부산대학교 도서관

Data analytics is pivotal in assessing the techni-cal characteristics and performance of Battery Energy Storage Systems (BESS), underpinning BESS modeling, optimization, and control. PNNL has collected diverse and comprehensive real-world BESS operational datasets in collaboration with the Electric Power Research Institute and multiple Washington State utilities, allowing for BESS analytics and modeling. However, raw datasets frequently harbor anomalies from measurement errors and equipment malfunctions, impacting BESS reliability and analysis accuracy. To address the challenge, this paper presents a methodology for the rapid detection of anomalous charge or discharge cycles within BESS operational data, expediting the cleaning process while ensuring data integrity. Using case studies from real BESS operational datasets, we demonstrate that the proposed method detects anomalies and aids in their resolution, improving system performance characterization. It also reveals recurring data anomaly sources, offering insights for data cleaning. Practitioners can gain valuable insights from the identified anomalous cycles in the real-world datasets along with the investigative process for root cause analyses and essential data cleaning steps.