Abstract
In order to accurately identify the key lines in the photovoltaic (PV) grid-connected system, an identification method based on the improved PageRank algorithm is proposed. Firstly, the correlation matrix reflecting the electrical characteristics of the system is constructed using the line current-carrying rate, line breaking power flow transfer rate and line coupling rate, to replace the original network topology matrix. Secondly, through the entropy method, a comprehensive evaluation index based on electrical betweenness, load deviation rate and voltage shock rate is constructed to improve the distribution of the initial PageRank (PR) value of the PV grid-connected system. To study the changes’ impact of PVs active power outputs on the identification results of key lines in the Multi-PV power system, the HGWO-SVM (Hybrid Grey Wolves Optimized Support Vector Machine) algorithm was used to obtain the PVs daily outputs prediction curves and obtain fixed outputs of PVs at different periods, so as to study the impact of the variation of PV daily output on the key line identification. Taking the IEEE 39-node system containing multi-PV as an example, the identification results show that the improved PageRank algorithm is superior to the original method in line identification accuracy. The HGWO-SVM algorithm by adaptively modifying the cross operator and mutation operator also has a certain improvement in prediction accuracy. The changes of PVs daily outputs have different degree of influence on the line criticality (namely final PR value) during periods of high light intensity and other periods of light intensity.
Highlights
With the rapid development of power industry, large-scale interconnection of power systems has become the norm
The period of low output has little impact on the line identification results, and the key line identification results are similar to the key line identification results of the unconnected photovoltaic; while in the high light intensity period of noon, that is, the PVs output reaches 75% and above during this period, It can be clearly found that the key lines of the system have changed
The improved PageRank algorithm is used to identify the key lines of the IEEE 39-node system, and on this basis, the influence of multi-PV grid connection power changes on the line identification is considered
Summary
With the rapid development of power industry, large-scale interconnection of power systems has become the norm. Most of the above methods do not consider the new energy access to the system, or the method of considering the new energy grid connection only reflects the difference between whether the system is connected or not For this reason, this paper considers the line current carrying rate under N-1 fault, the line breaking power transfer rate and the line coupling rate constitute the correlation index, and the electrical betweenness, load deviation rate and voltage impact rate constitute a comprehensive evaluation index. (2) Determine the initial PR value of the line based on the comprehensive evaluation index of electrical betweenness, load deviation rate and voltage shock rate. Based on the above-mentioned topology matrix G and the determined initial PR value allocation, a system key line identification method based on the improved PageRank algorithm is established. There are different degrees of difference, and this method ignores the sequence of identification lines and only considers the results of line identification, which is roughly consistent with the above six methods, which confirms the correctness of the method used in this article
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