What type of data analysis technique is used to detect anomalies in ESS performance data?

Statistical anomaly detection techniques are commonly used to detect anomalies in Energy Storage System (ESS) performance data. These techniques involve analyzing historical data to establish a baseline of normal operating behavior and then identifying data points that deviate significantly from this baseline. One common approach is to use statistical process control (SPC) charts, such as control charts and Shewhart charts, to monitor key performance indicators (KPIs) like voltage, current, temperature, and State of Charge (SOC). These charts define upper and lower control limits based on the historical data, and any data points that fall outside these limits are flagged as anomalies. Another technique is to use clustering algorithms, such as k-means clustering, to group similar data points together and identify outliers that do not belong to any of the clusters. Machine learning algorithms, such as one-class support vector machines (SVMs) and isolation forests, can also be used to learn the normal operating behavior of the ESS and then identify anomalies as data points that are significantly different from the learned pattern. Time series analysis techniques, such as autoregressive integrated moving average (ARIMA) models, can be used to forecast future performance based on historical data and then identify anomalies as deviations from the forecast. These anomaly detection techniques help operators identify potential problems with the ESS early on, enabling them to take corrective actions and prevent failures.