Abstract
Reliability of an oil fuel distribution system can be achieved by considering multiple attributes. Load-point indicators consisted of the frequency of failures, average duration of an outage and average annual outage time. Indicators of system performance were SAIFI and SAIDI and the method of the study was the Learning Vector Quantization (LVQ). Decisions were made by considering the multi-attribute oil fuel distribution system to determine high reliability, which was simulated on the agent. The use of agents in the simulation to determine the oil fuel distribution system reliability helped visualize the oil fuel distribution in meeting the quality of fuels received by consumers and made it easier to learn the problems faced by oil fuel distribution.
Highlights
Rising world oil prices have tremendous impacts on Indonesia’s economy
The benchmark of distribution system reliability includes how often the distribution system experiences a delay, how long a delay occurs and how quickly is the distribution system to recover to its previous normal condition. (Portugal and Esteban, 2014)
System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI) input vectors produced a feeder with the shortest distance from the input vector to class 1 or the class with high reliability
Summary
Rising world oil prices have tremendous impacts on Indonesia’s economy. The burden of oil fuel subsidy borne by the government is becoming increasingly high. Reliability of fuel distribution system is measured by the oil fuel availability and service level from the system to the end-users. (González et al, 2009) Based on these indications, multiple attributes can be considered to achieve a highly reliable distribution system, such as the load points, SAIFI and SAIDI. An agent can help determine a highly reliable oil fuel distribution system with by considering multiple attributes. Flowchart of the study A classification process is required to determine the reliability of the distribution system by the use of the attributes of load points and system
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