to the desired implementation such as comparison of individual features of a data vector received from the pmu fourier transforms to compare power measurements and standard deviations voting methods for determining standard deviations and so on if so yes then the window contains an anomaly and thus the start time of the window the pmu and the outlyingness measure are output and appended to the database at 207 if not no then the flow at 207 is skipped and proceeds to 208 wherein the calculated feature vector is appended to the database and then the flow proceeds back to 203 by applying the anomaly monitoring method on sliding windows of new pmu data as they become available example implementations can detect anomalies in close to real time e g within minutes of occurrence in example implementations there is a time and location module configured to estimate the time and location of the event underlying each cluster of anomalies occurring close together in time that affects three or more pmus in descriptions herein one such event is assumed for each cluster the inputs to this module can include a set of previous events in the power grid the actual or estimated times and locations of these events are assumed to be known from some other data source and or analysis depending on the desired implementation further inputs can include the current cluster of anomalies for which an estimate of the underlying event is sought from the anomaly detection module the time pmu and magnitude of each anomaly in the cluster can be known the output is the estimated time and location of the event underlying the current cluster of anomalies in example implementations there is a geographic location based method which is configured for detecting the.