In this paper, a sequence-to-sequence deep learning architecture based on the bidirectional gated recurrent unit (Bi-GRU) for type recognition and time location of combined power quality disturbance ...is proposed. Especially, the proposed methodology can determine the type of each element in input sequence, which is different from existing sequence-to-sequence model employing encoder-decoder network. First, the input sequence is normalized and batched. Second, deep features are extracted from input sequence by constructing Bi-GRU recurrent neural network, where multiple Bi-GRU layers are stacked together in both forward direction and backward direction. Third, according to aforementioned extracted features, fully connected layer and Softmax are employed to calculate the corresponding probability indicating the category that each element in input sequence is classified to. Fourth, Argmax or Top_K operation is further integrated to determine the type of each element in input sequence by selecting the maximal probability. Finally, the type is recognized, and meanwhile, starting-ending times of disturbances are also located just at the moment when the type is changed. The proposed model is further validated and tested by synthetic signals and practical field signals, respectively. Experimental results demonstrate that the accuracy of type recognition is over 98% for 96 kinds of disturbances including single and combined disturbances with signal-to-noise ration being 20 dB. Besides, the starting-ending times are also located with the absolute error less than six sampling points when sampling frequency is 256 points per cycle with noisy environment.
The pulse width modulation inverter is widely applied in independent PV renewable generation applications and uninterruptible power supplies. Many control schemes are investigated for the inverter ...feeding non‐linear loads, aiming to achieve the desired performance. However, the model currently proposed is mostly based on a linear load, which is not suitable for the condition with the non‐linear load. In this paper, the uncontrolled diode rectifier load model based on the AC and DC link power balances is analysed in detail first. Then, the rectifier inverter control system mathematical model is presented. The mathematical model derivation considers the rectifier load, especially non‐linear dead zone circuit performance that is closer to practical application conditions. Based on the proposed model, the output voltage control strategy with capacitor current feedback is further given. The operating principle of the proposed model and control scheme is analysed in detail. Additionally, the conclusions are validated by simulations and experimental results.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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.
It is well known that when applying wireless power transfer technology to energy storage loads, higher-order compensation networks can implement the load-independent output characteristics and ...simplify the control system. Analysis methods based on the equivalent model can effectively reduce the design complexity of high-order compensation networks. However, additional analysis is required to maintain zero phase angle of input voltage and input current and implement soft switching during the entire charge process simultaneously, when these analysis methods are adopted. Therefore, a composite network model, which is able to systematically analyze the load-independent output characteristics, zero phase angle, and soft-switching conditions of the higher-order compensation network, is proposed in this paper. To verify the reliability and effectiveness of the proposed composite network model, a 100-W lab prototype charger with the double-sided LCC compensation circuit has been built. Moreover, the composite model can be applied to other higher-order networks.
Wind power prediction is of great importance in enhancing wind energy penetration. This paper proposes a novel wind power prediction method which combining three-level decomposition with optimized ...prediction method. In the decomposition part, the Wavelet Packet Decomposition (WPD) is introduced as the first level decomposition, then the obtained sub-series are further decomposed by Variable Mode Decomposition (VMD). At last, Singular Spectrum Analysis (SSA) is carried out for each Intrinsic Mode Function (IMF), and the dominant component and residual components are separated as the input of the prediction. In the prediction part, Kernel Extreme Learning Machine (KELM) is adopted to complete the multi-steps wind power prediction. In this paper, an Improved Grey Wolf Optimization (IGWO) algorithm with redesign of the hierarchy and architecture is proposed, which especially suitable for optimizing wind power prediction. Finally, ten different models are compared, and the results show that the proposed method in this paper can extract the trend information of wind power greatly and has achieved excellent accuracy in short-term wind power prediction.
As an efficient wireless power transmission system for energy storage devices, load-independent output characteristics, zero phase angle and soft switching are required simultaneously. Consequently, ...a multi-objective parametric design method is proposed based on the output characteristics of the double-sided inductor-capacitor-inductor (LCC) compensation network in this paper. With the proposed method, two output modes of constant current and constant voltage can be implemented at the different resonant frequencies, which reduces the complexity of system control. In addition, the condition of zero phase angle in both modes is proposed. Another contribution of the proposed method is that soft switching in constant current mode and constant voltage mode can be achieved only by designing the compensation capacitor parameters, without any additional control strategy. Finally, in order to verify the correctness and feasibility of the proposed method, a 100 W double-sided LCC compensation network experimental platform is established. The experimental results show that the load-independent output characteristics and near unit power factor operation are achieved during the entire charging process.
Widespread adoption of electric vehicles (EVs) would significantly increase the electrical load demand in power distribution networks. Most previous studies investigated EV charging demand based on ...drivers’ trip habits, but the impact of psychological bearing ability (PBA) about the range anxiety on EV drivers’ charging decision are ignored. Here a novel forecast method considering drivers’ PBA for predicting nodal charging demand of EVs is proposed. The charging decision model considering PBA is established based on an improved Richards model, and the spatial‐temporal dynamics model is established based on the non‐homogeneous Markov chain (NMC) and random trip chain. Meanwhile, the Monte Carlo simulation (MCS) is adopted to avoid the disaster of dimensionality in large scale EVs charging problem. The proposed method is illustrated by an actual system integrated traffic network and power grid. The simulation results demonstrate that drivers’ PBA will significantly affect the charging decision, then changes the spatial‐temporal distribution of charging power demand. The conclusion is that the drivers with lower PBA have a higher charging demand, and the impact of drivers’ PBA on charging power has a close relation with the initial battery level (IBL) of EVs.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
This paper focuses on voltage source inverters used in new energy user power supply and energy storage power supply systems. If it is equipped with linear load, the power quality of output voltage ...can be easily guaranteed. If it is equipped with a nonlinear load, the system is affected by output impedance, and the output voltage is easy to be distorted. In this paper, the mechanism of output voltage distortion of inverter with a nonlinear load is studied. It is concluded that the distortion is caused by the voltage drop of nonlinear load current in the output impedance of the inverter. We proposed an embedded repetitive control compensator to improve the output voltage quality. In addition, we give out the Bode diagram parameters select method. The simulation results show that the proposed control strategy is feasible and effective.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The temporal and spatial distribution of electric vehicle charging load (EVCL) is closely related with the drivers’ trip behavior, which has a highly uncertainty. The access of EVCL will increase the ...risk of safe and stable operation of the distribution network. To evaluate the impact of EVCL access to system’s static voltage stability, we propose a novel probability evaluation method based on the combination of static voltage stability evaluation model and probabilistic power flow (PPF). Firstly, considering steady state operation, dynamic operation and ultimate operation of the distribution network, the three different static voltage stability evaluation models are established respectively. Then, the probabilistic power flow model is adopted to simulate the impact of EVCL on the power flow of the distribution network. To improve the low calculation efficiency of the PPF model based on the Monte Carlo sampling method, the Latin hypercube sampling (LHS) is used. The LHS-PPF model not only ensures calculation accuracy but also improves calculation efficiency. Finally, based on MATLAB 2020a simulation software, the proposed method is programmed in M language. The results show that the access of EVCL will obviously decrease the system anti-interference capability, and increase the risk of the system approaching limit operation state. The case studies verify the correctness and effectiveness of the proposed method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The finite set state machine theory belongs to the field of computer science. It is used to model and create multitasking systems. The control and modulation system for power electronics converters ...is a multitasking system as well. To increase the control speed without being constrained by the triangular wave module, the finite set state machine theory can be applied to converter modulation. In this paper, a modulation technique for finite state machines was proposed. This strategy fully uses the relationship between the affective state and converter switch states. Then, the structure of the modulation modular is presented in detail. This paper has analyzed the mathematical model of the finite set state machine modular. Finally, finite set state machine theory is used to Boost converter modulation modular to control the output voltage stability. Additionally, the effectiveness of the modulation strategy is validated by the experimental results.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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