example, an adaptive mechanism can be introduced to dynamically adjust the width and height of the control range according to real-time voltage monitoring data. In addition, the topology and load distribution of the power grid can be considered to optimize the division of the control area to improve the efficiency and effect of voltage control.

[0114] Further, alternatives include using graph theory methods to determine the control area, dividing the control area by analyzing the connectivity of the power grid and the importance of the nodes. It is also possible to consider using optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, to automatically find the best control area division scheme to achieve the optimal effect of voltage control.

[0115] In some embodiments, the voltage deviation correction for each node includes:

[0116] According to the voltage level range, the data of each power supply area is divided into multiple sections along the feeder. First, the voltage value of the central node before and after voltage adjustment is obtained based on the voltage difference between the edge of the area and the adjacent area, and then the voltage value and power factor of other nodes before and after voltage adjustment are obtained.

[0117] It should be noted that this embodiment involves the process of correcting the voltage deviation of each node in the distribution network. The voltage deviation correction here refers to adjusting the voltage of each node in the distribution network to make it closer to the set voltage level range to ensure the stability and reliability of power supply. The voltage level range refers to the voltage range specified in the power system, and the voltage of the node needs to be controlled within this range to ensure the normal operation of the equipment.

[0118] Specifically, voltage deviation correction first requires dividing the data of the power supply area into multiple sections along the feeder. Feeder refers to the line that transmits electric energy in the distribution network. Then, based on the voltage difference between the edge of the area and the adjacent area, the voltage value of the central node before and after voltage adjustment is obtained. The central node here refers to a reference node in the control area, and its voltage value is instructive for the voltage adjustment of the entire control area. Next, the voltage values ​​and power factors of other nodes before and after voltage adjustment are obtained. The power factor refers to the cosine value of the phase difference between voltage and current in an AC circuit, which affects the efficiency of the power grid and the quality of electric energy.

[0119] Preferably, the voltage deviation correction process can be further refined. For example, advanced data analysis techniques, such as machine learning algorithms, can be used to predict and identify the trend of voltage deviation, so as to adjust the voltage more accurately. In addition, a real-time feedback mechanism can be introduced to dynamically adjust the control strategy according to the actual effect after voltage adjustment, so as to achieve faster and more accurate voltage control.

[0120] Further alternatives include using different voltage control algorithms, such as fuzzy control or model predictive control, which can handle complex grid dynamics and nonlinear problems, provide more flexible and effective voltage adjustment strategies, and also consider factors such as grid load changes and weather conditions to achieve more comprehensive voltage deviation correction.

[0121] In some embodiments, the distribution network equivalent circuit model is:

[0122]

[0123] in, is the voltage phasor at the sending end, is the receiving terminal voltage phasor, is the line equivalent impedance, is the line current phasor.

[0124] It should be noted that this embodiment describes a method for constructing an equivalent circuit model of a distribution network, which is the basis for analyzing and calculating the behavior of voltage and current in a power grid. An equivalent circuit model refers to simplifying a complex distribution network into a model consisting of basic circuit elements such as resistors, inductors, and capacitors in order to calculate voltage and power losses. In this model, the sending-end voltage phasor and the receiving-end voltage phasor represent the voltage conditions at the sending and receiving ends of the power grid, respectively, and they are key parameters for analyzing power grid performance.

[0125] Specifically, the parameter setting in the distribution network equivalent circuit model involves the equivalent impedance and current phasor of the line. The equivalent impedance of the line refers to the degree of resistance to the flow of current. It consists of two parts: resistance and reactance. It can be determined by measuring the physical parameters of the line (such as length, cross-sectional area, material, etc.). The line current phasor refers to the magnitude and phase of the current flowing through the line, which can be measured by a current sensor.

[0126] More specifically, in this model, the relationship between the sending-end voltage phasor and the receiving-end voltage phasor can be expressed by the following formula: ,in is the voltage phasor at the sending end, is the receiving terminal voltage phasor, is the line equivalent impedance, is the line current phasor.

[0127] Preferably, the construction of the distribution network equivalent circuit model can be further refined. For example, the nonlinear characteristics of the line, such as the skin effect and the proximity effect, which become significant at high current density, can be considered. In addition, the influence of temperature and environmental factors on the line impedance can be introduced to improve the accuracy of the model.

[0128] Further alternatives include using more complex circuit models, such as distributed parameter models that consider the line, which can more accurately describe the characteristics of long-distance transmission lines. At the same time, it is also possible to consider using numerical simulation software, such as PSCAD or MATPOWER, to simulate and analyze the dynamic behavior of the distribution network, which can provide more detailed network analysis and optimization tools.

[0129] In some embodiments, the voltage loss calculation model is:

[0130]

[0131] in, is the voltage loss, is the active power, is the reactive power, is the line resistance, is the line reactance, is the line voltage.

[0132] It should be noted that this embodiment involves the construction of a voltage loss calculation model, which is a key step for evaluating voltage loss in a distribution network. Voltage loss refers to the phenomenon that voltage drops due to factors such as line resistance during power transmission. In this model, active power and reactive power are two basic parameters in the power system, representing the power that actually does work and the power related to the exchange of magnetic field energy, respectively.

[0133] Specifically, the parameter settings in the voltage loss calculation model involve the resistance and reactance of the line, as well as the voltage level. Line resistance refers to the degree of resistance of the line to the flow of current, which can be determined by measuring the physical parameters of the line. Line reactance refers to the inductive resistance of the line to the alternating current, which is related to the length of the line and the geometry of the conductor.

[0134] More specifically, in this model, the voltage loss can be calculated by the following formula: ,in is the voltage loss, is the line resistance, is the active power, is the line reactance, is the reactive power.

[0135] Preferably, the voltage loss calculation model can be further refined. For example, non-ideal factors of the line can be considered, such as line non-uniformity and poor contact, which may affect the calculation of voltage loss. In addition, the effect of ambient temperature on line resistance can be introduced, because changes in temperature will change the resistivity of the material.

[0136] Further alternatives include using more accurate numerical simulation methods, such as finite element analysis, to simulate the voltage loss of the line. At the same time, it is also possible to consider using intelligent algorithms, such as genetic algorithms or particle swarm optimization algorithms, to optimize line parameters to minimize voltage loss. These methods can provide more accurate voltage loss predictions and more effective grid optimization strategies.

[0137] The above-mentioned embodiments of the present invention have the following beneficial effects: the digital distribution network voltage active management method and related system involved in the present invention can realize accurate evaluation and effective control of the voltage state of the distribution network. Through real-time voltage monitoring data and intelligent prediction algorithms, it is possible to grasp the voltage change trend in advance, discover potential voltage problems in time, and use voltage and reactive power coordinated control strategies for adjustment to ensure that the voltage of each node is stable within a reasonable range, which helps to improve the stability of the distribution network operation and reduce equipment failures and power quality degradation caused by voltage fluctuations.

[0138] Based on the in-depth analysis of the stable voltage value of each node, the key nodes of voltage fluctuation are obtained, and the control area and control path data are constructed, so that the key areas can be managed in a targeted manner. According to the control path data, a reasonable control parameter range can be set to avoid excessive adjustment of the voltage and reactive power coordination control strategy, ensuring the effectiveness of control and the safety of the power grid.

[0139] Furthermore, under the criterion of minimizing the sum of squared voltage deviations, the equivalent circuit model of the distribution network and the voltage loss calculation model are used to search for the optimal reactive compensation point and compensation capacity, which can achieve accurate correction of the voltage deviation of each node, thereby significantly improving the power quality of the entire distribution network, meeting the needs of various power users for high-quality power supply, reducing the operating risks of the power system, and improving the overall performance and economic benefits of the power system. At the same time, the specific technical means and models in each claim cooperate with each other, providing a comprehensive, systematic and efficient solution for the voltage management of the digital distribution network from multiple aspects.

[0140] A digital distribution network voltage active management system 200 according to some embodiments includes:

[0141] Voltage status assessment module: used to assess the voltage status of the distribution network based on real-time voltage monitoring data and intelligent prediction algorithms. This module includes a data acquisition submodule, which is used to obtain voltage data collected by high-precision voltage sensors installed at key nodes of the distribution network;

[0142] The trend estimation submodule estimates the current voltage change trend based on historical voltage data and current load conditions;

[0143] The parameter selection submodule selects the optimal prediction algorithm parameters based on the voltage change trend and grid topology;

[0144] The model generation submodule obtains the corresponding prediction model according to the prediction algorithm parameters and generates a voltage prediction curve;

[0145] The early warning design submodule uses the threshold judgment method to design the parameters of the voltage abnormality early warning mechanism based on the voltage prediction curve;

[0146] The evaluation and adjustment submodule uses the early warning mechanism to evaluate the voltage data of different nodes and adjusts the voltage state of the distribution network using the control coefficients of the corresponding prediction algorithm parameters;

[0147] Key node and control area determination module: used to obtain the key nodes of voltage fluctuation in the stable voltage value of each node, and build the control area according to the key nodes. This module includes a data processing submodule, which performs data preprocessing, feature extraction and voltage fluctuation quantification calculation on the data collected by the voltage sensor to obtain the voltage fluctuation degree results of each node;

[0148] The potential key node search submodule searches for the location of the maximum voltage fluctuation degree of each node as a potential key node;

[0149] The correlation analysis submodule performs correlation analysis on the voltage fluctuation degree of multiple adjacent nodes in segments;

[0150] The key node determination submodule takes the node segment with the largest average fluctuation coefficient as the key node segment, selects the node position according to the relative fluctuation coefficient threshold, extracts the voltage amplitude information of each selected node position, and selects the node with the largest voltage amplitude change as the voltage fluctuation key node;

[0151] The control area construction submodule extends the control range to the surrounding related nodes according to the key nodes, obtains the control path data, and sets the control parameter range according to the control path data to limit the adjustment range of the voltage and reactive power coordinated control strategy;

[0152] Voltage deviation correction module: It is used to search for the optimal reactive compensation point and compensation capacity under the criterion of minimizing the sum of squares of voltage deviation, based on the distribution network equivalent circuit model and voltage loss calculation model, and perform voltage deviation correction on each node. This module includes a segmentation submodule, which divides the data of each power supply area into multiple segments along the feeder according to the voltage level interval;

[0153] The voltage value acquisition submodule first obtains the voltage value of the central node before and after voltage adjustment based on the voltage difference between the edge of the area and the adjacent area, and then obtains the voltage value and power factor of other nodes before and after voltage adjustment;

[0154] The deviation correction submodule searches for the optimal reactive power compensation point and compensation capacity based on the distribution network equivalent circuit model and voltage loss calculation model, and corrects the node voltage deviation accordingly.

[0155] It is understandable that the modules recorded in the digital distribution network voltage active management system are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the digital distribution network voltage active management method are also applicable to the digital distribution network voltage active management system and the modules contained therein, and will not be repeated here.

[0156] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0157] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for active voltage management of a digital distribution network, characterized in that: The steps include: Based on real-time voltage monitoring data and intelligent prediction algorithms, the voltage status of the distribution network is evaluated, and then the voltage and reactive power coordinated control strategy is used to adjust the voltage to obtain the stable voltage value of each node; Obtain the key nodes of voltage fluctuation from the stable voltage values ​​of each node, construct a control area based on the key nodes, extend the control range to the surrounding related nodes, and obtain the control path data; Set the control parameter range according to the control path data to limit the adjustment range of the voltage and reactive power coordinated control strategy; Under the principle of minimizing the sum of squares of voltage deviation, based on the distribution network equivalent circuit model and voltage loss calculation model, the optimal reactive power compensation point and compensation capacity are searched, and the voltage deviation of each node is corrected. The coordinate range of the control area is: in, and The minimum and maximum coordinate values ​​of the control area in the direction of the node number; and The minimum and maximum coordinate values ​​of the control area in the direction of the voltage level number; The coordinates of the key nodes of voltage fluctuation are , is the coordinate value of the key node of voltage fluctuation in the direction of node number; It is the coordinate value of the key node of voltage fluctuation in the direction of voltage level number; is the control range width in the direction of node number, It is the control range height in the direction of voltage level number.

2. A method for active voltage management of a digital distribution network according to claim 1, characterized in that: The intelligent prediction algorithm is a prediction algorithm based on time series analysis; The mathematical model of the voltage-reactive power coordinated control strategy is: in, is the reactive power compensation amount, For Node The actual voltage, is the node voltage target value, is the equivalent reactance between the node and the power supply.

3. A method for active voltage management of a digital distribution network according to claim 2, characterized in that: The evaluating of the voltage state of the distribution network includes: The voltage data collected by high-precision voltage sensors installed at key nodes of the distribution network are used as basic data for analysis. The current voltage change trend is estimated based on historical voltage data and current load conditions, and the optimal prediction algorithm parameters are selected based on the voltage change trend and grid topology. A corresponding prediction model is obtained according to the prediction algorithm parameters, and a voltage prediction curve is obtained according to the model characteristics; According to the voltage prediction curve, the threshold judgment method is used to design the parameters of the voltage abnormality warning mechanism; The early warning mechanism is used to evaluate the voltage data of different nodes respectively, and then the control coefficient of the corresponding prediction algorithm parameter is used to adjust the voltage state of the distribution network.

4. A method for active voltage management of a digital distribution network according to claim 3, characterized in that: The optimal prediction algorithm parameters are determined as follows: For each node, by comparing the error between the predicted voltage value and the actual voltage value under different parameters, the mean square error corresponding to each parameter is calculated; The mean square error calculation formula is: is the predicted voltage value, is the actual voltage value, is the number of data points.

5. The method for active voltage management of a digital distribution network according to claim 1, characterized in that: The step of obtaining the voltage fluctuation key node from the stable voltage value of each node includes: The data collected by the voltage sensor are sequentially processed through data preprocessing, feature extraction and voltage fluctuation quantification to obtain the voltage fluctuation degree results of each node; Search for the location of the maximum voltage fluctuation degree of each node as a potential key node, perform correlation analysis on the voltage fluctuation degrees of multiple adjacent nodes in segments, and statistically analyze the relative fluctuation coefficient of each node and the average fluctuation coefficient of nodes in the segment; The node segment with the largest average fluctuation coefficient is taken as the key node segment, and the node position is selected according to the relative fluctuation coefficient threshold, the voltage amplitude information of each selected node position is extracted, and the node with the largest voltage amplitude change is selected as the key node of voltage fluctuation.

6. A method for active voltage management of a digital distribution network according to claim 1, characterized in that: The voltage deviation correction for each node includes: According to the voltage level range, the data of each power supply area is divided into multiple sections along the feeder. First, the voltage value of the central node before and after voltage adjustment is obtained based on the voltage difference between the edge of the area and the adjacent area, and then the voltage value and power factor of other nodes before and after voltage adjustment are obtained.

7. A method for active voltage management of a digital distribution network according to claim 1, characterized in that: The equivalent circuit model of the distribution network is: in, is the voltage phasor at the sending end, is the receiving terminal voltage phasor, is the line equivalent impedance, is the line current phasor.

8. A method for active voltage management of a digital distribution network according to claim 1, characterized in that: The voltage loss calculation model is: in, is the voltage loss, is the active power, is the reactive power, is the line resistance, is the line reactance, is the line voltage.

9. A digital distribution network voltage active management system, used to implement a digital distribution network voltage active management method according to any one of claims 1 to 8, characterized in that: include: Voltage status assessment module: used to assess the voltage status of the distribution network based on real-time voltage monitoring data and intelligent prediction algorithms. This module includes a data acquisition submodule, a trend estimation submodule, a parameter selection submodule, a model generation submodule, an early warning design submodule, and an assessment and adjustment submodule; Key node and control area determination module: used to obtain the key nodes of voltage fluctuation in the stable voltage values ​​of each node, and construct the control area according to the key nodes. This module includes a data processing submodule, a potential key node search submodule, a correlation analysis submodule, a key node determination submodule and a control area construction submodule; Voltage deviation correction module: It is used to search for the optimal reactive compensation point and compensation capacity under the criterion of minimizing the sum of squared voltage deviations, based on the distribution network equivalent circuit model and voltage loss calculation model, and perform voltage deviation correction on each node. This module includes a segmentation submodule, a voltage value acquisition submodule, and a deviation correction submodule.

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