Wind power plant power regulation and electric energy quality evaluation method

Abstract

The invention relates to the technical field of power regulation, in particular to a wind power plant power regulation and electric energy quality evaluation method, which comprises the following steps of: acquiring wind speed, air temperature and air pressure data based on internal monitoring equipment of a wind power plant, and synchronizing the monitoring data with power grid state data and wind power output data to obtain a synchronous environment data set; according to the method, the deviation between the current output parameters of the wind power plant and the demand parameters of the power grid can be analyzed through data synchronization, the output parameters are adjusted according to the deviation, the load response of the power grid is optimized, the output power of the wind power plant is monitored and adjusted in real time, and the instant demand of the power grid is matched by adjusting the rotating speed of the fan and the blade angle; the overall stability of the power grid is improved, and the economical efficiency and reliability of the wind power plant are improved by continuously monitoring the output voltage and frequency stability of the wind power plant, evaluating the electric energy quality, effectively identifying and marking the electric energy quality abnormity and further adjusting the control strategy and optimizing the electric energy output quality.

Description

Technical Field

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

[0002] The field of power regulation technology mainly involves the real-time adjustment of power in energy systems to meet the needs and stability of the power grid. In particular, in the application of wind farms, power regulation technology is particularly critical due to the volatility and unpredictability of wind power generation. Power regulation technology includes the adjustment of wind turbine output power due to wind speed changes, interface management between the system and the power grid, and frequency and voltage control. Ensure that when the wind speed is above or below a certain set threshold, the wind farm can effectively adjust the output power, thereby protecting the stability of the power grid and improving energy efficiency. Power regulation also involves the ability to respond quickly to emergencies.

[0003] Among them, wind farm power regulation and power quality assessment methods cover the strategies and technologies for implementing power regulation in wind farms, as well as methods for evaluating and optimizing power quality. Its core purpose is to accurately control the power generation of wind farms to meet the operating requirements and standards of the power grid, while ensuring the efficiency and safety of power transmission through power quality assessment. It helps to improve the economy and reliability of wind farms, and is of great significance to the stable operation and sustainable development of the entire power grid.

[0004] Traditional regulation methods have certain limitations in dealing with the volatility of wind power generation, especially in the instant matching between grid demand and wind power output. Traditional methods fail to make accurate adjustments in real time, resulting in insufficient grid stability and power efficiency. Traditional methods of power quality assessment rely only on periodic detection rather than continuous monitoring, which limits the ability to respond immediately to quality fluctuations, resulting in the inability of wind farms to effectively adjust output during peak or trough wind speeds, affecting the load balance of the grid, increasing the risk of grid operation, reducing energy utilization efficiency, and affecting the overall economy and sustainability of the grid. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a method for wind farm power regulation and power quality assessment.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for wind farm power regulation and power quality assessment, comprising the following steps: S1: Based on the internal monitoring equipment of the wind farm, wind speed, temperature and air pressure data are collected, and the monitoring data are synchronized with the grid status data and wind power output data to obtain a synchronized environmental data set; S2: Based on the synchronized environmental data set, analyzing the deviation between the current output parameters of the wind farm and the grid demand parameters, adjusting the output parameters of the wind farm, and obtaining optimized adjustment parameters; S3: Based on the synchronized environmental data set and the optimized adjustment parameters, monitor the deviation between the current power output of the wind farm and the power required by the power grid, and adjust the output power of the wind farm according to the real-time data to obtain adjusted power output data; S4: Based on the adjusted power output data, monitor the stability of the wind farm output voltage and frequency, record the power quality fluctuations beyond the preset range, mark the abnormal data points, evaluate the quality of the wind farm output power, and obtain the power quality evaluation result; S5: Based on the power quality assessment result, analyzing the frequency and voltage fluctuation data of the wind farm power, adjusting the voltage and frequency control strategies, and obtaining an optimized regulation strategy; S6: Based on the optimized regulation strategy, analyze the improvement effect of power output and power quality, evaluate the impact of the regulation strategy on the wind farm operation efficiency, and obtain wind farm performance optimization information.

[0007] As a further solution of the present invention, the synchronized environmental data set includes synchronized wind speed data, temperature data, air pressure data and grid power data, the optimized regulation parameters include adjusted voltage setting values ​​and frequency setting values, the adjusted power output data include adjusted power output values ​​and power output time periods, the power quality assessment results include voltage stability data, frequency stability data and abnormal fluctuation records, the optimized regulation policy includes set voltage regulation parameters, frequency regulation parameters and response control measures, and the wind farm performance optimization information includes power output comparison results, power quality improvement data and regulation strategy effect evaluation.

[0008] As a further solution of the present invention, based on the internal monitoring equipment of the wind farm, the wind speed, temperature and air pressure data are collected, and the monitoring data is synchronized with the grid status data and the wind power output data to obtain the synchronized environmental data set. Specifically, the steps are as follows: S101: Based on the internal monitoring equipment of the wind farm, the wind speed, temperature and air pressure data of the wind farm are collected, and a timestamp is added to each data point to obtain time-stamped environmental data; S102: Based on the time-stamped environmental data, matching the data, the grid status data and the wind power output data, merging and synchronizing the data in time series through data alignment, and obtaining a time-aligned data set; S103: Based on the time-aligned data set, data cleaning is performed to remove abnormal and incomplete data, and data redundancy and errors caused by time misalignment are removed to obtain a synchronized environment data set.

[0009] As a further solution of the present invention, based on the synchronized environmental data set, the deviation between the current output parameters of the wind farm and the grid demand parameters is analyzed, and the output parameters of the wind farm are adjusted to obtain the optimized adjustment parameters. Specifically, the steps are: S201: Based on the synchronous environment data set, compare the current voltage and frequency output parameters of the wind farm with the grid demand parameters, identify the deviation between the parameters, perform quantitative analysis on the deviation, and obtain parameter deviation analysis results; S202: Based on the parameter deviation analysis result, using a fuzzy logic control algorithm, adjusting the voltage and frequency settings of the wind farm, adjusting the power output parameters of the wind farm to match the current parameter requirements of the power grid, and obtaining adjustment parameter data; S203: Based on the adjustment parameter data, the control settings of the wind farm are updated, the output values ​​of voltage and frequency are reconfigured, and the consistency between the wind farm power output parameters and the grid demand parameters is verified through feedback control to obtain optimized adjustment parameters.

[0010] As a further solution of the present invention, the fuzzy logic control algorithm is according to the formula:

[0011] Calculate the adjusted power output parameters, where: Indicates The deviation assessment level between the grid demand parameters and wind power output parameters at each data point, Indicates Adjustment measures preset based on wind farm real-time data at each data point, Indicates The environmental factors on each data point have an impact on , and For the The adjusted weight coefficient for each data point is is the total number of data points.

[0012] As a further solution of the present invention, based on the synchronized environmental data set and the optimized adjustment parameters, the deviation between the current power output of the wind farm and the power demanded by the power grid is monitored, and the output power of the wind farm is adjusted according to the real-time data. The steps of obtaining the adjusted power output data are specifically as follows: S301: Based on the synchronized environmental data set and the optimized adjustment parameters, monitor the current power output of the wind farm, compare and analyze the instant deviation between the current power output and the power demand of the power grid, record the deviation value, and obtain real-time power deviation data; S302: Based on the real-time power deviation data, combined with the current external wind speed, temperature and air pressure data, adjust the speed and blade angle of the wind turbine, monitor the adjustment effect, match the instantaneous load demand of the power grid, record the parameter changes during the adjustment process, and obtain the power output data during the adjustment; S303: Based on the power output data during adjustment, monitor the adjusted wind power output, sort out and record the power output values ​​and corresponding time periods obtained during the adjustment process, and obtain the adjusted power output data.

[0013] As a further solution of the present invention, based on the adjusted power output data, the stability of the wind farm output voltage and frequency is monitored, power quality fluctuations beyond a preset range are recorded, and abnormal data points are marked to evaluate the quality of the wind farm output power. The steps of obtaining the power quality evaluation result are specifically as follows: S401: Based on the adjusted power output data, monitor the stability of the wind farm output voltage and frequency, identify the fluctuation of the wind farm output voltage and frequency within a preset range, and obtain output stability monitoring data; S402: Based on the output stability monitoring data, identifying power quality fluctuation points that exceed a preset stability range, recording abnormal events and fluctuation periods of wind farm power output, and obtaining abnormal power fluctuation records; S403: Based on the abnormal power fluctuation record, abnormal events are summarized and analyzed, and the power quality of the wind farm is evaluated by using a weighted average method in combination with the frequencies and impacts of various abnormal events to obtain a power quality evaluation result.

[0014] As a further solution of the present invention, the weighted average method is according to the formula:

[0015] Calculate the power quality score where, Score the power quality, For the The weight of an abnormal event, For the The impact score of an abnormal event, For the The voltage fluctuation data corresponding to each event, For the The frequency fluctuation data corresponding to each event, is the total number of abnormal events.

[0016] As a further solution of the present invention, based on the power quality assessment result, the frequency and voltage fluctuation data of the wind farm power are analyzed, and the voltage and frequency control strategies are adjusted to obtain the optimized adjustment strategy. Specifically, the steps are as follows: S501: Based on the power quality assessment result, analyze the hourly voltage and frequency fluctuation data of the wind farm item by item, identify the time period with the largest fluctuation range, and obtain fluctuation source diagnosis data; S502: Based on the fluctuation source diagnosis data, identify the control parameters that need to be adjusted, adjust the voltage and frequency control parameters of the wind farm, optimize the data fluctuation, and generate adjustment measure information; S503: Based on the adjustment measure information, the adjusted control parameters are applied in a simulation environment to simulate the adjustment effect, and according to the simulation result, the adjustment measures are optimized to generate an optimized adjustment strategy.

[0017] As a further solution of the present invention, based on the optimized regulation strategy, the improvement effect of power output and power quality is analyzed, the influence of the regulation strategy on the operation efficiency of the wind farm is evaluated, and the steps of obtaining the wind farm performance optimization information are specifically as follows: S601: Based on the optimized regulation strategy, collect the operation data of the wind farm, monitor the adjusted power output and power quality, record the changes of power and power quality, and obtain the adjusted operation data; S602: Based on the adjusted operation data, analyze the stability change of the power grid before and after the adjustment, evaluate the implementation effect of the regulation strategy, monitor the synchronization between the power grid load and the wind power output, and obtain the power grid stability analysis information; S603: Based on the grid stability analysis information, evaluate the impact of the regulation strategy on the wind farm operation efficiency, identify the improvement effect of the wind farm power output and power quality, and obtain wind farm performance optimization information.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through data synchronization, the deviation between the current output parameters of the wind farm and the grid demand parameters can be analyzed, and the output parameters can be adjusted accordingly to match the grid demand, optimize the load response of the grid, and monitor and adjust the output power of the wind farm in real time. By adjusting the wind turbine speed and blade angle to match the immediate demand of the grid, the wind farm's adaptability to grid fluctuations is enhanced, and the overall stability of the grid is improved. Through continuous monitoring of the output voltage and frequency stability of the wind farm, the power quality is evaluated, and by analyzing the fluctuations, the power quality abnormalities are effectively identified and marked, and then by adjusting the control strategy, the power output quality is optimized, and the economy and reliability of the wind farm are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the main steps of the present invention; Figure 2 It is a schematic diagram of the refinement of S1 of the present invention; Figure 3It is a schematic diagram of the refinement of S2 of the present invention; Figure 4 It is a schematic diagram of the refinement of S3 of the present invention; Figure 5 It is a schematic diagram of the refinement of S4 of the present invention; Figure 6 It is a detailed schematic diagram of S5 of the present invention; Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0022] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a method for wind farm power regulation and power quality assessment, comprising the following steps: S1: Based on the internal monitoring equipment of the wind farm, wind speed, temperature and air pressure data are collected, and each data point is marked with a timestamp. The monitoring data is synchronized with the grid status data and wind power output data using the timestamp alignment method, and time series integration is performed to eliminate time deviation and obtain a synchronized environmental data set; S2: Based on the synchronous environmental data set, analyze the deviation between the current output parameters of the wind farm and the grid demand parameters, adjust the output parameters of the wind farm, including voltage and frequency, to match the grid demand parameters, and obtain the optimized adjustment parameters; S3: Based on the synchronized environmental data set and optimized adjustment parameters, the deviation between the current power output of the wind farm and the power required by the power grid is monitored. According to the real-time data, the output power of the wind farm is adjusted, including adjusting the speed of the wind turbine and the angle of the blades to match the instant changes in the power grid load, and the adjusted power output data is obtained; S4: Based on the adjusted power output data, monitor the stability of the wind farm output voltage and frequency, record the power quality fluctuations beyond the preset range, mark the abnormal data points, evaluate the quality of the wind farm output power, and obtain the power quality evaluation results; S5: Based on the power quality assessment results, analyze the frequency and voltage fluctuation data of the wind farm power, adjust the voltage and frequency control strategies, optimize the power output quality, and obtain the optimized regulation strategy; S6: Based on the optimized regulation strategy, by obtaining the actual operation data of the wind farm, analyzing the improvement effect of power output and power quality, and combining the changes in the overall stability of the power grid, evaluating the impact of the regulation strategy on the operation efficiency of the wind farm, and obtaining the wind farm performance optimization information; The synchronized environmental data set includes synchronized wind speed data, temperature data, air pressure data and grid power data; the optimized regulation parameters include adjusted voltage setting values ​​and frequency setting values; the adjusted power output data include adjusted power output values ​​and power output time periods; the power quality assessment results include voltage stability data, frequency stability data and abnormal fluctuation records; the optimized regulation policy includes set voltage regulation parameters, frequency regulation parameters and response control measures; the wind farm performance optimization information includes power output comparison results, power quality improvement data and regulation strategy effect evaluation.

[0023] See also Figure 2 Based on the internal monitoring equipment of the wind farm, the wind speed, temperature and air pressure data are collected, and each data point is marked with a timestamp. The monitoring data is synchronized with the grid status data and wind power output data using the timestamp alignment method, and the time series is integrated to eliminate the time deviation. The specific steps to obtain the synchronized environmental data set are as follows: S101: Based on the internal monitoring equipment of the wind farm, the wind speed, temperature and air pressure data of the wind farm are collected, and a timestamp is added to each data point to obtain the time-stamped environmental data. Specifically, the process is as follows; In S101, wind speed, temperature and air pressure data are collected based on the internal monitoring equipment of the wind farm. In this process, the monitoring equipment measures every 10 seconds, records the wind speed, temperature and air pressure each time, and automatically performs quality screening through the data processing unit. The screening criteria are: if "the current measurement value > the upper limit of the device measurement or the current measurement value < the lower limit of the device measurement", it is marked as abnormal quality. Qualified data is then assigned an accurate timestamp generated by the control system. Qualified data is organized in the format of "time series data = timestamp + wind speed data + temperature data + air pressure data" to ensure that the data can be accurately tracked and referenced in subsequent processing to obtain time-stamped environmental data.

[0024] S102: Based on the time-stamped environmental data, the data, the grid status data and the wind power output data are matched, and the time series of the data are merged and synchronized through data alignment to obtain the time-aligned data set. The specific process is as follows; In S102, based on the time-stamped environmental data, the data, the grid status data and the wind power output data are matched to ensure data integrity and time consistency. The data time tag is used to compare the timestamps of all data, and the formula "data time offset = maximum timestamp - minimum timestamp" is used to calculate the maximum time deviation in the data set. By time-calibrating the data, all data points are ensured to be accurately aligned, and the analysis errors that may be caused by time differences are eliminated. The calibrated data sets are processed in a unified format for subsequent efficient data analysis and application, and a time-aligned data set is obtained.

[0025] S103: Based on the time-aligned data set, the process of removing abnormal and incomplete data through data cleaning processing, and removing data redundancy and errors caused by time misalignment to obtain the synchronized environment data set is as follows; In S103, based on the time-aligned data set, data cleaning is performed to remove abnormal and incomplete data, and an abnormal detection mechanism is applied to identify and process data points that do not conform to the normal range of environmental parameters. The abnormality judgment adopts "deviation value = current data point value - historical average value of environmental data". If the deviation value exceeds three times the standard deviation, the data point is marked as abnormal. After the abnormal data is eliminated, the continuity and consistency of the data are checked through the time series analysis tool to eliminate redundancy and errors caused by time dislocation or data transmission delay. The data set is finally synchronized, and "synchronized data = corrected data point / total number of data points" is adopted to ensure that the obtained data set is optimized in both time and quality, and a synchronized environmental data set is obtained.

[0026] See also Figure 3 ,Based on the synchronous environment data set, the deviation between the current output parameters of the wind farm and the grid demand parameters is analyzed, and the output parameters of the wind farm, including voltage and frequency, are adjusted to match the grid demand parameters. The steps to obtain the optimized adjustment parameters are as follows: S201: Based on the synchronous environment data set, compare the current voltage and frequency output parameters of the wind farm with the grid demand parameters, identify the deviation between the parameters, perform quantitative analysis on the deviation, and obtain the parameter deviation analysis result. The specific process is as follows; In S201, based on the synchronous environmental data set, the current voltage and frequency output parameters of the wind farm are compared with the grid demand parameters, the real-time voltage and frequency data of the wind farm are collected, the data are compared with the grid demand parameters, and the formula "parameter deviation = actual output parameter - standard demand parameter" is used to calculate the deviation between the two. The deviation is statistically analyzed, and the mean value and coefficient of variation of the deviation are calculated to quantify the degree and distribution of the deviation and obtain the parameter deviation analysis results.

[0027] S202: Based on the parameter deviation analysis results, the fuzzy logic control algorithm is used to adjust the voltage and frequency settings of the wind farm, and the power output parameters of the wind farm are adjusted to match the current parameter requirements of the power grid. The specific process of obtaining the adjustment parameter data is as follows; In S202, based on the parameter deviation analysis results, the voltage and frequency settings of the wind farm are adjusted using a fuzzy logic control algorithm. By adjusting the settings of the voltage and frequency controllers, the power output parameters of the wind farm are adjusted to match the current parameter requirements of the power grid, and the adjusted parameter data are obtained.

[0028] Fuzzy logic control algorithm, according to the formula:

[0029] Calculate the adjusted power output parameters, where: Indicates The deviation assessment level between the grid demand parameters and wind power output parameters at each data point, Indicates Adjustment measures preset based on wind farm real-time data at each data point, Indicates The environmental factors on each data point have an impact on , and For the The adjusted weight coefficient for each data point is is the total number of data points.

[0030] The specific execution process of the formula is as follows: Calculate the difference between grid demand and wind power output , considering environmental factors For example, the influence of wind speed and temperature on wind power output is combined through the coefficient and Weighted adjustment demand determines the adjustable operation of the wind farm and the associated adjustment weights , the weighted demand and adjustment measures are synthesized by multiplication to calculate the adjusted power output parameters , confirm the weight coefficient , and The specific values ​​are obtained through historical data analysis and real-time performance feedback, using statistical methods and machine learning techniques to optimize parameters to improve the accuracy and efficiency of the regulation strategy.

[0031] S203: Based on the adjustment parameter data, the control settings of the wind farm are updated, the output values ​​of voltage and frequency are reconfigured, and the consistency between the wind farm power output parameters and the grid demand parameters is verified through feedback control to obtain the optimized adjustment parameters. In S203, based on the adjustment parameter data, the control settings of the wind farm are updated, the output values ​​of voltage and frequency are reconfigured, the adjusted voltage and frequency parameters are applied to the control system of the wind farm, and the feedback control mechanism is implemented. By real-time monitoring of the matching degree between the adjusted output parameters and the grid demand parameters, the formula "matching degree = 1-|adjusted parameters-grid demand parameters| / grid demand parameters" is used to evaluate the effect of the adjustment. If the matching degree is lower than the preset threshold, it is automatically adjusted again until the optimal match is achieved to ensure the consistency between the wind farm power output parameters and the grid demand parameters, and obtain the optimized adjustment parameters.

[0032] See also Figure 4 Based on the synchronized environmental data set and optimized adjustment parameters, the deviation between the current power output of the wind farm and the power demand of the power grid is monitored. According to the real-time data, the output power of the wind farm is adjusted, including adjusting the speed of the wind turbine and the angle of the blades to match the instant changes in the power grid load. The steps to obtain the adjusted power output data are as follows: S301: Based on the synchronous environment data set and the optimized adjustment parameters, the current power output of the wind farm is monitored, the instant deviation between the current power output and the power demand of the power grid is compared and analyzed, the deviation value is recorded, and the process of obtaining the real-time power deviation data is specifically as follows; In S301, based on the synchronized environmental data set and optimized adjustment parameters, the current power output of the wind farm and the instant deviation from the power demanded by the power grid are continuously monitored. In order to accurately measure and record the power deviation, high-precision power monitoring equipment is used to record the real-time power of the wind farm once a minute and simultaneously record the power demand of the power grid. Each data record includes the power output of the wind farm and the power demanded by the power grid, as well as a timestamp, to ensure the timeliness and accuracy of the data. The power deviation is calculated using the formula "power deviation = current power output - power demanded by the power grid", and the power deviation data is updated in real time, and detailed data including the deviation value and time are recorded to obtain real-time power deviation data.

[0033] S302: Based on the real-time power deviation data, combined with the current external wind speed, temperature and air pressure data, the speed and blade angle of the wind turbine are adjusted, and the adjustment effect is monitored to match the instantaneous load demand of the power grid, and the parameter changes during the adjustment process are recorded to obtain the power output data during the adjustment process. In S302, based on the real-time power deviation data and combined with the current external environmental data, such as wind speed, temperature and air pressure, the speed and blade angle of the wind turbine are adjusted, and the necessary adjustment parameters are calculated according to the real-time monitored environmental conditions and power deviation data. The formulas include: "Speed ​​adjustment amount = power deviation × wind speed adjustment coefficient" and "Blade angle adjustment amount = power deviation × air pressure adjustment coefficient", where the adjustment coefficient is obtained by historical data analysis. After the adjustment is implemented, the effect of the adjustment is continuously monitored, and real-time data comparison is used to verify whether the speed and blade angle adjustments of the wind turbine effectively match the immediate load demand of the power grid. All adjustment parameters and result data are recorded to obtain the power output data during the adjustment.

[0034] S303: Based on the power output data during adjustment, monitor the adjusted wind power output, sort out and record the power output values ​​and corresponding time periods obtained during the adjustment process, and the process of obtaining the adjusted power output data is as follows; In S303, subsequent monitoring and data collation are performed based on the power output data during adjustment. After the adjustment measures are implemented, the wind power output is continuously monitored, the power output value after each adjustment and the corresponding time period are recorded, the power output in each time period after adjustment and the degree of matching with the grid demand are recorded, the long-term effect of the blade angle and speed adjustment is evaluated, the stability and efficiency of the wind farm output are confirmed, and the adjusted power output data is obtained.

[0035] See also Figure 5 Based on the adjusted power output data, the stability of the wind farm output voltage and frequency is monitored, the power quality fluctuations beyond the preset range are recorded, and the abnormal data points are marked to evaluate the quality of the wind farm output power. The specific steps for obtaining the power quality evaluation results are as follows: S401: Based on the adjusted power output data, the stability of the wind farm output voltage and frequency is monitored to identify the fluctuation of the wind farm output voltage and frequency within a preset range, and the process of obtaining the output stability monitoring data is specifically as follows; In S401, based on the adjusted power output data, the stability of the wind farm output voltage and frequency is continuously monitored, the voltage and frequency data are recorded, and the data is analyzed in real time to detect whether there are fluctuations exceeding the preset threshold. The fluctuation analysis uses the formula "fluctuation rate = (maximum recorded value - minimum recorded value) / preset reference value" to calculate the voltage and frequency fluctuations in each monitoring cycle to ensure that any fluctuations beyond the normal range can be identified and recorded in a timely manner to generate output stability monitoring data.

[0036] S402: Based on the output stability monitoring data, identify the power quality fluctuation points that exceed the preset stability range, record the abnormal events and fluctuation periods of the wind farm power output, and obtain the abnormal power fluctuation record. The specific process is as follows; In S402, based on the output stability monitoring data, the power quality fluctuation is analyzed and identified, and the specific point where the fluctuation exceeds the preset stability range is determined. The formula "abnormal determination = whether the current fluctuation rate is greater than the allowed fluctuation rate" is used to identify the abnormal fluctuation point of the power quality. For each identified abnormal point, the relevant time period and detailed fluctuation data are automatically recorded, including the amplitude, duration and possible cause analysis of the fluctuation, to form an abnormal power fluctuation record, providing direct evidence and detailed description of the wind farm power output stability problem.

[0037] S403: Based on the abnormal power fluctuation record, the abnormal events are summarized and analyzed, and the power quality of the wind farm is evaluated by using the weighted average method and combining the frequency and impact of multiple abnormal events to obtain the power quality evaluation result. The specific process is as follows; In S403, based on the abnormal power fluctuation records, abnormal events are summarized and analyzed, and the weighted average method is used to analyze the frequency and impact of various abnormal events, quantify the power quality of the wind farm, and identify the main influencing factors to form a power quality assessment result.

[0038] Weighted average method, according to the formula:

[0039] Calculate the power quality score where, Score the power quality, For the The weight of an abnormal event, For the The impact score of an abnormal event, For the The voltage fluctuation data corresponding to each event, For the The frequency fluctuation data corresponding to each event, is the total number of abnormal events.

[0040] The specific execution process of the formula is as follows: Get the impact score of each abnormal event And its corresponding voltage fluctuation data and frequency fluctuation data , and then assign a weight to each score and fluctuation data , and then calculate the weighted impact of each event , then sum all weighted impacts and divide by the total weight to get the weighted average score of power quality , in order to evaluate the power quality of the wind farm. The determination is obtained through statistical analysis of historical data, ensuring that each weight reflects the actual impact and frequency of the corresponding abnormal event, thereby ensuring the accuracy and reliability of the power quality score as a whole.

[0041] See also Figure 6 Based on the power quality assessment results, the frequency and voltage fluctuation data of the wind farm power are analyzed, the voltage and frequency control strategies are adjusted, and the power output quality is optimized. The specific steps of obtaining the optimized regulation strategy are as follows: S501: Based on the power quality assessment results, the hourly voltage and frequency fluctuation data of the wind farm are analyzed item by item, the time period with the largest fluctuation range is identified, and the specific process of obtaining the fluctuation source diagnosis data is as follows; In S501, based on the power quality assessment results, the hourly voltage and frequency fluctuation data of the wind farm are analyzed. By setting a specific data analysis model, the fluctuation amplitude of each hour is calculated using the formula "hourly fluctuation amplitude = maximum voltage change - minimum voltage change + maximum frequency change - minimum frequency change". These data are analyzed to identify the time period with the largest fluctuation range in a day, with special attention paid to abnormal time periods that exceed the conventional preset fluctuation range. By focusing on analyzing the specific data of key time periods, the main source of fluctuation can be effectively diagnosed and fluctuation source diagnostic data can be obtained.

[0042] S502: Based on the fluctuation source diagnosis data, the control parameters that need to be adjusted are identified, the voltage and frequency control parameters of the wind farm are adjusted, the data fluctuation is optimized, and the process of generating adjustment measure information is specifically as follows; In S502, based on the fluctuation source diagnosis data, the key control parameters that cause abnormal fluctuations are identified. Multiple control parameters that affect voltage and frequency stability, such as wind turbine blade angle, speed and control logic, are analyzed. After determining the parameters that need to be adjusted, the formula "adjustment amount = standard parameter - current parameter value" is used to calculate the specific adjustment amount of each parameter. The control parameters are precisely adjusted to reduce data fluctuations, enhance overall stability, generate adjustment measure information, and record all adjusted parameters and expected impact effects.

[0043] S503: Based on the adjustment measure information, the adjusted control parameters are applied to the simulation environment to simulate the adjustment effect, and the adjustment measures are optimized according to the simulation results to generate the optimized adjustment strategy. In S503, based on the adjustment measure information, the adjusted control parameters are applied to the simulation environment to predict the actual effect of the adjustment. The simulation software is used to simulate the operation of the wind farm according to the adjustment measure information to observe the changes in voltage and frequency. During the simulation process, attention is paid to the time period previously identified as having large fluctuations to verify the effectiveness of the adjustment measures. Based on the simulation results, the adjustment measures are optimized to ensure that the adjustment of each parameter can achieve the expected stabilization effect and generate an optimized regulation strategy.

[0044] See also Figure 7 Based on the optimized regulation strategy, by obtaining the actual operation data of the wind farm, analyzing the improvement effect of power output and power quality, and combining the changes in the overall stability of the power grid, evaluating the impact of the regulation strategy on the operation efficiency of the wind farm, the specific steps to obtain the wind farm performance optimization information are as follows: S601: Based on the optimized regulation strategy, collect the operation data of the wind farm, monitor the adjusted power output and power quality, record the changes of power and power quality, and obtain the adjusted operation data. The specific process is as follows; In S601, based on the optimized regulation strategy, the operation data of the wind farm is collected to monitor the adjusted power output and power quality. Key indicators such as voltage, frequency, and power are collected regularly, and the collected data is analyzed in real time to calculate the changes in power and power quality. The formula is such as "power change rate = (observed power - baseline power) / baseline power × 100%", and the power quality change is calculated similarly. Through recording, the direct effect of the adjustment measures can be understood in detail, and the adjusted operation data record is formed to provide basic data for analysis and optimization.

[0045] S602: Based on the adjusted operation data, analyze the stability change of the power grid before and after the adjustment, evaluate the implementation effect of the regulation strategy, monitor the synchronization of the power grid load and the wind power output, and obtain the power grid stability analysis information. The specific process is as follows; In S602, based on the adjusted operating data, the stability changes of the power grid before and after the adjustment are analyzed and evaluated. The impact of the wind farm adjustment measures on the overall stability of the power grid, especially the impact of power and voltage fluctuations on the power grid load, is considered. By comparing the power grid operation data before and after the adjustment, such as the load fluctuation and frequency deviation of the power grid, the formula "stability improvement = (initial deviation - current deviation) / initial deviation" is used to quantify the effect of the regulation strategy. At the same time, the synchronization of the power grid load and the wind power output is monitored to ensure a good match between the two. Through analysis, grid stability analysis information is generated to help evaluate the effectiveness of the overall regulation measures.

[0046] S603: Based on the grid stability analysis information, the influence of the regulation strategy on the wind farm operation efficiency is evaluated, the improvement effect of the wind farm power output and power quality is identified, and the process of obtaining the wind farm performance optimization information is specifically as follows; In S603, based on the grid stability analysis information, the impact of the regulation strategy on the wind farm operating efficiency is evaluated, and the improvement effect of power output and power quality is identified. The various performance indicators of the wind farm after the implementation of the regulation strategy, such as power efficiency, energy consumption and maintenance requirements, are analyzed. The evaluation indicators include power efficiency improvement and maintenance cost savings. The calculation formula includes "efficiency improvement = (power after improvement - power before improvement) / power before improvement", as well as the corresponding cost-benefit analysis. The data and analysis results are sorted to generate wind farm performance optimization information.

[0047] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for wind farm power regulation and power quality assessment, characterized in that: The following steps are involved: Based on the internal monitoring equipment of the wind farm, wind speed, temperature and air pressure data are collected, and the monitoring data is synchronized with the grid status data and wind power output data to obtain a synchronized environmental data set; Based on the synchronized environmental data set, analyzing the deviation between the current output parameters of the wind farm and the grid demand parameters, adjusting the output parameters of the wind farm, and obtaining optimized adjustment parameters; Based on the synchronized environmental data set and the optimized adjustment parameters, the deviation between the current power output of the wind farm and the power demanded by the power grid is monitored, and the output power of the wind farm is adjusted according to the real-time data to obtain adjusted power output data; Based on the adjusted power output data, the stability of the wind farm output voltage and frequency is monitored, power quality fluctuations beyond a preset range are recorded, and abnormal data points are marked to evaluate the quality of the wind farm output power and obtain a power quality evaluation result; Based on the power quality assessment results, analyzing the frequency and voltage fluctuation data of the wind farm power, adjusting the voltage and frequency control strategies, and obtaining an optimized regulation strategy; Based on the optimized regulation strategy, the improvement effect of power output and power quality is analyzed, the influence of the regulation strategy on the operation efficiency of the wind farm is evaluated, and the performance optimization information of the wind farm is obtained.

2. The method for wind farm power regulation and power quality assessment according to claim 1, characterized in that: The synchronized environmental data set includes synchronized wind speed data, temperature data, air pressure data and grid power data; the optimized regulation parameters include adjusted voltage setting values ​​and frequency setting values; the adjusted power output data include adjusted power output values ​​and power output time periods; the power quality assessment results include voltage stability data, frequency stability data and abnormal fluctuation records; the optimized regulation policy includes set voltage regulation parameters, frequency regulation parameters and response control measures; and the wind farm performance optimization information includes power output comparison results, power quality improvement data and regulation strategy effect evaluation.

3. The method for wind farm power regulation and power quality assessment according to claim 1, characterized in that: Based on the internal monitoring equipment of the wind farm, wind speed, temperature and air pressure data are collected, and the monitoring data is synchronized with the grid status data and wind power output data to obtain the synchronized environmental data set. The specific steps are as follows: Based on the internal monitoring equipment of the wind farm, the wind speed, temperature and air pressure data of the wind farm are collected, and a timestamp is added to each data point to obtain time-stamped environmental data; Based on the time-stamped environmental data, the data, the grid status data and the wind power output data are matched, and the data are merged and synchronized in time series through data alignment to obtain a time-aligned data set; Based on the time-aligned data set, data cleaning is performed to remove abnormal and incomplete data, and remove data redundancy and errors caused by time misalignment, so as to obtain a synchronized environment data set.

4. The method for wind farm power regulation and power quality assessment according to claim 1, characterized in that: Based on the synchronized environmental data set, the deviation between the current output parameters of the wind farm and the grid demand parameters is analyzed, and the output parameters of the wind farm are adjusted to obtain the optimized adjustment parameters. Specifically, the steps are as follows: Based on the synchronous environment data set, comparing the current voltage and frequency output parameters of the wind farm with the grid demand parameters, identifying the deviation between the parameters, performing quantitative analysis on the deviation, and obtaining parameter deviation analysis results; Based on the parameter deviation analysis results, the voltage and frequency settings of the wind farm are adjusted using a fuzzy logic control algorithm, the power output parameters of the wind farm are adjusted to match the current parameter requirements of the power grid, and adjustment parameter data is obtained; Based on the adjustment parameter data, the control settings of the wind farm are updated, the output values ​​of voltage and frequency are reconfigured, and the consistency of the wind farm power output parameters and the grid demand parameters is verified through feedback control to obtain optimized adjustment parameters.

5. The method for wind farm power regulation and power quality assessment according to claim 4, characterized in that: The fuzzy logic control algorithm is based on the formula: Calculate the adjusted power output parameters, where: Indicates The deviation assessment level between the grid demand parameters and wind power output parameters at each data point, Indicates Adjustment measures preset based on wind farm real-time data at each data point, Indicates The environmental factors on each data point have an impact on , and For the The adjusted weight coefficient for each data point is is the total number of data points.

6. The method for wind farm power regulation and power quality assessment according to claim 1, characterized in that: Based on the synchronized environmental data set and the optimized adjustment parameters, the deviation between the current power output of the wind farm and the power demanded by the power grid is monitored, and the output power of the wind farm is adjusted according to the real-time data to obtain the adjusted power output data. Specifically, the steps are as follows: Based on the synchronized environmental data set and the optimized adjustment parameters, the current power output of the wind farm is monitored, the instant deviation between the current power output and the power demand of the power grid is compared and analyzed, the deviation value is recorded, and the real-time power deviation data is obtained; Based on the real-time power deviation data, combined with the current external wind speed, temperature and air pressure data, the speed and blade angle of the wind turbine are adjusted, and the adjustment effect is monitored to match the instantaneous load demand of the power grid, and the parameter changes during the adjustment process are recorded to obtain the power output data during the adjustment; Based on the power output data during adjustment, the adjusted wind power output is monitored, and the power output values ​​and corresponding time periods obtained during the adjustment process are sorted and recorded to obtain the adjusted power output data.

7. The method for wind farm power regulation and power quality assessment according to claim 1, characterized in that: Based on the adjusted power output data, the stability of the wind farm output voltage and frequency is monitored, power quality fluctuations beyond a preset range are recorded, and abnormal data points are marked to evaluate the quality of the wind farm output power. The steps of obtaining the power quality evaluation result are specifically as follows: Based on the adjusted power output data, the stability of the wind farm output voltage and frequency is monitored, the fluctuation of the wind farm output voltage and frequency within a preset range is identified, and output stability monitoring data is obtained; Based on the output stability monitoring data, power quality fluctuation points that exceed a preset stability range are identified, abnormal events and fluctuation periods of wind farm power output are recorded, and abnormal power fluctuation records are obtained; Based on the abnormal power fluctuation records, abnormal events are summarized and analyzed, and the power quality of the wind farm is evaluated by using a weighted average method combined with the frequencies and impacts of multiple abnormal events to obtain a power quality evaluation result.

8. The method for wind farm power regulation and power quality assessment according to claim 7, characterized in that: The weighted average method is based on the formula: Calculate the power quality score where, Score the power quality, For the The weight of an abnormal event, For the The impact score of an abnormal event, For the The voltage fluctuation data corresponding to each event, For the The frequency fluctuation data corresponding to each event, is the total number of abnormal events.

9. The method for wind farm power regulation and power quality assessment according to claim 1, characterized in that: Based on the power quality assessment results, the frequency and voltage fluctuation data of the wind farm power are analyzed, and the voltage and frequency control strategies are adjusted to obtain the optimized regulation strategy. Specifically, the steps are as follows: Based on the power quality assessment results, the hourly voltage and frequency fluctuation data of the wind farm are analyzed item by item, the time period with the largest fluctuation range is identified, and fluctuation source diagnosis data is obtained; Based on the fluctuation source diagnosis data, identifying the control parameters that need to be adjusted, adjusting the voltage and frequency control parameters of the wind farm, optimizing the data fluctuation, and generating adjustment measure information; Based on the adjustment measure information, the adjusted control parameters are applied in a simulation environment to simulate the adjustment effect, and according to the simulation result, the adjustment measures are optimized to generate an optimized adjustment strategy.

10. The method for wind farm power regulation and power quality assessment according to claim 1, characterized in that: Based on the optimized regulation strategy, the improvement effect of power output and power quality is analyzed, the influence of the regulation strategy on the operation efficiency of the wind farm is evaluated, and the steps of obtaining the wind farm performance optimization information are specifically as follows: Based on the optimized regulation strategy, the operation data of the wind farm is collected, the adjusted power output and power quality are monitored, the changes of power and power quality are recorded, and the adjusted operation data is obtained; Based on the adjusted operating data, analyze the stability changes of the power grid before and after the adjustment, evaluate the implementation effect of the regulation strategy, monitor the synchronization between the power grid load and the wind power output, and obtain the power grid stability analysis information; Based on the grid stability analysis information, the influence of the regulation strategy on the wind farm operation efficiency is evaluated, the improvement effect of the wind farm power output and power quality is identified, and the wind farm performance optimization information is obtained.

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