Hybrid Electrical Fault Detection Dataset (Real + Synthetic)

This dataset is titled Hybrid Electrical Fault Detection Dataset (Real and Synthetic) and contains 22,000 labeled instances of electrical faults in three-phase power systems. It was created by combining two real-world datasets obtained from Kaggle: the Electrical Fault Detection dataset, which contains measurements of voltage and current with labeled fault types, and the Electric Fault-Line Detection dataset, which provides similar electrical measurements with different fault labels. By integrating these two datasets and generating additional synthetic samples, we produced a larger, more balanced, and standardized dataset suitable for machine learning and research purposes. Each row contains measurements of phase currents (current_A, current_B, current_C) and phase voltages (voltage_A, voltage_B, voltage_C), a fault label describing the type of fault, a timestamp, and the source of the data, indicating whether the row is from one of the original datasets or was synthetically generated. The fault labels include healthy conditions and specific faults such as AG fault, BG fault, CG fault, AB fault, BC fault, and ABC fault. The synthetic samples were generated using a statistical modeling and signal simulation approach. First, medians and variability measures were calculated from the real datasets to define realistic ranges for voltages and currents. Sinusoidal signals were then simulated with randomized amplitudes, phases, and small added noise to resemble natural fluctuations in electrical systems. Controlled perturbations were applied depending on the fault type. For example, in a single-phase-to-ground fault, the corresponding phase voltage was reduced and the current increased. For double-phase faults, voltages of the affected phases were reduced while currents were amplified. Triple-phase faults were simulated by reducing all phase voltages while adjusting currents to maintain realistic behavior. This approach ensures that synthetic measurements are representative of real-world faults, providing diversity and coverage across all fault types while addressing class imbalance.本数据集命名为混合式电力故障检测数据集（真实与合成），包含22000条带标注的三相电力系统（three-phase power systems）电力故障样本。该数据集由Kaggle平台上的两份真实世界数据集融合构建而成：其一为电力故障检测数据集（Electrical Fault Detection dataset），该数据集包含带故障类型标注的电压与电流测量数据；其二为电力线路故障检测数据集（Electric Fault-Line Detection dataset），其提供了带有不同故障标注的同类电气测量数据。通过整合这两份数据集并生成额外的合成样本，我们构建了规模更大、类别分布更均衡且标准化程度更高的数据集，可适用于机器学习（machine learning）与相关研究场景。每条样本行包含相电流（phase currents）current_A、current_B、current_C与相电压（phase voltages）voltage_A、voltage_B、voltage_C的测量数据、描述故障类型的故障标签（fault label）、时间戳（timestamp）以及数据来源字段，用以标注该样本源自原始数据集还是合成生成。故障标签涵盖正常运行状态，以及A相接地故障（AG fault）、B相接地故障（BG fault）、C相接地故障（CG fault）、AB相间故障（AB fault）、BC相间故障（BC fault）与ABC三相故障（ABC fault）等故障类型。 合成样本采用统计建模与信号仿真方法生成。首先，从真实数据集计算得到中位数与离散度指标，以此确定电压与电流的合理取值范围。随后，模拟生成带有随机幅值、相位与小幅附加噪声的正弦信号，以模拟电力系统中的自然波动。根据故障类型施加受控扰动：例如，对于单相接地故障，对应相的电压会降低且电流会升高；对于两相故障，受影响相的电压会降低而电流会被放大；对于三相故障，则通过降低所有相的电压并调整电流以维持符合实际的运行特性。该方法可确保合成测量数据能够反映真实电力故障的特征，在覆盖所有故障类型并保证样本多样性的同时，有效解决了类别不平衡问题。