# Optimum System for Diagnosing Power Quality in Electrical Microgrids
## 6 Conclusions  
The increasing importance of PQ necessitates analysis of microgrids from both the supplier's and the consumer's perspectives. Therefore, an electrical measurement network accessible to both is essential. It should also be scalable according to the current microgrid nodes, easily installable, and meet the standards for measuring electrical parameters.  
To overcome the indicated challenges, this paper presents an electrical measurement network (DiagElect) based on low-cost, open-hardware devices to guarantee its accessibility.  
The network simultaneously measures voltage and current at each node of a microgrid according to the established standards. These data are suitable for calculating the waveforms spectra and for comparing results from the different nodes of the microgrid, thereby facilitating any PQ analysis, including the detection and classification of both stationary and non-stationary PQDs, and the identification of nodes that inject harmonics.  
DiagElect, completely designed and built by the authors of this paper, consists of multiple measurement devices (SDMDs) connected to each microgrid node, along with a communication network that includes hardware and software elements for connecting all SDMDs, requesting and collecting measurements, and processing the collected data. The structure of DiagElect makes it a complete and versatile system that is easily portable, connectable, and extensible.  
In addition, the experimental results obtained for both the SDMD and DiagElect verify their good performance for each proposed application. The PQ analysis chosen to verify the usability of DiagElect has been the identification of the distorting nodes in a microgrid, because it is one of the most complex. As a future work, DiagElect could be assessed in additional PQ analysis and a new algorithm could be developed capable of reliably identifying harmonic sources, which would improve upon the current literature.  
Conceptualization, G.G.-R., R.S.-H. and J.M.A.; methodology, G.G.-R. and R.S.-H.; software, G.G.-R. and A.D.M.; validation, G.G.-R., R.S.-H. and A.D.M.; formal analysis, G.G.-R., [PERSON]-H. and J.M.A.; investigation, G.G.-R., [PERSON] and A.D.M.; resources, [PERSON] and J.M.A.; data curation, [PERSON]-R., [PERSON] and A.D.M.; writing--original draft preparation, [PERSON]-R.; writing--review and editing, [PERSON]-H. and J.M.A.; visualization, [PERSON]-R. and [PERSON]; supervision, [PERSON]-H. and J.M.A.; project administration, [PERSON]-H.; funding acquisition, [PERSON]-H. All authors have read and agreed to the published version of the manuscript.  
This paper is part of the project \"Development of a system based on Industrial IoT for the analysis of electrical power in power systems\", grant number UHU-202023, funded by the European Commission (EC), the University of Huelva (Q7150008F) and the Andalusia Government (S4111001F). The author [PERSON] is enjoying an FPU grant funded by the Spanish Ministry of Universities for the training of university teaching staff during his PhD period.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.