What this category of data looks like
Wind power investment research data mainly comes from wind turbine SCADA monitoring systems, ground meteorological observatories, power grid dispatching ledgers, project feasibility study documents, and operation and maintenance logs. SCADA systems collect real-time operating data for individual wind turbines, updated once per minute. Meteorological data is updated hourly. Project documents are static structured reports and text descriptions, with updates triggered by project progress.
Single operating data entries include fields such as active power (unit: MW), hub wind speed (unit: m/s), blade pitch angle (unit: °), nacelle temperature (unit: ℃), and grid connection status (boolean value). Time-series data is stored in Parquet format, while project documents are mostly in PDF or structured CSV format.
What constraints do these characteristics impose on the database and operations link
The multi-source heterogeneity and differences in update frequencies of wind power data require the database to support both time-series indexing and semi-structured text parsing. SCADA data with high real-time requirements must be stored separately in a time-series database to avoid competing for query resources with static document data.
Wind power data has scenarios where multiple units are mixed, such as active power being recorded in both kW and MW. Unit unified mapping must be completed before data ingestion. Large volumes of time-series data will increase storage expansion pressure, so an automatic archiving strategy must be configured to migrate historical data beyond the retention period to cold storage.
Investment research analysis requires cross-multi-source data association, so the database must support multi-table JOIN and field unified verification to avoid data inconsistencies affecting analysis results.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_CONNECTION_TIMEOUT | 30 seconds | Wind power real-time operating data queries require fast response; timeouts will cause failures in investment research tool calls |
BATCH_SYNC_SIZE | 5000 records/batch | Wind power time-series data has large per-batch data volumes, avoiding database overload from a single synchronization run |
TIMESERIES_INDEX_RETENTION | 180 days | Investment research analysis typically requires reviewing up to six months of operating data; overly long index retention will consume excessive storage resources |
PARSE_FIELD_UNIT_MAPPING | Match preset mapping rules by field name | Wind power data has mixed unit scenarios, requiring unified conversion to investment research standard units |
QUERY_RESULT_LIMIT | Top 20 records | Investment research analysis requires multi-dimensional samples; limiting returned records avoids redundant data interfering with analysis |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test against your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The database query plugin returns a
400status code with a prompt indicating incorrect message format. Cause: The passed SQL statement contains unescaped single quotes, and parameters are not passed via variable binding. - Phenomenon: No output is generated when calling a database connection during local deployment. Cause: The database port is not opened in the security group rules for the deployment node, and a correct access whitelist is not configured.
- Phenomenon: Execution fails when passing SQL via variables, but works when entering SQL manually. Cause: Variables are not escaped, leading to corrupted SQL syntax.
How to confirm proper configuration
Execute a time-series data query for a single wind turbine, and verify that the units of the returned fields match the preset standards. Perform a batch synchronization of historical data, and verify that the number of completed synchronization records matches the total source data volume. Call a SQL query with variables, and verify that the syntax of the statement after variable replacement is correct. Simulate a multi-source data association query for an investment research scenario, and verify that the returned results meet analysis requirements.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.