Wind power station operation and maintenance system

Abstract

The invention belongs to the technical field of operation and maintenance systems, and particularly relates to a wind power station operation and maintenance system which comprises a data acquisition module, a data processing module, a fault analysis and tracing module, a new project construction assisting module and a design and assembly scheme updating prompting module. According to the system, through the synergistic effect of the fault analysis and tracing module, the new project construction auxiliary module and the design and assembly scheme updating prompt module, the operation and maintenance efficiency, the design reliability and the equipment service life of the wind power plant are remarkably improved. According to the system, fault analysis, design optimization and dynamic updating are deeply fused through a data-driven closed-loop management mechanism, and the limitation that a traditional wind power plant operation and maintenance system depends on artificial experience is broken through.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation and maintenance systems, and in particular relates to an operation and maintenance system for a wind farm station. Background Art

[0002] The operation and maintenance of existing wind farms face many challenges, mainly including the following aspects: 1. Wind farm equipment generates a large amount of operating data, but existing systems are insufficiently capable of analyzing and utilizing this data, failing to fully leverage the data's value to optimize equipment operation and maintenance strategies.

[0003] 2. New wind farm projects often face uncertainties in the design and construction stages. Due to the lack of analysis and utilization of historical fault data, they are prone to repeating past mistakes, leading to similar problems in new projects.

[0004] 3. Even if new designs, assembly schemes or new materials are available, the existing system lacks an effective mechanism to update and apply these improvements in a timely manner, resulting in the equipment being unable to benefit from the latest technological advances in a timely manner.

[0005] Existing technologies mainly monitor equipment status through sensors, use voiceprint data for fault warning, and predict faults based on multi-source data. However, these systems mostly focus on the implementation of a single function, lack the ability to comprehensively manage the entire process of wind farm operation and maintenance, and cannot solve the above-mentioned technical problems.

[0006] For example, the invention patent with patent publication number CN113836762B discloses a digital mirror simulation display system for wind turbines and wind farms, which focuses on achieving comprehensive display and optimized control of wind farms through digital twin technology.

[0007] The invention patent with patent announcement number CN114611424B discloses a large wind turbine blade life prediction method that integrates voiceprint data and CAE algorithms. Its core lies in life prediction and simulation analysis, and it also cannot solve the above technical problems. Summary of the Invention

[0008] In response to the above technical problems, the present invention provides a wind farm operation and maintenance system, which significantly improves the operation and maintenance efficiency, design reliability and equipment life of the wind farm through the synergy of a fault analysis and tracing module, a new project investment and construction auxiliary module, and a design and assembly plan update prompt module.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is: A wind farm operation and maintenance system, comprising: Data acquisition module: includes wind speed and direction sensors, wind turbine blade stress sensing devices, vibration sensors, microphone arrays, and salt spray concentration sensors, used to collect wind conditions, equipment stress, vibration, voiceprint data, and ambient salt spray concentration in real time; Data processing module: After pre-processing the collected data, the equipment health score is generated through a multi-parameter fusion algorithm; Fault Analysis and Traceability Module: This module uses a random forest model to distinguish internal and external factors of a fault, constructs a cause-and-effect diagram for internal factors, and traces the root causes of design defects, manufacturing errors, or component aging. It then generates improvement measures and verifies their effectiveness through digital twin simulation. New Project Investment and Construction Assistance Module: This module extracts geographical environmental parameters for new projects, matches them with cases with a similarity greater than 85% in the historical case library, generates design solutions for salt spray corrosion resistance and turbulence resistance through digital twin simulation, and outputs risk warnings. Design and assembly solution update prompt module: This module establishes a knowledge graph association between fault types and new solutions, performs accelerated life testing and voiceprint anomaly detection on updated solutions, and generates priority ranking and visual prompts based on fault severity and update cost. Maintenance management module: performs maintenance tasks and records blockchain evidence logs.

[0010] In the fault analysis and tracing module, the classification model input features of the random forest model are wind speed fluctuation rate, vibration main frequency amplitude, soundprint anomaly index and environmental salt spray concentration.

[0011] In the fault analysis and tracing module: the nodes in the causal graph associate the fault phenomenon with the root component, and the edge weight represents the strength of the causal relationship; the indicators of digital twin simulation verification include vibration drop rate, temperature stabilization time and voiceprint abnormality probability.

[0012] The new project investment and construction auxiliary module includes: Geographic environment parameter extraction unit, which analyzes the longitude and latitude, terrain roughness level and annual average salt spray concentration in planning documents; The case matching unit uses the BERT model to calculate the semantic similarity between the text description of the new project and the historical cases; The risk-resistant simulation unit injects extreme wind shear and grid harmonic parameters into the digital twin model to evaluate the design robustness.

[0013] The design and assembly plan update prompt module includes: Knowledge graph association unit: the fault type node connects the solution node and the applicable model node; Accelerated Life Test Unit: Cyclic loads are applied to updated components to monitor whether the stress concentration factor is less than 1.2.

[0014] The blockchain evidence log of the maintenance management module includes: Operation timestamp, unit number, maintenance type, replacement parts batch number and maintenance personnel digital signature; Automatic maintenance work order assignment rules: Prioritize matching the closest operation and maintenance team with matching skill tags.

[0015] In the data processing module: The multi-parameter fusion algorithm adopts a dynamic weighted model with the weight distribution as follows: vibration data 40%, voiceprint features 30%, stress data 20%, and environmental parameters 10%.

[0016] In the data processing module, the health score threshold is set as: ≥80 points for normal, 60-79 points for warning, and <60 points for emergency alarm.

[0017] In the data acquisition module: the wind speed and direction sensor is arranged on the top of the tower; the wind turbine blade stress sensing device is a fiber grating sensor.

[0018] Compared with the prior art, the present invention has the following beneficial effects: By combining the random forest classification model with multi-source data (vibration spectrum, soundprint characteristics, and environmental parameters), we can quickly distinguish between internal and external factors of the fault, avoiding ineffective maintenance caused by misjudgment.

[0019] Based on the cause-and-effect diagram analysis method, a fault root cause map is constructed to locate internal factors such as design defects and manufacturing errors. The effectiveness of improvement measures is verified by combining digital twin simulation to reduce the recurrence rate of similar faults.

[0020] Through geographic environment parameter matching and semantic analysis of historical case libraries, customized design solutions such as anti-turbulence and anti-corrosion are automatically recommended to reduce repetitive design errors.

[0021] Digital twin simulations are used to test design solutions in multiple scenarios, including extreme wind conditions and power grid fluctuations (e.g., tower corrosion simulation in salt spray environments). This allows for early identification of potential risks and generates reinforcement recommendations, reducing the failure rate of new projects. Historical failure data is leveraged to guide new project development (e.g., reusing salt spray-resistant coating solutions), fostering a mechanism for accumulated experience and iterative optimization.

[0022] Based on knowledge graph association technology, it automatically matches verified solutions, eliminating the subjectivity of manual screening. It generates priority sorting rules based on fault severity, update cost, and expected impact (e.g., formula: priority = 0.5 × fault score + 0.3 × cost factor + 0.2 × impact score).

[0023] This system uses a data-driven closed-loop management mechanism to deeply integrate fault analysis, design optimization and dynamic updates, breaking through the limitations of traditional wind farm operation and maintenance systems that rely on manual experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0026] like Figure 1 As shown, a wind farm operation and maintenance system includes a data acquisition module, a data processing module, a fault analysis and tracing module, a new project investment and construction auxiliary module, and a design and assembly plan update prompt module.

[0027] The data acquisition module includes a fiber grating sensor installed at the root of the wind turbine blade, a wind speed and direction sensor installed at the top of the tower, a microphone array set in the cabin, and the ambient salt spray concentration is collected by a salt spray concentration sensor.

[0028] Further, the implementation of the fault analysis and tracing module Input maintenance record text (such as abnormal gearbox noise, abnormal oil temperature) and sensor data before the fault occurs (including vibration spectrum, soundprint time-frequency graph, and ambient salt spray concentration).

[0029] The random forest algorithm is used, and the input features include wind speed fluctuation rate, vibration main frequency amplitude, soundprint anomaly index, and salt spray concentration.

[0030] A fault causal relationship graph is constructed in the graph database. The nodes represent the fault type (such as bearing wear), components (gearbox) and design parameters (such as material heat treatment process), and the edges represent the causal relationship (such as material defect → stress concentration → crack extension). The edge weights are obtained through historical fault statistics.

[0031] A digital twin model of the gearbox is established, fault parameters (such as tooth surface roughness) are input, the operating status after the improvement plan (replacing carburized gears) is simulated, and the vibration amplitude, oil temperature and sound pattern abnormality probability indicators are output.

[0032] Compare key parameters before and after improvement, such as the vibration amplitude, oil temperature, and probability of sound pattern abnormality of a certain gearbox.

[0033] Example 1: A motor overheated in a turbine at an offshore wind farm. The system identified the problem as salt spray intrusion caused by aging of the seal ring. Cause-and-effect diagram analysis traced the root cause to insufficient corrosion resistance of the material. The corrective measure was to replace the fluororubber seal ring.

[0034] Furthermore, the implementation of the auxiliary module for new project investment and construction Extract geographic environment parameters and generate description text.

[0035] Match the newly created project description with the project text in the historical case library, and select cases with a similarity greater than 85% as references.

[0036] Import historical fault data that matches the case (e.g., high tower resonance frequency) and set simulation boundary conditions (e.g., turbulence intensity).

[0037] Compared with tower reinforcement solutions (increasing wall thickness, adding constrained damping layers, and changing cross-sectional shapes), simulation results show that adding constrained damping layers can reduce the probability of resonance.

[0038] Example 2: In the early stages of planning a certain mountain wind farm, the system matched a historical case (complex terrain leading to severe wake effect), and the system recommended a solution combining staggered arrangement and adaptive yaw.

[0039] Further, the implementation of the design and assembly plan update prompt module Build a knowledge graph with nodes including fault type (such as loose bolts), solutions (such as smart gaskets), and applicable models.

[0040] Find connected solutions and prioritize those validated through virtual testing.

[0041] Perform accelerated life tests on the updated (verified) solution to monitor whether its stress concentration factor exceeds the threshold.

[0042] After the update, the unit's trouble-free time and health score are counted.

[0043] Example 3: The vibration of the units in a certain wind farm exceeds the standard due to bearing wear. Coated bearings are recommended.

[0044] Furthermore, an encryption algorithm is adopted and the encryption key is dynamically updated; the maintenance operation log is recorded in the blockchain to ensure that the log cannot be tampered with.

[0045] Only the preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the above embodiments.

Claims

1. A wind farm operation and maintenance system, characterized in that: include: Data acquisition module: includes wind speed and direction sensors, wind turbine blade stress sensing devices, vibration sensors, microphone arrays, and salt spray concentration sensors, used to collect wind conditions, equipment stress, vibration, voiceprint data, and ambient salt spray concentration in real time; Data processing module: After pre-processing the collected data, the equipment health score is generated through a multi-parameter fusion algorithm; Fault Analysis and Traceability Module: This module uses a random forest model to distinguish internal and external factors of a fault, constructs a cause-and-effect diagram for internal factors, and traces the root causes of design defects, manufacturing errors, or component aging. It then generates improvement measures and verifies their effectiveness through digital twin simulation. New Project Investment and Construction Assistance Module: This module extracts geographical environmental parameters for new projects, matches them with cases with a similarity greater than 85% in the historical case library, generates design solutions for salt spray corrosion resistance and turbulence resistance through digital twin simulation, and outputs risk warnings. Design and assembly solution update prompt module: This module establishes a knowledge graph association between fault types and new solutions, performs accelerated life testing and voiceprint anomaly detection on updated solutions, and generates priority ranking and visual prompts based on fault severity and update cost. Maintenance management module: performs maintenance tasks and records blockchain evidence logs.

2. A wind farm operation and maintenance system according to claim 1, characterized in that: In the fault analysis and tracing module, the classification model input features of the random forest model are wind speed fluctuation rate, vibration main frequency amplitude, soundprint anomaly index and environmental salt spray concentration.

3. A wind farm operation and maintenance system according to claim 1, characterized in that: In the fault analysis and tracing module: the nodes in the causal graph associate the fault phenomenon with the root component, and the edge weight represents the strength of the causal relationship; the indicators of digital twin simulation verification include vibration drop rate, temperature stabilization time and voiceprint abnormality probability.

4. The wind farm operation and maintenance system according to claim 1, characterized in that: The new project investment and construction auxiliary module includes: Geographic environment parameter extraction unit, which analyzes the longitude and latitude, terrain roughness level and annual average salt spray concentration in planning documents; The case matching unit uses the BERT model to calculate the semantic similarity between the text description of the new project and the historical cases; The risk-resistant simulation unit injects extreme wind shear and grid harmonic parameters into the digital twin model to evaluate the design robustness.

5. The wind farm operation and maintenance system according to claim 1, characterized in that: The design and assembly plan update prompt module includes: Knowledge graph association unit: the fault type node connects the solution node and the applicable model node; Accelerated Life Test Unit: Cyclic loads are applied to updated components to monitor whether the stress concentration factor is less than 1.

2.

6. A wind farm operation and maintenance system according to claim 1, characterized in that: The blockchain evidence log of the maintenance management module includes: Operation timestamp, unit number, maintenance type, replacement parts batch number and maintenance personnel digital signature; Automatic maintenance work order assignment rules: Prioritize matching the closest operation and maintenance team with matching skill tags.

7. A wind farm operation and maintenance system according to claim 1, characterized in that: In the data processing module: The multi-parameter fusion algorithm adopts a dynamic weighted model with the weight distribution as follows: vibration data 40%, voiceprint features 30%, stress data 20%, and environmental parameters 10%.

8. A wind farm operation and maintenance system according to claim 1 or 7, characterized in that: In the data processing module, the health score threshold is set as: ≥80 points for normal, 60-79 points for warning, and <60 points for emergency alarm.

9. The wind farm operation and maintenance system according to claim 1, characterized in that: In the data acquisition module: the wind speed and direction sensor is arranged on the top of the tower; the wind turbine blade stress sensing device is a fiber grating sensor.

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