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Key Technology and Engineering Application of Pre Decision System of Quasi Online Dispatching Control for Massive Power Grid Analysis: quasi online navigation system for power system operation

Based on the principles of (1) dispatching information system (OMS), (2) dispatching data acquisition and monitoring control system (SCADA), (3) wide area measurement system (WAMS) with multi data sources and quasi online time scale, under the existing pre-warning and pre-decision mechanism of Henan Electric Power Dispatching, this paper introduces the key technology and engineering application of quasi online dispatching control pre decision system for massive power grid analysis in Henan power grid. Based on the summary of scientific research and engineering research and development in recent five years, the concept and model of operation control quasi online navigation system for power system operation dispatching control strategy formulation system is proposed, which takes into account the historical state, current state and future state. Firstly, an open 4-layer architecture from data source processing to power grid early warning and pre decision-making process is proposed. Secondly, on this basis above, taking “the future state of the next week” as the core time scale, the technical route of short-term cycle analysis and operation control decision-making of three data sources, including OMS, SCADA and WAMS, is proposed, in which a pre decision-making system of quasi online dispatching control for Henan power grid is developed. The engineering application of the system, to a certain extent, reduces the workload of system operation analysts and power grid planners, and improves the level of power grid security control. The system transforms the “artificial intelligence” offline mode of making medium and long-term conservative operation rules into the quasi online medium and short-term fine operation rules mode of “artificial intelligence”, and promotes the transformation of scheduling early warning and pre-decision-making from “semi artificial and semi intelligent” scheduling mechanism to “intelligent” massive data, multi artificial intelligence algorithm, intelligent early warning and pre decision-making. Finally, the technical problems to be improved are pointed out.