Wind turbine generator master control system and method based on PLC

Abstract

The invention discloses a wind turbine generator master control system and method based on a PLC, and relates to the technical field of wind driven generators, and the wind turbine generator master control system based on the PLC comprises a wind turbine generator power generation capability evaluation module, a wind turbine generator power generation task distribution module, a wind turbine generator operation monitoring and adjusting module and a wind turbine generator operation monitoring module. The starting process of each wind turbine generator is monitored through the PLC, the power generation state of the wind turbine generator can be comprehensively evaluated, reasonable distribution of power generation tasks is achieved, the power generation efficiency is improved, continuous and stable operation of a main control system of the wind turbine generator is ensured, meanwhile, faults can be diagnosed according to power generation capacity correction parameters, unit operation is controlled, stable operation of a wind power plant is ensured, and the power generation efficiency is improved. The problems of incomplete monitoring of the operation process of the wind turbine generator, simple data processing, inaccurate power generation task adjustment and the like are solved, maintenance personnel can quickly position and repair faults conveniently, and the troubleshooting time and cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine generators, and in particular to a PLC-based wind turbine generator set main control system and method. Background Art

[0002] With the increasing demand for clean energy, wind power generation has developed rapidly as a renewable energy technology. Coastal areas, open plains and mountainous areas have abundant wind resources, which are suitable for the construction of large-scale wind farms. With the construction of large-scale wind farms in the region, the demand for monitoring of wind turbines is also increasing.

[0003] Prior art, such as the invention patent with announcement number: CN111594384B, is a control method and main control system for a voltage source wind turbine, including determining the operating mode of the wind turbine based on whether the power grid is in a large frequency disturbance condition; when the wind turbine operates in a current source operating mode, based on the generator torque command output by the variable speed and pitch control loop and the generator torque command required for the support frequency output by the power grid frequency active support control loop, determining the generator torque output command sent to the converter and the operating mode of the wind turbine; when the wind turbine operates in a voltage source operating mode, based on the generator torque command output by the variable speed and pitch control loop and the generator torque required for virtual synchronization, determining the generator torque output command sent to the converter and the operating mode of the wind turbine.

[0004] The prior art, such as the invention patent with announcement number: CN114233572B, is a control system for the integrated linkage between the central control of a wind farm and the main control of a wind turbine, including: a tower base controller, a cabin controller, a yaw inverter, a pitch inverter, a power converter, an anemometer, a control panel, an internal optical fiber communication bus and a data acquisition server; the tower base controller and the cabin controller realize the integrated control of the tower base controller and the cabin controller by means of an internal optical fiber communication bus, and the cabin controller acts as a coupler to realize the data interaction of multiple interfaces and protocols of the yaw inverter, the pitch inverter, the power converter and the anemometer.

[0005] From the above scheme, it can be seen that the current wind turbine main control system has certain limitations in the design of control methods. It only focuses on some operating modes and control links, that is, it performs corresponding adjustment processing based on the actual operating level data of a single dimension. However, the output of the wind turbine in actual operation will be suppressed by external factors. If it only relies on the mode adjustment under actual operation, there may be insufficient timeliness in the adjustment, which will lead to excessive actual adjustment range and cause operating losses of the wind turbine, making it difficult to achieve precise adjustment of the power generation task. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a PLC-based wind turbine master control system and method, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a PLC-based wind turbine main control system, which is characterized by: including: a wind turbine power generation capacity evaluation module, which is used to monitor the startup process of each wind turbine through PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operating parameters of each wind turbine and wind farm environmental data, and analyze the power generation status evaluation index of each wind turbine.

[0008] The wind turbine generator set power generation task allocation module is used to determine the power generation mode of each wind turbine generator set according to the start-up correction coefficient of each wind turbine generator set and the power generation status evaluation index of each wind turbine generator set.

[0009] The wind turbine operation monitoring and adjustment module is used to obtain the operating status data of each wind turbine within a preset monitoring period, analyze the power generation status verification index of each wind turbine, and adaptively adjust the power generation task of each wind turbine based on the power generation status verification index of each wind turbine.

[0010] The wind turbine operation monitoring module is used to upload the real-time power generation status information of each wind turbine to the PLC remote monitoring center and monitor the power generation process of the wind turbine in real time.

[0011] The second aspect of the present invention provides a PLC-based wind turbine master control method, which is characterized by comprising the following steps: S1, monitoring the startup process of each wind turbine through PLC, collecting startup data of each wind turbine, analyzing the startup correction coefficient of each wind turbine, synchronously obtaining the historical operating parameters of each wind turbine and wind farm environmental data, and analyzing the power generation status evaluation index of each wind turbine.

[0012] S2, determining the power generation mode of each wind turbine generator set according to the start-up correction coefficient of each wind turbine generator set and the power generation status evaluation index of each wind turbine generator set.

[0013] S3, within a preset monitoring period, obtaining the operating status data of each wind turbine set, analyzing the power generation status verification index of each wind turbine set, and adaptively adjusting the power generation task of each wind turbine set based on the power generation status verification index of each wind turbine set.

[0014] S4, uploading the real-time power generation status information of each wind turbine to the PLC remote monitoring center to monitor the power generation process of the wind turbine in real time.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0016] (1) The present invention provides a wind turbine main control system and method based on PLC. The PLC monitors the startup process of each wind turbine and can comprehensively evaluate the power generation status of the wind turbine, thereby realizing reasonable allocation of power generation tasks in the initial startup stage and improving power generation efficiency. The adjustment in the initial stage ensures the timeliness of the adjustment and avoids the operation loss of the wind turbine due to excessive actual adjustment range. At the same time, the power generation status information is uploaded to the PLC remote monitoring center for real-time monitoring to ensure the continuous and stable operation of the wind turbine main control system. The present invention also includes a fault diagnosis module that can diagnose faults and control the operation of the unit according to the power generation capacity correction parameters to ensure the stable operation of the wind farm. The present invention solves the problems of incomplete monitoring of the operation process of the wind turbine, simple data processing and inaccurate adjustment of power generation tasks, which is convenient for maintenance personnel to quickly locate and repair faults and reduce the time and cost of troubleshooting.

[0017] (2) By analyzing the power generation status evaluation indicators of each wind turbine, the present invention can clarify the power generation performance and reliability of the wind turbine under the current environment, provide a basis for rationally determining the power generation mode, accurately distinguish between high-efficiency and low-efficiency power generation modes, and improve the power generation efficiency of the entire wind farm. At the same time, through comprehensive consideration of environmental data and the unit's own data, it can better adapt to the power generation needs under different environmental conditions, ensure the stable operation of the wind turbine, and improve the operating efficiency and operation management level of the wind farm.

[0018] (3) The present invention adaptively adjusts the power generation task of each wind turbine based on the power generation status verification index of each wind turbine. It can make timely and reasonable adjustments to the power generation task according to the real-time operating status data of the wind turbine, which helps the wind turbine to always maintain the best power generation state and improve the power generation efficiency. At the same time, it can reduce manual intervention, improve the automation level and management efficiency of wind farm operation, and ensure stable power generation in the wind farm.

[0019] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of system module connection of the present invention;

[0021] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0023] See also Figure 1 As shown, an embodiment of the present invention provides a PLC-based wind turbine main control system, which is characterized by: including: a wind turbine power generation capacity evaluation module, which is used to monitor the startup process of each wind turbine through PLC, collect startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operating parameters of each wind turbine and wind farm environmental data, and analyze the power generation status evaluation index of each wind turbine.

[0024] It should be noted that PLC stands for Programmable Logic Controller, which refers to programmable logic controller.

[0025] In this embodiment, the start-up correction coefficient of each wind turbine is analyzed, and the specific process is as follows: the start-up data of each wind turbine includes the start-up feedback time of each wind turbine, the generator start-up acceleration time, the blade start-up acceleration time, the generator output voltage, the generator output current and the main shaft torque.

[0026] It should be noted that the startup data of each wind turbine generator set is collected by a set of sensor devices installed on the wind turbine generator set, and the set of sensor devices includes a speed sensor, a voltage sensor, a current sensor, and the like.

[0027] The startup feedback time of a wind turbine refers to the time from when the wind turbine receives the startup command to when the PLC control system of the wind turbine feeds back that the wind turbine has reached the rated startup state.

[0028] The generator start-up acceleration time and the blade start-up acceleration time refer to the time it takes for the generator and blade speeds to reach the rated speed from zero.

[0029] Main shaft torque refers to the torsional moment borne by the main shaft (the shaft connecting the wind rotor and the generator) in a wind turbine.

[0030] According to the startup data of each wind turbine generator set, the startup index of each wind turbine generator set is obtained through analysis and processing.

[0031] The startup index of each wind turbine generator set is a numerical result of quantifying the startup data of each wind turbine generator set and is used to characterize the stability of the startup state of the wind turbine generator set.

[0032] In a specific embodiment, the startup index of each wind turbine generator set is obtained in the following manner:

[0033] The ideal startup data of the wind turbine generator set stored in the database is extracted, wherein the ideal startup data of the wind turbine generator set includes a reference startup feedback time of the wind turbine generator set, a reference startup acceleration time of the generator, a reference startup acceleration time of the blades, an ideal output voltage of the generator, an ideal output current of the generator and an ideal main shaft torque.

[0034] It should be noted that the ideal startup data of the wind turbine generator set stored in the database is obtained by averaging the historical data of the startup data of the wind turbine generator set before the database is established.

[0035]

[0036] Among them, C i is the startup index of the i-th wind turbine, a i is the startup feedback time of the i-th wind turbine, a0 is the reference startup feedback time, b i is the generator start-up acceleration time of the i-th wind turbine, b0 is the generator reference start-up acceleration time, c i is the blade startup acceleration time of the i-th wind turbine, c0 is the blade reference startup acceleration time, d i is the generator output voltage of the i-th wind turbine, d0 is the ideal generator output voltage, f i is the generator output current of the i-th wind turbine, f0 is the ideal generator output current, g i is the main shaft torque of the i-th wind turbine, g0 is the ideal main shaft torque, x1 is the weight of the startup feedback time, x2 is the weight of the generator startup acceleration time, x3 is the weight of the blade startup acceleration time, x4 is the weight of the generator output voltage, x5 is the weight of the generator output current, x6 is the weight of the main shaft torque, i is the number of each wind turbine, i = 1, 2, ..., m, m is the number of wind turbines, and e is a natural constant.

[0037] It should be noted that the starting feedback time weight, the generator starting acceleration time weight, the blade starting acceleration time weight, the generator output voltage weight, the generator output current weight and the main shaft torque weight all have a value range between 0 and 1. In this embodiment, the starting feedback time weight is the starting feedback time weight preset in the database, which indicates the numerical value of the influence of the starting feedback time on the starting index of the wind turbine generator set. The generator starting acceleration time weight is the generator starting acceleration time weight preset in the database, which indicates the numerical value of the influence of the generator starting acceleration time on the starting index of the wind turbine generator set. The blade starting acceleration time weight is the blade starting acceleration time weight preset in the database, which indicates the numerical value of the influence of the blade starting acceleration time on the starting index of the wind turbine generator set. The generator output voltage weight is the generator output voltage weight preset in the database, which indicates the numerical value of the influence of the generator output voltage on the starting index of the wind turbine generator set. The generator output current weight is the generator output current weight preset in the database, which indicates the numerical value of the influence of the generator output current on the starting index of the wind turbine generator set. The main shaft torque weight is the main shaft weight preset in the database. The torque weight represents the numerical value of the influence of the main shaft torque on the starting index of the wind turbine. When used, the preset starting feedback time weight, the generator starting acceleration time weight, the blade starting acceleration time weight, the generator output voltage weight, the generator output current weight and the main shaft torque weight can be directly obtained from the database. The corresponding relationship can be a preset mapping relationship. For example, the starting feedback time, the generator starting acceleration time, the blade starting acceleration time, the generator output voltage, the generator output current and the main shaft torque are respectively mapped with the starting feedback time weight, the generator starting acceleration time weight, the blade starting acceleration time weight, the generator output voltage weight, the generator output current weight and the main shaft torque weight preset in the database to form a mapping set. The real-time starting feedback time, the generator starting acceleration time, the blade starting acceleration time, the generator output voltage, the generator output current and the main shaft torque are respectively input into the mapping set to obtain the starting feedback time weight, the generator starting acceleration time weight, the blade starting acceleration time weight, the generator output voltage weight, the generator output current weight and the main shaft torque weight, wherein the mapping relationship is one-to-one correspondence.

[0038] It should also be noted that in this embodiment, the wind turbine startup index is obtained by processing the startup feedback time of the wind turbine, the generator startup acceleration time, the blade startup acceleration time, the generator output voltage, the generator output current and the main shaft torque, taking into account the mutual influence between these parameters. For example, if the generator startup acceleration time and the blade startup acceleration time are short, it means that the mechanical and electrical parts of the wind turbine can quickly reach a stable operating state, which usually shortens the startup feedback time. If the generator can quickly establish a stable output voltage and current to meet the conditions for the control system to determine that the startup is successful, then the startup feedback time will be shortened. On the contrary, if the generator output voltage and current rise slowly or fluctuate greatly and cannot quickly meet the requirements, the startup feedback time will be extended. During the startup process, sufficient main shaft torque is a prerequisite for ensuring that the generator and blades can accelerate the startup smoothly. During the startup process of the wind turbine, the blades capture wind energy and convert it into mechanical energy, which is transmitted to the generator through the main shaft to drive the generator to rotate. If the blade startup acceleration time is short, it can quickly reach a certain speed and provide sufficient power for the generator, then the generator startup acceleration time will be shortened accordingly. According to Ohm's law, when the internal resistance and external load of the generator are constant, the output voltage and output current of the generator are interrelated. When the output voltage of the generator increases, if the external load remains unchanged, the output current will also increase accordingly. Conversely, when the output voltage decreases, the output current will also decrease.

[0039] In a specific embodiment, by analyzing the startup indicators of each wind turbine set, the mechanical and electrical performance of the wind turbine set during startup can be understood, which helps to timely discover potential problems in the startup process, provide a basis for optimizing the startup control strategy, improve the reliability and efficiency of the wind turbine set startup, reduce the risk of startup failure, and ensure that the wind turbine set can quickly and stably enter the normal power generation state.

[0040] The wind turbine correction coefficient corresponding to each startup index interval stored in the database is extracted, and the wind turbine correction coefficient corresponding to each wind turbine startup index is mapped and extracted, and marked as each wind turbine startup correction coefficient.

[0041] In a specific embodiment, the starting correction coefficient of each wind turbine can more accurately reflect the actual situation of the starting status of the wind turbine, comprehensively consider and correct various complex factors in the starting process, and more reasonably evaluate the starting capability of the wind turbine when determining the power generation mode, providing a reliable basis for subsequent targeted operations according to different power generation modes, thereby improving the overall power generation efficiency and operation stability of the wind farm.

[0042] In this embodiment, historical operating parameters of each wind turbine and wind farm environmental data are obtained, wherein: the historical operating parameters of each wind turbine include the historical average trouble-free time, historical average daily power generation, historical total maintenance times and historical average startup feedback time of each wind turbine.

[0043] It should be noted that the historical mean fault-free time refers to the average normal operation time between two adjacent faults among multiple faults during the past operation of the wind turbine generator set.

[0044] The historical operating parameters of each wind turbine generator set are extracted from the maintenance management system of the wind farm.

[0045] Wind farm environmental data includes the current wind speed, wind direction, temperature and humidity of the wind farm.

[0046] It should be noted that the wind farm environmental data is collected by a built-in sensor device of the wind turbine, and the sensor device includes an anemometer, a temperature sensor and a humidity sensor.

[0047] In this embodiment, the power generation status evaluation index of each wind turbine is analyzed, and the specific process is as follows: extracting the reference historical operating parameters and reference environmental data of the wind turbine stored in the database.

[0048] The reference historical operating parameters of the wind turbine generator set include a reference average trouble-free time, a reference average daily power generation, a reference total maintenance times and a reference average startup feedback time of the wind turbine generator set.

[0049] The reference environmental data include the reference wind speed of the wind farm, the rotor angle of the yaw system of the wind turbine, the reference temperature and the reference humidity.

[0050] It should be noted that the reference historical operating parameters and reference environmental data of the wind turbines stored in the database are obtained by averaging the historical data of the wind turbine operating parameters and wind farm environmental data before the database is established.

[0051] Based on the historical operating parameters of each wind turbine, wind farm environmental data, reference historical operating parameters of the wind turbine and reference environmental data, analysis and processing are performed to obtain the power generation status evaluation index of each wind turbine. The power generation status evaluation index of each wind turbine is used to characterize the power generation performance and reliability of each wind turbine in the current environment.

[0052] In a specific embodiment, the power generation status evaluation index of each wind turbine generator set is obtained in the following specific process:

[0053]

[0054] Among them, D i is the evaluation index of the power generation status of the i-th wind turbine, hi is the historical mean time between failures of the i-th wind turbine, h0 is the reference mean time between failures of the wind turbine, j i is the historical average daily power generation of the i-th wind turbine, j0 is the reference average daily power generation of the wind turbine, k i is the total number of historical maintenance of the i-th wind turbine, k0 is the total number of reference maintenance of the wind turbine, m i is the historical average startup feedback time of the i-th wind turbine, m0 is the reference average startup feedback time of the wind turbine, n i is the current wind speed of the wind farm, n0 is the reference wind speed of the wind farm, p i is the current wind direction of the wind farm, p i0 is the rotor angle of the yaw system of the i-th wind turbine, q i is the current temperature of the wind farm, q0 is the reference temperature of the wind farm, r i is the current humidity of the wind farm, r0 is the reference humidity of the wind farm, y1 is the weight of the historical average fault-free time, y2 is the weight of the historical average daily power generation, y3 is the weight of the total number of historical maintenance times, y4 is the weight of the historical average startup feedback time, y5 is the weight of wind speed, y6 is the weight of wind direction, y7 is the weight of temperature, y8 is the weight of humidity, i is the number of wind turbines, i=1,2,...,m, m is the number of wind turbines, and e is a natural constant.

[0055] It should be understood that the softsign function is a built-in function in Pytnon.

[0056] It should be noted that the weight of the historical average fault-free time, the weight of the historical average daily power generation, the weight of the total number of historical maintenance times, the weight of the historical average startup feedback time, the weight of the wind speed, the weight of the wind direction, the weight of the temperature and the weight of the humidity all have a value range between 0 and 1. In this embodiment, the weight of the historical average fault-free time is the weight of the historical average fault-free time preset in the database, which indicates the numerical value of the influence of the historical average fault-free time on the wind turbine generator set power generation status evaluation index. The weight of the historical average daily power generation is the weight of the historical average daily power generation preset in the database, which indicates the numerical value of the influence of the historical average daily power generation on the wind turbine generator set power generation status evaluation index. The weight of the historical maintenance The total number of weights is the total number of historical maintenance weights preset in the database, which indicates the numerical value of the influence of the total number of historical maintenance on the wind turbine generator set power generation status evaluation index. The historical average startup feedback time weight is the historical average startup feedback time weight preset in the database, which indicates the numerical value of the influence of the historical average startup feedback time on the wind turbine generator set power generation status evaluation index. The wind speed weight is the wind speed weight preset in the database, which indicates the numerical value of the influence of wind speed on the wind turbine generator set power generation status evaluation index. The wind direction weight is the wind direction weight preset in the database, which indicates the numerical value of the influence of wind direction on the wind turbine generator set power generation status evaluation index. The temperature weight is the temperature weight preset in the database. The value represents the numerical value of the influence of temperature on the wind turbine generator set power generation status evaluation index. The humidity weight is the humidity weight preset in the database, which represents the numerical value of the influence of humidity on the wind turbine generator set power generation status evaluation index. When used, the preset historical average fault-free time weight, historical average daily power generation weight, historical total maintenance number weight, historical average startup feedback time weight, wind speed weight, wind direction weight, temperature weight and humidity weight can be directly obtained from the database. The corresponding relationship can be a preset mapping relationship, for example, the historical average fault-free time, the historical average daily power generation, the historical total maintenance number, the historical average startup feedback time, wind speed, wind direction, temperature and humidity weight. Humidity forms a mapping set with the historical average fault-free time weight, historical average daily power generation weight, historical total maintenance number weight, historical average startup feedback time weight, wind speed weight, wind direction weight, temperature and humidity weight preset in the database, and the real-time historical average fault-free time, historical average daily power generation, historical total maintenance number, historical average startup feedback time, wind speed, wind direction, temperature and humidity are respectively input into the mapping set to obtain the historical average fault-free time weight, historical average daily power generation weight, historical total maintenance number weight, historical average startup feedback time weight, wind speed weight, wind direction weight, temperature weight and humidity weight, and the mapping relationship is one-to-one correspondence.

[0057] It should also be noted that in this embodiment, the wind turbine generator set power generation status evaluation index is obtained by processing the historical operating parameters of the wind turbine generator set and the wind farm environmental data, taking into account the mutual influence between these parameters. For example, a longer historical mean fault-free time usually means that the wind turbine generator set has more time in normal operation, thereby generating more electricity. A large number of maintenance times may indicate that the unit has more problems or is prone to failure, which will lead to a shorter fault-free time. The unit with a longer historical mean fault-free time has higher system stability and reliability, which may make its startup feedback time relatively stable and shorter, because during the fault-free operation, the various components of the unit, such as the control system, electrical system and mechanical transmission system, are in a better state and can complete the startup process more efficiently. Under good environmental conditions, such as suitable wind speed, wind direction, temperature and humidity, the risk of wear and failure of the wind turbine generator set is relatively low, which is conducive to extending the historical mean fault-free time. A shorter historical average startup feedback time helps to improve the power generation efficiency of the wind turbine generator set. If the unit can be started quickly, it can start generating electricity earlier, thereby increasing the historical average daily power generation.

[0058] In a specific embodiment, by analyzing the power generation status evaluation indicators of each wind turbine set, the power generation performance and reliability of the wind turbine set in the current environment can be clarified, providing a basis for reasonable determination of the power generation mode, achieving accurate distinction between high-efficiency and low-efficiency power generation modes, and improving the power generation efficiency of the entire wind farm. At the same time, through comprehensive consideration of environmental data and the unit's own data, it can better adapt to the power generation needs under different environmental conditions, ensure the stable operation of the wind turbine set, and improve the operating efficiency and operation management level of the wind farm.

[0059] The wind turbine generator set power generation task allocation module is used to determine the power generation mode of each wind turbine generator set according to the start-up correction coefficient of each wind turbine generator set and the power generation status evaluation index of each wind turbine generator set.

[0060] The power generation modes of each wind turbine generator set include a low-efficiency power generation mode and a high-efficiency power generation mode.

[0061] In this embodiment, the power generation mode of each wind turbine is determined, and the specific process is as follows: according to the start-up correction coefficient of each wind turbine and the power generation status evaluation index of each wind turbine, the power generation capacity indicator parameter of each wind turbine is obtained through comprehensive analysis and processing.

[0062] The power generation capacity indicator parameters of each wind turbine generator set are numerical results of quantifying the start-up correction coefficient of each wind turbine generator set and the power generation state evaluation index of each wind turbine generator set, and are used to characterize the stability of the start-up capacity of each wind turbine generator set.

[0063] In a specific embodiment, the power generation capacity indicator parameters of each wind turbine are obtained in the following manner:

[0064] A i = sinh[(D i *θ i ) 2 ],

[0065] Among them, A i is the index parameter of the power generation capacity of the i-th wind turbine, D i is the evaluation index of the power generation status of the i-th wind turbine, θ i is the startup correction coefficient of the i-th wind turbine group, i is the number of each wind turbine group, i=1,2,...,m, and m is the number of wind turbine groups.

[0066] In a specific embodiment, by analyzing the power generation capacity indicator parameters of each wind turbine set, the power generation mode of the wind turbine set can be determined more accurately, and high-efficiency and low-efficiency power generation modes can be distinguished, so as to reasonably arrange the power generation tasks, so that the high-efficiency units can give full play to their power generation capacity, and the low-efficiency units can be properly handled, thereby improving the power generation efficiency of the entire wind farm and providing important decision-making basis for the operation management and optimization of the wind farm.

[0067] Extract the wind turbine generation capacity index threshold preset in the database.

[0068] It should be understood that the wind turbine generator set power generation capacity index threshold is extracted from the database and is used to reflect the critical value of the wind turbine generator set power generation capacity.

[0069] In a specific embodiment, the threshold value of the wind turbine generation capacity index is pre-set in the database and can be obtained by a variety of methods. For example, a large number of wind turbine generation capacity parameter sample data under different operating environments and different operating conditions are collected, and these sample data are subjected to detailed statistical analysis and processing to obtain the wind turbine generation capacity evaluation value, and then these evaluation values ​​are weighted averaged or calculated by taking specific percentile values ​​to obtain the wind turbine generation capacity index threshold value, or experienced experts in the field of wind power generation can determine the limit of the wind turbine generation capacity based on their long-term accumulated experience to set the threshold value.

[0070] If the power generation capacity indicator parameter of a wind turbine set is less than the power generation capacity index threshold of the wind turbine set, the power generation mode of the wind turbine set is marked as a low-efficiency power generation mode; if the power generation capacity indicator parameter of a wind turbine set is greater than or equal to the power generation capacity index threshold of the wind turbine set, the power generation mode of the wind turbine set is marked as a high-efficiency power generation mode.

[0071] It should be understood that if the power generation capacity indicator parameter of a wind turbine is less than the wind turbine power generation capacity index threshold, it means that the power generation capacity of the wind turbine is relatively weak under the current operating conditions, and the overall power generation efficiency is at a low level, so its power generation mode is marked as a low-efficiency power generation mode, and its power generation task is reduced. If the power generation capacity indicator parameter of a wind turbine is greater than or equal to the wind turbine power generation capacity index threshold, it means that the wind turbine has a strong power generation capacity under the current operating environment and its own state, and can convert wind energy into electrical energy more efficiently. It performs well in wind energy capture, generator operation, and adaptation to the environment, and the overall power generation efficiency has reached a high level. Therefore, its power generation mode is marked as a high-efficiency power generation mode, and it can be reasonably used and managed as a unit with outstanding power generation efficiency in the wind farm.

[0072] It should also be noted that the generator in the low-efficiency power generation mode can reduce its power generation task through PLC remote control, such as adjusting the blade pitch angle, changing the output frequency of the inverter, etc.

[0073] The wind turbine operation monitoring and adjustment module is used to obtain the operating status data of each wind turbine within a preset monitoring period, analyze the power generation status verification index of each wind turbine, and adaptively adjust the power generation task of each wind turbine based on the power generation status verification index of each wind turbine.

[0074] In this embodiment, the power generation status verification index of each wind turbine is analyzed, and the specific analysis process is as follows: the operating status data of each wind turbine includes the generator speed change, blade speed change, generator average output power and grid side average voltage of each wind turbine.

[0075] It should be noted that the generator speed variation and blade speed variation refer to the difference between the maximum and minimum values ​​of the generator speed and blade speed of the wind turbine in operation within a preset monitoring period.

[0076] Based on the operating status data of each wind turbine set, the power generation status verification index of each wind turbine set is obtained through analysis and processing.

[0077] The power generation status verification index of each wind turbine generator set is a numerical result of quantifying the operating status data of each wind turbine generator set, and is used to characterize the power generation performance and rationality of the operating status of each wind turbine generator set during operation.

[0078] In a specific embodiment, the power generation status verification index of each wind turbine generator set is obtained in the following manner:

[0079] The reference operating state data stored in the database is extracted, wherein the reference operating state data includes a reference speed change of the generator, a reference speed change of the blades, a reference generator average output power and a reference grid-side average voltage.

[0080] It should be noted that the reference operating status data stored in the database is obtained by performing mean processing on the operating status data of the wind turbine generator set before establishing the database.

[0081]

[0082] Among them, B i is the power generation status verification index of the i-th wind turbine, α i is the change in generator speed of the i-th wind turbine, α0 is the change in generator reference speed, β i is the blade speed change of the i-th wind turbine, β0 is the blade reference speed change, γ i is the average output power of the generator of the i-th wind turbine, γ0 is the average output power of the reference generator, δ i is the average grid-side voltage of the i-th wind turbine, δ0 is the reference grid-side average voltage, z1 is the weight of the generator speed change, z2 is the weight of the blade speed change, z3 is the weight of the generator average output power, z4 is the weight of the grid-side average voltage, i is the number of each wind turbine, i = 1, 2, ..., m, m is the number of wind turbines, and e is a natural constant.

[0083] It should be noted that the weight of the generator speed change, the blade speed change, the average generator output power and the average grid-side voltage all have a value range between 0 and 1. In this embodiment, the weight of the generator speed change is a preset weight of the generator speed change in the database, which indicates the numerical value of the influence of the generator speed change on the wind turbine generator state verification index. The weight of the blade speed change is a preset weight of the blade speed change in the database, which indicates the numerical value of the influence of the blade speed change on the wind turbine generator state verification index. The weight of the average generator output power is a preset weight of the average generator output power in the database, which indicates the numerical value of the influence of the average generator output power on the wind turbine generator state verification index. The weight of the average grid-side voltage is a preset weight of the average grid-side voltage in the database, which indicates the influence of the average grid-side voltage on the wind turbine generator. The numerical value of the influence degree of the power generation status verification index can be directly used to obtain the preset generator speed change weight, blade speed change weight, generator average output power weight and grid-side average voltage weight from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the generator speed change, blade speed change, generator average output power and grid-side average voltage are respectively mapped with the generator speed change weight, blade speed change weight, generator average output power weight and grid-side average voltage weight preset in the database to form a mapping set. The real-time generator speed change, blade speed change, generator average output power and grid-side average voltage are respectively input into the mapping set to obtain the generator speed change weight, blade speed change weight, generator average output power weight and grid-side average voltage weight, wherein the mapping relationship is one-to-one correspondence.

[0084] It should also be noted that in this embodiment, the wind turbine generator power generation status verification index is obtained by processing the generator speed change, blade speed change, generator average output power and grid-side average voltage of the wind turbine generator, taking into account the mutual influence between these parameters. For example, in a wind turbine generator, the blades are connected to the generator through components such as the hub and the main shaft to form a mechanical transmission system. When the blade speed changes, the generator speed will be directly affected by this mechanical transmission system. At the same time, the change in blade speed means a change in the amount of wind energy captured. When the blade speed increases, the captured wind energy increases, and the mechanical energy transmitted to the generator will also increase, thereby increasing the generator speed. According to the law of electromagnetic induction, the output voltage of the generator is related to the speed and magnetic field strength of the generator. When the generator speed changes, the speed at which its internal winding cuts the magnetic lines of force changes, thereby affecting the output voltage. Under the condition of a certain load, according to the power formula P=UI, the voltage change will cause the output power to change. The grid-side voltage will also limit the power output of the generator. If the grid-side voltage is too low, the generator may not be able to output according to the rated power, because the too low voltage will limit the output current of the generator. According to the power formula, the power output will also be affected.

[0085] In a specific embodiment, by analyzing the power generation status verification index of each wind turbine set, abnormal conditions in the power generation process can be discovered in a timely manner, providing a basis for adaptively adjusting the power generation task, ensuring that the wind turbine set always maintains a good power generation state, improving power generation efficiency, and ensuring the stable operation of the wind farm.

[0086] In this embodiment, the power generation task of each wind turbine is adaptively adjusted based on the power generation status verification index of each wind turbine. The specific adjustment method is as follows: extract the first power generation status verification index of the wind turbine and the second power generation status verification index of the wind turbine preset in the database.

[0087] It should be understood that the first verification index of the power generation state of the wind turbine set and the second verification index of the power generation state of the wind turbine set are extracted from the database and are used to reflect the critical value of the power generation state of the wind turbine set.

[0088] In a specific embodiment, the first verification index of the power generation state of the wind turbine set and the second verification index of the power generation state of the wind turbine set are pre-set in the database and can be obtained by a variety of methods. For example, a large amount of detailed operating state data of the wind turbine set at different operating stages can be collected, and the data can be deeply statistically analyzed and processed for these collected rich sample data, such as calculating the mean, median, standard deviation and other statistical indicators of various types of data, and determining the first verification index of the power generation state of the wind turbine set and the second verification index of the power generation state of the wind turbine set through a specific algorithm. The cluster analysis method can be used to cluster the data according to the quality of the power generation state, and select the verification index corresponding to the data near the boundaries of different clusters as the initial setting value.

[0089] It should also be noted that the first verification index of the wind turbine power generation status refers to the minimum value of the wind turbine power generation status verification index stored in the database, and the second verification index of the wind turbine power generation status refers to the maximum value of the wind turbine power generation status verification index stored in the database.

[0090] Extract the wind turbine generation status verification index adjustment value preset in the database.

[0091] According to the first verification index of the power generation status of the wind turbine set, the second verification index of the power generation status of the wind turbine set and the adjustment value of the power generation status verification index of the wind turbine set, the power generation capacity correction parameters of each wind turbine set are analyzed.

[0092] The power generation capacity correction parameters of each wind turbine are compared with the power generation capacity index threshold of the wind turbine, and the adaptive adjustment power generation mode of each wind turbine is analyzed, and each wind turbine is adaptively adjusted according to the corresponding adaptive adjustment power generation mode.

[0093] In a specific embodiment, the power generation capacity correction parameter of each wind turbine is obtained in the following manner:

[0094]

[0095] Among them, A i ′ is the correction parameter of the power generation capacity of the i-th wind turbine, A i is the index parameter of the power generation capacity of the i-th wind turbine, B i is the verification index of the power generation status of the i-th wind turbine, B1 is the first verification index of the power generation status of the wind turbine, B2 is the second verification index of the power generation status of the wind turbine, is the wind turbine generator set power generation status verification index adjustment value, i is the number of each wind turbine generator set, i=1,2,...,m, and m is the number of wind turbine generator sets.

[0096] It should be noted that if the power generation status verification index of a wind turbine set is less than the first power generation status verification index of the wind turbine set, it means that the power generation status of the wind turbine set is at a poor level, and there may be problems such as power generation performance significantly lower than expected and unstable operating status. It is necessary to adjust its power generation task, and it may be necessary to reduce the power generation task to avoid a greater impact on the overall power generation efficiency of the wind farm. If the power generation status verification index of a wind turbine set is greater than the second power generation status verification index of the wind turbine set, it means that the power generation status of the wind turbine set is at a good level, and it may perform well in both power generation performance and operating status. It can be appropriately considered to increase its power generation task to make full use of its power generation capacity and improve the overall power generation efficiency of the wind farm. If the power generation status verification index of a wind turbine set is greater than or equal to the first power generation status verification index of the wind turbine set and less than or equal to the second power generation status verification index of the wind turbine set, it means that the power generation status of the wind turbine set is at a normal level, and its power generation performance and operating status are within the expected range. There is no need to adjust its power generation task for the time being, and the current operating status and power generation task arrangement can continue to be maintained to maintain the stable operation and power generation efficiency of the wind farm.

[0097] In a specific embodiment, by adaptively adjusting the power generation task of each wind turbine set based on the power generation status verification index of each wind turbine set, the power generation task can be timely and reasonably adjusted according to the real-time operating status data of the wind turbine set, which helps the wind turbine set to always maintain the best power generation state and improve the power generation efficiency. At the same time, it can reduce manual intervention, improve the degree of automation and management efficiency of wind farm operation, and ensure stable power generation of the wind farm.

[0098] The wind turbine operation monitoring module is used to upload the real-time power generation status information of each wind turbine to the PLC remote monitoring center and monitor the power generation process of the wind turbine in real time.

[0099] In a specific embodiment, the PLC-based wind turbine main control system also includes: a wind turbine fault diagnosis module, which is used to analyze wind turbine fault information based on the power generation capacity correction parameters of each wind turbine. The specific process is as follows: extract the wind turbine power generation capacity correction parameter threshold preset in the database.

[0100] It should be understood that the wind turbine generator set power generation capacity correction parameter threshold is extracted from the database and is used to reflect the critical value of the wind turbine generator set power generation capacity after correction.

[0101] In a specific embodiment, the threshold value of the correction parameter of the power generation capacity of the wind turbine is pre-set in the database and can be obtained by a variety of methods. For example, the sample data of the power generation capacity correction parameters of many wind turbines under different working conditions (such as different wind speeds, temperatures, humidity and other environmental conditions, as well as different operating hours, maintenance conditions, etc.) can be collected, and these sample data can be deeply statistically analyzed, such as analyzing the distribution law of the data, calculating its mean, standard deviation and other statistics, and according to the distribution characteristics of the data, a suitable method can be used to determine the threshold value, such as using the mean plus or minus a certain multiple of the standard deviation as the threshold range, or dividing the sample data into different intervals, observing the actual operating performance of the wind turbine in each interval (such as whether a fault occurs, the change in power generation efficiency, etc.), and determining the appropriate threshold value based on these performances, thereby providing a key basis for the stable operation and fault judgment of the wind turbine.

[0102] If the power generation capacity correction parameter of a wind turbine is less than or equal to the power generation capacity correction parameter threshold of the wind turbine, the fault information of the wind turbine is marked as a faulty wind turbine, the fault information of the wind turbine is uploaded to the remote monitoring center synchronously, and the operation of the wind turbine is stopped through PLC control.

[0103] If the power generation capacity correction parameter of a wind turbine set is greater than the power generation capacity correction parameter threshold of the wind turbine set, the operation control of the wind turbine set will not be performed.

[0104] It should be understood that if the power generation capacity correction parameter of a wind turbine is less than or equal to the power generation capacity correction parameter threshold of the wind turbine, it means that the power generation capacity of the wind turbine is in a poor state after correction, and there may be problems such as difficulty in starting the wind turbine, low power generation efficiency, poor operating stability or insufficient environmental adaptability. In order to ensure the stable power generation of the wind farm and avoid the expansion of the impact of the fault, it is necessary to stop the operation of the wind turbine through PLC control and upload the fault information to the remote monitoring center. If the power generation capacity correction parameter of a wind turbine is greater than the power generation capacity correction parameter threshold of the wind turbine, it means that the power generation capacity of the wind turbine after correction is at a normal level and can meet the needs of stable power generation under different working conditions. In order to ensure the continuous and efficient operation of the wind turbine, there is no need to make additional adjustments to its operating status for the time being.

[0105] See also Figure 2 As shown, an embodiment of the present invention provides a PLC-based wind turbine master control method, which is characterized by comprising the following steps: S1, monitoring the startup process of each wind turbine through PLC, collecting startup data of each wind turbine, analyzing the startup correction coefficient of each wind turbine, synchronously acquiring the historical operating parameters of each wind turbine and wind farm environmental data, and analyzing the power generation status evaluation index of each wind turbine.

[0106] S2, determining the power generation mode of each wind turbine generator set according to the start-up correction coefficient of each wind turbine generator set and the power generation status evaluation index of each wind turbine generator set.

[0107] S3, within a preset monitoring period, obtaining the operating status data of each wind turbine set, analyzing the power generation status verification index of each wind turbine set, and adaptively adjusting the power generation task of each wind turbine set based on the power generation status verification index of each wind turbine set.

[0108] S4, uploading the real-time power generation status information of each wind turbine to the PLC remote monitoring center to monitor the power generation process of the wind turbine in real time.

[0109] In a specific embodiment, a PLC-based wind turbine main control system and method are provided, and the startup process of each wind turbine is monitored by PLC, and the power generation status of the wind turbine can be comprehensively evaluated, so as to realize reasonable allocation of power generation tasks in the initial startup stage and improve power generation efficiency. The adjustment in the initial stage ensures the timeliness of the adjustment and avoids the operation loss of the wind turbine due to excessive actual adjustment range. At the same time, the power generation status information is uploaded to the PLC remote monitoring center for real-time monitoring to ensure the continuous and stable operation of the wind turbine main control system. It also includes a fault diagnosis module, which can diagnose faults and control the operation of the unit according to the correction parameters of the power generation capacity to ensure the stable operation of the wind farm, and solves the problems of incomplete monitoring of the operation process of the wind turbine, simple data processing and inaccurate adjustment of power generation tasks, so as to facilitate maintenance personnel to quickly locate and repair faults and reduce the time and cost of troubleshooting.

[0110] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0111] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. The PLC-based wind turbine main control system is characterized by: include: The wind turbine generation capacity evaluation module is used to monitor the startup process of each wind turbine through PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operating parameters of each wind turbine and the wind farm environment data, and analyze the power generation status evaluation indicators of each wind turbine; The wind turbine generator set power generation task allocation module is used to determine the power generation mode of each wind turbine generator set according to the start-up correction coefficient of each wind turbine generator set and the power generation status evaluation index of each wind turbine generator set; The wind turbine operation monitoring and adjustment module is used to obtain the operation status data of each wind turbine within a preset monitoring period, analyze the power generation status verification index of each wind turbine, and adaptively adjust the power generation task of each wind turbine based on the power generation status verification index of each wind turbine; The wind turbine operation monitoring module is used to upload the real-time power generation status information of each wind turbine to the PLC remote monitoring center and monitor the power generation process of the wind turbine in real time.

2. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The specific process of analyzing the startup correction coefficient of each wind turbine generator set is as follows: The start-up data of each wind turbine generator set includes the start-up feedback time of each wind turbine generator set, the generator start-up acceleration time, the blade start-up acceleration time, the generator output voltage, the generator output current and the main shaft torque; According to the startup data of each wind turbine generator set, the startup index of each wind turbine generator set is obtained by analysis and processing; Extract the wind turbine correction coefficient corresponding to each startup index interval stored in the database, and map and extract the wind turbine correction coefficient corresponding to each wind turbine startup index, and mark it as the startup correction coefficient of each wind turbine; The startup index of each wind turbine generator set is a numerical result of quantifying the startup data of each wind turbine generator set and is used to characterize the stability of the startup state of the wind turbine generator set.

3. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The historical operating parameters of each wind turbine and wind farm environmental data are obtained, wherein: The historical operating parameters of each wind turbine include the historical average trouble-free time, historical average daily power generation, historical total maintenance times and historical average startup feedback time of each wind turbine; Wind farm environmental data includes the current wind speed, wind direction, temperature and humidity of the wind farm.

4. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The specific process of analyzing the evaluation index of the power generation status of each wind turbine is as follows: Extracting reference historical operating parameters and reference environmental data of wind turbines stored in a database; Based on the historical operating parameters of each wind turbine, wind farm environmental data, reference historical operating parameters of the wind turbine and reference environmental data, analysis and processing are performed to obtain the power generation status evaluation index of each wind turbine. The power generation status evaluation index of each wind turbine is used to characterize the power generation performance and reliability of each wind turbine in the current environment.

5. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The specific process of determining the power generation mode of each wind turbine is as follows: According to the start-up correction coefficient of each wind turbine and the power generation status evaluation index of each wind turbine, the power generation capacity index parameters of each wind turbine are obtained through comprehensive analysis and processing; Extracting the wind turbine generation capacity index threshold preset in the database; If the power generation capacity indicator parameter of a wind turbine set is less than the power generation capacity index threshold of the wind turbine set, the power generation mode of the wind turbine set is marked as a low-efficiency power generation mode; if the power generation capacity indicator parameter of a wind turbine set is greater than or equal to the power generation capacity index threshold of the wind turbine set, the power generation mode of the wind turbine set is marked as a high-efficiency power generation mode.

6. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The specific analysis process of analyzing the power generation status verification index of each wind turbine is as follows: The operating status data of each wind turbine generator set includes the change in generator speed, the change in blade speed, the average generator output power and the average voltage on the grid side of each wind turbine generator set; Based on the operating status data of each wind turbine, the power generation status verification index of each wind turbine is obtained by analysis and processing; The power generation status verification index of each wind turbine generator set is a numerical result of quantifying the operating status data of each wind turbine generator set, and is used to characterize the power generation performance and rationality of the operating status of each wind turbine generator set during operation.

7. The PLC-based wind turbine master control system according to claim 6 is characterized in that: The power generation task of each wind turbine is adaptively adjusted based on the power generation status verification index of each wind turbine. The specific adjustment method is as follows: Extracting a first verification index of a wind turbine generator set power generation status and a second verification index of a wind turbine generator set power generation status preset in a database; Extracting the wind turbine generator set power generation status verification index adjustment value preset in the database; Analyze the power generation capacity correction parameters of each wind turbine according to the first verification index of the wind turbine power generation status, the second verification index of the wind turbine power generation status and the adjustment value of the wind turbine power generation status verification index; The power generation capacity correction parameters of each wind turbine are compared with the power generation capacity index threshold of the wind turbine, and the adaptive adjustment power generation mode of each wind turbine is analyzed, and each wind turbine is adaptively adjusted according to the corresponding adaptive adjustment power generation mode.

8. The PLC-based wind turbine master control system according to claim 7 is characterized in that: The specific acquisition method of the power generation capacity correction parameter of each wind turbine is as follows: Among them, A i ′ is the correction parameter of the power generation capacity of the i-th wind turbine, A i is the index parameter of the power generation capacity of the i-th wind turbine, B i is the verification index of the power generation status of the i-th wind turbine, B1 is the first verification index of the power generation status of the wind turbine, B2 is the second verification index of the power generation status of the wind turbine, is the wind turbine generator set power generation status verification index adjustment value, i is the number of each wind turbine generator set, i=1,2,...,m, m is the number of wind turbine generator sets.

9. The PLC-based wind turbine master control system according to claim 7 further comprises: The wind turbine fault diagnosis module is used to correct the parameters according to the power generation capacity of each wind turbine and analyze the fault information of the wind turbine. The specific process is as follows: Extracting the threshold value of wind turbine generating capacity correction parameter preset in the database; If the power generation capacity correction parameter of a wind turbine is less than or equal to the power generation capacity correction parameter threshold of the wind turbine, the fault information of the wind turbine is marked as a faulty wind turbine, the fault information of the wind turbine is uploaded to the remote monitoring center synchronously, and the operation of the wind turbine is stopped through PLC control.

10. A PLC-based wind turbine master control method, characterized in that: The following steps are involved: S1, monitor the startup process of each wind turbine through PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operating parameters of each wind turbine and wind farm environmental data, and analyze the power generation status evaluation indicators of each wind turbine; S2, determining the power generation mode of each wind turbine generator set according to the start-up correction coefficient of each wind turbine generator set and the power generation status evaluation index of each wind turbine generator set; S3, within a preset monitoring period, obtaining the operating status data of each wind turbine set, analyzing the power generation status verification index of each wind turbine set, and adaptively adjusting the power generation task of each wind turbine set based on the power generation status verification index of each wind turbine set; S4, uploading the real-time power generation status information of each wind turbine to the PLC remote monitoring center to monitor the power generation process of the wind turbine in real time.

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