The Design And Study Of Online Battery Detecting

As the DC backup power of power system when AC power failure occurs, storage battery takes on to provide uninterrupted power supply under the malfunction for each kind of DC operating mechanism. While, both at home and overseas, there have repeatedly been events that the electrical network collapses because of the battery breakdown. Although the power plant has the regular overhaul plan every year, however, because of the long time interval, it cannot effectively prevent the above events.In view of the present regular overhaul plan deficiencies, this article proposed a online battery detecting system design proposal based on SCM and solid state relay, the goal of which lies in the storage battery running status online, real-time detecting, and reminding the power plant staff promptly of the possibly trouble in storage battery. This system select P89V51RD2 as the central processing unit, companied with MAX818, the WDT circuit, 62256, the external data memory and GAL16V8D, the PLD device, which take on to collect the controlling pins, as the peripheral device. Referring to the function, the system can be divided into running parameter detecting subsystem, time maintaining subsystem, indicating subsystem and the serial communication subsystem, and so on. As it comes to soft part, this paper select Keil and Delphi as the developing tools, one for the hypogynous monitor, and the other for the upper monitor.The problem of battery failure in abnormal conditions will effect operations of electrical network seriously. Except real-time running parameter detecting, the hidden trouble should also be forecast. Based on the in-deep study of existing grey forecast model, this paper present an improved battery capacity grey forecast GM(1,1) model, which can. This method can realize the early failure prediction of the battery, and reduce the times of the traditional discharge test for battery capacity, thus prolongs the service life of the battery. Forecast examples show that the improved GM(1,1) forecast model of the battery capacity, comparing with the traditional GM(1,1) forecast model, has the higher modeling accuracy, and can satisfy the engineering requirements.