# Maintenance Management of a Transmission Substation with Optimization
## 6 Discussion  
In the field of maintenance of electric power system devices and networks, time-based maintenance (TBM) is still used widely. Nevertheless, a large number of companies are gradually introducing condition-based maintenance (CBM). An upgrade of such maintenance system is RCM, wherein the frequency of maintenance interventions is defined with regard to the state and importance of the EPS element. RCM maintenance methodology considers the reliability of the entire system and not only individual elements, which is the key advantage of this maintenance methodology.  
An RCM model was developed and tested for transmission substations. For this purpose, in the first step, input data were defined for the indicators of operational reliability, technical condition of devices, and maintenance costs. These data were obtained on the basis of results of diagnostics and monitoring of elements, as well as from the calculations of parameters for the entire Slovenian EPS. The RCM model was upgraded with an optimization algorithm that enables it to find the optimal maintenance costs at the guaranteed reliability of operation and the most suitable combination of generation, consumption, and power flows (SS). Improper maintenance of the EPS elements causes various risks. These risks can be managed by the introduction of maintenance optimization. For the maintenance costs to be reduced, it is necessary to know the limit of acceptable risks. The maintenance optimization mode presented in this paper was tested practically in one of the Slovenian transmission substations. The developed algorithm is universal and can be used for any TS and included in the entire process of EPS maintenance, i.e., in the asset management of any transmission system operator.  
A limitation of use of the proposed algorithm can, in the case of very large maintenance systems (e.g., a few thousands of elements), be problematic. In such a case, the use of conventional optimization algorithms, such as the one used in this paper, could jeopardize the stability of the optimization process and the accuracy of the obtained solutions.  
With the proposed new algorithm for calculating maintenance periods using the optimization process, we confirmed the hypothesis that analyzing the past data of operation, events, and maintenance costs enables a direct impact on maintenance process for the future periods. With this maintenance concept, we maintain the reliability of operation of EPS elements and of their technical condition. At the same time, we reduce maintenance costs. A comparison of the existing (TBM) maintenance concept and the proposed one confirms savings. The proposed computations showed 25.53% saving in the first year, 18.76% in the second year, and 6.77% in the third year. This was the confirmation of the second hypothesis.  
Further research work in the field of EPS elements' maintenance enables extensions of the optimization model on all substations and all transmission lines in the transmission system. Testing with different maintenance periods could be performed for more extensive comparisons. It would also be possible to perform research of the use and testing of different optimization algorithms, as well as introduction of new approaches, such as the deep learning maintenance process.  
Conceptualization, P.K. and J.R.; methodology, P.K. and L.B.; software, P.K. and J.R.; validation, L.B., J.P., and J.R.; formal analysis, J.P.; investigation, L.B. and J.P.; resources, L.B. and J.P.; data curation, L.B.; writing--original draft preparation, P.K.; writing--review and editing, J.P. and J.R.; visualization, P.K.; supervision, J.P. and J.R.; project administration, J.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.