###### Abstract
A well-designed lossy compression algorithm only throws out information that is not necessary for the data to be reconstructed. The work in [2; 3] provides the first, and up to this point the only, \"lossy\" compression algorithms proposed for power signals. The cyclic and residual histograms provide reduced, two-dimensional representations of OSG data and capture the likelihood that a power signal's magnitude achieves a specific value at a given sample point within a cycle and over time. The code converting an OSG file to power signal histograms is available via a GitHub repository [4]. While the compression algorithm is designed for high-voltage power transmission, the GitHub repository facilitates (i) porting it to another programming language, such as C or VHDL, (ii) implementing the code on a different hardware platform, such as an Advanced RISC Machine (ARM) or a Field-Programmable Gate Array (FPGA), and (iii) using it in other research and development fields, such as electrified railway infrastructure [5; 6]. Figure 1: \(Cont\). This study utilizes Incipient Fault Prediction (IFP) to validate the power signal histogram's ability to store actionable information for event forensics. IFD utilizes waveform analytics to identify the faults occurring within a recorded fault. There has been demonstrated success with the identification of various event types that occur during grid operations [3, 7, 8, 9, 10, 11, 12]. While these methods help identify and validate the occurrence of events, Incipient Fault Prediction (IFP) targets predicting these events before they occur, allowing for event prevention through preemptive measures. This (i) maintains nominal grid operations, (ii) prevents hardware from being damaged, and (iii) reduces risk to maintenance crews. The work in [13] demonstrates the ability to use an increase in the frequency of previous events to forecast the likelihood of a more critical failure but does not use the recorded waveform data, only the log of prior events. The work in [14] presents a correlation-based approach that accurately predicts an event ten milliseconds before it occurs. Current research has shown that the earliest means of detection possible is only forty seconds before an event occurs on a deployed grid [15]. The authors of [16] predict a series of line interruptions, voltage dips, and ground faults with 89% true-positive accuracy one hour preceding the fault. The authors used Root-Means Squared (RMS) data sampled at 1 Hz and a random forest classifier to predict fault likelihood. The work in [2] introduces IFD using cyclic and residual histograms as data-efficient storage techniques. While the data are reduced, these histograms are designed for IFD. This work assesses the viability of using these histograms for IFP. The deployed DFRs are designed to perform only standard grid monitoring operations. Thus, the computational capability of the DFR is limited. These computational limitations include a reduced processor, memory, and storage. Because of these limitations, any new functionality must be implemented computationally efficiently to ensure that it does not interfere with standard operations. The contributions of this work are summarized as follows:  
* The proposed power signals histogram compression method reduces data storage requirements by 99.54% compared to the original waveform. * The proposed power signal histogram compression method can be run on currently deployed DFR hardware without interrupting standard operations, eliminating the need for new hardware to be implemented. * The ability of the histograms to store actionable information is assessed using three use cases: Capacitive Voltage Transformer (CVT) failure, arcing due to a loose connection, and DFR sampling errors. All use case data are recorded by DFRs deployed within an operational power transmission system. * The power signals histogram compression enables an IFP that is statistical; thus, it does not require Machine Learning/Deep Learning (ML/DL).