Electric energy quality management capability assessment method and equipment of converter device, and storage medium

Abstract

The embodiment of the invention provides a power quality management capability evaluation method and equipment of a converter device and a storage medium. The invention relates to the field of electric energy quality treatment. The method comprises the following steps: acquiring a state feature vector of a target converter device under a target working condition; inputting the state feature vector into a governance capability evaluation model to obtain a governance capability potential index of the target converter device under the target working condition; according to the governance capability potential index, determining a governance capability evaluation value of the target converter device; and determining a potential evaluation result of the target converter device under the target working condition according to the governance capability evaluation value. The method is used for achieving the technical effect of improving the evaluation accuracy of the electric energy quality management capability of the converter device.

Description

Technical Field

[0001] The present application relates to the field of power quality management, and in particular to a method, device and storage medium for evaluating the power quality management capability of a converter device. Background Art

[0002] With the rapid development of distributed energy and e-mobility, typical regulation resources such as photovoltaics, energy storage, and electric vehicle charging stations are being connected to distribution networks in large numbers. These devices are generally connected to the grid through power electronic converters. While they control active power, they also possess certain reactive power regulation and harmonic mitigation capabilities, becoming a new type of power quality management resource. Traditional mitigation devices, such as static VAR compensators and active power filters, are typically dedicated devices. However, these "multi-converter devices" offer a degree of dispatchability and collaborative control potential. While fulfilling their primary functions, they can also passively or actively participate in power quality management. However, because their operating state is affected by factors such as sunlight, state of charge, and charging power, converters' management capabilities vary significantly under different operating conditions. The lack of unified evaluation standards and dynamic analysis methods hinders their effective deployment and management as a management resource in distribution networks.

[0003] Existing research on the participation of multiple converters in power quality management has mostly focused on evaluating the management effects of a single device under specific operating conditions, and most of it relies on static parameter settings, ignoring the dynamic changes in the operating status of various converters under actual operating conditions. This makes it difficult to reflect the management potential of the equipment under different load levels, resource states, and grid conditions. In addition, there is currently a lack of a universal evaluation method for multiple devices and multiple operating conditions, making it impossible to achieve a unified measurement and comparison of the management capabilities of different types of converters, making it difficult to support their large-scale coordinated scheduling and optimized utilization in actual projects. Therefore, the existing technology has the technical problem of poor accuracy in evaluating the power quality management capabilities of converters. Summary of the Invention

[0004] The embodiments of the present application provide a method, device and storage medium for evaluating the power quality management capability of a converter device, so as to achieve the technical effect of improving the accuracy of the evaluation of the power quality management capability of the converter device.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating the power quality management capability of a converter device, comprising:

[0006] Obtaining a state characteristic vector of a target converter device under a target operating condition;

[0007] The state eigenvector is input into the governance capability evaluation model to obtain the governance capability potential index of the target converter under the target working condition;

[0008] Determine the governance capability evaluation value of the target converter device based on the governance capability potential index;

[0009] According to the management capability evaluation value, the potential evaluation result of the target converter device under the target operating conditions is determined; wherein the potential evaluation result is used to characterize the power quality management capability of the target converter device.

[0010] In one possible implementation, the training process of the governance capability assessment model includes:

[0011] Obtaining a sample state feature vector set, the sample state feature vector set including sample state feature vectors of multiple converter devices under different operating conditions, and sample governance capability assessment values ​​corresponding to the sample state feature vectors;

[0012] Clustering multiple sample state feature vectors in the sample state feature vector set to obtain an operating condition cluster set, where the operating condition cluster set includes multiple operating condition categories and sample state feature vectors corresponding to each operating condition category;

[0013] The state feature vector corresponding to each operating condition category is input into the governance capability assessment model to obtain the governance capability potential index corresponding to the sample state feature vector;

[0014] According to the mapping relationship between the governance capacity potential index and the characteristic vector, the predicted governance capacity evaluation value corresponding to the sample state characteristic vector is determined;

[0015] The governance capability assessment model is trained based on the preset loss function, sample governance capability assessment values, and predicted governance capability assessment values.

[0016] In one possible implementation, training a governance capability assessment model based on a preset loss function, sample governance capability assessment values, and predicted governance capability assessment values ​​includes:

[0017] The loss value between the sample governance capability assessment value and the predicted governance capability assessment value is calculated based on the preset loss function;

[0018] The network parameters of the governance capability evaluation model are updated according to the loss value until the loss function converges to obtain a trained governance capability evaluation model.

[0019] In a possible implementation, obtaining a state feature vector of a target current converter under a target operating condition includes:

[0020] Obtaining real-time operating data of the target converter;

[0021] Perform time series filling of real-time operation data based on time series prediction method;

[0022] Standardize the real-time operation data that has been filled with time series data to eliminate or replace the data that exceeds the set threshold in the real-time operation data;

[0023] The normalized real-time operation data is normalized to obtain the state feature vector.

[0024] In one possible implementation, the governance capability potential indicators include harmonic governance potential, reactive power regulation potential, and voltage stability contribution factor. Determining the governance capability evaluation value of the target converter device based on the governance capability potential indicators includes:

[0025] Based on the preset weight values, the harmonic control potential, reactive power regulation potential and voltage stability contribution factor are weighted and summed to obtain the control capability evaluation value of the target converter device.

[0026] In one possible implementation, determining a potential evaluation result of a target converter device under a target operating condition based on the governance capability evaluation value includes:

[0027] Compare the governance capacity assessment value with the preset level threshold to determine the potential level corresponding to the governance capacity assessment value;

[0028] The potential level is determined as a potential evaluation result of the target converter device under the target operating conditions.

[0029] In a possible implementation, after determining a potential evaluation result of the target flow converter under the target operating condition, the method further includes:

[0030] When the amount of real-time operation data accumulates to a preset amount, a new sample state feature vector is obtained according to the accumulated real-time operation data;

[0031] Update the governance capability assessment model based on the new sample state feature vector.

[0032] In one possible implementation, after determining the potential assessment result, the method further includes:

[0033] Based on the potential assessment results, the power quality indicators to be controlled of the target converter device under the target operating conditions are determined, and control operations are performed on the power quality indicators to be controlled; wherein, the power quality indicators to be controlled include at least one or more of the following: harmonic indicators, reactive power regulation indicators and voltage indicators.

[0034] In a second aspect, an embodiment of the present application provides a device for evaluating the power quality management capability of a converter device, comprising:

[0035] An acquisition module, used for acquiring a state characteristic vector of a target converter device under a target operating condition;

[0036] The first processing module is used to input the state characteristic vector into the governance capability evaluation model to obtain the governance capability potential index of the target converter device under the target working condition;

[0037] The second processing module is used to determine the governance capability evaluation value of the target converter device according to the governance capability potential index;

[0038] The third processing module is used to determine the potential evaluation result of the target converter device under the target operating conditions based on the management capability evaluation value; wherein the potential evaluation result is used to characterize the power quality management capability of the target converter device.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor;

[0040] The memory stores computer-executable instructions;

[0041] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0043] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0044] The power quality management capability assessment method, device, and storage medium for a converter device provided in embodiments of the present application obtain a state feature vector of a target converter device under target operating conditions; input the state feature vector into a management capability assessment model to obtain a management capability potential index of the target converter device under the target operating conditions; determine a management capability assessment value of the target converter device based on the management capability potential index; and determine a potential assessment result of the target converter device under the target operating conditions based on the management capability assessment value. This achieves the technical effect of improving the accuracy of the power quality management capability assessment of the converter device. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] Figure 1 Schematic diagram of the process of evaluating the power quality management capability of the converter device provided in this application Figure 1 ;

[0047] Figure 2 Schematic diagram of the process of evaluating the power quality management capability of the converter device provided in this application Figure 2 ;

[0048] Figure 3 Schematic diagram of the process of evaluating the power quality management capability of the converter device provided in this application Figure 3 ;

[0049] Figure 4 A schematic diagram of the structure of the power quality management capability evaluation device for the converter device provided in this application;

[0050] Figure 5 This is a hardware diagram of the power quality management capability evaluation device of the converter device provided in this application.

[0051] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0052] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and approaches consistent with certain aspects of the present application, as detailed in the appended claims.

[0053] With the rapid development of distributed energy and e-mobility, typical regulation resources such as photovoltaics, energy storage systems, and electric vehicle charging stations are being connected to distribution networks in large numbers. These devices are typically connected to the grid via power electronic converters. In addition to controlling active power, they also perform certain reactive power regulation and harmonic control functions, making them a new type of power quality management resource. Traditionally, power systems have relied primarily on specialized devices such as static VAR compensators and active power filters for power quality management. However, these "multi-converter devices" are increasingly demonstrating greater application value due to their dispatchability and potential for coordinated control. While fulfilling their primary functions, these devices can both passively respond to grid demands and actively participate in power quality management, providing greater flexibility for grid operation.

[0054] However, the operating state of these multi-device converters is affected by numerous factors, including light intensity, state of charge, and charging power, resulting in significant variations in their performance under different operating conditions. The current lack of unified evaluation standards and dynamic analysis methods makes it difficult to accurately measure their performance under varying operating conditions, limiting their effective deployment and precise management within distribution networks. Current academic research and engineering applications primarily focus on the performance of a single device under specific operating conditions, primarily evaluating the performance based on static parameter settings. This approach fails to fully account for the dynamic state changes of various converters during actual operation and fails to fully reflect the device's performance under varying load levels, resource states, and grid conditions. More critically, existing technologies lack a universal evaluation method for multiple devices and operating conditions, making it difficult to uniformly measure and compare the performance of different converter types. This technical shortcoming hinders the large-scale coordinated scheduling and optimal utilization of multi-device converters in engineering practice. Ultimately, this results in inaccurate assessments of the power quality performance of existing converters, directly impacting the overall operational quality and management efficiency of the power grid.

[0055] The power quality management capability assessment method, device, and storage medium for a converter device provided in embodiments of the present application obtain a state feature vector of a target converter device under target operating conditions; input the state feature vector into a management capability assessment model to obtain a management capability potential index of the target converter device under the target operating conditions; determine a management capability assessment value of the target converter device based on the management capability potential index; and determine a potential assessment result of the target converter device under the target operating conditions based on the management capability assessment value. This achieves the technical effect of improving the accuracy of the power quality management capability assessment of the converter device.

[0056] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0057] Figure 1 Schematic diagram of the process of evaluating the power quality management capability of the converter device provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0058] S101. Obtain a state feature vector of a target converter device under a target operating condition.

[0059] In this embodiment, by collecting multidimensional state parameters of the target converter under specific operating conditions, a feature vector accurately reflects the device's real-time operating status. This state feature vector not only provides a quantitative basis for subsequent governance capability assessment, but also enables a comprehensive characterization of the device's complex operating state through systematic parameter integration, effectively addressing the assessment bias caused by information loss in traditional assessment methods.

[0060] Optionally, the state characteristic vector of the target converter device under the target working condition includes a grid state parameter and a converter device state parameter, and the state characteristic vector of the target converter device under the target working condition is obtained according to the grid state parameter and the converter device state parameter. Wherein, the grid state parameter includes the fundamental current I rms , fundamental voltage U rms , each harmonic current / voltage I h 、U h , active power P, reactive power Q, total harmonic distortion THD, voltage deviation ΔU; the state parameters of the converter device include: photovoltaic active power P PV , Energy storage / charging pile charging power P ch , Energy storage / charging pile discharge power P dis The state eigenvector can be expressed as: .

[0061] S102: Input the state characteristic vector into a governance capability evaluation model to obtain a governance capability potential index of the target converter device under target operating conditions.

[0062] In this embodiment, the multi-dimensional state characteristic vector obtained in the early stage is used as an input parameter and imported into a pre-built governance capability assessment model. With the help of the algorithm logic and parameter weight system built into the model, the governance potential of the target converter device under specific working conditions is quantitatively evaluated, thereby generating a potential index with clear reference value. This step eliminates the subjectivity and arbitrariness existing in traditional manual evaluations through standardized data processing procedures and a unified evaluation framework, making the evaluation results of different equipment and different working conditions comparable and interpretable. In addition, the generation of governance capability potential indicators provides a quantitative basis for subsequent evaluation value calculations and potential analysis, making the evaluation process more systematic and standardized, and providing a reliable basis for the optimal scheduling and coordinated control of multi-element converter devices, thereby promoting the development of power quality governance technology towards precision and intelligence, and effectively improving the stability and reliability of distribution network operation.

[0063] S103. Determine the governance capability evaluation value of the target converter device according to the governance capability potential index.

[0064] In this embodiment, the potential index is converted into an operational evaluation value through a quantitative model, thereby achieving accurate characterization of the dynamic adjustment capability of the converter under different working conditions. This process not only retains the equipment performance boundary information contained in the potential index, but also eliminates the dimensional differences between different working conditions through normalization of the evaluation value, making cross-scenario and cross-device performance comparison possible.

[0065] S104. Determine a potential evaluation result of the target converter device under the target operating condition based on the governance capability evaluation value.

[0066] In this embodiment, the potential assessment results are used to characterize the power quality management capabilities of the target converter. By comparing and analyzing the management capability assessment value with preset performance thresholds or grading standards, the actual management capability level of the target converter under current operating conditions and its potential for improvement can be quickly and accurately determined. This process not only achieves quantitative grading of equipment performance status, but also dynamically adjusts the assessment criteria to adapt to the complexity of different operating conditions, making the assessment results more targeted and practical. This provides a scientific basis for the intelligent upgrade and energy efficiency management of the converter, promotes the transformation of the health management of power electronic equipment from experience-driven to data-driven, and ultimately improves the reliability and economic efficiency of the entire power system.

[0067] The power quality management capability assessment method, device, and storage medium for a converter device provided in embodiments of the present application obtain a state feature vector of a target converter device under target operating conditions; input the state feature vector into a management capability assessment model to obtain a management capability potential index of the target converter device under the target operating conditions; determine a management capability assessment value of the target converter device based on the management capability potential index; and determine a potential assessment result of the target converter device under the target operating conditions based on the management capability assessment value. This achieves the technical effect of improving the accuracy of the power quality management capability assessment of the converter device.

[0068] Figure 2 Schematic diagram of the process of evaluating the power quality management capability of the converter device provided in this application Figure 2 ,like Figure 2 As shown, this embodiment Figure 1 Based on the embodiment, the training process of the governance capability evaluation model in the power quality governance capability evaluation method of the converter device is described in detail. The method includes:

[0069] S201. Obtain a sample state feature vector set, where the sample state feature vector set includes sample state feature vectors of multiple converter devices under different operating conditions, and sample governance capability assessment values ​​corresponding to the sample state feature vectors.

[0070] In this embodiment, a set of sample state feature vectors is obtained, which covers the sample state feature vectors of multiple converter devices under different operating conditions and the corresponding sample governance capability evaluation values. The technical effect is that by constructing a broadly representative sample data set, a rich and reliable foundation is provided for the training and verification of the governance capability evaluation model, so that the model can fully learn the complex mapping relationship between the state characteristics of the converter devices under different working conditions and the governance capability evaluation values, thereby significantly improving the generalization ability and evaluation accuracy of the model.

[0071] S202 : Clustering multiple sample state feature vectors in the sample state feature vector set to obtain an operating condition cluster set.

[0072] In this embodiment, the operating condition cluster set includes multiple operating condition categories and sample state feature vectors corresponding to each operating condition category. By clustering, state feature vectors with similar operating characteristics are grouped into the same operating condition category. This effectively addresses the generalization problem of the evaluation model caused by the complex and changing operating conditions in the actual operation of the converter. This enables model training to perform differentiated learning for different operating condition categories, significantly improving the adaptability and evaluation accuracy of the governance capability evaluation model for various operating conditions. This promotes the transformation of converter state evaluation from single indicator analysis to multi-dimensional operating condition correlation evaluation, thereby improving the reliability analysis and optimization decision-making capabilities of the power system under complex operating conditions.

[0073] S203. Input the state characteristic vector corresponding to each operating condition category into the governance capability assessment model to obtain the governance capability potential index corresponding to the sample state characteristic vector; determine the predicted governance capability assessment value corresponding to the sample state characteristic vector based on the mapping relationship between the governance capability potential index and the characteristic vector.

[0074] In this embodiment, the state characteristic vector corresponding to each operating condition category is input into the governance capability evaluation model to obtain the governance capability potential index corresponding to the sample state characteristic vector, and the predicted governance capability evaluation value is determined based on the mapping relationship between the governance capability potential index and the characteristic vector. The dynamic relationship between the state characteristics of the converter device and the governance capability under different operating conditions is accurately captured by modeling according to different operating conditions. This not only retains the model's adaptability to complex working conditions, but also realizes the conversion from potential performance space to actual operational evaluation system through the mapping of potential indicators to evaluation values, effectively solving the problem of inaccurate evaluation of traditional single models in multiple working condition scenarios.

[0075] S204. Calculate the loss value between the sample governance capability assessment value and the predicted governance capability assessment value based on the preset loss function; update the network parameters of the governance capability assessment model based on the loss value until the loss function converges to obtain a trained governance capability assessment model.

[0076] In this embodiment, the loss value between the sample governance capability assessment value and the predicted governance capability assessment value is calculated according to the preset loss function, and the network parameters of the governance capability assessment model are updated accordingly until the loss function converges to obtain a trained model. By constructing a closed-loop feedback mechanism, the model can continuously optimize its own parameters to minimize the prediction deviation. This process not only uses the backpropagation algorithm to accurately adjust the model weights to fit the complex nonlinear relationship in the sample data, but also ensures that the model learns the essential mapping law between the state feature vector and the governance capability assessment value through the constraints of the loss function, thereby significantly improving the generalization ability and evaluation accuracy of the model under different working conditions.

[0077] S205. Obtain a new sample state feature vector; and update the governance capability assessment model based on the new sample state feature vector.

[0078] In this embodiment, continuous training and model fine-tuning are carried out in combination with new sample state feature vectors, and a sliding window mechanism and incremental learning strategy are introduced. When new sample state feature vectors accumulate to a certain batch, they are automatically added to the training sample set, lightweight model retraining is performed, and model parameters are updated, thereby achieving model self-optimization and ensuring that the evaluation model remains effective under conditions such as device state changes and environmental fluctuations.

[0079] Figure 3 Schematic diagram of the process of evaluating the power quality management capability of the converter device provided in this application Figure 3 ,like Figure 3 As shown, this embodiment Figure 1 Based on the embodiment, a method for evaluating the power quality management capability of a converter device is described in detail. The method includes:

[0080] S301. Acquire real-time operating data of a target converter; perform time series filling on the real-time operating data based on a time series prediction method; perform standardization on the time-series filled real-time operating data to eliminate or replace data exceeding a set threshold in the real-time operating data; perform normalization on the standardized real-time operating data to obtain a state feature vector.

[0081] In this embodiment, the real-time operation data includes the grid state parameters and the converter state parameters, wherein the grid state parameters include: fundamental current I rms , fundamental voltage U rms , each harmonic current / voltage I h 、U h , active power P, reactive power Q, total harmonic distortion THD, voltage deviation ΔU; the state parameters of the converter device include: photovoltaic active power P PV , Energy storage / charging pile charging power P ch , Energy storage / charging pile discharge power P dis .

[0082] Optionally, a time series prediction method based on a long short-term memory network is used to fill in the missing data caused by communication interruption or sampling anomaly to ensure the continuity and integrity of the data in the time dimension; the outliers in each dimension are identified based on the standard score Z-score statistical method, and the data exceeding the set threshold is eliminated or replaced to avoid extreme values ​​from interfering with the subsequent modeling process; the normalization method is used to uniformly map each feature variable to the standard distribution range of [0,1] to eliminate the influence of different dimensions on the model learning process.

[0083] S302: Input the state characteristic vector into the governance capability evaluation model to obtain the governance capability potential index of the target converter device under the target working condition.

[0084] S303: Based on preset weight values, perform weighted summation on the harmonic control potential, reactive power regulation potential, and voltage stability contribution factor to obtain a control capability evaluation value of the target converter device.

[0085] In this embodiment, the governance capability potential indicators include harmonic governance potential, reactive power regulation potential, and voltage stability contribution factor.

[0086] Optionally, harmonic control potential is used to measure the ability of a device to participate in harmonic control. It is defined as the current margin ratio that can be used for harmonic injection / absorption at the current operating point without affecting the main function of the device. It can be obtained according to the following formula:

[0087]

[0088]

[0089] Among them, H pot Harmonic control potential; I maxavailable Indicates the maximum current amplitude allowed for harmonic compensation in the remaining capacity of the device, I rated is the rated current of the device; I rms is the fundamental current; I h is the voltage.

[0090] Reactive power regulation potential is defined as the ratio of the device's available reactive power regulation capacity to its rated capacity under current operating conditions. It is related to the PV active power, energy storage charging and discharging power, and charging and discharging power of the charging pile. It can be obtained using the following formula:

[0091]

[0092]

[0093] Among them, Q pot is the reactive power regulation potential; Q maxavailableis the maximum available reactive capacity; S rated is the rated capacity of the converter; P out is the active power output of the converter.

[0094] The voltage stability contribution factor is used to measure the theoretical contribution of a device to node voltage stability and can be obtained through sensitivity analysis:

[0095]

[0096] Among them, V stab is the voltage stability contribution factor; Q pot It is the potential for reactive power regulation; U rms / Q is the sensitivity of the node voltage to the reactive power output of the device, which can be estimated through voltage / reactive power sampling data or obtained indirectly using the power flow sensitivity matrix. It is closely related to the access location of the converter device.

[0097] Based on the preset weight values, the harmonic control potential, reactive power regulation potential and voltage stability contribution factor are weighted and summed to obtain the control capability evaluation value of the target converter device according to the following formula:

[0098]

[0099] Among them, R total is the governance capacity assessment value; H pot is the harmonic control potential; Q pot is the reactive power regulation potential; V stab is the voltage stability contribution factor; α is the weight of harmonic control potential; β is the weight of reactive power regulation potential, and γ is the weight of voltage stability contribution factor; α, β, and γ can be weighted according to different grid control objectives.

[0100] S304. Compare the governance capability evaluation value with a preset level threshold to determine the potential level corresponding to the governance capability evaluation value; determine the potential level as a potential evaluation result of the target converter device under the target operating condition.

[0101] In this embodiment, the governance capability evaluation values ​​can be graded to determine the potential level of the governance capability evaluation value. total >0.8 is set to level I, which means high governance potential; 0.5 <R total ≤0.8 is set to Level II; R total ≤0.5 is set as Level III.

[0102] S305. Determine the power quality indicators to be controlled of the target converter device under the target operating conditions based on the potential assessment result, and perform control operations on the power quality indicators to be controlled.

[0103] In this embodiment, the power quality indicators to be managed include at least one or more of the following: harmonic indicators, reactive power regulation indicators, and voltage indicators. Based on the obtained potential assessment results, the management capability assessment value of the target converter device in the potential assessment results under the target operating conditions can be determined, where the management capability assessment value is obtained based on the harmonic management potential, reactive power regulation potential, and voltage stability contribution factor. Optionally, threshold judgments can be performed on the values ​​of the harmonic management potential, reactive power regulation potential, and voltage stability contribution factor, respectively. For those that do not meet the preset threshold adjustment, the value can be determined as the corresponding power quality management indicator to be managed, and then management operations can be performed on the power quality indicator to be managed.

[0104] In one possible implementation, when the value of the harmonic control potential generated by the target converter exceeds a preset threshold, power quality control operations can be performed on the harmonic indicators of the target converter. The operation methods include but are not limited to putting active filters or waveless filters into operation, so as to reduce the harmonic content in the power grid to within the standard range.

[0105] In one possible implementation, when the value of the reactive power regulation potential generated by the target converter exceeds a preset threshold, power quality management operations can be performed on the reactive power regulation index of the target converter. The operation methods include but are not limited to reasonable switching of reactive compensation devices, such as capacitor banks or static VAR generators, to improve the power factor of the power grid and stabilize the voltage.

[0106] In one possible implementation, when the value of the voltage stability contribution factor generated by the target converter device exceeds a preset threshold, power quality management operations can be performed on the voltage indicators of the target converter device. The operation methods include but are not limited to adjusting the dynamic voltage regulator to stabilize the input voltage of the converter device by adjusting the output voltage of the target converter device itself.

[0107] The aforementioned power quality management capability assessment method for converter devices can, on the one hand, adapt to different seasonal conditions, load fluctuations, and device access, avoiding the subjectivity and limitations of traditional methods that rely on manually set scenarios. By establishing a unified management potential indicator system and standardized scoring method, it enables horizontal comparison and hierarchical management of the management capabilities of devices such as photovoltaic inverters, energy storage converters, and charging pile converters, thereby improving the basic capabilities for coordinated management of multiple devices. Furthermore, based on the obtained potential assessment results, management operations can be performed on the target converter device's power quality indicators to be managed, thereby improving the target converter device's power management, accurately assessing the converter device's power quality management capabilities, and effectively adjusting the converter device based on its power quality management capabilities, thereby effectively improving the converter device's power quality management capabilities.

[0108] Figure 4 The schematic diagram of the power quality management capability evaluation device for the converter device provided in this application is as follows: Figure 4 As shown, the power quality management capability evaluation device 40 of the converter device provided in this embodiment includes:

[0109] An acquisition module 401 is configured to acquire a state feature vector of a target converter device under a target operating condition;

[0110] The first processing module 402 is configured to input the state feature vector into a governance capability evaluation model to obtain a governance capability potential index of the target converter device under target operating conditions;

[0111] The second processing module 403 is used to determine the governance capability evaluation value of the target converter device according to the governance capability potential index;

[0112] The third processing module 404 is used to determine a potential evaluation result of the target converter device under the target operating condition according to the control capability evaluation value; wherein the potential evaluation result is used to characterize the power quality control capability of the target converter device.

[0113] In a possible implementation, the first processing module 402 is further configured to:

[0114] Obtaining a sample state feature vector set, the sample state feature vector set including sample state feature vectors of multiple converter devices under different operating conditions, and sample governance capability assessment values ​​corresponding to the sample state feature vectors;

[0115] Clustering multiple sample state feature vectors in the sample state feature vector set to obtain an operating condition cluster set, where the operating condition cluster set includes multiple operating condition categories and sample state feature vectors corresponding to each operating condition category;

[0116] The state feature vector corresponding to each operating condition category is input into the governance capability assessment model to obtain the governance capability potential index corresponding to the sample state feature vector;

[0117] According to the mapping relationship between the governance capacity potential index and the characteristic vector, the predicted governance capacity evaluation value corresponding to the sample state characteristic vector is determined;

[0118] The governance capability assessment model is trained based on the preset loss function, sample governance capability assessment values, and predicted governance capability assessment values.

[0119] In a possible implementation, the first processing module 402 is further configured to:

[0120] The loss value between the sample governance capability assessment value and the predicted governance capability assessment value is calculated based on the preset loss function;

[0121] The network parameters of the governance capability evaluation model are updated according to the loss value until the loss function converges to obtain a trained governance capability evaluation model.

[0122] In a possible implementation, the first processing module 402 is further configured to:

[0123] Obtaining real-time operating data of the target converter;

[0124] Perform time series filling of real-time operation data based on time series prediction method;

[0125] Standardize the real-time operation data that has been filled with time series data to eliminate or replace the data that exceeds the set threshold in the real-time operation data;

[0126] The normalized real-time operation data is normalized to obtain the state feature vector.

[0127] In one possible implementation, the governance capability potential indicator includes harmonic governance potential, reactive power regulation potential, and voltage stability contribution factor; and based on the governance capability potential indicator, a governance capability evaluation value of the target converter device is determined. The second processing module 403 is further configured to:

[0128] Based on the preset weight values, the harmonic control potential, reactive power regulation potential and voltage stability contribution factor are weighted and summed to obtain the control capability evaluation value of the target converter device.

[0129] In a possible implementation, the third processing module 404 is further configured to:

[0130] Compare the governance capacity assessment value with the preset level threshold to determine the potential level corresponding to the governance capacity assessment value;

[0131] The potential level is determined as a potential evaluation result of the target converter device under the target operating conditions.

[0132] In a possible implementation, the third processing module 404 is further configured to:

[0133] When the amount of real-time operation data accumulates to a preset amount, a new sample state feature vector is obtained according to the accumulated real-time operation data;

[0134] Update the governance capability assessment model based on the new sample state feature vector.

[0135] In a possible implementation, the third processing module 404 is further configured to:

[0136] Based on the potential assessment results, the power quality indicators to be controlled of the target converter device under the target operating conditions are determined, and control operations are performed on the power quality indicators to be controlled; wherein, the power quality indicators to be controlled include at least one or more of the following: harmonic indicators, reactive power regulation indicators and voltage indicators.

[0137] The power quality management capability assessment device of the converter device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar, and are not described in detail in this embodiment.

[0138] Figure 5 This is a hardware diagram of the power quality management capability evaluation device for the converter device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0139] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that the at least one processor 501 performs the above method.

[0140] The specific implementation process of the processor 501 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0141] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0142] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0143] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0144] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0145] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0146] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0147] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0148] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via an interface, method, or unit, and may be electrical, mechanical, or otherwise.

[0149] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0151] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0152] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0153] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for evaluating the power quality management capability of a converter device, characterized in that: The method comprises: Obtaining a state characteristic vector of a target converter device under a target operating condition; Inputting the state characteristic vector into a governance capability evaluation model to obtain a governance capability potential index of the target converter device under the target operating condition; Determining a governance capability evaluation value of the target converter device according to the governance capability potential indicator; According to the management capability evaluation value, a potential evaluation result of the target converter device under the target operating condition is determined; wherein the potential evaluation result is used to characterize the power quality management capability of the target converter device.

2. The method according to claim 1, characterized in that The training process of the governance capability assessment model includes: Acquire a sample state feature vector set, wherein the sample state feature vector set includes sample state feature vectors of a plurality of converter devices under different operating conditions, and sample governance capability evaluation values ​​corresponding to the sample state feature vectors; Clustering a plurality of the sample state feature vectors in the sample state feature vector set to obtain an operating condition cluster set, wherein the operating condition cluster set includes a plurality of operating condition categories and a sample state feature vector corresponding to each of the operating condition categories; Inputting the state feature vector corresponding to each of the operating condition categories into the governance capability assessment model to obtain a governance capability potential index corresponding to the sample state feature vector; Determine the predicted governance capability assessment value corresponding to the sample state feature vector based on the mapping relationship between the governance capability potential indicator and the feature vector; The governance capability assessment model is trained according to a preset loss function, the sample governance capability assessment value and the predicted governance capability assessment value.

3. The method according to claim 2, characterized in that The training of the governance capability assessment model according to the preset loss function, the sample governance capability assessment value, and the predicted governance capability assessment value includes: Calculate the loss value between the sample governance capability evaluation value and the predicted governance capability evaluation value according to the preset loss function; The network parameters of the governance capability evaluation model are updated according to the loss value until the loss function converges to obtain the trained governance capability evaluation model.

4. The method according to claim 1, wherein The obtaining of the state characteristic vector of the target converter device under the target working condition includes: Acquiring real-time operating data of the target converter; Performing time series filling on the real-time operation data based on a time series prediction method; performing normalization processing on the time-series padded real-time operation data to eliminate or replace data exceeding a set threshold in the real-time operation data; The normalized real-time operation data is normalized to obtain the state feature vector.

5. The method according to claim 1, wherein The governance capability potential indicators include harmonic governance potential, reactive power regulation potential, and voltage stability contribution factor; and determining the governance capability evaluation value of the target converter device based on the governance capability potential indicators includes: Based on preset weight values, the harmonic control potential, the reactive power regulation potential and the voltage stability contribution factor are weighted and summed to obtain a control capability evaluation value of the target converter device.

6. The method according to claim 1, wherein Determining the potential evaluation result of the target inverter device under the target operating condition according to the governance capability evaluation value includes: Comparing the governance capability assessment value with a preset level threshold to determine the potential level corresponding to the governance capability assessment value; The potential level is determined as a potential evaluation result of the target inverter device under the target operating condition.

7. The method according to any one of claim 1, characterized in that After determining the potential evaluation result of the target flow converter under the target operating condition, the method further includes: When the amount of real-time operation data accumulates to a preset amount, obtaining a new sample state feature vector according to the accumulated real-time operation data; The governance capability assessment model is updated based on the new sample state feature vector.

8. The method according to any one of claims 1 to 7, characterized in that After determining the potential assessment result, the method further includes: Based on the potential assessment result, the power quality indicators to be controlled of the target converter device under the target operating conditions are determined, and control operations are performed on the power quality indicators to be controlled; wherein the power quality indicators to be controlled include at least one or more of the following: harmonic indicators, reactive power regulation indicators and voltage indicators.

9. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 8 when executed by a processor.

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