This paper presents the details and difficulties on asset management process of power apparatus used in power network. The actual condition of power apparatus is far more important than its age. The regulatory and market forces in the current competitive energy market that are challenging the electric utility require more proactive methods of utility asset management.
The utilities try to implement from the classical time based maintenance to a condition-based, predictive maintenance program using the concepts of reliability centered maintenance. Many diagnostic and condition monitoring techniques are developed for in-service power apparatus. Precise information about particular aspects of an assets performance is presented in a user-friendly way. Management group monitors the asset and network
performance, investment and operating costs, and develop policy and standards, leading to an overall strategy for the network. On line static and dynamic data is the essential ingredient to effective asset management.
The cost of condition monitoring can be recovered by reducing maintenance costs. Life cycle
cost of an apparatus can be broken down into three distinct areas: purchase cost with installation, operations and maintenance, and decommissioning. The essence of asset management is to reduce the cost of keeping the asset in service and extending the period for which the asset provides satisfactory service. Assets age differ at different rates depending on the duty imposed on them. The current technology suggests that effective reliable age can be predicted more scientifically by blending financial aspects and condition indicators. The trend of condition indicators can predict potential failures and enabling corrective maintenance which is normally less expensive than repair following a catastrophic failure. More organized knowledge base needs to be developed to achieve this goal. This paper reviews the asset management process, the simple theory of power apparatus failure and the existing condition monitoring methods to predict that and the modern trend of intelligent asset decision process. To read more please click here