Improving availability and maintenance by measuring battery impedance : a case study of electric power plant

Abstract

The objectives of this thesis are to improve availability and maintenance of battery. The battery is an essential back-up power source of control equipment in electricity generating process. When main power supply fails, if the battery not functioning properly, it may cause the process to shutdown. Normally there is routine maintenance schedule of battery, but it cannot determine the availability of battery. The availability can be determined by load testing method. This method requires specific testing equipment, which is a resistor bank and a long length of time for testing. Although the result of load test can be used to determine the availability, the test is time consuming and costly, requiring the load to be taken off, and the result of test cannot used to predict tha availability of battery. This study starts from learning the battery component, measurement of capacity and availability of battery. The impacts of battery capability are also included. Instead of relying on the traditional load testing impedance testing approach is the method that will be implemented in this thesis. Impedance is the internal resistance of battery. The value of impedance to the availability of battery has strong correlation. The impedance testing can be performed during battery system on-line. It is unnecessary to take the load off. Impedance of each battery cell will be compared with the average impedance value of battery inthe same system. The cell that has deviation over 20% of system average is required to be repaired or planned for replacing. In order to confirm the accuracy of impedance testing, load testing will be performed after the impedance test. The result of this thesis will be implemented for other power plants of the Electricity Generating Authority of Thailand in order to improve the availability and maintenance of battery. Furthermore, the impedance testing method is capable of reducing test cost about 56%, and time duration for testing.