Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Long-term load forecasting (LTLF) models play an important role in the strategic planning of power systems around the globe. Obtaining correct decisions on power network expansions or restrictions based on predictions help substantially reduce the power grid infrastructure costs. The classical approach of LTLF is limited to the usage of artificial neural networks (ANN) or regression-based approaches along with a large set of historical electricity load, weather, economy and population data. Considering the drawbacks of classical methods, this paper introduces a novel sequence to sequence hybrid convolutional neural network and long short-term memory (CNN-LSTM) model to forecast the monthly peak load for a time horizon of three years. These drawbacks include, lack of sensitivity to changing trends over long time horizons, difficulty of fitting large number of variables and complex relationships, etc. [1]. Forecasting time interval plays a key role in LTLF. Therefore, using monthly peak load avoids unnecessary complications while providing all essential information for a good long-term strategical planning. The accuracy of the proposed method is verified by the load data of “New South Wales (NSW)”, Australia. The numerical results show that, proposed method has achieved higher prediction accuracy compared to the existing work on long-term load forecasting.

CLLC-type dc transformer (CLLC-DCT) is very popular in the hybrid ac-dc microgrid thanks to its high-power density advantage and good bidirectional power transfer capacity. In the hybrid ac/dc microgrid, the open-loop control is always utilized by the CLLC-DCT to cooperate with the bidirectional interlinking converter to realize the power and voltage conversion between the ac and dc bus. This paper first studies the circuit parameters design of the open-loop controlled CLLC-DCT with consideration of such a realistic problem: The real inductors/capacitors values are actually different with their theoretically designed values due to the operation power and temperature variation. To solve this problem, a robust circuit parameters design scheme is proposed for the CLLC-DCT in this paper. With the proposed scheme, the designed CLLC-DCT exhibits good power transmission and voltage regulation ability in the hybrid ac/dc microgrid even when its actual inductors/capacitors values vary with the practical power and temperature. The robust design method is experimentally verified in a hybrid ac/dc microgrid prototype.

With the penetration of renewable generation and tightly regulated power electronic loads, the power quality and stability of modern DC microgrids are greatly challenged. To solve this problem, energy storage systems (ESSs) are widely proposed. Unfortunately, most of the existing control methods for ESSs can only ensure the stability of DC microgrids with small signal disturbances, which may not be satisfying for realtime applications where large signal disturbances exist. Moreover, they are usually designed for low boost ratio and low power converters, negating their suitability for grid-scale applications. To ensure the large signal stability of DC microgrids using high boost ratio interleaved converter interfaced ESSs, this paper proposes a new backstepping control strategy with finite-time disturbance observers. Its effectiveness is verified by simulation and experiments carried out on an interleaved double dual boost converter (IDDBC).

The interleave boost converter (IBC) has been used as the interface converter between the low voltage electrical energy sources, such as lithium-ion battery banks, solar panels and fuel cells, and the DC-bus of DC microgrids. With increasing penetration of tightly regulated power electronic loads, which behave as constant power loads (CPLs), the stability of microgrid DC bus voltage that is fed by the IBC is threatened by the loads‚ negative incremental impedance feature. To ensure the stability of bus voltage, this paper proposes a nonlinear disturbance observer (NDO) based sliding mode control algorithm. The proposed algorithm with excellent robustness, low computational burden and no extra hardware cost. A generalized average state-space model is proposed to facilitate the control design. In addition, a NDO is employed to estimate the output power of IBC rapidly and accurately. To verify the effectiveness of the proposed algorithm, simulation and experimental results are presented.