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In spite of its several advantages, a classic direct power control (DPC) of doubly fed induction generators (DFIGs) driven by variable speed wind turbines has some drawbacks. In this paper, a simple and robust total sliding mode controller (TSMC) is designed to improve the classical DPC performance without complicating the overall scheme. The TSMC is designed to regulate the DFIG stator active and reactive powers. Two integral switching functions are selected for describing the switching surfaces of the active and reactive powers. Reaching phase stability problem of the classical sliding mode controller is avoided in the proposed TSMC. Neither current control loops nor accurate values of machine parameters are required in the proposed scheme. In addition, axes transformation of the stator voltage and current are eliminated. The grid side converter is controlled based on DPC principle to regulate both DC-link voltage and total reactive power. The feasibility of the proposed DPC scheme is validated through simulation studies on a 1.5 MW wind power generation system. The performance of the proposed and conventional DPC schemes is compared under different operating conditions. ª 2015 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Summary In this paper, an energy management strategy of direct current (DC) microgrids (MGs) is presented. The proposed MG consists of fuel cells, batteries, and supercapacitors, along with associated DC/DC and DC/AC converters. Coordination between different sources of MG is implemented using a multi‐agent system. Multi‐agent algorithm is implemented using an open source agent building toolkit, Java Agent Development framework (JADE). The energy source components are modeled and implemented using MatLab/Simulink (power system library). The proposed energy management strategy is designed using proportional‐integral (PI) controllers, which is implemented using the JADE. Interface between MatLab/Simulink and multi‐agent system (JADE) is done with the help of MACSimJX. The proposed multi‐agent framework is presented and the interface between the JADE and MatLab/Simulink is described in details. Then, the design and implementation steps of the PI controller using JADE are presented. Simulation work is carried out, and the results show that the proposed multi‐agent‐based controller effectively coordinated with variable loads in MG.