**5. Discussions**

The investigations presented in this paper correspond to the recent amendment to the PQ standard EN 50160:2015 [2] in comparison to the previous version EN 50160:2010 [1]. The main issues in relation to the development of the mentioned standard are the influence of the requirement for the assessed PQ parameters to preserve the limits 100% of the observed time in comparison to the previous requirement of 95% of the time of observation, as well as influence of the suggestion to use a 1 min aggregation time interval in the case of a LV power system in comparison to the classical 10 min aggregation. Additionally, the issue of the aggregation interval is extended in the paper for the analysis of the influence of the aggregation interval on correlation analysis between PQ parameters and weather conditions. The formulated problems can have a meaning in analysis of the integration of distributed energy resources with a power system in the light of increasing requirements for power quality parameters and increasing concentration of distributed generation in power systems.

In order to highlight mentioned issues, the results of the investigation of a real measurement of a 100 kW photovoltaic power plant directly connected to a LV power system is presented. In relation to the requirement for the assessed PQ parameters to preserve the limits 100% of the observed time, it can be concluded that such requirement can be hard to obtain in selected cases. For example, the investigated 15th harmonic in voltage in the PCC of the investigated 100 kW PV power plant does not preserve the requirement for 100% of the observed time, but has a positive assessment for the requirement of 95% of the observed time. It should be emphasized that the flagging concept was implemented, and the investigated measurement data are free of events which might have affected the assessment by extremal values. A similar conclusion can be formulated in the case of a variation in frequency demand. A more restricted limit for the permissible level of frequency variation and demand for 100% of the observed time causes the assessment to be negative when the requirements

related to the amendment to the standard EN 50160:2015 [2] is applied, but would be positive according to requirements of the previous version EN 50160:2010 [1].

Novel power quality analysis is not only concentrated on PQ parameters but also finds a relation between external components and their impact on power quality. In the case of integration of distributed generation with a power system, a prominent example is the influence of weather conditions on power quality. A tool used for the assessment can be a correlation analysis. Thus, an additional aim of the paper is to investigate the influence of the aggregation time interval on the correlation analysis. Generally, it can be concluded that the obtained result of the investigated differences indicates that the correlation analysis using 10 min and 1 min aggregation intervals are characterized by comparative level of correlation coefficient. The application of different aggregation time intervals does not change the direction of the correlation but has an influence on the absolute value of the correlation coefficient. The 10 min data are more smoothed than the 1 min time series due to the averaging process over the 10 min and correlation coefficients obtained using 10 min aggregation are slightly higher. Only in case of flicker severity, expressed by parameter *P*st, which is sensitive even for single voltage fluctuations, is the difference of the correlation coefficient noticeable.

The obtained results indicate the need for further investigation of the sensitivity of the assessment when new requirements for power quality limits are created or a shorter aggregation time interval is considered. The advantage of the application of a shorter aggregation time interval is the enhancement of the observability of the investigated objects. However, it has an impact on extended requirements for the measurement devices and increases the time and computational power required for analysis due to the extended size of the power quality database.
