# Software Integration of Power System Measurement Devices with AI Capabilities
## 1 Introduction  
In recent years, there has been a significant improvement in the devices available for monitoring electrical systems focused on different applications, in terms of their performance, variety, cost, availability communication capabilities, acquisition, or processing software. An interesting review of the different monitoring devices that appear in a smart grid can be found in [1], where the fundamental characteristics and applications of three of the most common ones are briefly described: a phasor measurement unit (PMU), a power quality equipment (PQM), and a smart meter (SM). This performance improvement has been enhanced, among others, by the notable increase in distributed energy resource (DER) environments, photovoltaic (PV) panels, and electric vehicle charging, which pose substantial challenges from a power quality point of view [2, 3]. In this way, monitoring devices are enabling the development of variousapplications at different levels. For example, they have been used in fault detection: using power quality monitoring data to estimate fault location on distribution feeders [4], islanding the location of a ground fault [5], automatic fault location, and high impedance faults location on distribution networks using PMUs [6; 7; 8]. Another major set of applications involves the estimation of the state of the distribution system [9; 10; 11]. In [12], a recent and comprehensive survey is presented on the estimation of the state of the power system using multiple data sources. Another area of interest involves the applications of real-time power flow monitoring and control in microgrids [13] or a stability study using renewable sources [14]. These previous applications are typically carried out using both PQMs and PMUs, while for Non-Intrusive Load monitoring (NILM), applications for distributed energy resources (DERs) [15], residential settings [16], or industrial settings [17] both SMs and PQMs are used. On a different note, the availability of large volumes of generated data has made it common to use Artificial Intelligence techniques for these tasks [18]: recent reviews of machine learning techniques in power system security and stability [19; 20], fault diagnosis [21], and NILM applications [16; 17; 22] can be found. Despite the benefits, integrating these devices poses significant challenges. Each electrical monitoring device has unique characteristics and requirements, such as transmission speed and synchronization, that differentiate it from others. As a result, each device typically operates within its own specialized software. In addition, each manufacturer often provides proprietary software that is easy to use with their equipment but incompatible with others. This creates a highly heterogeneous environment where linking data from different sources is challenging. However, in grids where different electrical monitoring devices are used simultaneously, it is highly desirable to have all these variables unified in a single data stream. A large number of applications would benefit from handling joint data [23]. It is therefore of interest to implement an architecture based on open-source tools that allows and facilitates the integration of measurements from different devices, provides real-time access and interaction with the data, and includes the possibility of implementing systems based on Artificial Intelligence. Data must be stored synchronously, despite the varying levels of synchronism among the devices. Having synchronized measurements facilitates the correlation of measurements from different and distant equipment, even if the comparison is limited by the lower-quality equipment. Alternative software tools are available in these bibliography [24; 25; 26; 27], although they have limitations in terms of flexibility and scalability when given the different applications for which they are intended. For example, in [24], trend data from the PQM are not taken into account; only event data registered from the PQM event recorder are considered.