Wind power plant construction commanding and dispatching method and system
By building equipment status evaluation and command and dispatch models, using machine learning and Internet of Things technology, the problem of inefficient dispatching in traditional wind farm construction has been solved, efficient and accurate construction equipment management and dynamic adjustment have been achieved, and the overall efficiency and safety of wind farm construction have been improved.
Patent Information
- Application Number
- CN202510219566.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-26
- Publication Date
- 2025-07-22
AI Technical Summary
The traditional wind farm construction command and dispatch method is difficult to comprehensively and accurately evaluate the status and operation needs of construction equipment, resulting in insufficiency of scheduling and lack of real-time response and dynamic adjustment capabilities to uncertain factors.
Build a device status evaluation model and command and scheduling model, use machine learning and Internet of Things technology to evaluate device status in real time and output optimized scheduling strategies based on historical data and real-time operation information.
It improves scheduling efficiency and accuracy, enhances the ability to respond to uncertain factors, optimizes resource allocation, and improves management level and security.
Smart Images
Figure CN120355116A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of wind farm construction management and intelligent scheduling. Specifically, it relates to a method and system for wind farm construction command and dispatch. Background Art
[0002] With the continuous growth of the global demand for renewable energy, wind power generation, as a clean and renewable energy form, has been increasingly emphasized in its development and utilization. The construction of wind farms, as an important link in the wind power generation industry chain, directly affects the economic and environmental benefits of wind power projects. However, the construction process of wind farms is complex, involving various construction equipment and a large amount of human and material resources. How to efficiently and orderly command and dispatch these resources has become a major challenge in wind farm construction.
[0003] Traditional methods for wind farm construction command and dispatch mainly rely on manual experience and planning arrangements, and there are many deficiencies in this method. First of all, manual judgment is often limited by personal experience and knowledge level, making it difficult to comprehensively and accurately evaluate the status and operation requirements of construction equipment, resulting in low scheduling efficiency and resource waste. Secondly, there are many uncertain factors in the process of wind farm construction, such as weather changes, equipment failures, etc. These factors may affect the construction progress and equipment status, while traditional scheduling methods often lack the ability to respond to these factors in real time and make dynamic adjustments. Summary of the Invention
[0004] The purpose of the present invention is to provide a method and system for wind farm construction command and dispatch, aiming to solve the problems that traditional methods are difficult to comprehensively and accurately evaluate the status and operation requirements of construction equipment, resulting in low scheduling efficiency and resource waste, and lacking the ability to respond to uncertain factors in real time and make dynamic adjustments.
[0005] The present invention is achieved through the following technical solutions:
[0006] A method for wind farm construction command and dispatch includes the steps of:
[0007] Obtaining the construction data of historical wind farms, and constructing an equipment status evaluation model and a command and dispatch model based on machine learning;
[0008] Obtaining the real-time operation information of each construction equipment in the current wind farm, and performing real-time verification on the obtained real-time operation information to obtain a verification data set;
[0009] Using the verification data set as the input of the equipment status evaluation model, and evaluating the real-time status of each construction equipment through the equipment status evaluation model to obtain a real-time status evaluation result;
[0010] Use the real-time status evaluation results and real-time job requirements as the input of the command and dispatch model, and output the command and dispatch strategy through the command and dispatch model;
[0011] Deploy each construction equipment according to the command and dispatch strategy, and monitor the working status of each construction equipment in real time.
[0012] Optionally, the specific process of obtaining the construction data of historical wind farms and constructing a status evaluation model based on machine learning is as follows:
[0013] Collect the corresponding construction equipment data in several historical wind farm projects, preprocess the collected construction equipment data to obtain an equipment data set, and divide the preprocessed data set into a training set, a validation set and a test set;
[0014] Based on machine learning algorithms, construct an initial equipment status evaluation model according to the characteristics and complexity of the preprocessed data set, and train the initial equipment status evaluation model through the training set;
[0015] During the training process of the initial equipment status evaluation model, verify the training degree of the initial equipment status evaluation model through the validation set;
[0016] Use the test set to evaluate the performance of the trained initial equipment status evaluation model to obtain the equipment status evaluation model.
[0017] Optionally, the specific process of constructing the command and dispatch model is as follows:
[0018] Collect historical data related to the construction scheduling of wind farms, preprocess the collected historical data to obtain a scheduling data set;
[0019] Based on machine learning algorithms, construct an initial command and dispatch model, and train the initial command and dispatch model through the scheduling data set;
[0020] During the training process, use the actual scheduling cases and results in the historical data as the training set to train the initial command and dispatch model, and iteratively optimize the parameters and structure of the initial command and dispatch model;
[0021] Evaluate the performance of the trained initial command and dispatch model through the cross-validation method to obtain the command and dispatch model.
[0022] Optionally, the specific process of obtaining the real-time operation information of each construction equipment in the current wind farm and performing real-time verification on the obtained real-time operation information to obtain a verification data set is as follows:
[0023] Through the Internet of Things technology, collect operation information in real time from the sensors installed on each construction equipment in the current wind farm;
[0024] Perform a primary verification on the collected real-time operation information, remove abnormal data caused by sensor failures or data transmission errors, and obtain a primary verification set;
[0025] Through preset data verification rules, perform a secondary verification on the data in the primary verification set. The secondary verification includes data range verification, data consistency verification, and data logical relationship verification;
[0026] Organize the real-time operation information that has passed the secondary verification into a structured data set as the verification data set.
[0027] Optionally, the specific process of using the verification data set as the input of the equipment status evaluation model, evaluating the real-time status of each construction equipment through the equipment status evaluation model, and obtaining the real-time status evaluation result is as follows:
[0028] Input the verification data set that has passed the real-time verification into the constructed and trained equipment status evaluation model;
[0029] The equipment status evaluation model analyzes and evaluates the real-time status of each construction equipment according to the input verification data set;
[0030] During the evaluation process, the equipment status evaluation model comprehensively judges whether the equipment is in a normal state, whether there are potential faults, or whether maintenance is required based on the equipment's historical operation data, current operation parameters, and environmental impact factors;
[0031] Output the real-time status evaluation results of each construction equipment. The real-time status results include the health status, performance level, estimated remaining life of the equipment, as well as the fault prediction type and risk level.
[0032] Optionally, the specific process of using the real-time status evaluation result and the real-time operation requirement as the input of the command and dispatch model, and outputting the command and dispatch strategy through the command and dispatch model is as follows:
[0033] Combine the real-time status evaluation results of each construction equipment output by the equipment status evaluation model with the real-time operation requirements of the current wind farm to obtain a comprehensive data set;
[0034] Input the comprehensive data set into the command and dispatch model, and output the command and dispatch strategy through the command and dispatch model according to the real-time status of the equipment, the operation requirements, and the overall construction plan of the wind farm.
[0035] Optionally, set corresponding weights for the availability, operation efficiency, cost-effectiveness, and construction safety of the equipment. With the goal of maximizing equipment utilization rate, reducing equipment failure risks, and construction costs, perform iterative calculations through the command and dispatch model to obtain the optimal command and dispatch strategy.
[0036] Optionally, the specific process of deploying each construction equipment according to the command and dispatch strategy and real-time monitoring the working status of each construction equipment is as follows:
[0037] According to the command and dispatch strategy output by the command and dispatch model, formulate a construction equipment deployment plan, which includes the specific operation location, operation task, operation time, and operation sequence of each equipment;
[0038] Send the construction equipment deployment plan to the wind farm site to guide the construction team to deploy and dispatch each construction equipment according to the plan;
[0039] After the equipment starts to operate, continuously collect real-time operation data from the sensors installed on the equipment through the Internet of Things technology;
[0040] Perform real-time analysis on the collected real-time operation data, monitor the working status of the equipment, and judge whether there are abnormal fluctuations or deviations from the preset working range for each construction equipment.
[0041] Optionally, use the real-time monitored equipment working status data as feedback and input it into the equipment status evaluation model and the command and dispatch model to optimize the model parameters of the equipment status evaluation model and the command and dispatch model.
[0042] The present invention also provides a wind farm construction command and dispatch system for implementing the above-mentioned wind farm construction command and dispatch method, including:
[0043] A data collection module for obtaining the construction data of historical wind farms and the real-time operation information of each construction equipment in the current wind farm;
[0044] A data verification module for performing real-time verification on the obtained real-time operation information to obtain a verification data set;
[0045] An equipment status evaluation module with a pre-built and trained equipment status evaluation model, which is used to take the verification data set output by the data verification module as input, analyze and evaluate the real-time status of each construction equipment, and output the real-time status evaluation results of each construction equipment;
[0046] A command and dispatch module with a pre-built and trained command and dispatch model, which is used to take the real-time status evaluation results output by the equipment status evaluation module and the real-time operation requirements of the current wind farm as input, and output the optimal command and dispatch strategy;
[0047] A deployment and monitoring module for formulating a construction equipment deployment plan according to the command and dispatch strategy, sending it to the wind farm site for execution, collecting the real-time operation data of the equipment through the Internet of Things technology, performing real-time analysis, and monitoring the working status of the equipment;
[0048] The model optimization module is used to take the working status data of each construction equipment monitored in real time as feedback and input it into the equipment status evaluation model and the command and dispatch model, and iteratively optimize the parameters and structures of the equipment status evaluation model and the command and dispatch model.
[0049] The technical solution of the present invention has at least the following advantages and beneficial effects:
[0050] Improve scheduling efficiency and accuracy: By using the construction data of historical wind farms to construct the equipment status evaluation model and the command and dispatch model, it is possible to quickly and accurately output the command and dispatch strategy based on real-time operation information and equipment status; compared with the traditional manual scheduling method, the scheduling efficiency and accuracy are greatly improved, and the resource waste and schedule delays caused by human judgment errors are reduced.
[0051] Enhance the ability to cope with uncertainties: By monitoring the equipment status and operation requirements in real time and dynamically adjusting the command and dispatch strategy, the ability to cope with uncertain factors in the process of wind farm construction is effectively enhanced, ensuring the smooth progress of the construction process.
[0052] Optimize resource allocation: According to the real-time status evaluation results and operation requirements, equipment scheduling is carried out, realizing the reasonable allocation and efficient utilization of resources. It can not only reduce the construction cost and improve the economic benefits, but also help to reduce the impact on the environment and achieve green and sustainable wind farm construction.
[0053] Improve the management level: By introducing an intelligent command and dispatch method, the management level of wind farm construction is improved. Managers can more intuitively understand the equipment status and operation progress, which is convenient for making more scientific and reasonable decisions; at the same time, it also provides strong support for the digital transformation of wind farm construction.
[0054] Enhance safety: Monitoring the equipment status in real time and scheduling accordingly helps to timely discover potential safety hazards, take preventive measures, and avoid accidents. It not only guarantees the safety of construction personnel, but also protects the integrity of the equipment and reduces the economic losses caused by accidents. BRIEF DESCRIPTION OF THE DRAWINGS
[0055] Figure 1 It is a schematic flow chart of the wind farm construction command and dispatch method according to Embodiment 1 of the present invention;
[0056] Figure 2 It is a schematic structural diagram of the wind farm construction command and dispatch system according to Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION
[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention generally described and illustrated in the figures herein can be arranged and designed in a variety of different configurations.
[0058] Embodiment 1
[0059] Refer to Figure 1 , a wind farm construction command and dispatch method, including the steps:
[0060] Step 1: Obtain the construction data of historical wind farms, and construct an equipment status evaluation model and a command and dispatch model based on machine learning.
[0061] In this embodiment, the specific process of obtaining the construction data of historical wind farms and constructing a status evaluation model based on machine learning is as follows:
[0062] Collect the corresponding construction equipment data in several historical wind farm projects, including but not limited to equipment type, operation time, fault records, maintenance records, environmental parameters (such as temperature, humidity, wind speed), etc.;
[0063] Preprocess the collected construction equipment data, including cleaning the collected data to remove duplicate, missing, or invalid data records; performing standardization or normalization processing on the data to ensure that the dimensions of different features are consistent, and obtaining an equipment data set;
[0064] Divide the preprocessed data set into a training set, a validation set, and a test set, and the ratio of the training set, the validation set, and the test set is 70%, 15%, 15%; based on machine learning algorithms, select appropriate machine learning algorithms according to the characteristics and complexity of the preprocessed data set, such as support vector machine (SVM), random forest (RF), neural network (NN), etc., to construct an initial equipment status evaluation model. In this embodiment, the support vector machine is used to construct the initial equipment status evaluation model. Assume that the training data set is {(x1,y1),(x2,y2),…,(x n ,y n )}, where x represents the feature vector and y represents the class label. In this embodiment, the class label can be expressed as normal or faulty; the objective function J of the initial equipment status evaluation model is shown in the following formula (1):
[0065]
[0066] where w represents the normal vector of the hyperplane and b represents the bias term, ° iThe slack variable is denoted as such and is used to handle the situation where data points may not be strictly separable. C represents the penalty parameter, which is used to control the degree of penalty for misclassification. The constraint conditions are shown in the following formula (2):
[0067]
[0068] In the non-linear case, by introducing the kernel function K(x i , x j ), the input data is mapped to a high-dimensional space, and the objective function and constraint conditions can be modified accordingly. The modified objective function and constraint conditions are shown in the following formulas (3) and (4):
[0069]
[0070] Among them, a i is the Lagrange multiplier. By solving the above optimization problem, the parameters of the initial model for equipment status evaluation can be obtained.
[0071] The initial model for equipment status evaluation is trained using the training set; during the training process of the initial model for equipment status evaluation, the training degree of the initial model for equipment status evaluation is verified using the validation set; the performance of the trained initial model for equipment status evaluation is evaluated using the test set to obtain the equipment status evaluation model.
[0072] In this embodiment, the specific process of constructing the command and dispatch model is as follows:
[0073] Collect historical data related to the construction dispatch of the wind farm, including but not limited to equipment usage, operation efficiency, dispatch decisions, environmental parameters (such as wind speed, wind direction, temperature, etc.), energy output, etc.; preprocess the collected historical data, remove duplicate, invalid or abnormal data records to ensure the accuracy and consistency of the data, extract key features related to dispatch decisions from the original data, such as equipment efficiency, operation requirements, weather conditions, etc., perform normalization processing on the feature data to eliminate the dimensional differences between different features to obtain the dispatch data set; based on machine learning algorithms, construct an initial command and dispatch model, set the input features (such as equipment status, operation requirements, environmental parameters, etc.) and output targets (such as the optimal dispatch strategy) of the model, and train the initial command and dispatch model using the dispatch data set; during the training process, use the actual dispatch cases and results in the historical data as the training set to train the initial command and dispatch model, and iteratively optimize the parameters and structure of the initial command and dispatch model; evaluate the performance of the trained initial command and dispatch model through the cross-validation method to obtain the command and dispatch model.
[0074] Step 2: Obtain the real-time operation information of each construction equipment in the current wind farm, and perform real-time verification on the obtained real-time operation information to obtain the verification data set.
[0075] In this embodiment, the specific process of obtaining the real-time operation information of each construction device in the current wind farm and performing real-time verification on the obtained real-time operation information to obtain a verification data set is as follows:
[0076] Through the Internet of Things technology, the operation information is collected in real time from the sensors installed on each construction device in the current wind farm; by using the Internet of Things technology, through the sensors deployed on each construction device in the wind farm, the operation information of the device is collected in real time. These sensors can include, but are not limited to, temperature sensors, humidity sensors, vibration sensors, pressure sensors, etc., which are used to monitor the operating parameters and environmental conditions of the device. According to the characteristics of the device and the operation requirements, a reasonable data collection frequency is set. For key devices or devices that require high-precision monitoring, the data collection frequency can be increased to ensure real-time performance. The collected data is transmitted to the data center or cloud server by wireless or wired means for subsequent processing and analysis.
[0077] Perform a primary verification on the collected real-time operation information. By setting data thresholds, data range limits, etc., abnormal data caused by sensor failures or data transmission errors can be removed to obtain a primary verification set; clean the data after the primary verification to remove duplicate, invalid, or redundant data records to ensure the accuracy and consistency of the data.
[0078] Perform a secondary verification on the data in the primary verification set through preset data verification rules. The secondary verification includes data range verification, data consistency verification, and data logical relationship verification; check whether the data is within the specified range, for example, whether environmental parameters such as temperature and humidity exceed the normal working range of the device; check whether there are contradictions or inconsistencies between the data collected by different sensors. For example, the data collected by the vibration sensor and the temperature sensor should be coordinated with each other and should not be contradictory; according to the operation logic and operation process of the device, check whether there is a reasonable logical relationship between the data. For example, the startup time of the device should be earlier than the running time, and the shutdown time should be later than the running time, etc.
[0079] Organize the real-time operation information that has passed the secondary verification into a structured data set as the verification data set, including information such as device number, data type, data value, time stamp, etc., for subsequent analysis and processing; store the organized verification data set in a database or data warehouse for long-term storage and query. For abnormal data or potential problems found during the verification process, an alarm signal is sent in a timely manner to notify relevant personnel for handling; record the key information and processing results during the verification process for subsequent analysis and improvement.
[0080] Step 3: Use the verification data set as the input of the device status evaluation model, and evaluate the real-time status of each construction device through the device status evaluation model to obtain a real-time status evaluation result.
[0081] In this embodiment, the verification data set is used as the input of the equipment status evaluation model. The specific process of evaluating the real-time status of each construction equipment through the equipment status evaluation model to obtain the real-time status evaluation result is as follows:
[0082] Input the verified verification data set into the constructed and trained equipment status evaluation model. The equipment status evaluation model first extracts key features from the input verification data set, including equipment type, operating time, current operation parameters (such as power, rotation speed, etc.), environmental parameters (such as temperature, humidity, wind speed, etc.), and historical failure records and maintenance records, etc.
[0083] The equipment status evaluation model analyzes and evaluates the real-time status of each construction equipment according to the input verification data set. Based on the extracted features, the equipment status evaluation model will perform a series of calculations, including feature weighting, non-linear transformation, classification or regression, etc., to evaluate the real-time status of the equipment.
[0084] During the evaluation process, the equipment status evaluation model comprehensively judges whether the equipment is in a normal state, whether there are potential faults or whether maintenance is required according to the equipment's historical operation data, current operation parameters, and environmental impact factors;
[0085] Output the real-time status evaluation results of each construction equipment. The real-time status results include the health status, performance level, estimated remaining life of the equipment, as well as the fault prediction type and risk level. Through visualization tools (such as dashboards, charts, etc.) or report generation tools, display the real-time status evaluation results to relevant personnel so that they can understand the equipment status in a timely manner and take corresponding measures. Store the real-time status evaluation results in the database for subsequent query and analysis. This helps to track the status changes of the equipment and provides data support for future equipment management and maintenance. Using the real-time status evaluation results as feedback input into the equipment status evaluation model helps the model to self-learn and optimize. For example, if the model performs poorly in certain situations, its performance can be improved by increasing training data or adjusting model parameters. According to the real-time status evaluation results, the operation of the entire wind farm construction command and dispatch system can be optimized. For example, if certain equipment frequently fails, its operation plan can be adjusted or the maintenance frequency can be increased to reduce the impact of the failure on the overall construction.
[0086] Step Four: Use the real-time status evaluation results and real-time operation requirements as the input of the command and dispatch model, and output the command and dispatch strategy through the command and dispatch model.
[0087] In this embodiment, the specific process of using the real-time status evaluation results and real-time operation requirements as the input of the command and dispatch model and outputting the command and dispatch strategy through the command and dispatch model is as follows:
[0088] The real - time status evaluation results of each construction equipment output by the equipment status evaluation model are combined with the real - time operation requirements of the current wind farm to obtain a comprehensive data set; the comprehensive data set is input into the command and dispatch model. According to the real - time status of the equipment, operation requirements, and the overall construction plan of the wind farm, the command and dispatch strategy is output through the command and dispatch model. The real - time status evaluation results include information such as the health status, performance level, estimated remaining life, fault prediction type, and risk level of the equipment. These information are integrated into a comprehensive status evaluation report; at the same time, the real - time operation requirements of the current wind farm are collected, which include task allocation, time requirements, priorities, etc. in each construction area. This information may come from the management system of the wind farm, construction plan, or instructions from on - site commanders; the integrated real - time status evaluation results and real - time operation requirements are converted into a format recognizable by the command and dispatch model, such as JSON, XML, or a specific data structure. Then, this data is passed as input to the command and dispatch model.
[0089] In this embodiment, corresponding weights are set for the availability, operation efficiency, cost - effectiveness, and construction safety of the equipment. Aiming at maximizing equipment utilization rate, reducing equipment failure risk, and construction cost, through iterative calculation of the command and dispatch model, an optimal command and dispatch strategy is obtained. Inside the command and dispatch model, first, the model parameters are initialized, including the availability weight, operation efficiency weight, cost - effectiveness weight, and construction safety weight of the equipment, etc. These weights are preset according to the operation objectives and strategies of the wind farm. The command and dispatch model performs optimization calculations based on the input real - time status evaluation results and real - time operation requirements. Optimization calculations can be carried out through multi - objective optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, or simulated annealing algorithms, etc. The goal of the model is to maximize equipment utilization rate, reduce equipment failure risk, and construction cost while meeting all operation requirements. After optimization calculations, the command and dispatch model generates an optimal command and dispatch strategy. This strategy usually includes detailed information such as the specific operation location, operation task, operation time, and operation sequence of each construction equipment.
[0090] Step Five: Deploy each construction equipment according to the command and dispatch strategy and monitor the working status of each construction equipment in real - time.
[0091] In this embodiment, the specific process of deploying each construction equipment according to the command and dispatch strategy and monitoring the working status of each construction equipment in real - time is as follows:
[0092] Analyze the command and dispatch strategy output by the command and dispatch model. According to the command and dispatch strategy output by the command and dispatch model, formulate a construction equipment deployment plan, which includes the specific operation locations, operation tasks, operation times, and operation sequences of each piece of equipment. Based on the analyzed command and dispatch strategy and combined with the actual situation of the wind farm (such as terrain, meteorological conditions, equipment types, etc.), formulate a detailed construction equipment deployment plan. This plan should ensure that the equipment can efficiently and safely complete the specified operation tasks. Issue the formulated construction equipment deployment plan to the on-site construction personnel through the management system or wireless communication equipment of the wind farm to ensure that the construction personnel can accurately understand and execute the plan.
[0093] Issue the construction equipment deployment plan to the wind farm site to guide the construction team to deploy and dispatch each construction equipment according to the plan; the construction personnel transport each construction equipment to the specified operation location according to the deployment plan and carry out necessary installation and commissioning work; after the equipment deployment is completed, start the operation in the time sequence of the plan. At the same time, ensure that the equipment operators have the corresponding qualifications and skills and can operate the equipment proficiently; after the equipment starts to operate, collect operation data in real time from the sensors installed on the equipment through Internet of Things technology, such as operating parameters, energy consumption, vibration conditions, etc. These data will be used for subsequent equipment status monitoring and evaluation.
[0094] After the equipment starts to operate, continuously collect real-time operation data from the sensors installed on the equipment through Internet of Things technology; conduct real-time analysis on the collected real-time operation data to monitor the working status of the equipment and judge whether there are abnormal fluctuations or deviations from the preset working range for each construction equipment. Conduct real-time analysis on the collected real-time operation data to monitor the working status of the equipment. If it is found that the equipment has abnormal fluctuations or deviations from the preset working range, immediately trigger the early warning mechanism and notify the relevant personnel for handling. Combine the real-time status evaluation results output by the equipment status evaluation model to predict potential faults of the equipment. Once it is predicted that the equipment may have a fault, immediately formulate a maintenance plan and arrange maintenance personnel for repair. If the equipment fault or abnormal situation affects the overall construction plan of the wind farm, it is necessary to adjust the command and dispatch strategy in a timely manner according to the actual situation to ensure the smooth progress of the construction plan.
[0095] In this embodiment, use the equipment working status data monitored in real time as feedback and input it into the equipment status evaluation model and the command and dispatch model to optimize the model parameters of the equipment status evaluation model and the command and dispatch model. Adjust the parameters and structure of the model through an iterative optimization algorithm to improve the accuracy and reliability of the model. According to the operation objectives and actual situation of the wind farm, continuously optimize the operation process and method of the entire wind farm construction command and dispatch system to improve the overall performance and efficiency of the system.
[0096] Embodiment 2
[0097] Based on Embodiment 1, with reference to Figure 2 , this embodiment provides a wind farm construction command and dispatch system for implementing the wind farm construction command and dispatch method, including:
[0098] A data acquisition module 101, configured to obtain the construction data of the historical wind farm and the real-time operation information of each construction device in the current wind farm;
[0099] A data verification module 102, configured to perform real-time verification on the obtained real-time operation information to obtain a verification data set;
[0100] An equipment status evaluation module 103, built with a pre-constructed and trained equipment status evaluation model, configured to use the verification data set output by the data verification module as input, analyze and evaluate the real-time status of each construction device, and output the real-time status evaluation results of each construction device;
[0101] A command and dispatch module 104, built with a pre-constructed and trained command and dispatch model, configured to use the real-time status evaluation results output by the equipment status evaluation module and the real-time operation requirements of the current wind farm as input, and output the optimal command and dispatch strategy;
[0102] A deployment and monitoring module 105, configured to formulate a construction equipment deployment plan according to the command and dispatch strategy, and issue it to the wind farm site for execution, collect the real-time operation data of the equipment through the Internet of Things technology, perform real-time analysis, and monitor the working status of the equipment;
[0103] A model optimization module 106, configured to use the working status data of each construction device monitored in real time as feedback, input it into the equipment status evaluation model and the command and dispatch model, and iteratively optimize the parameters and structures of the equipment status evaluation model and the command and dispatch model.
[0104] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A method for construction command and dispatch of a wind farm, characterized in that, Including the steps: Obtain the construction data of historical wind farms, and construct an equipment status evaluation model and a command and dispatch model based on machine learning algorithms; Obtain the real-time operation information of each construction equipment in the current wind farm, perform real-time verification on the obtained real-time operation information, and obtain a verification data set; Use the verification data set as the input of the equipment status evaluation model, evaluate the real-time status of each construction equipment through the equipment status evaluation model, and obtain the real-time status evaluation result; Use the real-time status evaluation result and the real-time operation requirements as the input of the command and dispatch model, and output the command and dispatch strategy through the command and dispatch model; Deploy each construction equipment according to the command and dispatch strategy, and monitor the working status of each construction equipment in real time.
2. The wind farm construction command and dispatch method according to claim 1, characterized in that, The specific process of obtaining the construction data of historical wind farms and constructing a status evaluation model based on machine learning algorithms is as follows: Collect the construction equipment data corresponding to several historical wind farm projects, preprocess the collected construction equipment data to obtain an equipment data set, and divide the preprocessed data set into a training set, a validation set, and a test set; Based on machine learning algorithms, construct an initial equipment status evaluation model according to the characteristics and complexity of the preprocessed data set, and train the initial equipment status evaluation model through the training set; During the training process of the initial equipment status evaluation model, verify the training degree of the initial equipment status evaluation model through the validation set; Use the test set to evaluate the performance of the trained initial equipment status evaluation model to obtain the equipment status evaluation model.
3. The wind farm construction command and dispatch method according to claim 1, wherein, The specific process of constructing the command and dispatch model is as follows: Collect historical data related to wind farm construction scheduling, preprocess the collected historical data to obtain a scheduling data set; Based on machine learning algorithms, construct an initial command and dispatch model, and train the initial command and dispatch model through the scheduling data set; During the training process, use the actual scheduling cases and results in the historical data as the training set to train the initial command and dispatch model, and iteratively optimize the parameters and structure of the initial command and dispatch model; Evaluate the performance of the trained initial command and dispatch model through the cross-validation method to obtain the command and dispatch model.
4. The wind farm construction command and dispatch method according to claim 1, characterized in that, The specific process of obtaining the real-time operation information of each construction equipment in the current wind farm, performing real-time verification on the obtained real-time operation information, and obtaining a verification data set is as follows: Through Internet of Things technology, collect operation information in real time from the sensors installed on each construction equipment in the current wind farm; Perform primary verification on the collected real-time operation information, remove abnormal data caused by sensor failures or data transmission errors, and obtain a primary verification set; Through preset data verification rules, perform secondary verification on the data in the primary verification set. The secondary verification includes data range verification, data consistency verification, and data logical relationship verification; Organize the real-time operation information that has passed the secondary verification into a structured data set as the verification data set.
5. The wind farm construction command and dispatch method according to claim 1, wherein The specific process of using the verification data set as the input of the equipment status evaluation model, evaluating the real-time status of each construction equipment through the equipment status evaluation model, and obtaining the real-time status evaluation result is as follows: Input the verified data set after real-time verification into the constructed and trained device status evaluation model; Based on the input verified data set, the device status evaluation model analyzes and evaluates the real-time status of each construction device; During the evaluation process, the device status evaluation model comprehensively judges whether the device is in a normal state, whether there are potential faults or whether maintenance is required according to the device's historical operation data, current operation parameters, and environmental impact factors; Output the real-time status evaluation results of each construction device. The real-time status results include the device's health status, performance level, estimated remaining life, as well as the type of fault prediction and risk level.
6. The wind farm construction command and dispatch method according to claim 1, wherein, The specific process of using the real-time status evaluation results and real-time operation requirements as the input of the command and dispatch model and outputting the command and dispatch strategy through the command and dispatch model is as follows: Combine the real-time status evaluation results of each construction device output by the device status evaluation model with the real-time operation requirements of the current wind farm to obtain a comprehensive data set; Input the comprehensive data set into the command and dispatch model, and output the command and dispatch strategy through the command and dispatch model according to the real-time status of the device, operation requirements, and the overall construction plan of the wind farm.
7. The wind farm construction command and dispatch method according to claim 6, wherein, Set corresponding weights for the device's availability, operation efficiency, cost-effectiveness, and construction safety. Aiming at maximizing the device utilization rate, reducing the device failure risk and construction cost, perform iterative calculations through the command and dispatch model to obtain the optimal command and dispatch strategy.
8. The wind farm construction command and dispatch method according to claim 1, wherein, The specific process of deploying each construction device according to the command and dispatch strategy and monitoring the working status of each construction device in real time is as follows: According to the command and dispatch strategy output by the command and dispatch model, formulate a construction device deployment plan, which includes the specific operation location, operation task, operation time, and operation sequence of each device; Send the construction device deployment plan to the wind farm site to guide the construction team to deploy and dispatch each construction device according to the plan; After the device starts operating, continuously collect real-time operation data from the sensors installed on the device through Internet of Things technology; Perform real-time analysis on the collected real-time operation data, monitor the working status of the device, and judge whether there are abnormal fluctuations or deviations from the preset working range for each construction device.
9. The wind farm construction command and dispatch method according to claim 8, wherein, Use the real-time monitored device working status data as feedback and input it into the device status evaluation model and the command and dispatch model to optimize the model parameters of the device status evaluation model and the command and dispatch model.
10. A wind farm construction command and dispatch system for implementing the wind farm construction command and dispatch method according to any one of claims 1-9, characterized in that, Including: A data collection module for obtaining the construction data of the historical wind farm and the real-time operation information of each construction device in the current wind farm; A data verification module for performing real-time verification on the obtained real-time operation information to obtain a verified data set; A device status evaluation module with a constructed and trained device status evaluation model built in, which is used to take the verified data set output by the data verification module as input, analyze and evaluate the real-time status of each construction device, and output the real-time status evaluation results of each construction device; The command and dispatch module, which has a built and trained command and dispatch model inside, is used to take the real-time status evaluation results output by the equipment status evaluation module and the real-time operation requirements of the current wind farm as inputs, and output the optimal command and dispatch strategy; The deployment and monitoring module is used to formulate a construction equipment deployment plan according to the command and dispatch strategy, issue it to the wind farm site for execution, collect the real-time operation data of the equipment through Internet of Things technology, conduct real-time analysis, and monitor the working status of the equipment; The model optimization module is used to take the working status data of each construction equipment monitored in real time as feedback, input it into the equipment status evaluation model and the command and dispatch model, and iteratively optimize the parameters and structures of the equipment status evaluation model and the command and dispatch model.