Study Of The Data Compression Algorithm In The Scada System

As an important component of SCADA system, historical database saves important data in SCADA system day and night, so it provides first-hand information for management analysis of processing failures. In addition, historical database plays a great part in trend analysis, event processing, report printing and other services. With the reform and development of the railway and smooth progress of The sixth speed, the PSCADA system develop to the direction of large systems and networked control systems. The information which collected by the dispatch center in the new PSCADA distributed system is growing. The data must be effectively compression to ensure the vast amounts of data to real-time storage, to save storage costs as much as possible and improve storage efficiency. This paper researches the historical data compression strategy and the compression algorithm with the premise of fully considering of the characteristics of real-time history of the PSCADA system database and historical data. The findings are summarized as follows:1) The real-time history database system in SCADA system. Firstly, the characteristics of the real-time historical database are analyzed. Secondly, we describe the data distribution in the database and data reading and writing process. Finally, according to the characteristics of data in the historical database, we clarify the feasibility and necessity of data compression.2) Lossless compression technique is researched. Firstly, we introduce the concept principle, classification and application of data compression. Secondly, we explain fully the principle and implementation of the lossless compression algorithms include compression algorithm based on the statistical model, LZ series based on the dictionary model and other algorithms through examples. Finally,we implement three coding algorithms including the PPM encoding, the LZW encoding, the BWT encoding by C++.3) Lossy compression techniques are researched. The SDT algorithm which is most common in the real-time historical data compression is researched. We can see that the compression parameters E affect the compression quality and performance in the SDT algorithm through the simulation. Once E is does not match with the actual data characteristics, There will be severely limitation in the performance of the SDT algorithm. So we study an improved SDT algorithm which can adaptively adjust the E, and do the simulations.4) The application of the data compression technology in real-time history database is researched in this paper. The data compression and decompression module are achieved through the dynamic link library. The overall framework of the data compression module is reaearched. Different signals.such as:timestamp, switch type, integer type and analog type,have different recording methods.