## 5 Discussion
In case the sag continues for a longer period (minute), both sum meter and consumer MUT will sense the same voltage dip, and that will not be an issue for the method. Finally, some outlier rejection rules may be introduced if voltage sags cause distortions of meter readings and unexpectedly high PCC or meter gain error values. To mention additional influencing factors for future analysis, it has to be noted that power losses in 3-phase distribution systems are influenced by the unbalance of load in different electrical phases [66; 67]. At the moment of PCC, due to a single-phase load switching, the balancing conditions in the distribution grid are changing instantaneously. Consequently, the change of power losses measured by sum meter includes unbalanced conditions, caused power losses. Influence of phase load unbalance on power losses was investigated in [68; 69], showing that it may contribute up to a \(3.7\div 6.4\%\) increase of power losses. Though such a size of losses seems comparable and even less than the error of PCC estimation by the presented method, it is important to investigate its influence in more detail in the future. Considering that instantaneous power consumption in all phases and in some implementations neutral current is available from a 3-phase sum meter, the unbalance factor before and after the power step could be obtained and used as inputs for \(dP_{w}\) and \(dP_{mL}\) correction. Implementation of the method in the existing monitoring systems may require some modifications to their current functionality. At this stage of development, this can be discussed only in a preliminary fashion. Some manufacturers of MDMS (Meter Data Management Systems) among the features list anomaly detection, but no more information is made open [70]. The deployment of various anomaly detection systems can either be based on readings accessible from smart meters. Siemens EnergyIP Mosaic, Landis+Gyr's Revelo are examples of AMI vendor-agnostic MDMS, promoting anomaly detection features. However, they are proprietary and not open for new developed techniques integration without vendor cooperation. Data storage and analytics could be integrated into (1) MDMS owned or operated by utilities or (2) operated by DSO-independent vendors. Though federated systems preserving user data privacy are discussed in scientific papers, the majority of methods described for anomaly detection are still based on centralized processing. In the application we consider in this paper a real-time detection is not mandatory. Indeed, it could last for weeks and months before a reliable detection of meter error is triggered. Every new application imposes certain requirements on the data collection system. Acquisition of electrical data from a smart meter at the rate of 1 s is commonly available over P1 interface. Therefore, technical solutions can rely on P1 local reading of measurements and delivering data to centralized processing servers. The amount of data to be transmitted over a communication channel (GSM, PLC, Wifi, etc.) and storage of data is a typical concern. An event-driven technique, as discussed in this publication, requires high time resolution sampling (1 s) of electrical quantities, but only surrounding the moment of the power event. Therefore, detection of a power event should be implemented in the firmware of a smart meter and V/I/P samples 10 s before and after the power step delivered to the central server. At the same time, 20 s V/I/P readings from sum meter has to be requested and delivered to the same central server. Some implementation scenarios and load on the communication infrastructure have already been discussed in our previous publication [37].