Wind power plant electric energy quality prediction and evaluation method

Abstract

The invention provides a wind power plant electric energy quality prediction and evaluation method, and belongs to the technical field of electric energy evaluation. The method comprises the following steps: 1, collecting basic information of a wind power plant, and configuring a sensor to obtain real-time parameter data of the wind power plant; 2, preprocessing the real-time parameter data to obtain standard parameter data, and analyzing electric energy parameter index values of all operation nodes of the wind power plant based on load flow calculation; 3, constructing a wind power plant index system according to the standard parameter data of the wind power plant; 4, constructing a wind power plant model according to the wind power plant index system, inputting the electric energy parameter index value of each operation node, and predicting and evaluating the electric energy quality of the wind power plant in combination with the standard parameter data; and 5, configuring the wind power plant model to the common bus, analyzing the electric energy quality on the common bus, and optimizing the wind power plant model. The wind power plant electric energy quality and operation efficiency are improved, and the electric energy evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy assessment, and in particular to a method for predicting and assessing electric energy quality in a wind farm. Background Art

[0002] At present, there are a lot of studies on wind farm power quality assessment methods in China, mainly including the monitoring and assessment of wind farms and the prediction and assessment of access systems for new, rebuilt, and expanded projects. Among them, point-to-point methods and cut-set methods are used to calculate various indicators of power quality, and the results are superimposed with field measurements for evaluation. Most of them are simulation studies on the impact of a certain wind farm on the power system and its governance, and the simulation processing lacks accuracy and comprehensiveness.

[0003] Therefore, the present invention proposes a method for predicting and evaluating power quality of a wind farm. Summary of the invention

[0004] The present invention provides a method for predicting and evaluating the power quality of a wind farm, which is used to construct a wind farm power index system and a prediction model through real-time data collection and power flow calculation, optimize power quality, and improve the operating efficiency of the wind farm. Ultimately, through common bus analysis and model optimization, efficient and safe operation of the wind farm is achieved.

[0005] In one aspect, the present invention provides a method for predicting and evaluating power quality of a wind farm, comprising: Step 1: Collect basic information of the wind farm and configure sensors to obtain real-time parameter data of the wind farm; Step 2: Preprocess the real-time parameter data to obtain standard parameter data, and analyze the power parameter index values ​​of all operating nodes of the wind farm based on power flow calculation; Step 3: Construct a wind farm index system based on the standard parameter data of the wind farm; Step 4: Build a wind farm model based on the wind farm index system, input the power parameter index value of each operating node and combine it with the standard parameter data to predict and evaluate the wind farm power quality; Step 5: Configure the wind farm model to the common bus, analyze the power quality on the common bus and optimize the wind farm model.

[0006] On the other hand, basic information of the wind farm is collected, including: Collect the geographic coordinates of the wind farm and determine the geographic information of the wind farm; Get the location of all equipment in the wind farm and get detailed information about the equipment; Analyze the target monitoring power parameter types of the wind farm based on the wind farm's geographic information and equipment specific information.

[0007] On the other hand, sensors are configured to obtain real-time parameter data of the wind farm, including: According to the target monitoring electric energy parameter type of the wind farm, matching the corresponding sensor type; Select the installation location and configuration parameters of the sensor according to the specific information of the device, and complete the sensor configuration; Wireless communication network technology is selected to connect all sensors in the wind farm, and sensors are used to obtain real-time parameter data of the wind farm in real time.

[0008] On the other hand, the real-time parameter data is preprocessed to obtain standard parameter data, including: Acquire real-time parameter data of any electric energy parameter type, acquire statistical characteristics of the real-time parameter data, and fill missing values ​​in the real-time parameter data based on the statistical characteristics to obtain first data; Obtain the standard unit of the first data of any electric energy parameter type according to the electric energy parameter type-standard unit mapping table, and perform unit unification to obtain the second data; The second data is standardized by a Z-score algorithm to obtain standard parameter data.

[0009] On the other hand, the power parameter index values ​​of all operating nodes of the wind farm are analyzed based on the power flow calculation, including: According to the topological structure of the wind farm, a power line framework model is constructed, wherein each wind farm device in the power line framework model is an operating node; Determine the power balance equation for each running node as follows: ;in, represents the active power of the ith running node, represents the reactive power of the ith operating node, represents the real part of the preset admittance matrix, represents the imaginary part of the preset admittance matrix, represents the mean voltage amplitude of the standard parameter data of the i-th operating node, represents the voltage amplitude of the standard parameter data at the jth time point of the ith operating node, n represents that there are n time points in the ith node, represents the voltage phase of the standard parameter data at the jth time point of the i-th operating node; Under the premise that any operating node achieves power balance, the power parameter index value of the operating node is calculated according to the power flow: ;in, represents the power parameter index value of the i-th running node, The partial derivative Jacobian matrix representing the voltage magnitude and phase.

[0010] On the other hand, a wind farm index system is constructed based on the standard parameter data of the wind farm, including: Constructing a multi-level indicator system according to the wind farm's electric energy parameter types and preset standard classification names, wherein the preset standard classification names contain corresponding types of electric energy parameter type names; First, the importance of each preset standard classification name is evaluated according to the expert review database, and the corresponding first impact value is assigned; For all types of electric energy parameters under any preset standard classification name, a judgment matrix is ​​constructed based on the expert review library, where each element in the judgment matrix Indicates the relative importance of electric energy parameter type a and electric energy parameter type b; Then the second impact value of any electric energy parameter type is: ;in, represents the second impact value of the kth electric energy parameter type, m represents a total of m preset standard classification names, represents the maximum eigenvalue of the judgment matrix of the pth preset standard classification name, Indicates the relative importance of power parameter type p and power parameter type k; Finally, a wind farm index system is obtained, including: a first-layer preset standard classification name and its first impact value, a second-layer electric energy parameter type and its second impact value.

[0011] On the other hand, a wind farm model is constructed based on the wind farm index system, and the power parameter index value of each operating node is input and combined with the standard parameter data to predict and evaluate the power quality of the wind farm, including: Constructing a wind farm power quality model framework, importing all indicators of the wind farm indicator system of the wind farm as framework nodes, and importing the power quality mathematical model as an algorithm framework to obtain a first model; Inputting the standard electric energy parameters of each wind farm node into the first model, obtaining the basic model index, and generating the second model; Input the power parameter index value of any operating node into the second model, evaluate the power quality of the operating node based on the impact value of the wind farm index system, obtain the power quality score of the operating node, compare the deviation with the operating status score of the operating node actually inspected on the spot, and obtain the final model index; The first model is imported according to the final model index to generate the final wind farm model.

[0012] On the other hand, the wind farm model is configured to the common bus, the power quality on the common bus is analyzed and the wind farm model is optimized, including: According to the bus configuration parameters of the wind farm, the final wind farm model is configured to the common bus of the wind farm; Start the final wind farm model to analyze its short-term fluctuations on the common bus and obtain real-time power quality assessment results; Obtain the impact of short-term fluctuations of actual wind farms on the power quality of the common bus, obtain the real power quality assessment results, compare the real-time power quality assessment results with the real power quality assessment results, and obtain the real model error; The model indicators and wind farm indicator system of the final wind farm model are optimized according to the actual model error.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for predicting and evaluating the power quality of a wind farm, which is used to construct a wind farm power index system and a prediction model through real-time data collection and power flow calculation, optimize power quality, and improve the operating efficiency of the wind farm. Ultimately, through common bus analysis and model optimization, efficient and safe operation of the wind farm is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of a method for predicting and evaluating power quality of a wind farm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: like Figure 1 As shown, a method for predicting and evaluating power quality of a wind farm provided by an embodiment of the present invention includes: Step 1: Collect basic information of the wind farm and configure sensors to obtain real-time parameter data of the wind farm; Step 2: Preprocess the real-time parameter data to obtain standard parameter data, and analyze the power parameter index values ​​of all operating nodes of the wind farm based on power flow calculation; Step 3: Construct a wind farm index system based on the standard parameter data of the wind farm; Step 4: Build a wind farm model based on the wind farm index system, input the power parameter index value of each operating node and combine it with the standard parameter data to predict and evaluate the wind farm power quality; Step 5: Configure the wind farm model to the common bus, analyze the power quality on the common bus and optimize the wind farm model.

[0018] In this embodiment, the wind farm is an electricity production system composed of a plurality of wind turbines, which convert wind energy into electrical energy through wind turbines and transmit it to the power grid.

[0019] In this embodiment, the basic information includes: geographical location, scale, rated power of wind turbines, grid access method, etc.

[0020] In this embodiment, the real-time parameter data refers to dynamic data obtained from various sensors, measuring equipment and monitoring systems in the wind farm, including wind speed, wind direction, power generation, etc.

[0021] In this embodiment, the standard parameter data refers to the real-time parameter data of the wind farm that has been preprocessed and normalized.

[0022] In this embodiment, the power flow calculation is used to calculate the voltage and power flow of each node in the power system and the operating status of the system. Its purpose is to determine the power transmission and power distribution between each node (such as generators, transformers, load points, etc.) of the power system under given load and power generation conditions.

[0023] In this embodiment, the operating node is a device node in the power system, which is used to represent the electrical status of the components in the power grid.

[0024] In this embodiment, the wind farm index system is a set of standardized indicators for quantitatively evaluating the power quality, operating status and overall performance of the wind farm, including indicators such as wind speed, voltage, current, capacity, and availability.

[0025] In this embodiment, the wind farm model refers to a mathematical model used to simulate changes in power quality of the wind farm under different operating conditions.

[0026] In this embodiment, power quality refers to whether the power provided in the power system meets the usage standards and requirements, ensuring the normal operation of the equipment, avoiding damage and ensuring system efficiency.

[0027] In this embodiment, the common bus refers to a centralized electrical connection point for power transmission and exchange between multiple power sources or load devices.

[0028] In this embodiment, optimization refers to achieving better power quality, improving the operating efficiency of the wind farm and reducing the instability of the power system by adjusting and improving the wind farm model and its operating parameters.

[0029] The working principle and beneficial effects of the above technical solution are: through real-time data collection and flow calculation, a wind farm power parameter index system is constructed, and the model is used to predict and evaluate the power quality. The optimized wind farm model works in conjunction with the public bus to effectively improve the power quality, enhance the wind farm operation efficiency, and ensure system stability.

[0030] Embodiment 2: Based on the above embodiment 1, basic information of the wind farm is collected, including: Collect the geographic coordinates of the wind farm and determine the geographic information of the wind farm; Get the location of all equipment in the wind farm and get detailed information about the equipment; Analyze the target monitoring power parameter types of the wind farm based on the wind farm's geographic information and equipment specific information.

[0031] In this embodiment, the geographic coordinates refer to the position of a point on the surface of the earth represented by a specific coordinate system.

[0032] In this embodiment, geographic information refers to various information related to a certain geographic location, including the coordinates, geographic features, environmental conditions, climate data of the location, and natural and man-made facilities around the location.

[0033] In this embodiment, the equipment specific information refers to the detailed data and parameters related to all equipment in the wind farm, such as rated voltage, brand, model, power, design wind speed, etc. The equipment types include: wind turbines, substations, monitoring equipment, communication facilities, etc.

[0034] In this embodiment, the electric energy parameter type refers to various electrical parameters used to evaluate the impact of a wind farm on the power quality of a power grid, including voltage fluctuation, frequency, power, and the like.

[0035] The working principle and beneficial effects of the above technical solution are: by collecting the geographical coordinates and equipment location information of the wind farm, analyzing the layout and equipment distribution of the wind farm, and thus determining the type of electric energy parameters to be monitored, accurately identifying key monitoring points, improving the accuracy of electric energy data and monitoring efficiency, and providing strong support for subsequent electric energy optimization and fault warning.

[0036] Embodiment 3: Based on the above embodiment 2, configuring sensors to obtain real-time parameter data of the wind farm includes: According to the target monitoring electric energy parameter type of the wind farm, matching the corresponding sensor type; Select the installation location and configuration parameters of the sensor according to the specific information of the device, and complete the configuration of the sensor; Wireless communication network technology is selected to connect all sensors in the wind farm, and sensors are used to obtain real-time parameter data of the wind farm in real time.

[0037] In this embodiment, the sensor types include: wind speed, wind direction, voltage, current, etc.

[0038] In this embodiment, the configuration parameters refer to specific technical and operating conditions that need to be considered when installing and configuring each sensor.

[0039] In this embodiment, wireless communication network technologies include: WiFi, 5G, Bluetooth and other technologies.

[0040] The working principle and beneficial effects of the above technical solution are: by matching the sensor type with the wind farm target monitoring power parameters, reasonably configuring the sensors and selecting appropriate locations, real-time data collection is achieved. Wireless communication technology ensures efficient data transmission, improves the monitoring accuracy and response speed of the wind farm, and helps to optimize the operating efficiency in real time.

[0041] Embodiment 4: On the basis of the above-mentioned embodiment 2, the real-time parameter data is preprocessed to obtain standard parameter data, including: Acquire real-time parameter data of any electric energy parameter type, acquire statistical characteristics of the real-time parameter data, and fill missing values ​​in the real-time parameter data based on the statistical characteristics to obtain first data; Obtain the standard unit of the first data of any electric energy parameter type according to the electric energy parameter type-standard unit mapping table, and perform unit unification to obtain the second data; The second data is standardized by a Z-score algorithm to obtain standard parameter data.

[0042] In this embodiment, statistical features are key indicators for describing and summarizing a data set, including mean, variance, median, etc.

[0043] In this embodiment, missing values ​​refer to the lack of valid data at certain positions in the data set.

[0044] In this embodiment, the first data is an electric energy parameter data set after missing values ​​are filled based on statistical features.

[0045] In this embodiment, the electric energy parameter type-standard unit mapping table is a table used to map different types of electric energy parameters to their corresponding standard units.

[0046] In this embodiment, the standard unit refers to the international standard unit corresponding to the electric energy parameter type-standard unit mapping table.

[0047] In this embodiment, the second data is the result of converting the first data into a standard unit.

[0048] In this embodiment, the Z-score algorithm is a method for converting data into standardized data with zero mean and unit standard deviation. ; X represents the data point, represents the mean (average) of the sample, Represents the standard deviation of the sample.

[0049] The working principle and beneficial effects of the above technical solution are: filling missing values ​​through statistical features, unit unification and Z-score standardization to process power data to ensure data integrity and consistency. The standardized data is helpful for subsequent analysis and model building, and improves the accuracy and reliability of wind farm operation monitoring and optimization.

[0050] Embodiment 5: On the basis of the above-mentioned embodiment 1, the power parameter index values ​​of all operating nodes of the wind farm are analyzed based on the power flow calculation, including: According to the topological structure of the wind farm, a power line framework model is constructed, wherein each wind farm device in the power line framework model is an operating node; Determine the power balance equation for each running node as follows: ;in, represents the active power of the ith running node, represents the reactive power of the ith operating node, represents the real part of the preset admittance matrix, represents the imaginary part of the preset admittance matrix, represents the mean voltage amplitude of the standard parameter data of the i-th operating node, represents the voltage amplitude of the standard parameter data at the jth time point of the ith operating node, n represents that there are n time points in the ith node, represents the voltage phase of the standard parameter data at the jth time point of the i-th operating node; Under the premise that any operating node achieves power balance, the power parameter index value of the operating node is calculated according to the power flow: ;in, represents the power parameter index value of the i-th running node, The Jacobian matrix of the partial derivatives representing the voltage magnitude and phase.

[0051] In this embodiment, the topology structure refers to how various devices in the system (such as generators, loads, transformers, lines, etc.) are connected and arranged through power lines, wires, switches and other devices.

[0052] In this embodiment, the power line framework model is a mathematical model used to describe the flow of power between various nodes in the system.

[0053] In this embodiment, active power is the power used to actually do work in the power system, representing the actual consumption or transmission of electrical energy.

[0054] In this embodiment, reactive power is a form of power in the power system, which is consumed and generated by the establishment and change of electric and magnetic fields.

[0055] In this embodiment, the preset admittance matrix represents the electrical coupling relationship between the nodes of each wind farm, and each element of the matrix represents the electrical admittance relationship between the nodes: In this embodiment, the electric energy parameter index value is an important measure used to describe the electrical state of each node in the system.

[0056] In this embodiment, the partial derivative Jacobian matrix refers to a matrix that describes the sensitivity of each variable in the system to power changes.

[0057] In this embodiment, the amplitude refers to the size of the voltage.

[0058] In this embodiment, the phase refers to the phase angle of the voltage.

[0059] The working principle and beneficial effects of the above technical solution are: by constructing a wind farm power line framework model, combining the power balance equation with the flow calculation, the power parameter indicators of the operating nodes are calculated in real time. The partial derivative Jacobian matrix of the voltage amplitude and phase is used to improve the calculation accuracy and optimize the power dispatch and operation efficiency of the wind farm.

[0060] Embodiment 6: On the basis of the above-mentioned embodiment 2, a wind farm index system is constructed according to the standard parameter data of the wind farm, including: Constructing a multi-level indicator system according to the wind farm's electric energy parameter types and preset standard classification names, wherein the preset standard classification names contain corresponding types of electric energy parameter type names; First, the importance of each preset standard classification name is evaluated according to the expert review database, and the corresponding first impact value is assigned; For all types of electric energy parameters under any preset standard classification name, a judgment matrix is ​​constructed based on the expert review library, where each element in the judgment matrix Indicates the relative importance of electric energy parameter type a and electric energy parameter type b; Then the second impact value of any electric energy parameter type is: ;in, represents the second impact value of the kth electric energy parameter type, m represents a total of m preset standard classification names, represents the maximum eigenvalue of the judgment matrix of the pth preset standard classification name, Indicates the relative importance of power parameter type p and power parameter type k; Finally, a wind farm index system is obtained, including: a first-layer preset standard classification name and its first impact value, a second-layer electric energy parameter type and its second impact value.

[0061] In this embodiment, the preset standard classification name is a standard for grouping and classifying the electric energy parameters of the wind farm, including: power output, load, reliability, etc.

[0062] In this embodiment, the multi-level indicator system is a hierarchical structure used for evaluation, analysis and optimization of complex systems.

[0063] In this embodiment, the expert review database is a data set containing the evaluations, judgments and opinions of domain experts on certain issues.

[0064] In this embodiment, the judgment matrix is ​​a matrix used to describe the relative importance between types of electric energy parameters.

[0065] In this embodiment, the first impact value refers to the relative importance of each preset standard classification name in the overall evaluation system.

[0066] In this embodiment, the second impact value is the relative importance of each power parameter type relative to all other power parameter types.

[0067] The working principle and beneficial effects of the above technical solution are: evaluate the importance of wind farm power parameters through the expert review database, build a multi-level indicator system, and calculate the impact value of each power parameter type in combination with the judgment matrix. Optimize wind farm power parameters through hierarchical management to improve decision-making accuracy and system operation efficiency.

[0068] Embodiment 7: On the basis of the above-mentioned embodiment 1, a wind farm model is constructed according to the wind farm index system, and the power parameter index value of each operating node is input and combined with the standard parameter data to predict and evaluate the power quality of the wind farm, including: Constructing a wind farm power quality model framework, importing all indicators of the wind farm indicator system of the wind farm as framework nodes, and importing the power quality mathematical model as an algorithm framework to obtain a first model; Inputting the standard electric energy parameters of each wind farm node into the first model, obtaining the basic model index, and generating the second model; Input the power parameter index value of any operating node into the second model, evaluate the power quality of the operating node based on the impact value of the wind farm index system, obtain the power quality score of the operating node, compare the deviation with the operating status score of the operating node actually inspected on the spot, and obtain the final model index; The first model is imported according to the final model index to generate the final wind farm model.

[0069] In this embodiment, the power quality model framework is a multi-level and multi-dimensional evaluation system that evaluates the power quality of the wind farm through a systematic approach.

[0070] In this embodiment, the framework node refers to each key element, parameter or variable in the model, and these elements are the core parts of the entire evaluation system for describing and analyzing power quality.

[0071] In this embodiment, the algorithm architecture is a multi-level framework, involving algorithm flow parts from data collection and processing to power quality assessment.

[0072] In this embodiment, the first model refers to a basic model that combines the index system of the wind farm and the mathematical model of power quality.

[0073] In this embodiment, the second model is a model obtained after optimization based on the first model.

[0074] In this embodiment, the basic model indicators are preliminary calculation results in the process of wind farm power quality assessment, including model deviation, accuracy, true negative rate, etc.

[0075] In this embodiment, the power quality score is a result of quantitatively evaluating the power quality status of the wind farm operation nodes.

[0076] In this embodiment, the operating status score is a comprehensive evaluation result of the operating status of a certain node in the wind farm within a specific time period.

[0077] In this embodiment, the final model index is a comprehensive evaluation index of the wind farm power quality obtained through a process of step-by-step calculation, adjustment and verification.

[0078] The working principle and beneficial effects of the above technical solution are: by constructing a wind farm power quality model framework, combining an indicator system and a mathematical model, the power quality of the operating nodes is evaluated in real time and compared with the field survey data, the model accuracy is optimized, and the wind farm operating efficiency and power quality management level are improved.

[0079] Embodiment 8: On the basis of the above-mentioned embodiment 7, the wind farm model is configured to the common bus, the power quality on the common bus is analyzed and the wind farm model is optimized, including: According to the bus configuration parameters of the wind farm, the final wind farm model is configured to the common bus of the wind farm; Start the final wind farm model to analyze its short-term fluctuations on the common bus and obtain real-time power quality assessment results; Obtain the impact of short-term fluctuations of actual wind farms on the power quality of the common bus, obtain the real power quality assessment results, compare the real-time power quality assessment results with the real power quality assessment results, and obtain the real model error; The model indicators and wind farm indicator system of the final wind farm model are optimized according to the actual model error.

[0080] In this embodiment, the bus configuration parameters refer to the electrical configuration characteristics of the bus system in the wind farm, such as capacity, voltage level, topology, etc.

[0081] In this embodiment, short-term fluctuation refers to the rapid change of power quality parameters such as voltage, frequency, and power in the power system within a short time range.

[0082] In this embodiment, the real-time power quality assessment result is a power quality indicator of the current state obtained by analyzing the power quality based on real-time data and a wind farm model during the operation of the power system of the wind farm.

[0083] In this embodiment, the actual power quality assessment result is based on actual wind farm operation data and public grid feedback information, and assesses the specific impact of the wind farm on the power quality of the grid under actual operation conditions.

[0084] In this embodiment, the real model error refers to an error generated according to the difference between the real power quality assessment result and the real-time power quality assessment result during the actual operation of the wind farm.

[0085] The working principle and beneficial effects of the above technical solution are: by configuring the final wind farm model with the common bus, analyzing the impact of short-term fluctuations on power quality, and comparing real-time and actual evaluation results, calculating model errors and optimizing the wind farm model and indicator system, the wind farm power quality prediction accuracy and system optimization level are improved.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting and evaluating power quality of a wind farm, characterized in that: include: Step 1: Collect basic information of the wind farm and configure sensors to obtain real-time parameter data of the wind farm; Step 2: Preprocess the real-time parameter data to obtain standard parameter data, and analyze the power parameter index values ​​of all operating nodes of the wind farm based on power flow calculation; Step 3: Construct a wind farm index system based on the standard parameter data of the wind farm; Step 4: Build a wind farm model based on the wind farm index system, input the power parameter index value of each operating node and combine it with the standard parameter data to predict and evaluate the wind farm power quality; Step 5: Configure the wind farm model to the common bus, analyze the power quality on the common bus and optimize the wind farm model.

2. A wind farm power quality prediction and evaluation method according to claim 1, characterized in that: Collect basic information about the wind farm, including: Collect the geographic coordinates of the wind farm and determine the geographic information of the wind farm; Get the location of all equipment in the wind farm and get detailed information about the equipment; Analyze the target monitoring power parameter types of the wind farm based on the wind farm's geographic information and equipment specific information.

3. A wind farm power quality prediction and evaluation method according to claim 2, characterized in that: Configure sensors to obtain real-time parameter data of wind farms, including: According to the target monitoring electric energy parameter type of the wind farm, matching the corresponding sensor type; Select the installation location and configuration parameters of the sensor according to the specific information of the device, and complete the configuration of the sensor; Wireless communication network technology is selected to connect all sensors in the wind farm, and sensors are used to obtain real-time parameter data of the wind farm in real time.

4. A wind farm power quality prediction and assessment method according to claim 2, characterized in that: Preprocess the real-time parameter data to obtain standard parameter data, including: Acquire real-time parameter data of any electric energy parameter type, acquire statistical characteristics of the real-time parameter data, and fill missing values ​​in the real-time parameter data based on the statistical characteristics to obtain first data; Obtain the standard unit of the first data of any electric energy parameter type according to the electric energy parameter type-standard unit mapping table, and perform unit unification to obtain the second data; The second data is standardized by a Z-score algorithm to obtain standard parameter data.

5. A wind farm power quality prediction and evaluation method according to claim 1, characterized in that: The power parameter index values ​​of all operating nodes of the wind farm are analyzed based on power flow calculation, including: According to the topological structure of the wind farm, a power line framework model is constructed, wherein each wind farm device in the power line framework model is an operating node; Determine the power balance equation for each running node as follows: ;in, represents the active power of the ith running node, represents the reactive power of the ith operating node, represents the real part of the preset admittance matrix, represents the imaginary part of the preset admittance matrix, represents the mean voltage amplitude of the standard parameter data of the i-th operating node, represents the voltage amplitude of the standard parameter data at the jth time point of the ith operating node, n represents that there are n time points in the ith node, represents the voltage phase of the standard parameter data at the jth time point of the i-th operating node; Under the premise that any operating node achieves power balance, the power parameter index value of the operating node is calculated according to the power flow: ;in, represents the power parameter index value of the i-th operating node, The partial derivative Jacobian matrix representing the voltage magnitude and phase.

6. A wind farm power quality prediction and assessment method according to claim 2, characterized in that: The wind farm index system is constructed based on the standard parameter data of the wind farm, including: Constructing a multi-level indicator system according to the wind farm's electric energy parameter types and preset standard classification names, wherein the preset standard classification names contain corresponding types of electric energy parameter type names; First, the importance of each preset standard classification name is evaluated according to the expert review database, and the corresponding first impact value is assigned; For all types of electric energy parameters under any preset standard classification name, a judgment matrix is ​​constructed based on the expert review library, where each element in the judgment matrix Indicates the relative importance of electric energy parameter type a and electric energy parameter type b; Then the second impact value of any electric energy parameter type is: ;in, represents the second impact value of the kth electric energy parameter type, m represents a total of m preset standard classification names, represents the maximum eigenvalue of the judgment matrix of the pth preset standard classification name, Indicates the relative importance of power parameter type p and power parameter type k; Finally, a wind farm index system is obtained, including: a first-layer preset standard classification name and its first impact value, a second-layer electric energy parameter type and its second impact value.

7. A wind farm power quality prediction and assessment method according to claim 1, characterized in that: The wind farm model is constructed according to the wind farm index system, and the power parameter index value of each operating node is input and combined with the standard parameter data to predict and evaluate the power quality of the wind farm, including: Constructing a wind farm power quality model framework, importing all indicators of the wind farm indicator system of the wind farm as framework nodes, and importing the power quality mathematical model as an algorithm framework to obtain a first model; Inputting the standard electric energy parameters of each wind farm node into the first model, obtaining the basic model index, and generating the second model; Input the power parameter index value of any operating node into the second model, evaluate the power quality of the operating node based on the impact value of the wind farm index system, obtain the power quality score of the operating node, compare the deviation with the operating status score of the operating node actually inspected on the spot, and obtain the final model index; The first model is imported according to the final model index to generate the final wind farm model.

8. A method for predicting and evaluating power quality of a wind farm according to claim 7, characterized in that: Configure the wind farm model to the common bus, analyze the power quality on the common bus and optimize the wind farm model, including: According to the bus configuration parameters of the wind farm, the final wind farm model is configured to the common bus of the wind farm; Start the final wind farm model to analyze its short-term fluctuations on the common bus and obtain real-time power quality assessment results; Obtain the impact of short-term fluctuations of actual wind farms on the power quality of the common bus, obtain the real power quality assessment results, compare the real-time power quality assessment results with the real power quality assessment results, and obtain the real model error; The model indicators and wind farm indicator system of the final wind farm model are optimized according to the actual model error.

Images