| --- |
| pretty_name: RelaySec-FEL |
| license: cc-by-4.0 |
| language: |
| - en |
| - zh |
| tags: |
| - tabular |
| - graph |
| - relay-protection |
| - secondary-circuits |
| - fault-localization |
| - fault-diagnosis |
| - power-systems |
| - multi-source-evidence |
| - production-measurements |
| configs: |
| - config_name: fault_cases |
| default: true |
| data_files: |
| - split: data |
| path: fault_cases.csv |
| - config_name: asset_devices |
| data_files: |
| - split: data |
| path: asset_devices.csv |
| - config_name: asset_ports |
| data_files: |
| - split: data |
| path: asset_ports.csv |
| - config_name: bays |
| data_files: |
| - split: data |
| path: bays.csv |
| - config_name: communication_records |
| data_files: |
| - split: data |
| path: communication_records.csv |
| - config_name: configuration_records |
| data_files: |
| - split: data |
| path: configuration_records.csv |
| - config_name: defect_workorders |
| data_files: |
| - split: data |
| path: defect_workorders.csv |
| - config_name: entity_alignment |
| data_files: |
| - split: data |
| path: entity_alignment.csv |
| - config_name: event_observations |
| data_files: |
| - split: data |
| path: event_observations.csv |
| - config_name: fault_labels |
| data_files: |
| - split: data |
| path: fault_labels.csv |
| - config_name: goose_records |
| data_files: |
| - split: data |
| path: goose_records.csv |
| - config_name: inspection_records |
| data_files: |
| - split: data |
| path: inspection_records.csv |
| - config_name: logical_functions |
| data_files: |
| - split: data |
| path: logical_functions.csv |
| - config_name: normal_status_records |
| data_files: |
| - split: data |
| path: normal_status_records.csv |
| - config_name: physical_links |
| data_files: |
| - split: data |
| path: physical_links.csv |
| - config_name: protection_events |
| data_files: |
| - split: data |
| path: protection_events.csv |
| - config_name: sampled_value_records |
| data_files: |
| - split: data |
| path: sampled_value_records.csv |
| - config_name: scd_relations |
| data_files: |
| - split: data |
| path: scd_relations.csv |
| - config_name: self_check_records |
| data_files: |
| - split: data |
| path: self_check_records.csv |
| - config_name: soe_events |
| data_files: |
| - split: data |
| path: soe_events.csv |
| - config_name: base_edges |
| data_files: |
| - split: data |
| path: static_graph/base_edges.csv |
| - config_name: base_nodes |
| data_files: |
| - split: data |
| path: static_graph/base_nodes.csv |
| - config_name: base_triples |
| data_files: |
| - split: data |
| path: static_graph/base_triples.tsv |
| sep: "\t" |
| - config_name: stations |
| data_files: |
| - split: data |
| path: stations.csv |
| - config_name: virtual_circuits |
| data_files: |
| - split: data |
| path: virtual_circuits.csv |
| - config_name: waveform_features |
| data_files: |
| - split: data |
| path: waveform_features.csv |
| --- |
| |
| # RelaySec-FEL |
| ## Relay Secondary-Circuit Fault Element Localization Dataset |
|
|
| **Production field measurements · 20,000 diagnostic cases · 8 substations · 2021–2025** |
|
|
| [Versioned archive](https://doi.org/10.5281/zenodo.23492959) · [All-version DOI](https://doi.org/10.5281/zenodo.21349356) · [Browse files](https://huggingface.co/datasets/james12221/RelaySec-FEL/tree/main) |
|
|
| RelaySec-FEL contains measurements and operational records collected from relay-protection secondary circuits during production operation. It supports **element-level fault localization**: ranking the devices, ports, physical links, and virtual circuits most likely to explain the observations in a diagnostic window. |
|
|
| The release brings together circuit structure, communication and protection events, inspection records, and fault labels. It is intended for research on graph-based diagnosis, multi-source evidence fusion, candidate ranking, and evidence-chain explanation under incomplete or conflicting observations. |
|
|
| ## Dataset overview |
|
|
| | Property | Release v1.0.1 | |
| |---|---:| |
| | Diagnostic cases | 20,000 | |
| | Record period | January 2021–December 2025 | |
| | Substations / bays | 8 / 128 | |
| | Unified event observations | 156,523 | |
| | Static graph nodes | 3,944 | |
| | SCD structural relations | 7,608 | |
| | Stored graph edges, including reverse relations | 15,216 | |
| | Fault-element labels | 20,000 | |
| | Inspection records | 19,798 | |
| | Defect and maintenance work orders | 8,917 | |
| | Data format | UTF-8 CSV; graph triples in TSV | |
| | License | CC BY 4.0 | |
|
|
| The static graph comprises **528 devices, 1,536 ports, 768 physical links, 600 virtual circuits, and 512 logical functions**. Logical functions provide structural context; the localization targets are devices, ports, physical links, and virtual circuits. |
|
|
| Exact table counts and structural validation statistics are provided in [`metadata.json`](https://huggingface.co/datasets/james12221/RelaySec-FEL/blob/main/metadata.json). |
|
|
| ## Data source and organization |
|
|
| The underlying data are **real measurements and records from production operation**. The release organizes these records into relational tables and diagnostic cases, together with a static graph derived from the circuit-structure tables. Measurement records, derived representations, and supervision tables have distinct roles in the diagnosis workflow. |
|
|
| The records cover protection events, sequence-of-events (SOE) logs, communication status, GOOSE and sampled-value records, waveform features, device self-checks, configuration checks, normal or recovered states, and inspections. Observations retain source, time, target, quality, and evidence-reliability fields where provided. |
|
|
| Controlled perturbations introduced for robustness experiments should be reported separately from the original production records. |
|
|
| ## Task definition |
|
|
| For each case in `fault_cases.csv`, combine the relevant circuit structure and available observations to return a ranked list of candidate fault elements: |
|
|
| ```text |
| Circuit structure + case observations + pre-fault inspection context |
| ↓ |
| Ranked candidate fault elements |
| ↓ |
| Supporting and opposing evidence paths |
| ``` |
|
|
| The candidate types are `Device`, `Port`, `PhysicalLink`, and `VirtualCircuit`. An observation target identifies where a symptom was recorded; the reference fault element identifies the failed component. These may differ because secondary circuits couple physical connections with logical communication mappings. |
|
|
| `fault_labels.csv` supplies the reference element for supervision and evaluation. Labels and maintenance outcomes are kept separate from inference inputs. |
|
|
| ## Files and relationships |
|
|
| | Data layer | Files | Role | |
| |---|---|---| |
| | Circuit structure | `stations.csv`, `bays.csv`, `asset_devices.csv`, `asset_ports.csv`, `physical_links.csv`, `logical_functions.csv`, `virtual_circuits.csv`, `scd_relations.csv` | Describe assets, physical connections, logical functions, and communication mappings. | |
| | Entity alignment | `entity_alignment.csv` | Map business-table identifiers to graph nodes. | |
| | Diagnostic cases | `fault_cases.csv` | Define case identifiers, time windows, station/bay context, and evaluation splits. | |
| | Unified evidence | `event_observations.csv` | Provide the common event representation and observation targets. | |
| | Source-specific records | `protection_events.csv`, `soe_events.csv`, `communication_records.csv`, `goose_records.csv`, `sampled_value_records.csv`, `waveform_features.csv`, `self_check_records.csv`, `configuration_records.csv`, `normal_status_records.csv` | Preserve event details and measurements from individual record types. | |
| | Inspection context | `inspection_records.csv` | Supply health observations available before a case begins. | |
| | Supervision and verification | `fault_labels.csv`, `defect_workorders.csv` | Supply fault-element labels and maintenance outcomes for training, evaluation, and post-hoc verification. | |
| | Derived static graph | `static_graph/base_nodes.csv`, `static_graph/base_edges.csv`, `static_graph/base_triples.tsv` | Provide node, edge, and triple representations of the circuit structure. | |
|
|
| ### Core fields |
|
|
| | Table | Key fields | Interpretation | |
| |---|---|---| |
| | `fault_cases.csv` | `sample_id`, `station_id`, `bay_id`, `case_start_time`, `case_end_time`, `window_start_time`, `window_end_time`, `split` | Case identity, operating context, diagnosis window, and prescribed partition. | |
| | `event_observations.csv` | `event_id`, `sample_id`, `event_time`, `ingest_time`, `target_node_id`, `target_node_type`, `evidence_source`, `event_type`, `value_state`, `quality_flag` | Event identity, case membership, timing, target, source, state, and data quality. | |
| | `event_observations.csv` | `source_reliability`, `time_consistency`, `conflict_degree`, `credibility` | Evidence attributes available for reliability-aware modeling; these are not fault probabilities. | |
| | `entity_alignment.csv` | `node_id`, `node_type`, `field_table`, `primary_key` | Correspondence between graph nodes and business records. | |
| | `fault_labels.csv` | `sample_id`, `fault_node_id`, `fault_node_type`, `fault_mode`, `label_source`, `verification_method`, `label_confidence` | Reference fault element and label documentation. | |
| | `inspection_records.csv` | `node_id`, `inspection_time`, `health_score`, `status_code`, `defect_level` | Time-stamped inspection context associated with a graph node. | |
|
|
| Join cases and unified observations through `sample_id`. Match `target_node_id`, `fault_node_id`, and inspection `node_id` to graph `node_id`; use `entity_alignment.csv` for business identifiers. The unified evidence and source-specific tables provide related views of the records and should not be treated as independent evidence without checking their identifiers. |
|
|
| <details> |
| <summary>Complete table inventory and row counts</summary> |
|
|
| | File | Rows | |
| |---|---:| |
| | `fault_cases.csv` | 20,000 | |
| | `asset_devices.csv` | 528 | |
| | `asset_ports.csv` | 1,536 | |
| | `bays.csv` | 128 | |
| | `communication_records.csv` | 113,441 | |
| | `configuration_records.csv` | 10,469 | |
| | `defect_workorders.csv` | 8,917 | |
| | `entity_alignment.csv` | 3,944 | |
| | `event_observations.csv` | 156,523 | |
| | `fault_labels.csv` | 20,000 | |
| | `goose_records.csv` | 17,051 | |
| | `inspection_records.csv` | 19,798 | |
| | `logical_functions.csv` | 512 | |
| | `normal_status_records.csv` | 4,249 | |
| | `physical_links.csv` | 768 | |
| | `protection_events.csv` | 6,793 | |
| | `sampled_value_records.csv` | 7,133 | |
| | `scd_relations.csv` | 7,608 | |
| | `self_check_records.csv` | 4,958 | |
| | `soe_events.csv` | 3,438 | |
| | `static_graph/base_edges.csv` | 15,216 | |
| | `static_graph/base_nodes.csv` | 3,944 | |
| | `static_graph/base_triples.tsv` | 19,160 | |
| | `stations.csv` | 8 | |
| | `virtual_circuits.csv` | 600 | |
| | `waveform_features.csv` | 7,133 | |
|
|
| </details> |
|
|
| ## Load the data |
|
|
| ### Local archive |
|
|
| Extract `RelaySec-FEL-v1.0.1.zip` and read the tables from the `relaysec-fel/` directory: |
|
|
| ```python |
| from pathlib import Path |
| import pandas as pd |
| |
| root = Path("relaysec-fel") |
| cases = pd.read_csv(root / "fault_cases.csv") |
| events = pd.read_csv(root / "event_observations.csv") |
| nodes = pd.read_csv(root / "static_graph" / "base_nodes.csv") |
| labels = pd.read_csv(root / "fault_labels.csv") # Supervision/evaluation only |
| ``` |
|
|
| ### Hugging Face Datasets |
|
|
| Each table has its own configuration because the tables have different schemas. The default configuration is `fault_cases`. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| repo_id = "james12221/RelaySec-FEL" |
| cases = load_dataset(repo_id, "fault_cases", split="data") |
| events = load_dataset(repo_id, "event_observations", split="data") |
| |
| train_cases = cases.filter(lambda row: row["split"] == "train") |
| val_cases = cases.filter(lambda row: row["split"] == "val") |
| test_cases = cases.filter(lambda row: row["split"] == "test") |
| ``` |
|
|
| Here, `data` is the storage split for a complete table. The **benchmark partitions are the values in the `split` column of `fault_cases.csv`**, not the storage split name. Other configurations use the file stem, such as `asset_devices`, `fault_labels`, `base_nodes`, and `base_edges`. |
| |
| ### pandas |
| |
| For direct access to individual CSV tables: |
| |
| ```python |
| import pandas as pd |
| |
| base = "https://huggingface.co/datasets/james12221/RelaySec-FEL/resolve/main" |
| cases = pd.read_csv(f"{base}/fault_cases.csv") |
| events = pd.read_csv(f"{base}/event_observations.csv") |
| nodes = pd.read_csv(f"{base}/static_graph/base_nodes.csv") |
| |
| case_id = cases.loc[cases["split"].eq("test"), "sample_id"].iloc[0] |
| case_events = events.loc[events["sample_id"].eq(case_id)] |
| ``` |
| |
| The release contains data and graph representations. Model implementations are supplied separately. For reproducible experiments, report the dataset version and cite the versioned archive below. |
| |
| ## Evaluation protocol |
| |
| | Partition | Diagnostic cases | Share | |
| |---|---:|---:| |
| | Training (`train`) | 14,027 | 70.135% | |
| | Validation (`val`) | 2,948 | 14.740% | |
| | Test (`test`) | 3,025 | 15.125% | |
| |
| Use the supplied case partitions for comparable results. Report **Top-1 accuracy, Hit@k, and mean reciprocal rank (MRR)** over the candidate ranking, together with the candidate-scope rule and evaluation sample count. If MRR is truncated, state the cutoff explicitly. For different partitions, document how they were formed; the supplied case split should not be assumed to establish chronological or unseen-station generalization. |
| |
| To prevent information leakage: |
| |
| 1. Use `fault_labels.csv` for supervision and evaluation, and `defect_workorders.csv` for post-hoc verification. Exclude both from inference features, evidence aggregation, and candidate selection. |
| 2. Restrict observations to the information available at the diagnosis cutoff. Account for event time and ingestion time when defining online evaluation. |
| 3. Use an inspection record only when `inspection_time < case_start_time`. |
| 4. Fit learned preprocessing and model parameters on training data. Do not derive input features from validation/test fault labels or later maintenance outcomes. |
| |
| ## Data quality and scope |
| |
| Production records contain missing observations, suspect quality flags, delayed timestamps, repeated events, normal or recovered states, and cross-source conflicts. These attributes support evaluation of diagnosis methods under imperfect evidence and should be handled explicitly rather than silently discarded. |
| |
| The release metadata reports **zero event targets missing from the graph** and **zero labels without a corresponding diagnostic case**. These are structural consistency checks; they do not imply that every record is complete or every label is certain. Source, quality, verification, and confidence fields should be considered when interpreting individual cases. |
|
|
| Results on the released cases describe performance under the reported evaluation protocol. Generalization to other substations, configuration changes, and operating conditions requires separate evaluation. Candidate rankings and evidence paths support inspection prioritization; they do not, by themselves, establish a physical fault cause. |
|
|
| ## Citation |
|
|
| Please cite the versioned dataset archive: |
|
|
| > Zeus. (2026). *RelaySec-FEL: Relay Secondary-Circuit Fault Element Localization Dataset* (Version 1.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.23492959 |
|
|
| ```bibtex |
| @dataset{relaysec_fel_2026, |
| author = {Zeus}, |
| title = {{RelaySec-FEL}: Relay Secondary-Circuit Fault Element Localization Dataset}, |
| year = {2026}, |
| version = {1.0.1}, |
| publisher = {Zenodo}, |
| doi = {10.5281/zenodo.23492959}, |
| url = {https://doi.org/10.5281/zenodo.23492959} |
| } |
| ``` |
|
|
| The [all-version DOI](https://doi.org/10.5281/zenodo.21349356) resolves to the latest archived release. |
|
|
| ## License and contact |
|
|
| The dataset is distributed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/), as registered in the [Zenodo archive](https://zenodo.org/records/23492959). Attribute the dataset creator and cite the dataset when using or redistributing it. |
|
|
| For questions about the tables, evaluation protocol, or data quality, open a thread in the [dataset discussion area](https://huggingface.co/datasets/james12221/RelaySec-FEL/discussions). |
|
|
