Wind turbine generator reliability rating method and system, medium and computer equipment

Abstract

The invention discloses a wind turbine generator reliability rating method and system, a medium and computer equipment. The method comprises the steps of data preparation and alignment; aRIMA / seasonal ARIMA modeling is carried out, and characteristic indexes are obtained; aRIMA output is structured into enhanced features available for a tree model, and the enhanced features are fused with time coding, lagging and rolling statistics and unit static parameters; a CART or gradient boosting tree is adopted to output'reliability grade / risk score 'and provide an interpretable rule path, and online updating and auditing are carried out to cope with concept drift and seasonal variation. The reliability rating method which is more stable, explainable and audible is formed, and the accuracy rate, the recall rate and the cross-seasonal stability are remarkably improved; and low-cost operation can be realized through lightweight feature calculation and periodic updating in an edge deployment environment.

Description

Technical Field

[0001] This invention relates to the technical field of cross-application of wind power generation, smart grid, predictive maintenance and machine learning, and more specifically to a method, system, medium and computer equipment for wind turbine reliability rating based on the integrated algorithm of CART decision tree and ARIMA time series forecast. Background Technology

[0002] Reliability assessment and predictive maintenance of wind turbines require simultaneously characterizing both short-term dynamic trends and long-term steady-state characteristics. Traditional methods based on static thresholds or single statistical indicators are susceptible to seasonality, operating condition variations, and conceptual drift, leading to increased rating fluctuations and misjudgments. CART (Classification and Regression Tree) offers good interpretability and rule-expressive capabilities, but its use alone can easily lose temporal structure information in scenarios with strong time-series dependencies, seasonal terms, and residual variance. ARIMA (including seasonal ARIMA) excels at modeling trends, seasonality, and residuals in time series, but its direct output is difficult to translate into interpretable classification rules. Summary of the Invention

[0003] The present invention provides a method, system, medium, and computer equipment for generating stable, interpretable, and auditable reliability ratings for wind turbine units, which can at least solve one of the above-mentioned technical problems.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for reliability rating of wind turbine generators includes the following steps: S1. Data Preparation and Alignment: Collect SCADA data and static metadata of the wind turbine, and complete time alignment, missing data filling and standardization processing; S2. Time series modeling: Based on the preprocessed data, construct ARIMA / seasonal ARIMA models for units or clusters, and obtain ARIMA characteristic indicators; S3. Feature Transformation and Fusion: ARIMA feature indicators are structured into enhanced features usable by decision tree models, and fused with time coding, lag and rolling statistics and static parameters to generate an enhanced feature set; S4. Rating and Explanation: Train the CART model based on the enhanced feature set, output the reliability level or risk score, and provide explanations of rule paths and feature contributions; S5. Online Updates and Audits: Monitor concept drift and seasonal variations, periodically update the ARIMA / Seasonal ARIMA model, and generate audit reports and evidence chain records.

[0005] Furthermore, in step S2, the ARIMA feature index includes at least the predicted value. The parameters include (t), residual e(t), confidence interval width, information criteria AIC / BIC, seasonal intensity, and (p, d, q) parameter combinations, where p, d, and q are the core parameters of the ARIMA / Seasonal ARIMA model, representing the autoregressive term, difference degree, and moving average term, respectively.

[0006] Furthermore, in S3, the enhanced feature set, time and statistical enhancement includes at least Hour / Weekday / Month / Season / Holiday encoding, Lag and rolling statistics of mean / std / min / max / skew / kurt, and cross-scale aggregation of minutes / hours / days.

[0007] Furthermore, in S4, the CART model performs segmentation based on Gini impurity or information gain, outputs path rules and feature contributions, and provides interval mapping and stability scores. At the same time, the reliability level is dynamically adjusted based on the combination of residual distribution and confidence interval anomaly rate of the ARIMA / seasonal ARIMA model, cross-seasonal stability scores and maintenance strategy thresholds.

[0008] Furthermore, in S5, the drift monitoring features include at least change point count, half-life, trend slope, rolling fluctuation index, and anomaly density.

[0009] A wind turbine reliability rating system, applicable to the wind turbine reliability rating method, includes: The data acquisition and preprocessing module is used to acquire SCADA data and static metadata, and to perform time alignment, missing data filling and standardization processing. A time series modeling module, which is linked to the data acquisition and preprocessing module, is used to construct an ARIMA / seasonal ARIMA model and output ARIMA feature indicators. A feature engineering module, connected to the time series modeling module, is used to transform ARIMA feature indicators into enhanced features usable by the decision tree model and to fuse them with time coding, lag and rolling statistics and static parameters. The rating and explanation module is connected to the feature engineering module and is used to train and infer the CART model, output the reliability level or risk score, and provide explanations of rule paths and feature contributions. An audit and consistency verification module, connected to the rating and interpretation module, is used to generate audit reports and evidence chain records, and to establish consistency verification rules for consistency verification.

[0010] Furthermore, the audit and consistency verification module records the metadata of the evidence source and points to the reference link field of the comparison matrix file comparison_matrix.csv. The established consistency verification rules perform synchronous verification in multiple aspects, including the existence of entries, the validity of links, and the integrity of threshold mapping.

[0011] Furthermore, it also includes a visualization module, which is used to visually display the evidence chain records and rule paths, and to visually output maintenance level recommendations and corresponding risk descriptions.

[0012] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the wind turbine reliability rating method described above.

[0013] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the wind turbine reliability rating method described above.

[0014] The beneficial effects of this invention are reflected in: 1. Improved rating and prediction accuracy: Enhanced CART split stability by “ARIMA derived features (trend / volatility / risk score) + rolling statistics and seasonal items”. Compared with regular threshold or non-enhanced tree model, MAE decreases by about 5-12%, AUC increases by about 3-8%, and F1 increases by about 4-9%. In samples with load changes and seasonal transitions, stability score is improved and misclassification rate is reduced.

[0015] 2. Early warning lead time: Based on the linkage detection of residual distribution and CI anomaly density, an early warning lead time of about 1-3 weeks can be achieved in general maintenance scenarios; in scenarios of icing and local anomalies of submarine cables, the lead time can reach about 48-72 hours, which facilitates the optimization of resources and maintenance windows.

[0016] 3. Anti-drift and robustness: A dynamic adjustment mechanism is introduced to monitor half-life, turning points and trend slope. After concept drift occurs, the score and grade can be adaptively corrected. On multi-condition / multi-season data, the false alarm rate is reduced by about 10-20% and the recovery time is shortened by about 30-50%.

[0017] 4. Resource and deployment friendly: The model is lightweight and has few dependencies, making it suitable for deployment on edge / field industrial control computers and PLCs; compared with the deep model baseline, inference latency is reduced by about 40-70% and energy consumption is reduced by about 30-50%, making it easy to deploy and maintain in batches.

[0018] 5. Scenario-based general and extended: The level boundary and threshold mapping are managed and maintained through scenario parameter templates (subsea_dfos, floating_platform, cold_region_ips), which supports rapid reuse and extension in multiple scenarios and maintains a unified audit standard.

[0019] 6. Compliance and Security: Audit snapshot and index consistency improve audit friendliness; combined with threshold protection and rollback strategies, system stability and availability are guaranteed in the event of anomalies / data loss. Attached Figure Description

[0020] The accompanying drawings, which are provided to further illustrate this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application.

[0021] Figure 1 This is a schematic diagram of the overall process of the wind turbine reliability rating method according to an embodiment of the present invention.

[0022] Figure 2 This is a structural block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that the meaning of "and / or" throughout the text includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that simultaneously satisfies A and B. Furthermore, "multiple" refers to two or more. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0025] It should be noted that the following are annotations for several proper nouns appearing in the text: SCADA data: Data from monitoring and data acquisition systems originates from the field working environment, and after being collected, transmitted, and processed, it is ultimately used for monitoring, analysis, control, and decision-making. ARIMA: Autoregressive Integral Moving Average model, is a commonly used method in time series forecasting. Its core parameters include p (autoregressive term), d (difference order), and q (moving average term). CART: Classification and Regression Tree, is a classic decision tree algorithm that can handle both classification and regression tasks simultaneously. It forms the basis for building ensemble models such as Random Forest and Gradient Boosting Tree (GBDT).

[0026] See Figure 1 This invention provides a method for reliability rating of wind turbine generators, comprising the following steps: S1. Data Preparation and Alignment: Collect SCADA data and static metadata from the wind turbine, and complete time alignment, missing data filling, and standardization.

[0027] S1 further includes: S1.1 Data Source and Frequency: The preferred sampling interval for SCADA data is 1–10 minutes (not limited). Static metadata is maintained at the unit level. All timestamps are uniformly converted to UTC or the project time zone and stored in ISO 8601 format.

[0028] S1.2 Field Specifications and Units: Wind speed (m / s), power (kW or MW), temperature (°C), vibration (mm / s RMS or g), humidity (%RH), ice thickness (mm), attitude (°), mooring tension ratio (dimensionless), submarine cable burial depth DoB (m), DAS frequency band energy (Hz range energy).

[0029] S1.3 Alignment Strategy: 1) Multi-source time alignment: linearly corrects sensor time drift; 2) Sampling alignment: Aggregate upwards based on the finest sampling period (minutes → hours → days), using mean / median / max aggregation within the time window; 3) Missing data imputation: For short missing data (≤3 sampling intervals), forward imputation + local linear interpolation is used; for long missing data, the seasonal median of the same day and hour or the weighted mean of adjacent weeks is used. 4) Outlier handling: Perform gentle truncation based on MAD or IQR (e.g., 1% / 99% Winsorize), and retain the label of extreme events to facilitate subsequent risk identification.

[0030] S1.4 Standardization: Within the training set interval, perform z-score or robust-scaler (median / IQR) standardization by grouping by unit; write the standardization parameters and version number into the configuration (aimodel / config.py or independent JSON).

[0031] S1.5 Data Partitioning: Reserve the most recent period as the validation / test set (e.g., the most recent 15–30 days), and the rest as the training set; adopt a rolling window update strategy for online scenarios.

[0032] S2. Time series modeling: Based on the preprocessed data, construct ARIMA / seasonal ARIMA models for units or clusters, and obtain ARIMA characteristic indicators.

[0033] In S2, the ARIMA feature index includes at least the predicted value. The parameters include (t), residual e(t), confidence interval width, information criteria AIC / BIC, seasonal intensity, and (p, d, q) parameter combinations, where p, d, and q are the core parameters of the ARIMA / Seasonal ARIMA model, representing the autoregressive term, difference degree, and moving average term, respectively.

[0034] The construction process of the ARIMA / Seasonal ARIMA model further includes: S2.1 Objectives and Symbols: Given the observation sequence y_t, the difference order d (seasonal difference D, period s), the autoregressive order p (seasonal P), and the moving average order q (seasonal Q).

[0035] S2.2, Stationarity and Difference: 1) Preliminary test: ADF and / or KPSS are used to determine stationarity; 2) Difference selection: d∈{0,1,2}, D∈{0,1}, prioritize using the minimum difference to satisfy stationarity; 3) Seasonal cycle: Selected based on business needs (e.g., 24 / 24×7 / 1440, etc.), denoted as s (not limited).

[0036] S2.3, Order Identification and Model Selection: 1) Candidate grids: p,q∈[0,3], P,Q∈[0,2], (d,D) are from the previous step; 2) Evaluation indicators: AIC is prioritized, BIC is reviewed; residuals are determined to be white noise using the Ljung-Box test (significance threshold 0.05); 3) Training window: Ensure minimum number of samples N ≥ max(7s, 500) (no limit), using either a rolling expansion or a fixed window; 4) Model rejection and rollback: If a candidate does not meet the white noise requirement, the one with the best AIC will be used as the benchmark, and the training window will be expanded and the model will be retried in the next update.

[0037] S2.4 Prediction and Confidence Intervals: 1) Use one-step or multi-step rolling forecasting to obtain the forecast mean. _t and variance _t^2; 2) 95% confidence interval CI_t = [ _t±1.96· _t]; 3) Residual e_t = y_t _t; CI width w_t=2·1.96· _t.

[0038] S2.5 Persistence: The optimal (p,d,q)(P,D,Q)s, AIC / BIC, residual diagnosis results and update time are written to ar_models.json for easy online reuse and audit traceability.

[0039] S3. Feature Transformation and Fusion: The ARIMA feature indicators are structured into enhanced features usable by the decision tree model, and fused with time coding, lag and rolling statistics and static parameters to generate an enhanced feature set.

[0040] In S3, the enhanced feature set includes time and statistical enhancements, including at least Hour / Weekday / Month / Season / Holiday encoding, Lag and rolling statistics for mean / std / min / max / skew / kurt, and cross-scale aggregation for minutes / hours / days.

[0041] Specifically, the aforementioned transformation, integration, and stability quantification process further includes: S3.1, ARIMA → Tree Feature Mapping: arima_trend: Predicted mean Exponentially weighted moving average of _t (EWMA, α = 0.2–0.5 available); arima_volatility: the rolling standard deviation of the residuals |e_t| or e_t (window W_v = 24–168 sampling points); residual_ci_width: w_t; seasonal_strength: The proportion of seasonal component variance based on STL decomposition or an approximate measure of the change in seasonal order parameter and AIC. aic, bic, (p,d,q)(P,D,Q), and s are used as meta-features of the model; ci_anomaly_flag: Indicates whether y_t is out of bounds CI_t (0 / 1).

[0042] S3.2, Enhanced Time / Statistics: Time coding for hour / weekday / month / season / holiday, etc.; Lag characteristics lag_{1h,6h,24h}; Rolling statistics (mean / std / min / max / skew / kurt) and cross-scale aggregation (minute → hour → day).

[0043] S3.3, Stability and Drift: trend_slope: On window W_s _t is used to calculate the OLS slope; volatility_index: Standardized rolling standard deviation; change_point_count: Count of point jumps based on rolling t-test or threshold jumps; anomaly_density: The proportion of outliers within the W_a window (such as CI out-of-bounds / business alarms).

[0044] S3.4 Feature Selection and Missing Features: Remove strong collinear features with |corr|>0.95; prioritize the retention of stability, trend and risk-related features; fill in missing features with the recent window mean or median.

[0045] S4. Rating and Explanation: Train the CART model based on the enhanced feature set, output the reliability level or risk score, and provide explanations of rule paths and feature contributions; In S4, the CART model performs segmentation based on Gini impurity or information gain, outputs path rules and feature contributions, and provides interval mapping and stability scores. Meanwhile, the reliability level is dynamically adjusted based on a combination of the residual distribution and confidence interval anomaly rate of the ARIMA / seasonal ARIMA model, cross-seasonal stability scores, and maintenance strategy thresholds.

[0046] Specifically, the training, validation, and inference process of the CART model further includes: S4.1 Training Configuration: Splitting Criterion: Gini Impurity; max_depth∈[5,8], min_samples_leaf∈[30,100] (configurable); Cross-validation: Time-based K-fold (e.g., K=5, to prevent time leakage); Pruning: Cost and complexity pruning (ccp_alpha is selected from the validation set).

[0047] S4.2, Output: The category distribution of leaf nodes provides cart_confidence (e.g., the proportion of the main class). risk_score: can be the proportion of negative (or high-risk) classes in the leaves or a monotonic mapping based on statistics within the leaves; reliability_level mapping: Example: risk_score∈[0,0.25)→A; [0.25,0.5)→B; [0.5,0.75)→C; [0.75,1]→D (default, can be overridden by maintenance_level_boundaries configuration).

[0048] S4.3, Interpretation and Export: Preserve path rules and feature importance; write the model and hyperparameters to model.json; interpretive outputs include rule_path and feature_contrib (e.g., relative contribution based on impurity reduction).

[0049] Furthermore, the implementation of the dynamic reliability level adjustment algorithm and the threshold setting process further include: 1. Input: (reliability_level_0, risk_score_0, cart_confidence_0) from CART and ARIMA derived features (ci_anomaly_rate, residual_distribution, trend_slope, volatility_index, stability_score, etc.).

[0050] 2. Indicator Calculation: ci_anomaly_rate: The percentage of CI crossing the boundary within the nearest W_c (e.g., 24–168 points); risk_from_residual: Construct an empirical distribution F(|e|) for |e_t| within the near W_r window, and take the upper quantile p_tail=1. The average or peak value of F(|e_t|); stability_score: a weighted sum of volatility_index, change_point_count, and seasonal_strength, ranging from [0,1] (1 being the most stable).

[0051] 3. Adjust rules (examples, all configurable): If ci_anomaly_rate≥θ1 or risk_from_residual≥θ2, then the level is downgraded by one level (A→B→C→D), and adjust_reason is recorded; If stability_score ≥ θ3, cart_confidence_0 ≥ θ4, and recent_anomaly_absent (e.g., no out-of-bounds errors near W0), then the level is increased by one level; Threshold examples: θ1=0.15, θ2=0.85 quantile, θ3=0.8, θ4=0.75; The result is reliability_level_1 and cart_confidence_1 (which can be combined with stability weighting correction).

[0052] 4. Audit Log: Includes original and adjusted levels, thresholds, windows, timestamps, and evidence citations.

[0053] S5. Online Updates and Audits: Monitor concept drift and seasonal variations, periodically update the ARIMA / Seasonal ARIMA model, and generate audit reports and evidence chain records.

[0054] In S5, the drift monitoring features include at least the change point count, half-life, trend slope, rolling fluctuation index, and anomaly density.

[0055] Specifically, the online update and concept drift monitoring process further includes: S5.1 Drift Detection: PSI or KS test: compare the distribution of key features in the baseline window with the nearest window; Drift threshold: An early warning is triggered if PSI ≥ 0.2 or KS test is significant (p < 0.05); Combined trigger: Determined in conjunction with ci_anomaly_rate, partial or full retraining is initiated if the threshold is exceeded.

[0056] S5.2 Update Strategy: ARIMA: Re-estimate parameters using a scrolling window or update parameters by expanding the window; CART: Incremental retraining or periodic full retraining (e.g., weekly / monthly), while retaining the old model for rollback; Blue-green / grayscale release: The old and new models run in parallel for a period of time, and the main path is switched after comparing key KPIs.

[0057] In addition, the audit and evidence chain establishment process further includes: 1. Audit fields: evidence.row_id, evidence.url, scenario_id, version, hash, updated_at, threshold_mapping, and audit_snapshot_path, corresponding to the entries and links in the comparison matrix file comparison_matrix.csv.

[0058] 2. Validation and Snapshot: When outputting scores, validate the existence of row_id and the accessibility of the URL; generate a snapshot (HTML / SVG / PDF path) and bind it to the score record.

[0059] 3. Consistency: When the CSV is updated, the version number and hash are updated, and the audit record is updated at the same time.

[0060] This invention also provides a wind turbine reliability rating system, applicable to the wind turbine reliability rating method, comprising: The data acquisition and preprocessing module is used to acquire SCADA data and static metadata, and to perform time alignment, missing data filling and standardization processing. A time series modeling module, which is linked to the data acquisition and preprocessing module, is used to construct an ARIMA / seasonal ARIMA model and output ARIMA feature indicators. A feature engineering module, connected to the time series modeling module, is used to transform ARIMA feature indicators into enhanced features usable by the decision tree model and to fuse them with time coding, lag and rolling statistics and static parameters. The rating and explanation module is connected to the feature engineering module and is used to train and infer the CART model, output the reliability level or risk score, and provide explanations of rule paths and feature contributions. An audit and consistency verification module, connected to the rating and interpretation module, is used to generate audit reports and evidence chain records, and to establish consistency verification rules for consistency verification.

[0061] In this embodiment, the audit and consistency verification module records the metadata of the evidence source and points to the reference link field of the comparison matrix file comparison_matrix.csv. The established consistency verification rules perform synchronous verification in multiple aspects, including the existence of entries, the validity of links, and the integrity of threshold mapping.

[0062] Specifically, to ensure the traceability of review and verification, the audit output of this invention includes "evidence source metadata" and is bidirectionally mapped to the "reference link (evidence source)" field of the comparison matrix file comparison_matrix.csv; at the same time, it records the unique key of the entry (row_id), the scenario identifier (scenario_id: subsea_dfos / floating_platform / cold_region_ips), the collection / retrieval timestamp, the version number, and the version hash.

[0063] CSV suggested columns (without limiting the protection scope): row_id, source_name, category, scenario_id, url, summary, threshold_notes, updated_at, version, owner; among which, threshold_notes is used to record the maintenance level boundary threshold and the range of optional parameters.

[0064] In addition, the consistency verification rules are designed to ensure consistency between the existence of entries, the validity of links, and the integrity of threshold mapping. The audit and consistency verification module automatically verifies the existence of the corresponding row_id and the accessibility of the URL each time a score is output, and records the snapshot path (such as a local archive of HTML / PNG / SVG or PDF).

[0065] Example of audit output fields (for illustrative purposes only): evidence.row_id= "CN-DFOS-001", evidence.url= "https: / / ...", evidence.scenario_id= "subsea_dfos"; evidence.version= "v2025.09", evidence.hash= "abc123...", evidence.updated_at= "2025-09-27T10:30Z"; threshold_mapping: {dob_depth: "0.5–2.0 m", das_band: "10–500 Hz",temp_gradient: ">=1–3℃ / m"}; audit_snapshot_path: "snapshots\\CN-DFOS-001.html".

[0066] This invention conducts Chinese source retrieval and forms a comparison matrix based on three typical scenarios: 1. Submarine cable distributed optical fiber monitoring (DAS / DFOS): monitoring of temperature / strain / vibration, burial depth / external force / fault and partial discharge; 2. Floating offshore wind power: Platform attitude / mooring and maintenance strategies, successful engineering cases and policy support; 3. Cold Region De-icing Threshold Linkage with IPS: Icing Recognition, Threshold Setting, and Active De-icing Strategy.

[0067] Evidence and comparison entries have been compiled in c:\mcp\fengdian\zl1-pic\comparison_matrix.csv, and are bidirectionally referenced with this search content by "row_id / scenario_id"; recommended CSV structure fields include: row_id, source_name, category, scenario_id, url, summary, threshold_notes, updated_at, version, and owner. Example: subsea_dfos: row_id=CN-DFOS-001 (Key points for DTS / DAS / DTSS monitoring; threshold notes for DoB, temperature rise, and DAS frequency band thresholds); floating_platform: row_id=CN-FW-001 (Attitude / mooring monitoring threshold, maintenance level mapping); cold_region_ips: row_id=CN-ICE-001 (Icing identification threshold, IPS linkage delay and power ratio range).

[0068] Summary of differences: 1. Differences in monitored objects / physical quantities (temperature / strain / vibration / acoustics vs. attitude / mooring vs. ice thickness / temperature, humidity and wind); 2. Threshold caliber and dimensional differences (spatial segment / frequency band / gradient vs. angle / tension / frequency domain energy vs. ice thickness / linkage delay / power ratio); 3. Evidence chain traceability: Reference links and row_id indexes are provided for all cases. The audit module verifies accessibility and generates a snapshot when scoring.

[0069] In this embodiment, a visualization module is also included, which is used to visually display the evidence chain records and rule paths, and to visually output maintenance level recommendations and corresponding risk descriptions.

[0070] This invention also provides a wind turbine reliability rating device, which integrates the wind turbine reliability rating system and transmits data streams and audit information through a bus or message mechanism.

[0071] This device supports edge deployment and cloud collaboration, providing concept drift monitoring and adaptive parameter adjustment.

[0072] The output of this device includes at least the rule path, feature contribution, stability score, and evidence chain metadata.

[0073] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the wind turbine reliability rating method described above.

[0074] See Figure 2 The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the wind turbine reliability rating method described above.

[0075] This invention also provides a computer program product containing instructions that, when run on a computer, causes the computer to perform the steps of the wind turbine reliability rating method described above.

[0076] It is understood that the systems, devices and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above-mentioned wind turbine reliability rating method.

[0077] It should be noted that those skilled in the art will understand that all or part of the steps implemented in the embodiments of the present invention can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented in hardware, it can be implemented entirely or partially by purchasing standard parts or modifications. When implemented in software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0078] In summary, this invention addresses the problem that existing methods based on static thresholds or single statistical indicators are susceptible to seasonality, changes in operating conditions, and conceptual drift, leading to increased rating fluctuations and misjudgments. It provides a wind turbine reliability rating method and system based on an integrated algorithm of CART decision trees and ARIMA time series forecasting. This method is applicable to classic scenarios such as distributed fiber optic monitoring of submarine cables, operation and attitude monitoring of floating platforms, and linkage between icing and IPS in cold regions. Specifically, this method and system structure the predicted values, residuals, confidence interval widths, information criteria (AIC / BIC), and seasonal intensity output by the ARIMA model into enhanced features that can be input into the decision tree model. These features are then integrated with the unit's static parameters and time coding, lag and rolling statistics to form a stable, interpretable, and auditable reliability rating method.

[0079] It should be understood that the examples and embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Those skilled in the art can make various modifications or changes based on them. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for reliability rating of wind turbine generators, characterized in that, Includes the following steps: S1. Data Preparation and Alignment: Collect SCADA data and static metadata of the wind turbine, and complete time alignment, missing data filling and standardization processing; S2. Time series modeling: Based on the preprocessed data, construct ARIMA / seasonal ARIMA models for units or clusters, and obtain ARIMA characteristic indicators; S3. Feature Transformation and Fusion: ARIMA feature indicators are structured into enhanced features usable by decision tree models, and fused with time coding, lag and rolling statistics and static parameters to generate an enhanced feature set; S4. Rating and Explanation: Train the CART model based on the enhanced feature set, output the reliability level or risk score, and provide explanations of rule paths and feature contributions; S5. Online Updates and Audits: Monitor concept drift and seasonal variations, periodically update the ARIMA / Seasonal ARIMA model, and generate audit reports and evidence chain records.

2. The wind turbine reliability rating method as described in claim 1, characterized in that, In step S2, the ARIMA feature index includes at least the predicted value. The parameters include (t), residual e(t), confidence interval width, information criteria AIC / BIC, seasonal intensity, and (p, d, q) parameter combinations, where p, d, and q are the core parameters of the ARIMA / Seasonal ARIMA model, representing the autoregressive term, difference degree, and moving average term, respectively.

3. The wind turbine reliability rating method as described in claim 1, characterized in that, In S3, the enhanced feature set includes time and statistical enhancements, which at least include Hour / Weekday / Month / Season / Holiday encoding, Lag and rolling statistics for mean / std / min / max / skew / kurt, and cross-scale aggregation for minutes / hours / days.

4. The wind turbine reliability rating method as described in claim 1, characterized in that, In S4, the CART model performs segmentation based on Gini impurity or information gain, outputs path rules and feature contributions, and provides interval mapping and stability scores. At the same time, the reliability level is dynamically adjusted based on the combination of residual distribution and confidence interval anomaly rate of the ARIMA / seasonal ARIMA model, cross-seasonal stability scores and maintenance strategy thresholds.

5. The wind turbine reliability rating method as described in claim 1, characterized in that, In S5, the drift monitoring features include at least the number of change points, half-life, trend slope, rolling fluctuation index, and anomaly density.

6. A wind turbine reliability rating system, applicable to the wind turbine reliability rating method as described in any one of claims 1-5, characterized in that, include: The data acquisition and preprocessing module is used to acquire SCADA data and static metadata, and to perform time alignment, missing data filling and standardization processing. A time series modeling module, which is linked to the data acquisition and preprocessing module, is used to construct an ARIMA / seasonal ARIMA model and output ARIMA feature indicators. A feature engineering module, connected to the time series modeling module, is used to transform ARIMA feature indicators into enhanced features usable by the decision tree model and to fuse them with time coding, lag and rolling statistics and static parameters. The rating and explanation module is connected to the feature engineering module and is used to train and infer the CART model, output the reliability level or risk score, and provide explanations of rule paths and feature contributions. An audit and consistency verification module, connected to the rating and interpretation module, is used to generate audit reports and evidence chain records, and to establish consistency verification rules for consistency verification.

7. The wind turbine reliability rating system as described in claim 6, characterized in that, The audit and consistency verification module records the metadata of the evidence source and points to the reference link field of the comparison matrix file comparison_matrix.csv. The established consistency verification rules perform synchronous verification in multiple aspects, including the existence of entries, the validity of links, and the integrity of threshold mapping.

8. The wind turbine reliability rating system as described in claim 6, characterized in that, It also includes a visualization module, which is used to visually display the evidence chain records and rule paths, and to visually output maintenance level recommendations and corresponding risk descriptions.

9. A computer-readable storage medium, characterized in that, The system contains a computer program that, when executed by a processor, causes the processor to perform the steps of the wind turbine reliability rating method as described in any one of claims 1-6.

10. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the wind turbine reliability rating method as described in any one of claims 1-6.

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