Power grid low voltage treatment method, device and system

Abstract

The application discloses a power grid low-voltage treatment method, device and system, which is applied to the technical field of power system control and aims at solving the problems of poor adaptability and single treatment target of the power grid low-voltage treatment in the prior art. Specifically, the current weight coefficient is determined based on the typical operation characteristics of the power grid corresponding to the current time information; the current voltage deviation and the current harmonic distortion rate are subjected to fuzzy processing to obtain voltage change and harmonic change fuzzy processing results; the voltage change fuzzy processing result and the harmonic change fuzzy processing result are fused based on the current weight coefficient to obtain the activation strength of the target fuzzy rule; the area barycenter method is applied to perform defuzzification to obtain the reactive power compensation control quantity and the filter control quantity; and the power grid nodes are cooperatively treated according to the reactive power compensation control quantity and the filter control quantity. The current weight coefficient is dynamically determined through the current time information, the adaptive ability of the treatment is improved, and the cooperative treatment of the reactive power compensation and the filter enriches the treatment target.

Description

Technical Field

[0001] This application relates to the field of power system control technology, and more specifically, to a method, apparatus and system for managing low voltage in power grids. Background Technology

[0002] With the widespread application of power electronics technology, numerous nonlinear loads such as frequency converters in industrial equipment are connected to the power grid, leading to increasingly prominent harmonic pollution problems. In the field of low-voltage management in power systems, traditional methods primarily address voltage amplitude deviation by compensating for and adjusting it, maintaining voltage levels within acceptable ranges through on-load tap-changing transformers and capacitor switching. However, in actual power grid operation, voltage waveform distortion caused by harmonic distortion is prevalent. This periodic distortion can superimpose with voltage sags, creating complex voltage quality issues. Harmonic components not only cause overheating of electrical equipment and malfunctions of relay protection, but the resulting voltage waveform jitter can also couple with low-voltage problems, further deteriorating power supply quality.

[0003] In existing technologies, the TSK (Takagi-Sugeno-Kang) fuzzy classifier is applied to power grid voltage state identification. However, the rule base of such methods is usually based on fixed threshold settings, making it difficult to adapt to the uncertainties brought about by dynamic changes in power grid load. Furthermore, current power grid governance is limited to a single control objective, lacking a comprehensive control mechanism for the coordinated management of harmonic distortion and voltage sag, resulting in limited governance effectiveness. Simultaneously, existing methods fail to fully consider the differentiated needs of different installation scenarios (such as industrial and commercial areas, residential areas, and renewable energy plants), as well as the randomness and volatility brought about by renewable energy integration, leading to insufficient adaptability of governance strategies. At the implementation level, there is also a focus on the algorithm itself, lacking a complete closed-loop control architecture description for collaborative work with specific power electronic governance equipment, resulting in weak practicality of the solutions. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus and system for managing low voltage in power grids, in order to solve the problems of poor adaptability and single management target in existing low voltage management methods.

[0005] In a first aspect, the present invention provides a method for mitigating low voltage in power grids, comprising: Obtain the current voltage deviation, current harmonic distortion rate, and current time information of the power grid; Determine the current weighting coefficients based on the typical operating characteristics of the power grid corresponding to the current time information. The current voltage deviation and the current harmonic distortion rate are fuzzified to obtain the fuzzified results of voltage change and harmonic change. The activation intensity of the target fuzzy rule is obtained by fusing the voltage change fuzzification result and the harmonic change fuzzification result based on the current weight coefficients. Based on the target fuzzy rule and the activation intensity of the target fuzzy rule, the area centroid method is applied for defuzzification to obtain the reactive power compensation control quantity and the filter control quantity. Based on reactive power compensation control and filtering control, the power grid nodes are managed collaboratively.

[0006] In an optional implementation, the current voltage deviation and the current harmonic distortion rate are fuzzified to obtain fuzzified voltage change results and fuzzified harmonic change results, including: The current voltage deviation is mapped to the voltage deviation domain, and the first membership degree of the voltage deviation domain to which the current voltage deviation belongs is calculated using a triangular membership function. Each first membership degree is used as the fuzzification result of the voltage change. The voltage deviation domain is divided into six fuzzy subsets: negative large, negative medium, negative small, positive small, positive medium, and positive large. The current harmonic distortion rate is mapped to the harmonic distortion rate domain. The second membership degree of the harmonic distortion rate domain to which the current harmonic distortion rate belongs is calculated using a trapezoidal membership function. Each second membership degree is used as the fuzzification result of the harmonic variation. The harmonic distortion rate domain is divided into four fuzzy subsets: normal, mild, moderate, and severe.

[0007] In an optional implementation, the voltage variation fuzzification result and harmonic variation fuzzification result are fused based on the current weighting coefficients to obtain the activation intensity of the target fuzzy rule, including: The fuzzy control rules corresponding to the voltage change fuzzification result and the harmonic change fuzzification result in the preset fuzzy control rule library are used as the target fuzzy rules. For each target fuzzy rule, determine the minimum value of the corresponding membership degree in the voltage change fuzzification result and the harmonic change fuzzification result; use the product of the minimum value and the current weight coefficient as the activation intensity of the target fuzzy rule.

[0008] In an optional implementation, based on the target fuzzy rule and its activation intensity, the area centroid method is applied for defuzzification to obtain the reactive power compensation control quantity and the filter control quantity, including: Based on the area centroid method, the reactive power compensation centroid and filter control centroid corresponding to each target fuzzy rule are determined. The product of the activation intensity of each target fuzzy rule and the corresponding reactive power compensation centroid is determined as the first weighted centroid. The sum of all the first weighted centroids is used to obtain the first total weighted centroid. The ratio of the first total weighted centroid to the sum of the activation intensities of the target fuzzy rules is used as the reactive power compensation control quantity. The product of the activation intensity of each target fuzzy rule and the corresponding filter control centroid is determined as the second weighted centroid. The sum of all the second weighted centroids is used to obtain the second total weighted centroid. The ratio of the second total weighted centroid to the sum of the activation intensities of the target fuzzy rules is used as the filter control quantity.

[0009] Optionally, based on the typical operating characteristics of the power grid corresponding to the current time information, the current weighting coefficient is determined, including: Obtain the installation area scene label, current season information, and new energy power generation output forecast information of the power grid node; Based on current time information, installation area scene labels, current season information, and new energy power generation output prediction information, a scene feature vector is established; Based on a pre-set weighted strategy mapping model, the current weight coefficients are matched and output according to the scenario feature vectors. The weighted strategy mapping model includes a harmonic suppression priority strategy for industrial and commercial load areas, a voltage stability priority strategy for residential load areas, and a voltage dynamic support strategy for new energy access points.

[0010] Optionally, based on reactive power compensation control and filtering control variables, collaborative governance of grid nodes can be implemented, including: Convert the reactive power compensation control quantity and the filter control quantity into the control commands required by the power unit; Control commands are sent to the power units deployed at the grid nodes, driving the power units to generate and output corresponding reactive power compensation current and harmonic compensation current. After the reactive power compensation current and harmonic compensation current are output, the voltage and current signals are sampled at the common connection point of the grid node and obtained. Based on the voltage and current signals, the current voltage deviation and current harmonic distortion rate of the next control cycle are calculated.

[0011] In an optional implementation, after performing coordinated management of grid nodes based on reactive power compensation control and filtering control, the method further includes: Monitor the performance indicators of the power grid, including voltage recovery time and harmonic improvement rate; Based on performance metrics, the weight adjustment amount of the target fuzzy rule is calculated using a reinforcement learning algorithm, and the weight of the target fuzzy rule in the fuzzy control rule base is adjusted according to the weight adjustment amount.

[0012] In an optional implementation, it further includes: Get the total number of fuzzy control rules in the fuzzy control rule base; When the total number of rules exceeds a preset threshold, the rule with the smallest weight value will be deleted first until the total number of rules is no greater than the preset threshold.

[0013] In an optional implementation, it further includes: Continuously monitor the power grid operating status points, which consist of the current voltage deviation and the current harmonic distortion rate; When the number of times the state point meets the state deviation requirement within a preset time reaches a predetermined number, it is determined that a new operating condition has occurred in the power grid. The state deviation requirement is that the minimum geometric distance between the state point and the state center point corresponding to all fuzzy control rules in the fuzzy rule base continuously exceeds a preset distance threshold. New fuzzy control rules are generated based on the state point of the new operating condition, and the new fuzzy control rules are added to the fuzzy rule base.

[0014] In a second aspect, the present invention provides a low-voltage management device for power grids, comprising: The data acquisition unit is used to acquire the current voltage deviation, current harmonic distortion rate, and current time information of the power grid. The weight determination unit is used to determine the current weight coefficient based on the typical operating characteristics of the power grid corresponding to the current time information; The fuzzification unit is used to fuzzify the current voltage deviation and the current harmonic distortion rate to obtain the fuzzification results of voltage change and harmonic change. The fuzzy inference unit is used to fuse the fuzzification results of voltage change and harmonic change based on the current weight coefficients to obtain the activation intensity of the target fuzzy rule. The defuzzing unit is used to perform defuzzification based on the target fuzzy rule and the activation intensity of the target fuzzy rule, and to obtain the reactive power compensation control quantity and the filter control quantity by applying the area centroid method. The power grid control unit is used to coordinate the management of power grid nodes based on reactive power compensation control and filtering control quantities.

[0015] Thirdly, the present invention provides a power grid low voltage management system, comprising: a data acquisition unit, the aforementioned power grid low voltage management device, and a power unit; The data acquisition unit is located at the common connection point of the power grid node. The data acquisition unit is used to acquire voltage and current signals and transmit them to the power grid low voltage management device. The input terminal of the low voltage grid management device is connected to the output terminal of the data acquisition unit, and the output terminal of the low voltage grid management device is connected to the input terminal of the power unit. The output of the power unit is connected to the grid node; the power unit is used to receive the control commands converted by the low voltage management device of the grid according to the reactive power compensation control quantity and the filter control quantity, and generate and output the corresponding reactive power compensation current and harmonic compensation current to the grid node.

[0016] The beneficial effects of the embodiments of this application are as follows: In this embodiment, by introducing the typical operating characteristics of the power grid corresponding to the current time information and dynamically determining the weighting coefficients, the low-voltage management method can adapt to the temporal characteristics of power grid load changes and operating states, significantly improving the accuracy and adaptability of the management. By fuzzifying the voltage deviation and harmonic distortion rate and fusing the target fuzzy rule based on the weighting coefficients, the uncertainty and nonlinearity of power grid parameters are effectively solved, enhancing robustness and stability. Furthermore, the area centroid method is applied for defuzzification to obtain the reactive power compensation control quantity and the filtering control quantity, realizing the accurate calculation of the control quantity and avoiding over-compensation or under-compensation. Finally, through the coordinated management of reactive power compensation and filtering, the voltage quality of the power grid is comprehensively improved, harmonic pollution is reduced, the operating efficiency and economy of the power grid are improved, and the service life of equipment is extended, providing reliable technical support for the optimized operation of the smart grid.

[0017] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram outlining the general flow of the low voltage management method for power grids in the embodiments of this application; Figure 2 This is a diagram showing the division of the voltage deviation domain in the embodiments of this application; Figure 3 This is a membership function diagram of the voltage deviation in the embodiments of this application; Figure 4 This is a diagram showing the partitioning of the harmonic distortion rate domain in the embodiments of this application; Figure 5 This is a membership function diagram of harmonic distortion rate in the embodiments of this application; Figure 6 This is a diagram showing the division of the fuzzy control rule base in the embodiments of this application; Figure 7 This is a diagram showing the division of the domain of reactive power compensation control quantities in the embodiments of this application; Figure 8 This is a membership function diagram of the reactive power compensation control quantity in the embodiments of this application; Figure 9 This is a diagram showing the partitioning of the universe of discourse for the filter control quantity in the embodiments of this application; Figure 10 This is a membership function diagram of the filter control quantity in the embodiments of this application; Figure 11 This is a schematic diagram illustrating the specific process of the weight update method in the embodiments of this application; Figure 12 This is a schematic diagram illustrating the specific process of the fuzzy rule management method in the embodiments of this application; Figure 13 This is a schematic diagram illustrating the specific process of the fuzzy rule generation method in the embodiments of this application; Figure 14 This is a functional structure diagram of the low voltage control method device for power grids in the embodiments of this application; Figure 15 This is a system architecture diagram of the low voltage management system for power grids in this application embodiment. Detailed Implementation

[0019] To make the objectives, technical solutions, and beneficial effects of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] This application provides a method for mitigating low voltage in power grids, see below. Figure 1 As shown in the embodiments of this application, the general flow of the low voltage management method for power grids is as follows: Step 101: Obtain the current voltage deviation, current harmonic distortion rate, and current time information of the power grid.

[0021] In practical applications, voltage transformers and current harmonic analyzers deployed at grid nodes are used to measure grid operating parameters in real time. The current voltage deviation, calculated as a relative percentage according to national standards, reflects the degree of deviation of the node voltage from its rated value. The current voltage deviation can be calculated using the following formula. The current harmonic distortion rate quantifies the severity of current waveform distortion relative to the fundamental frequency and is a key indicator for assessing power quality. The current harmonic distortion rate can be calculated using the following formula. Current time information is obtained from a built-in real-time clock to identify the typical operating period of the grid, providing a basis for subsequent dynamic weight allocation. Typical operating periods include daytime peak loads and nighttime periods of high industrial harmonic incidence.

[0022]

[0023]

[0024] in, This is the current voltage deviation. This is the rated voltage of the power grid, typically 220V. This is the current grid voltage. It is the current harmonic distortion rate, I h It is the effective value of the h-th harmonic current, where h is the harmonic order. It is the effective value of the fundamental current (i.e., the first harmonic).

[0025] Step 102: Determine the current weighting coefficient based on the typical operating characteristics of the power grid corresponding to the current time information.

[0026] In practical applications, to improve the adaptability and accuracy of governance strategies, this method introduces multi-dimensional scenario features for intelligent weighted decision-making. Specifically, it includes the following sub-steps: Step 1021: Obtain the installation area scene label, current season information, and new energy power generation output forecast information of the power grid node.

[0027] Specifically, the installation area scene label is obtained by reading the pre-stored scene configuration file through the local configuration module, or by being sent to the local storage unit by the remote master station through the communication link and then read. The installation area scene label is used to identify the area type to which the power grid node belongs. The area type includes at least industrial and commercial load areas, residential load areas, and new energy power station access points. The current season information is obtained by extracting the current date information in real time from the system clock module and mapping it to the corresponding season, or by reading it directly from the seasonal configuration parameter file. The new energy power generation output prediction information is obtained remotely from the new energy power generation prediction system through the data communication interface, or by reading it from the locally cached prediction data queue. The new energy power generation output prediction information is the output prediction data of the photovoltaic power generation equipment or wind power generation equipment associated with the power grid node in the future within a preset time period. The preset time period can be 15 minutes.

[0028] Step 1022: Based on the current time information, installation area scene labels, current season information, and new energy power generation output prediction information, establish scene feature vectors.

[0029] Specifically, based on the current time information, installation area scene label, current season information, and new energy power generation output prediction information, the following digital processing is performed on the feature vector construction module: The current time information is mapped to a time period code value according to a preset time period division rule, which divides the 24 hours of each day into peak hours, valley hours, and flat hours; The installation area scene label is converted into a scene label code value by querying a preset scene label code lookup table, in which the industrial and commercial load area, residential load area, and new energy power station access point each correspond to a unique code vector; The current season information is converted into a season code value by querying a preset season code lookup table; The new energy power generation output prediction information is queried from a preset output level classification threshold table to determine the new energy output level code value; The time period code value, scene label code value, season code value, and new energy output level code value are arranged in a preset order and combined to form a scene feature vector.

[0030] Step 1023: Based on the preset weight strategy mapping model, match and output the current weight coefficients according to the scenario feature vector; wherein, the weight strategy mapping model includes a harmonic suppression priority strategy for industrial and commercial load areas, a voltage stability priority strategy for residential load areas, and a voltage dynamic support strategy for new energy access points.

[0031] Specifically, the scene feature vector is input into a pre-set weight strategy mapping model. The weight strategy mapping model matches the corresponding weight allocation strategy according to the scene label encoding value in the scene feature vector, and dynamically adjusts it according to the seasonal encoding value and the new energy output level encoding value, and outputs the current weight coefficient. The weight strategy mapping model is a decision table model built based on expert knowledge or a lightweight neural network model trained based on historical operating data. The weight strategy mapping model has built-in differentiated weight allocation strategies, including a harmonic suppression priority strategy for industrial and commercial load areas, a voltage stability priority strategy for residential load areas, and a voltage dynamic support strategy for new energy access points. For industrial and commercial load areas, a harmonic suppression priority strategy is adopted, assigning a first weight range (0.6 to 0.8) to the fuzzy control rules associated with the fuzzification results of harmonic variations. For residential load areas, a voltage stability priority strategy is adopted, assigning a second weight range (0.7 to 0.9) to the fuzzy control rules associated with the fuzzification results of voltage variations. For renewable energy power station access points, a dynamic voltage support strategy is adopted, dynamically adjusting the weight allocation based on the renewable energy output level coding value. When the renewable energy output level coding value corresponds to a high output level, the current weight coefficient tends to suppress voltage; when the renewable energy output level coding value corresponds to a low output level, the current weight coefficient returns to a balanced allocation. The weight strategy mapping model is superimposed with a seasonal adjustment factor. When the seasonal coding value is summer, the weight allocation of the voltage stability priority strategy is enhanced; when the seasonal coding value is winter, the weight allocation of the harmonic suppression priority strategy is enhanced.

[0032] In addition, to ensure system safety and stability, a high-priority forced correction mechanism is set up: real-time monitoring of the current harmonic distortion rate (THD) and the current voltage deviation (e(t)). When both THD ≥ 10% and |e(t)| ≥ 10% are simultaneously met, it is determined that an extreme operating condition has been entered, and the current weighting coefficient α is immediately forced to be set to 0.5 to ensure a balanced emergency response for voltage control and harmonic suppression.

[0033] Step 103: Perform fuzzification processing on the current voltage deviation and the current harmonic distortion rate to obtain the fuzzification results of voltage change and harmonic change.

[0034] In practical applications, the fuzzification of the current voltage deviation and the current harmonic distortion rate are performed in parallel as two independent processes. Predefined membership functions are used to convert the precise voltage deviation measurement and harmonic distortion rate calculation into corresponding fuzzy set membership degrees. This fuzzification method effectively quantifies the uncertainty of the input parameters, converting precise numerical inputs into semantic information that can be processed by fuzzy inference, laying the foundation for subsequent rule matching and inference processes.

[0035] Specifically, the current voltage deviation and the current harmonic distortion rate are fuzzified to obtain the fuzzified results of voltage changes and harmonic changes. This can be achieved, but is not limited to, the following methods: The current voltage deviation is mapped to the voltage deviation domain, and the first membership degree of the voltage deviation domain to which the current voltage deviation belongs is calculated using a triangular membership function. Each first membership degree is used as the fuzzification result of the voltage change. The voltage deviation domain is divided into six fuzzy subsets: negative large, negative medium, negative small, positive small, positive medium, and positive large. The current harmonic distortion rate is mapped to the harmonic distortion rate domain. The second membership degree of the harmonic distortion rate domain to which the current harmonic distortion rate belongs is calculated using a trapezoidal membership function. Each second membership degree is used as the fuzzification result of the harmonic variation. The harmonic distortion rate domain is divided into four fuzzy subsets: normal, mild, moderate, and severe.

[0036] In practical applications, the voltage deviation fuzzification processing channel maps the real-time acquired voltage deviation values ​​to a preset voltage deviation domain. (See also...) Figure 2 As shown, the voltage deviation domain is designed with a range of -20% to +20%, fully considering extreme operating conditions that may occur at the end of the power grid. The voltage deviation domain is finely divided into six fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), positive small (PS), positive medium (PM), and positive large (PB). This division method can accurately cover all operating states from severe undervoltage to severe overvoltage. (See reference...) Figure 3 As shown, each fuzzy subset is described using a triangular membership function. The triangular membership function has the advantages of low computational complexity and clear boundaries, enabling fast and accurate membership calculation. Based on the current voltage deviation, the membership degree of the current voltage deviation on the six fuzzy subsets is calculated in parallel by querying a preset triangular function parameter table, forming a fuzzified voltage change result containing multiple first membership degrees. In the harmonic distortion rate fuzzification processing channel, the calculated harmonic distortion rate is mapped to a preset harmonic distortion rate universe of discourse. (See also...) Figure 4 As shown, the harmonic distortion rate domain is designed within a range of 0% to 25%, strictly adhering to the national standard requirements for harmonic mitigation. The harmonic distortion rate domain is rationally divided into four fuzzy subsets: Normal, Mild, Moderate, and Severe. This classification method perfectly corresponds to the actual intensity of harmonic mitigation. (See also...) Figure 5As shown, each fuzzy subset is described using a trapezoidal membership function. The unique plateau interval of the trapezoidal membership function can accurately express the transition characteristics of harmonic levels, effectively avoiding frequent switching. Based on the calculated value of the current harmonic distortion rate, the membership degree of the current harmonic distortion rate on the four fuzzy subsets is calculated in parallel by querying a preset trapezoidal function parameter table, ultimately forming a fuzzy vector of harmonic variation results containing multiple second membership values.

[0037] Step 104: Based on the current weight coefficients, fuse the fuzzification results of voltage change and harmonic change to obtain the activation intensity of the target fuzzy rule.

[0038] In practical applications, dynamic weighting coefficients are introduced to achieve adaptive activation of fuzzy rules, specifically including two core steps: rule matching and intensity calculation. Based on the fuzzification results of voltage deviation and harmonic distortion rate, matching target fuzzy rules are selected from a pre-set fuzzy rule library. For each rule, the minimum matching degree of its antecedents is calculated, and this minimum value is multiplied by the current weighting coefficient to obtain the final activation intensity of the rule. This allows the weighting coefficients to directly adjust the influence of different rules; when focusing on voltage control, the activation intensity of voltage-related rules is enhanced; when focusing on harmonic suppression, the activation intensity of harmonic-related rules is increased. Through this fusion approach, the advantages of fuzzy inference in handling uncertainty are preserved, while the control strategy can be flexibly switched according to operating conditions, laying the foundation for the generation of subsequent precise control quantities.

[0039] In practical implementation, the fuzzification results of voltage change and harmonic change are fused based on the current weighting coefficients to obtain the activation intensity of the target fuzzy rule. This can be achieved, but is not limited to, the following methods: First, the fuzzy control rules corresponding to the fuzzification results of voltage change and harmonic change in the preset fuzzy control rule library are used as target fuzzy rules.

[0040] In practical applications, a fuzzy control rule base is pre-stored. The antecedent (IF part) of the fuzzy rule base consists of a combination of fuzzy subsets of voltage deviation and harmonic distortion rate. The consequent (THEN part) of the fuzzy rule base consists of a combination of fuzzy subsets of reactive power compensation control quantity and fuzzy subsets of filter control intensity. The fuzzy control rule base is organized in the form of a two-dimensional lookup table, with rows corresponding to the six fuzzy subsets of voltage deviation and columns corresponding to the four fuzzy subsets of harmonic distortion rate, containing at least 24 control rules. See details... Figure 6The voltage variation fuzzification result and harmonic variation fuzzification result are used as inputs. A parallel matching algorithm identifies all rules with non-zero membership degrees between the antecedent conditions and the current state. For example, when the voltage deviation membership degree shows 0.7 in the NM subset and the harmonic distortion rate has 0.8 membership degree in the Moderate subset, the rule "IF e(t)=NM AND THD=Moderate THEN Q=PM, F=Strong" will be selected as the target fuzzy rule. This matching process ensures that all potentially relevant control rules are included in subsequent calculations.

[0041] Then, for each target fuzzy rule, the minimum value of the corresponding membership degree in the voltage change fuzzification result and harmonic change fuzzification result is determined; the product of the minimum value and the current weight coefficient is used as the activation intensity of the target fuzzy rule.

[0042] In practical applications, during the activation intensity calculation stage, the following calculation process is performed for each target fuzzy rule: First, obtain the voltage deviation membership degree μ corresponding to the antecedent of the rule. e Harmonic distortion rate membership degree μ thd The basic matching degree is calculated using the following formula: β base = min(μ e , μ thd ) Where, β base It is the basic matching degree, μ e It is the membership degree of voltage deviation, μ thd It is the membership degree of harmonic distortion rate.

[0043] Subsequently, the base matching degree is multiplied by the current weight coefficient α to obtain the final activation strength of the rule, i.e., β. k = β base × α. Where α is the current weight coefficient, β k It is the final activation intensity.

[0044] Taking the rule "IF e(t)=NM AND THD=Moderate THEN..." as an example, if the current μ NM (e)=0.7, μ Moderate If (THD) = 0.8, then the basic matching degree is min(0.7, 0.8) = 0.7. Subsequently, a scalar multiplication operation is performed between the basic matching degree and the current weight coefficient α: β k= 0.7 × α. When operating during the daytime (α=0.7), the final activation strength of this rule is 0.49; under extreme conditions (α=0.5), the activation strength is adjusted to 0.35. This calculation process ensures that the weighting coefficient can directly modulate the influence of each rule. When focusing on voltage control, the activation strength of voltage-related rules is enhanced; when focusing on harmonic suppression, the activation strength of harmonic-related rules is correspondingly increased. Through this fusion mechanism, an adaptive rule activation strategy based on operating conditions is realized. This retains the advantages of traditional fuzzy inference in handling uncertainty, and achieves flexible switching of control objectives through dynamic adjustment of weighting coefficients, providing accurate weighted input for subsequent defuzzification.

[0045] Step 105: Based on the target fuzzy rule and the activation intensity of the target fuzzy rule, the area centroid method is applied for defuzzification to obtain the reactive power compensation control quantity and the filter control quantity.

[0046] In practical applications, the membership function system of the output variables is pre-constructed, such as... Figure 7 and Figure 9 As shown. For reactive power compensation control quantities, the output universe of discourse is defined as -100% to +100%, corresponding to the full capacity range of the static var generator. The universe of discourse for reactive power compensation control quantities is divided into six fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), positive small (PS), positive medium (PM), and positive large (PB). See also... Figure 8 As shown, each subset is described using a triangular membership function. For the filter control quantity, the output universe of discourse is defined as 0% to 100%, corresponding to the operating strength of the active power filter. This universe of discourse is divided into three fuzzy subsets: Weak, Medium, and Strong. (See also...) Figure 10 As shown, a trapezoidal membership function is used to describe the process. The area centroid method is applied to determine the reactive power compensation centroid and filter control centroid corresponding to each target fuzzy rule. The activation intensity of each target fuzzy rule is multiplied by its corresponding output centroid to obtain the weighted centroids. The total weighted centroid is obtained by summing the weighted centroids of all similar rules. Finally, the total weighted centroid is divided by the sum of the activation intensities of all rules to obtain the normalized reactive power compensation control quantity and filter control quantity. This method achieves smooth fusion of multi-rule outputs, ensuring continuous and stable control commands, effectively avoiding abrupt output changes, and guaranteeing precise control of power electronic equipment.

[0047] In practical implementation, based on the target fuzzy rule and its activation intensity, the area centroid method is applied for defuzzification to obtain the reactive power compensation control quantity and the filter control quantity. This can be achieved, but is not limited to, the following methods: First, based on the area centroid method, the reactive power compensation centroid and the filter control centroid corresponding to each target fuzzy rule are determined.

[0048] In practical applications, for each target fuzzy rule, the corresponding fuzzy subset of reactive power compensation control quantity and the fuzzy subset of filter control quantity are determined according to the rule consequent. The geometric area enclosed by the membership functions of the corresponding fuzzy subset of reactive power compensation control quantity is calculated, and the centroid of reactive power compensation is determined. The geometric area enclosed by the membership functions of the corresponding fuzzy subset of filter control quantity is calculated, and the centroid of filter control is determined.

[0049] Then, the product of the activation intensity of each target fuzzy rule and the corresponding reactive power compensation centroid is determined as the first weighted centroid, and the sum of all first weighted centroids is obtained to obtain the first total weighted centroid; the ratio of the first total weighted centroid to the sum of the activation intensities of the target fuzzy rules is used as the reactive power compensation control quantity; the product of the activation intensity of each target fuzzy rule and the corresponding filter control centroid is determined as the second weighted centroid, and the sum of all second weighted centroids is obtained to obtain the second total weighted centroid; the ratio of the second total weighted centroid to the sum of the activation intensities of the target fuzzy rules is used as the filter control quantity.

[0050] In practical applications, taking the calculation of reactive power compensation control quantities as an example, the activation intensity β of each objective fuzzy rule is... k As a weighting factor, the weighted average of the centroids of the fuzzy sets output by all activated rules is calculated. The specific calculation formula is as follows:

[0051] in, For reactive power compensation control, N is the number of target fuzzy rules, and centroid(Q) is the control variable. k Let β be the coordinates of the reactive power compensation centroid corresponding to the k-th target fuzzy rule. k Let be the activation strength of the k-th target fuzzy rule.

[0052] Similarly, the same weighted average mechanism is used to calculate the filter control quantity:

[0053] in, Here, is the filter control variable, N is the number of target fuzzy rules, and centroid(F) is the number of fuzzy rules. k Let β be the coordinates of the filter control centroid corresponding to the k-th target fuzzy rule. k Let be the activation strength of the k-th target fuzzy rule.

[0054] In this way, the continuity and smoothness of the control output are ensured by adopting this defuzzification method. Even when different rules produce conflicting suggestions, a reasonable compromise solution can be obtained through a weighted averaging mechanism. The reactive power compensation control quantity directly determines the reactive power output percentage of the static var generator, while the filter control quantity determines the harmonic compensation intensity of the active power filter, providing precise command input for subsequent power electronic equipment control.

[0055] Step 106: Perform coordinated management of power grid nodes based on reactive power compensation control and filtering control quantities.

[0056] In practical applications, the coordinated management of power grid nodes based on reactive power compensation control and filtering control can be achieved in the following ways, but is not limited to: Step 1061: Convert the reactive power compensation control quantity and the filter control quantity into the control commands required by the power unit.

[0057] Specifically, control commands are generated based on the equipment type and control mode of the backend power unit. The control mode can be determined based on external commands, and includes at least reactive power compensation mode, harmonic suppression mode, and reactive power and harmonic co-management mode. In reactive power compensation mode, the reactive power compensation control quantity is extracted and converted into a reactive current reference command, while the filter control quantity output is disabled. In harmonic suppression mode, the filter control quantity is extracted and converted into a harmonic compensation current reference command, while the reactive power compensation control quantity output is disabled. In reactive power and harmonic co-management mode, both reactive power compensation control quantity and filter control quantity are extracted simultaneously; the reactive power compensation control quantity is converted into a reactive current reference control command, and the filter control quantity is converted into a harmonic compensation current reference command. The power unit consists of a static var generator (SVA) and an active power filter (APF) deployed at the common connection point of the power grid. The control command format includes the duty cycle parameter of the pulse width modulation signal or the instantaneous current tracking command value. The SVA and APF each have independent power module drive circuits and switching device groups. The control commands are transmitted to the local control board of each power unit through an optical fiber communication link or a high-speed serial communication bus. The local control board parses the control commands and generates corresponding gate drive signals to control the on / off timing of the insulated gate bipolar transistor or silicon carbide power device, thereby achieving accurate output of reactive power compensation current and harmonic compensation current.

[0058] Step 1062: Send control commands to the power units deployed at the grid nodes to drive the power units to generate and output the corresponding reactive power compensation current and harmonic compensation current.

[0059] Specifically, control commands are sent to power units deployed at grid nodes via a communication bus. After receiving the control commands, the power units drive the static var generator (SVA) to output capacitive or inductive reactive power compensation current according to the reactive current reference command, and drive the active power filter (APF) to output harmonic compensation current with the same amplitude but opposite phase to the grid harmonic current according to the harmonic compensation current reference command. The real-time operating status of the SVA and APF is detected, including the DC-side capacitor voltage, power device junction temperature, and instantaneous output current. The command allocation timing of each power unit is adjusted according to the real-time operating status to eliminate the coupling interference between reactive power compensation current and harmonic compensation current at the point of common coupling, thus realizing the coordinated operation of multiple power units. The SVA adopts a voltage-oriented vector control strategy to achieve rapid dynamic response of reactive power, and the APF adopts a harmonic detection algorithm based on instantaneous reactive power theory to achieve selective compensation of specific harmonics. The two power units ensure the phase consistency of the output current through a unified time synchronization signal.

[0060] Step 1063: After the reactive power compensation current and harmonic compensation current are output, the voltage and current signals are sampled at the common connection point of the grid node and obtained. The current voltage deviation and current harmonic distortion rate of the next control cycle are calculated based on the voltage and current signals.

[0061] Specifically, after the reactive power compensation current and harmonic compensation current are output and stabilized, the three-phase voltage and current signals are sampled by voltage transformers and current transformers deployed at the common connection points of the power grid nodes. The actual voltage value of the power grid node is calculated based on the three-phase voltage signal. The actual voltage value is compared with the preset voltage reference value to obtain the current voltage deviation for the next control cycle. The current harmonic distortion rate for the next control cycle is calculated by performing fast Fourier transform harmonic analysis based on the three-phase current signal. The current voltage deviation and current harmonic distortion rate for the next control cycle are used as new input parameters, and the acquisition step is repeated to the collaborative governance step to form a rolling optimization control closed loop, realizing real-time dynamic governance of power grid voltage quality.

[0062] This application introduces typical power grid operating characteristics corresponding to current time information and dynamically determines weighting coefficients, enabling the low-voltage management method to adapt to the temporal characteristics of power grid load changes and operating states, significantly improving the accuracy and adaptability of the management. By fuzzifying voltage deviation and harmonic distortion rate and fusing the target fuzzy rule based on weighting coefficients, the uncertainty and nonlinearity of power grid parameters are effectively solved, enhancing robustness and stability. Furthermore, the area centroid method is applied for defuzzification to obtain reactive power compensation control and filtering control quantities, achieving accurate calculation of control quantities and avoiding over-compensation or under-compensation. Finally, through the coordinated management of reactive power compensation and filtering, the voltage quality of the power grid is comprehensively improved, harmonic pollution is reduced, power grid operating efficiency and economy are improved, and equipment lifespan is extended, providing reliable technical support for the optimized operation of smart grids.

[0063] In one possible implementation, see [reference] Figure 11 As shown, after the coordinated management of power grid nodes based on reactive power compensation control and filtering control, the following steps are also included: Step 201: Monitor the performance indicators of the power grid, including voltage recovery time and harmonic improvement rate.

[0064] In practical applications, the overall effect of each coordinated governance action is quantitatively evaluated according to a preset time period. Specifically, two key performance indicators are monitored: voltage recovery time and harmonic distortion reduction rate. Voltage recovery time, i.e., the time elapsed from the start of the control action until the voltage deviation recovers to the normal range specified by national standards (usually ±5% of the rated voltage), directly reflects the response speed and effectiveness of voltage governance. Harmonic distortion reduction rate is obtained by calculating the relative change in harmonic distortion rate before and after governance, specifically expressed as ΔTHD = (THD...). 前 -THD 后 ) / THD 前 ×100%, this indicator objectively quantifies the actual effect of harmonic mitigation. Here, ΔTHD represents the harmonic mitigation rate, and THD... 前 It is the harmonic distortion rate before treatment, THD 后 It is the harmonic distortion rate after treatment.

[0065] Step 202: Based on performance metrics, calculate the weight adjustment amount of the target fuzzy rule using a reinforcement learning algorithm, and adjust the weight of the target fuzzy rule in the fuzzy control rule base according to the weight adjustment amount.

[0066] In practical applications, based on the aforementioned performance indicators, a reinforcement learning algorithm is used to optimize the fuzzy rule base online. A learning framework centered on the Q-learning algorithm is established, with the voltage deviation level and harmonic distortion rate level of the power grid as state s, and the executed fuzzy control rule as action a, forming a "state-action" value evaluation system. Each fuzzy control rule maintains a corresponding weight Q in the rule base, which represents the long-term control value of the rule in a specific state. A batch update learning strategy is adopted, performing Q-value updates every 24 hours. The update rules adopt a temporal differential learning format. Qnew(s,a)=Qold(s,a)+η[R+γ maxQ(s′,a′) Qold(s,a)] Where Qnew(s,a) is the updated weight; Qold(s,a) is the original weight; η is the learning rate; R is the weight adjustment amount; γ is the discount factor; and maxQ(s′,a′) is the historical maximum weight. The learning rate η is set to 0.2 to ensure stable convergence of the learning process; the discount factor γ is set to 0.9 to take into account long-term returns.

[0067] The weight adjustment amount R can be determined using the following formula:

[0068] in, ΔTHD is the voltage recovery time, and ΔTHD is the harmonic mitigation rate. The first term in the formula reflects the requirement for rapid voltage recovery, while the second term characterizes the effectiveness of harmonic mitigation. The coefficients 0.6 and 0.4 reflect different emphases on voltage stability and harmonic quality, meeting the actual safety requirements of power grid operation.

[0069] In this way, through iterative updates, the weight values ​​of high-performing fuzzy control rules will gradually increase, gaining higher activation priority in subsequent control decisions; while the weight values ​​of poorly performing fuzzy control rules will decrease accordingly, gradually weakening their influence. This weight adjustment mechanism based on reinforcement learning can continuously learn from actual operational results and constantly optimize its control strategy, realizing the evolution from a fixed rule base to an adaptive rule base, significantly improving the adaptability and control efficiency of the governance device in a dynamic power grid environment.

[0070] In one possible implementation, see [reference] Figure 12 As shown, methods for managing low voltage in power grids also include: Step 301: Obtain the total number of fuzzy control rules in the fuzzy control rule base.

[0071] Step 302: When the total number of rules exceeds the preset threshold, the rule with the smallest weight value is deleted first until the total number of rules is no greater than the preset threshold.

[0072] In practical applications, the capacity status of the fuzzy rule base can be periodically checked. At fixed time intervals, the total number of currently stored fuzzy control rules is obtained by accessing the rule base's index structure. This total number of rules is compared to a preset threshold. When the total number of rules exceeds the preset threshold (preferably set to 50), a rule deletion process is initiated. Fuzzy control rules are sorted in ascending order according to their weight values, creating a sequence from lowest to highest weight. Deletion operations begin at the beginning of the sequence, removing rules with the lowest weight values ​​sequentially until the total number of rules does not exceed the preset threshold. This weight-priority-based deletion mechanism ensures that the retained rules have higher control value and practicality, effectively maintaining the overall quality of the fuzzy rule base. Through this maintenance mechanism, while maintaining self-learning capabilities, it avoids increased storage pressure and decreased real-time response performance caused by excessive rule base expansion, ensuring long-term stability and efficiency.

[0073] In addition, during the maintenance of the fuzzy control rule base, a rule merging mechanism is set up in addition to rule deletion to further improve the quality and efficiency of the rule base.

[0074] In practical applications, all fuzzy rules in the fuzzy rule base are periodically compared pairwise to calculate the output difference between rules. Specifically, the difference in the output vectors of fuzzy rules can be calculated using the following Euclidean distance formula:

[0075] in: It is the output difference between rule i and rule j; It is the reactive power compensation control quantity of rule i; It is the reactive power compensation control quantity of rule j; It is the filter strength control quantity for rule i; It is the filter strength control quantity for rule j.

[0076] When the output difference between two rules is less than a preset threshold (preferably set to 0.1), the two rules are considered to be highly similar and meet the merging condition. For rule pairs that meet the merging condition, the following merging operation is performed: First, the average weight of the two rules is calculated as the initial weight of the new rule; second, the voltage deviation range and harmonic distortion rate range in the antecedent of the rule are combined to form a new antecedent condition; finally, the reactive power compensation and filter intensity of the consequent of the rule are weighted and averaged to generate a new consequent conclusion. The merged new rule will inherit the core control characteristics of the original rule while covering a wider range of operating conditions. In addition, the weight value of the merged new rule is calculated using the following formula:

[0077] in, It is the weight of the new rules. It is the weight value of the original rule i. It is the weight value of the original rule j; It is the historical activation count of the original rule i. It represents the historical activation count of the original rule j.

[0078] This weighted calculation method ensures that frequently used and high-weight rules dominate the merging process, preserving the most valuable fuzzy control rules in the rule base. Through the rule merging mechanism, the size of the rule base can be effectively reduced while maintaining control performance, eliminating redundant rules, improving rule matching efficiency, and further optimizing response speed and stability. This mechanism, together with rule deletion and rule generation, constitutes a complete dynamic maintenance system for the rule base.

[0079] In one possible implementation, see [reference] Figure 13 As shown, methods for managing low voltage in power grids also include: Step 401: Continuously monitor the power grid operating status point consisting of the current voltage deviation and the current harmonic distortion rate.

[0080] Step 402: When the number of times the state point meets the state deviation requirement within a preset time reaches a predetermined number, it is determined that a new operating condition has occurred in the power grid; wherein, the state deviation requirement is that the minimum geometric distance between the state point and the state center point corresponding to all fuzzy control rules in the fuzzy rule base continuously exceeds a preset distance threshold; a new fuzzy control rule is generated based on the state point of the new operating condition, and the new fuzzy control rule is added to the fuzzy rule base.

[0081] In practical applications, the operating state points S(t) = (e(t), THD) in a two-dimensional state space are constructed using the real-time collected voltage deviation value e(t) and harmonic distortion rate THD. These state points are recorded with a fixed sampling period, and the historical records of the most recently preset number of state points are maintained within a sliding time window, providing a data foundation for subsequent identification of new operating conditions. For each newly collected state point, the following judgment process is executed: First, the Euclidean distance between the state point and the state center points corresponding to all existing rules in the rule base is calculated, and the minimum distance d is found. min When d min When the distance continuously exceeds a preset distance threshold (e.g., 10% of the universe diameter, i.e., 2.5%), the state point is marked as a "deviation point". The number of deviation points occurring within a preset time period (e.g., 30 minutes) is counted. When the number reaches a predetermined threshold (e.g., 3 times), a new operating condition is determined to have occurred in the power grid. During the new rule generation phase, a K-means clustering algorithm is used to cluster recently occurring deviation points, identifying representative new operating condition regions. For each newly discovered operating condition region, new fuzzy control rules are generated through the following steps: determining the membership function parameters of the rule's antecedent based on the cluster centers; determining the optimal consequent output value through simulation platform testing; and adding the verified new rules (false action rate <5%) to the fuzzy rule base. The initial weight of the new rule is set to the average weight of existing rules in the rule base to ensure a smooth transition.

[0082] Based on the above embodiments, this application provides a low-voltage management device for power grids, see below. Figure 14 As shown, the low voltage management device 500 provided in this application embodiment includes at least: The data acquisition unit 501 is used to acquire the current voltage deviation, current harmonic distortion rate and current time information of the power grid; The weight determination unit 502 is used to determine the current weight coefficient based on the typical operating characteristics of the power grid corresponding to the current time information; The fuzzification unit 503 is used to perform fuzzification processing on the current voltage deviation and the current harmonic distortion rate to obtain the fuzzification results of voltage change and harmonic change. The fuzzy inference unit 504 is used to fuse the fuzzification results of voltage change and harmonic change based on the current weight coefficients to obtain the activation intensity of the target fuzzy rule. The defuzzing unit 505 is used to perform defuzzification based on the target fuzzy rule and the activation intensity of the target fuzzy rule, and to obtain the reactive power compensation control quantity and the filter control quantity by applying the area centroid method. The power grid control unit 506 is used to coordinate the management of power grid nodes based on reactive power compensation control and filtering control quantities.

[0083] In one possible implementation, the blurring unit 503 is specifically used for: The current voltage deviation is mapped to the voltage deviation domain, and the first membership degree of the voltage deviation domain to which the current voltage deviation belongs is calculated using a triangular membership function. Each first membership degree is used as the fuzzification result of the voltage change. The voltage deviation domain is divided into six fuzzy subsets: negative large, negative medium, negative small, positive small, positive medium, and positive large. The current harmonic distortion rate is mapped to the harmonic distortion rate domain. The second membership degree of the harmonic distortion rate domain to which the current harmonic distortion rate belongs is calculated using a trapezoidal membership function. Each second membership degree is used as the fuzzification result of the harmonic variation. The harmonic distortion rate domain is divided into four fuzzy subsets: normal, mild, moderate, and severe.

[0084] In one possible implementation, the fuzzy inference unit 504 is specifically used for: The fuzzy control rules corresponding to the voltage change fuzzification result and the harmonic change fuzzification result in the preset fuzzy control rule library are used as the target fuzzy rules. For each target fuzzy rule, determine the minimum value of the corresponding membership degree in the voltage change fuzzification result and the harmonic change fuzzification result; use the product of the minimum value and the current weight coefficient as the activation intensity of the target fuzzy rule.

[0085] In one possible implementation, the defuzzification unit 505 is specifically used for: Based on the area centroid method, the reactive power compensation centroid and filter control centroid corresponding to each target fuzzy rule are determined. The product of the activation intensity of each target fuzzy rule and the corresponding reactive power compensation centroid is determined as the first weighted centroid. The sum of all the first weighted centroids is used to obtain the first total weighted centroid. The ratio of the first total weighted centroid to the sum of the activation intensities of the target fuzzy rules is used as the reactive power compensation control quantity. The product of the activation intensity of each target fuzzy rule and the corresponding filter control centroid is determined as the second weighted centroid. The sum of all the second weighted centroids is used to obtain the second total weighted centroid. The ratio of the second total weighted centroid to the sum of the activation intensities of the target fuzzy rules is used as the filter control quantity.

[0086] In one possible implementation, the weight determination unit 502 is specifically used for: Obtain the installation area scene label, current season information, and new energy power generation output forecast information of the power grid node; Based on current time information, installation area scene labels, current season information, and new energy power generation output prediction information, a scene feature vector is established; Based on a pre-set weighted strategy mapping model, the current weight coefficients are matched and output according to the scenario feature vectors. The weighted strategy mapping model includes a harmonic suppression priority strategy for industrial and commercial load areas, a voltage stability priority strategy for residential load areas, and a voltage dynamic support strategy for new energy access points.

[0087] In one possible implementation, the power grid control unit 506 is specifically used for: Convert the reactive power compensation control quantity and the filter control quantity into the control commands required by the power unit; Control commands are sent to the power units deployed at the grid nodes, driving the power units to generate and output corresponding reactive power compensation current and harmonic compensation current. After the reactive power compensation current and harmonic compensation current are output, the voltage and current signals are sampled at the common connection point of the grid node and obtained. Based on the voltage and current signals, the current voltage deviation and current harmonic distortion rate of the next control cycle are calculated.

[0088] In one possible implementation, the low voltage grid management device 500 further includes: a weight learning unit 507; The weighted learning unit is used to monitor the performance indicators of the power grid, including voltage recovery time and harmonic improvement rate. Based on the performance indicators, the weight adjustment amount of the target fuzzy rule is calculated by reinforcement learning algorithm, and the weight of the target fuzzy rule in the fuzzy control rule base is adjusted according to the weight adjustment amount.

[0089] In one possible implementation, the low voltage management device 500 further includes: a rule management unit 508; The rule management unit is used to obtain the total number of fuzzy control rules in the fuzzy control rule base; when the total number of rules exceeds the preset threshold, the rule with the smallest weight value is deleted first until the total number of rules is no greater than the preset threshold.

[0090] In one possible implementation, the low voltage management device 500 further includes: a fuzzy rule generation unit 509; The fuzzy rule generation unit continuously monitors the power grid operating state points, which consist of the current voltage deviation and the current harmonic distortion rate. When the number of times the state point meets the state deviation requirement within a preset time reaches a predetermined number, it determines that a new operating condition has occurred in the power grid. The state deviation requirement is that the minimum geometric distance between the state point and the state center point corresponding to all fuzzy control rules in the fuzzy rule library continuously exceeds a preset distance threshold. Based on the state point of the new operating condition, a new fuzzy control rule is generated and added to the fuzzy rule library.

[0091] It should be noted that the principle of the low voltage grid management device 500 provided in this application embodiment to solve the technical problem is similar to that of the low voltage grid management method provided in this application embodiment. Therefore, the implementation of the low voltage grid management device 500 provided in this application embodiment can refer to the implementation of the low voltage grid management method provided in this application embodiment, and the repeated parts will not be described again.

[0092] After introducing the low voltage management method and apparatus for power grids provided in the embodiments of this application, the low voltage management system for power grids provided in the embodiments of this application will be introduced next.

[0093] Based on the above embodiments, this application provides a low-voltage power grid management system, see reference. Figure 15 As shown, the low voltage grid management system 600 provided in this application embodiment includes at least: a data acquisition unit 610, the aforementioned low voltage grid management device 500, and a power unit 620; The data acquisition unit 610 is located at the common connection point of the power grid node. The data acquisition unit 610 is used to acquire voltage and current signals and transmit them to the power grid low voltage management device 500. The input terminal of the low voltage grid management device 500 is connected to the output terminal of the data acquisition unit 610, and the output terminal of the low voltage grid management device 500 is connected to the input terminal of the power unit 620. The output terminal of the power unit 620 is connected to the grid node; the power unit 620 is used to receive the control commands converted by the low voltage management device 500 according to the reactive power compensation control quantity and the filter control quantity, and generate and output the corresponding reactive power compensation current and harmonic compensation current to the grid node.

[0094] In practical applications, the data acquisition unit 610 is deployed at the common connection point of the power grid node. The data acquisition unit 610 includes at least a voltage transformer, a current transformer, a signal conditioning circuit, and an analog-to-digital converter. The primary side of the voltage transformer is connected in parallel to the three-phase bus of the power grid node for real-time sensing and acquisition of three-phase voltage signals. The primary side of the current transformer is connected in series to the three-phase incoming line of the power grid node for real-time acquisition of three-phase current signals. The secondary sides of the voltage transformer and the current transformer are electrically connected to the input terminals of the signal conditioning circuit of the data acquisition unit 610. After signal conditioning, the analog voltage and current signals are converted into digital signals by the analog-to-digital converter and transmitted to the data acquisition unit of the low-voltage management device of the power grid through an optical fiber communication link or a high-speed serial bus, providing raw data input for voltage deviation calculation and harmonic distortion rate analysis. The input of the low-voltage grid management device is connected to the output of the data acquisition unit 610 via a communication interface. This device receives real-time voltage and current data and executes the aforementioned low-voltage grid management method, generating reactive power compensation control and filtering control quantities. The output of the low-voltage grid management device is connected to the input of the power unit 620 via a control bus. This converts the reactive power compensation control and filtering control quantities into control commands recognizable by the power unit 620 and issues them accordingly. The power unit 620 is deployed at the common connection point of the grid node. The power unit 620 includes multiple power sub-units, each including a static var generator (SVA) and / or an active power filter. The AC side of the SVA is connected to the grid node via a filter reactor, and the DC side is equipped with an energy storage capacitor. The AC side of the active power filter is also connected to the grid node. After receiving control commands from the low-voltage management device, the power unit 620 outputs capacitive or inductive reactive power compensation current according to the reactive power compensation control quantity, thereby achieving rapid regulation of the grid voltage. The active power filter outputs a harmonic compensation current with the same amplitude but opposite phase to the grid harmonic current according to the filtering control quantity, thus achieving harmonic mitigation. The output terminal of the power unit 620 is electrically connected to the grid node, injecting the generated reactive power compensation current and harmonic compensation current into the grid to complete the coordinated management action.

[0095] In one possible implementation, the low-voltage grid management system further includes a local configuration module, a system clock module, a communication module, and a cache unit, all connected to the low-voltage grid management device. The local configuration module is used to pre-store scene configuration files that record scene tags for the installation area. The system clock module is used to extract current date information in real time and map it to generate current season information. The communication module is connected to a remote new energy power generation prediction system via a data communication interface to obtain output prediction data of associated photovoltaic or wind power generation equipment within a preset future time period. The communication module is also connected to the cache unit to write the output prediction data into the prediction data queue in the local cache.

[0096] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0097] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0098] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for managing low voltage in power grids, characterized in that, include: Obtain the current voltage deviation, current harmonic distortion rate, and current time information of the power grid; Determine the current weighting coefficients based on the typical operating characteristics of the power grid corresponding to the current time information. The current voltage deviation and the current harmonic distortion rate are fuzzified to obtain the voltage change fuzzification result and the harmonic change fuzzification result. The voltage change fuzzification result and the harmonic change fuzzification result are fused based on the current weight coefficients to obtain the activation intensity of the target fuzzy rule; Based on the target fuzzy rule and the activation intensity of the target fuzzy rule, the area centroid method is applied for defuzzification to obtain the reactive power compensation control quantity and the filter control quantity. Based on the reactive power compensation control quantity and the filtering control quantity, the power grid nodes are managed collaboratively.

2. The method for managing low voltage in power grids as described in claim 1, characterized in that, The process of fuzzifying the current voltage deviation and the current harmonic distortion rate to obtain fuzzified voltage change results and fuzzified harmonic change results includes: The current voltage deviation is mapped to the voltage deviation domain, and the first membership degree of the voltage deviation domain to which the current voltage deviation belongs is calculated using a triangular membership function. Each first membership degree is used as the fuzzification result of the voltage change. The voltage deviation domain is divided into six fuzzy subsets: negative large, negative medium, negative small, positive small, positive medium, and positive large. The current harmonic distortion rate is mapped to the harmonic distortion rate domain. The second membership degree of the harmonic distortion rate domain to which the current harmonic distortion rate belongs is calculated using a trapezoidal membership function. Each second membership degree is used as the fuzzification result of the harmonic variation. The harmonic distortion rate domain is divided into four fuzzy subsets: normal, mild, moderate, and severe.

3. The method for managing low voltage in power grids as described in claim 2, characterized in that, The process of fusing the fuzzification results of the voltage change and the harmonic change based on the current weighting coefficients to obtain the activation intensity of the target fuzzy rule includes: The fuzzy control rules corresponding to the fuzzification results of the voltage change and the fuzzification results of the harmonic change in the preset fuzzy control rule library are used as target fuzzy rules. For each target fuzzy rule, determine the minimum value of the corresponding membership degree in the voltage change fuzzification result and the harmonic change fuzzification result; the product of the minimum value and the current weight coefficient is used as the activation intensity of the target fuzzy rule.

4. The method for managing low voltage in power grids as described in claim 3, characterized in that, The process involves defuzzifying the target fuzzy rule and its activation intensity using the area centroid method to obtain the reactive power compensation control quantity and the filter control quantity, including: Based on the area centroid method, the reactive power compensation centroid and filter control centroid corresponding to each target fuzzy rule are determined. The product of the activation intensity of each target fuzzy rule and the corresponding reactive power compensation centroid is determined as the first weighted centroid. The sum of all the first weighted centroids is obtained as the first total weighted centroid. The ratio of the first total weighted centroid to the sum of the activation intensities of the target fuzzy rules is used as the reactive power compensation control quantity. The product of the activation intensity of each target fuzzy rule and the corresponding filter control centroid is determined as the second weighted centroid. All second weighted centroids are summed to obtain the second total weighted centroid. The ratio of the second total weighted centroid to the sum of the activation intensities of the target fuzzy rules is taken as the filter control quantity.

5. The method for managing low voltage in power grids as described in claim 1, characterized in that, The determination of the current weighting coefficient based on the typical operating characteristics of the power grid corresponding to the current time information includes: Obtain the installation area scene label, current season information, and new energy power generation output forecast information of the power grid node; Based on the current time information, the installation area scene label, the current season information, and the new energy power generation output prediction information, a scene feature vector is established; Based on a pre-set weighted strategy mapping model, the current weight coefficient is matched and output according to the scenario feature vector; wherein, the weighted strategy mapping model includes a harmonic suppression priority strategy for industrial and commercial load areas, a voltage stability priority strategy for residential load areas, and a voltage dynamic support strategy for new energy access points.

6. The method for managing low voltage in power grids as described in claim 1, characterized in that, The coordinated management of power grid nodes based on the reactive power compensation control quantity and the filtering control quantity includes: The reactive power compensation control quantity and the filter control quantity are converted into control commands required by the power unit. The control command is sent to the power unit deployed at the power grid node, driving the power unit to generate and output the corresponding reactive power compensation current and harmonic compensation current. After the reactive power compensation current and the harmonic compensation current are output, the voltage signal and the current signal are sampled at the common connection point of the power grid node, and the current voltage deviation and the current harmonic distortion rate for the next control cycle are calculated based on the voltage signal and the current signal.

7. The method for managing low voltage in power grids as described in any one of claims 1-6, characterized in that, After performing coordinated management of power grid nodes based on the reactive power compensation control quantity and the filtering control quantity, the process also includes: Monitor the performance indicators of the power grid, wherein the performance indicators include voltage recovery time and harmonic improvement rate; Based on the performance metrics, the weight adjustment amount of the target fuzzy rule is calculated using a reinforcement learning algorithm, and the weight of the target fuzzy rule in the fuzzy control rule base is adjusted according to the weight adjustment amount.

8. The method for managing low voltage in power grids as described in claim 7, characterized in that, Also includes: Obtain the total number of fuzzy control rules in the fuzzy control rule base; When the total number of rules exceeds a preset threshold, the rule with the smallest weight value is deleted first, until the total number of rules is no greater than the preset threshold.

9. The method for managing low voltage in power grids as described in claim 8, characterized in that, Also includes: Continuously monitor the power grid operating status point, which is composed of the current voltage deviation and the current harmonic distortion rate; When the number of times the state point meets the state deviation requirement within a preset time reaches a predetermined number, it is determined that a new operating condition has occurred in the power grid; wherein, the state deviation requirement is that the minimum geometric distance between the state point and the state center point corresponding to all fuzzy control rules in the fuzzy rule base continuously exceeds a preset distance threshold. New fuzzy control rules are generated based on the state points of the new operating conditions, and the new fuzzy control rules are added to the fuzzy rule library.

10. A low-voltage power grid management device, characterized in that, include: The data acquisition unit is used to acquire the current voltage deviation, current harmonic distortion rate, and current time information of the power grid. The weight determination unit is used to determine the current weight coefficient based on the typical operating characteristics of the power grid corresponding to the current time information; The fuzzification unit is used to fuzzify the current voltage deviation and the current harmonic distortion rate to obtain the voltage change fuzzification result and the harmonic change fuzzification result. The fuzzy inference unit is used to fuse the fuzzification results of the voltage change and the fuzzification results of the harmonic change based on the current weight coefficients to obtain the activation intensity of the target fuzzy rule; The defuzzing unit is used to perform defuzzification based on the target fuzzy rule and the activation intensity of the target fuzzy rule, using the area centroid method to obtain the reactive power compensation control quantity and the filter control quantity. The power grid control unit is used to perform coordinated management of power grid nodes based on the reactive power compensation control quantity and the filtering control quantity.

11. A low-voltage power grid management system, characterized in that, include: The data acquisition unit, the low voltage grid management device as described in claim 10, and the power unit; The data acquisition unit is located at the common connection point of the power grid node, and the data acquisition unit is used to acquire voltage signals and current signals and transmit them to the power grid low voltage management device. The input terminal of the low voltage grid management device is connected to the output terminal of the data acquisition unit, and the output terminal of the low voltage grid management device is connected to the input terminal of the power unit. The output terminal of the power unit is connected to the power grid node; the power unit is used to receive the control command converted by the low voltage management device of the power grid according to the reactive power compensation control quantity and the filter control quantity, and generate and output the corresponding reactive power compensation current and harmonic compensation current to the power grid node.

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