At present, the substation SCD model mainly aims at the modeling of primary equipment and secondary equipment, and the description of auxiliary equipment and substation area model is missing. According to the characteristics of equipment monitoring of the new generation centralized control station, the description of auxiliary equipment model and area model needs to be added to the original IEC61850 standard. In the first mock exam station, the current business requirements of centralized control station are analyzed. A unified model structure of master station is designed. Based on the definition of IEC61850 standard, a method and example of extending auxiliary device model and regional model are given. The proposed auxiliary equipment and area modeling method based on IEC61850 standard can make up for the shortcomings of the original standard, support the equipment monitoring of the new generation centralized control station and realize rich application functions.

This study focuses on maximum power point tracking (MPPT) control for photovoltaic (PV) power generation systems under partial shading conditions. A mathematic model of the partially shaded solar cell is built. Then, the output characteristics of the partial-shade array are analyzed. Based on the model of the PV battery and the concept of the average-state switch cycle, an average-state mathematical model of the PV power generation system using a boost circuit for the realization circuit is established. A sliding mode controller based on the integral sliding mode function is designed to realize MPPT in the PV power generation system. Finally, simulations in MATLAB/Simulink confirm the functionality and performance of the proposed controller.

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An integrated intelligence control method based on fuzzy and artificial intelligence (AI) is proposed aiming at combustion control of reheating furnace in steel rolling mill. Both fuzzy and AI strategies are used to solve problems of the bigger overshoot and the slower response in conventional hearth temperature control when gas pressure and heat value are frequently and acutely varied. The fuel-air ratio optimization and flux tracking modules based on AI respectively decrease fuel consumption and prolong the lifetime of actuators. The proposed method is implemented on an intelligence controller and distributed controllers, and the field test on two reheating furnaces in Lianyuan Iron and Steel Group Co. Ltd., Loudi, Hunan, China confirms that hearth temperature standard deviation, fuel consumption, and high temperature oxidation of billet are respectively decreased by 50%, 12%, and 10% over the current manual method. The proposed method delivers superior performance for reheating furnace in steel industry, but also it can be applied for other type furnaces in steel industry and in other industries, where further performance improvement might be achieved by adding a self-organizing capability to the fuzzy logic and AI control.