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Hybrid cascaded-parallel microgrid is becoming a new emerging structure to integrate multiple low-voltage power sources. This paper presents a unified distributed control strategy to implement power sharing control in hybrid cascaded-parallel microgrid under both resistive-inductive (RL) and resistive-capacitive (RC) load, where a sign function is introduced to automatically match load characteristic. Active power and reactive power regulators without frequency drop are developed, and low bandwidth communication network is employed to support power management and improve system redundancy. Furthermore, small signal model of hybrid cascaded-parallel microgrid with RL load and RC load is established. Also, small signal stability and dynamic performance of the proposed distributed control strategy is investigated. Simulation results show that the unified distributed control strategy is able to implement desirable power sharing under different load types with superior control performance. Also, the proposed control strategy is able to improve system redundancy and support plug-and-play operation of microgrid.

This paper presents an integrated series-parallel microgrid(MG), where a few distributed generation(DG) units in series type are used to form a DG string, then DG strings supply power to the common load in parallel type. With this structure, low-voltage devices could be integrated into MG without DC-DC boost converters. It provides a more flexible and efficient way to form higher voltage level MG. But it is difficult to find a decentralized control method to achieve power balance for this integrated system because of the difference between power transmission characteristics of parallel and series converter system. For this proposed MG in island mode, a unified distributed control strategy is proposed. With low bandwidth communication, accurate power sharing can be realized by exchanging information between the first converters connected to the bus of adjacent DG strings and between adjacent converters inside each DG string. Because the central controller is not used, the communication pressure of the system is reduced and the reliability is guaranteed. Simulation test has verified the feasibility and validity of the proposed control strategy.

Hybrid series-parallel microgrid is a new promising structure for integrating low voltage (LV) and remote sources efficiently. DG units are connected in series as a string locally, and the strings supply power in parallel. This paper proposes a locally-distributed and globally-decentralized (LDGD) control for this microgrid. Local DG units are controlled as a consistent string by distributed cooperative control, and global power demand among strings is shared in a decentralized manner. The frequency synchronization and proper power sharing are achieved under both the resistive-inductive and resistive-capacitive loads. Moreover, only local sparse low-bandwidth communication (LBC) networks are employed, which improves the reliability and reduces the communication costs. The overall system stability is analyzed and reasonable ranges of control parameters are given. Finally, the feasibility and effectiveness of the proposed solution are verified by simulation tests.

Precise estimation of effective wind speed plays an important role in the advanced controls aiming at maximizing wind power extraction and reducing loads on turbine components. This paper proposes a sensorless effective wind speed estimation algorithm based on the unknown input disturbance observer and the extreme learning machine for the variable-speed wind turbine. First, aerodynamic torque is accurately estimated through an unknown input disturbance observer where the rotor speed is the measured output of the wind turbine drive train system. Then, the aerodynamic characteristics of the wind turbine are approximated by an extreme learning machine model based nonlinear input-output mapping. Last, effective wind speed is estimated based on the extreme learning machine model, using the previously estimated aerodynamic torque by the unknown input disturbance observer, together with the measured rotor speed and pitch angle. The proposed algorithm is validated by simulation studies on a 1.5 MW variable-speed wind turbine system. To evaluate the performance of the proposed algorithm, a detailed comparison with the Kalman filter-based method has been made. Comparison results clearly demonstrate that effective wind speed estimated by the proposed method is more accurate than that by the Kalman filter-based method and that the computational efficiency is higher.