The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements, which prevents building a precise decision model. Furthermore, the diagnostic model is often provided with a mixture of redundant measurements that may deviate it from learning normal and fault distributions. This paper presents the effect of feature engineering on mitigating the aforementioned challenges in cyber-physical systems. Feature selection and dimensionality reduction methods are combined with decision models to simulate data-driven fault diagnosis in a 118-bus power system. A comparative study is enabled accordingly to compare several advanced techniques in both domains. Dimensionality reduction and feature selection methods are compared both jointly and separately. Finally, experiments are concluded, and a setting is suggested that enhances data quality for fault diagnosis.

翻译：信息物理电力系统中故障诊断系统的性能深受数据质量影响。此类系统产生海量数据，导致系统因计算成本过高而不堪重负。另一问题在于记录测量值中存在噪声干扰，阻碍了精确决策模型的构建。此外，诊断模型常面临冗余测量值的混合输入，这可能导致模型偏离正常状态与故障分布的学习目标。本文系统研究了特征工程在缓解信息物理系统上述挑战中的作用，将特征选择与降维方法相结合，构建决策模型以模拟118节点电力系统的数据驱动型故障诊断。在此基础上开展了对比研究，对两个领域中的多种先进技术进行横向评估。通过联合与分离两种方式比较了降维方法与特征选择技术。最终完成实验验证，并提出了能够提升故障诊断数据质量的优化配置方案。