A self-diagnosis and self-recovery method for an intelligent substation system

Abstract

The invention discloses a self-diagnosis and self-recovery method of an intelligent transformer substation system, which comprises the following steps that: (1) a monitoring server host regularly acquires a memory size occupied by core processes of the system, and self-recovery is started when the memory size exceeds a preset threshold, (2) abnormity detection is carried out by closed-loop self-checking between the monitoring server host and a monitoring client, and the self-recovery is started when the monitored abnormity frequency exceeds a preset threshold accumulatively; and (3) after the self-recovery is started, master and slave switching operation of double servers is carried out, and the host restarts generated abnormal service processes. The self-diagnosis and self-recovery method of the intelligent transformer substation system employs the characteristics of basic frame refreshing, data refreshing, alarm uploading and the like in an operating period of the system; a closed-loop timing self-checking function is formed between the monitoring server host and the monitoring client; and when a self-checking result corresponds with an abnormal condition, a self-recovery policy of the system is started by using a hot standby characteristic of the system so as to ensure long-term reliable operation of the system.

Description

technical field [0001] The invention belongs to the field of comprehensive automation of intelligent substations in power systems, and in particular relates to a self-diagnosis and self-recovery method for an intelligent substation system. Background technique [0002] During the long-term operation of the monitoring server in the substation, there may be abnormalities due to various reasons (such as some hidden loopholes), and serious abnormalities will bring bad effects to users. The common exceptions in the system are as follows: application crashes caused by continuous accumulation of memory leaks; system exceptions caused by continuous accumulation of system resource leaks such as sockets and file handles; application exceptions caused by process or site deadlocks; insufficient protection against illegal data processing Caused program exceptions (pointer exceptions, stack corruption), etc. When the above reasons occur in the core service process of the monitoring syste...

AI Technical Summary

Problems solved by technology

When the above reasons occur in the core service process of the monitoring system, the monitoring system will show the following characteristics: the real-time data of the online monitoring screen is not refreshed; the generated real-time alarm information cannot be uploaded to the operator station; the remote control command cannot be issued; No historical data; unable to query historical alarm information

Images