Partial Discharges(PD) are commonly produced in defects within the insulation systems of high voltage equipment. These discharges are typically nanosecond current pulses in the amplitude range of milli-amperes. A long term exposure of the insulation system to these partial discharges accelerate the aging mechanisms that eventually lead to the final breakdown of the insulation system. Such insulation breakdowns in High Voltage (HV) / Medium Voltage (MV) equipment typically involve arc-flash/fire hazards, posing safety threats. Moreover probable undelivered power and huge financial losses are also associated. Early detection of PD activity can provide warnings about pending insulation/device failures and hence, maintenance or repair activities can be scheduled before breakdown occurs. Moreover, clustering of PD due to different types of sources is of practical importance as it indicates the severity of defect and provides an insight into the time available for repair activities before complete breakdown. State of the art tools for electrical PD monitoring are expensive and cannot be economically deployed over a large network of HV/MV assets. Moreover, they employ classification schemes based on less robust PD features. This thesis marks the completion of the first stage in the process of building an open source, cost-effective, automated embedded online partial discharge detection tool for feature extraction and PD classification based on new, advanced robust features of partial discharges. As an outcome of this thesis, an embedded solution for real time PD detection and feature extraction was developed to facilitate future PD classification.