###### Abstract  
Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data's storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFF). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to 99.58%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event. nicipient fault prediction; power grid; smart grid; data compression; event forecasting; state estimation; real-time grid analysis; image registration 2020 20  
A Digital Fault Recorder (DFR) is a smart device employed by power utilities to comply with some North American Electric Corporation (NERC) standards and monitor key substations and transmission lines. The Electric Power Research Institute (EPRI) defines a DFR as a device capable of digitizing and recording oscillography (OSG) data (e.g., three-phase sinusoidal voltage), the sequence of events, and faults [1]. DFRs can monitor analog voltages, current lines, buses, and numerous digital inputs. Figure 1 provides examples of a Capacitive Voltage Transformer (CVT) event, Figure 0(a), and a loose connection-induced arcing event, Figure 0(b), recorded using a DFR sampling at 960 Hz and occurring on a 161 kV line. This work focuses on the DFR's continuous digitization and recording of the nominal (a.k.a., non-faulted) sinusoidal three-phase voltage and current waveforms. Due to the limited personnel, funds, time, and storage capacity, it is common for power utilities to not analyze or look at these waveforms and to overwrite the recordings every two to three weeks, thus hindering and even prohibiting the generation of actionable intelligence, incipient fault detection, and fault prediction by power utility personnel. Overwriting of the recorded data not only results in the potential loss of actionable information but also prevents the creation of long-term data libraries, which are essential to incipient fault forensics and the characterization of the behavior that precedes them, thus prohibiting the development and implementation of preventive or corrective measures. Currently, generalized lossless compression algorithms exist, such as Huffman Coding and Lempel-Ziv; ZIP and GZIP use a combination of the two but provide an approximate 5% file size reduction when compressing OSG files. Lossy compression algorithms exist but have to be tailored to the application. Some examples of lossy compression include MP3/4 or WAV for audio, JPEG or TIFF for images, and AV1 or H264/5 for video. JPEG compression typically can reduce the data required to store an image by around 90%. This data size reduction is due to the compression algorithm selecting and throwing away portions of the original data.