utility equipment, such as condition based monitoring (CBM) and modern diagnostic ... accurate data needed to optimize the scheduling of downtime, labor.
Identification of failure root causes using condition based monitoring data on a 33 kV switchgear
A.H.A. Bakar, H.A. Illias , M.K. Othman , H. Mokhlis
1. Introduction Nowadays, electrical utility companies are under tremendous pressure to reduce the maintenance cost due to the rising cost of operation. This has led to many diagnostic techniques of electrical utility equipment, such as condition based monitoring (CBM) and modern diagnostic techniques, e.g. systematic stochastic petri net based methodology and using computational intelligence. Through equipment diagnostics, the actual fault compartment can be identified and the duration of the repair can be estimated. A systematic stochastic petri net based methodology was used for the simulation of oil-immersed transformers fault diagnosis and the repair actions [1]. In this technique, transformer fault was detected using an integrated safety detector for sealed type transformer completely filled with oil and a Buchholz relay and an oil thermometer for transformer with conservator tank. From the simulation results, the transformer fault was able to be identified and the duration for transformer repair could be estimated. The adoption of modern CBM techniques are being implemented widely nowadays [2–4]. They are used to continuously monitor the function parameters and also to assess the deterioration of equipment before a malfunction of a system occurs. This approach is different than the conventional maintenance practices, as they are usually based on a fixed time interval between equipment inspections. When an inspection is due, the equipment is removed from service for testing. Some electrical utility companies in the world have increased the time interval between equipment inspections to reduce the maintenance cost. Although this practice reduces the cost in short term, it may have a detrimental effect on equipment reliability and system security. Therefore, a system which can provide reliable and accurate information to allow the utility to develop a conditionbased maintenance schedule is required. The CBM techniques were developed to provide simple and inexpensive methods for assessing the performance of equipment. The data acquired can be used for schedule maintenance. By targeting maintenance resources in this manner, electrical utility companies can increase the operational efficiency and reliability and reduce the maintenance cost. CBM is a maintenance method where the condition or health of the primary equipment is monitored and analysed for the earliest signs of impending failure [5,6]. It is a measurement performed at a scheduled time when the maintenance activities, i.e. preventive, routine, corrective are the most cost effective and before the equipment
loses its optimum performance. Equipment can be monitored using sophisticated instrumentation such as vibration, infrared analysis equipment or ultrasound technology. An effective CBM techniques offer several benefits, ranging from improved asset availability to the significant reduction of energy consumption. Instrumentation used as part of CBM should provide critical and accurate data needed to optimize the scheduling of downtime, labor and materials, increase the productivity and reduce the overall cost [7]. Two main difficulties in a comprehensive CBM analysis are identifying the root causes that contribute to equipment failure and using an appropriate mitigation plan to overcome the root cause of the failure. Therefore, in this paper, the actual site data obtained from CBM activities that have been carried out by an electrical utility company in Malaysia on a 33 kV switchgear from 2007 to 2010, were used to develop a failure root causes database. It was used to identify the root cause of a failure and to propose a failure mitigation plan for any CBM activity. CBM methods that have been used on site testing are ultrasound, thermoscanning and transient earth voltage (TEV) methods. The CBM data was used to create failure root causes database, at various compartments of the switchgear. The proposed method was tested using an actual site data obtained in 2011 to verify the effectiveness of the method, through a graphical user interface (GUI) that was developed. The proposed identification and mitigation plan of the root cause of failure can be said advantageous to many electrical utilities. This is because from the actual CBM activities on equipment, the actual root cause of a failure can be identified quickly using the proposed method. Thus, the time and cost of a repair can be reduced.
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