Stabilization Method Considering Disturbance Mitigation for DC Microgrids with Constant Power Loads

Liang, Haolan; Liu, Zhangjie; Liu, Hua

doi:10.3390/en12050873

Cited by 10 publications

(3 citation statements)

References 49 publications

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“…Under the pressure of environmental pollution and an energy crisis [1], the use of renewable energy sources, such as wind power, photovoltaic power, and biomass power, is rapidly increasing as alternatives to conventional sources [2][3][4][5][6][7][8]. Many countries have made several projections for large-scale integration of renewable energy sources into their grids [9][10][11]. Wind power generation is one of the fastest-increasing types of renewable energy generation [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

et al. 2019

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A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.

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Section: Introductionmentioning

confidence: 99%

Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

et al. 2019

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“…Due to the rapid development of society, the demand for energy consumption is rapidly increasing day by day [1]. As alternatives to conventional sources, the use of clean and renewable energy sources is rapidly increasing and is highly concerned by many countries around the world nowadays [2]. Under the strategic planning for 2030 carbon peak and 2060 carbon neutral in China, the use of clean and renewable energy sources will continue to increased and take up a larger proportion in China's grids.…”

Section: Introductionmentioning

confidence: 99%

Wind power short-term prediction over mountain area using a high-resolution WRF model

et al. 2021

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Accurate wind power prediction are crucial for power-grid integration and load balancing, as well as the safe and stable operation of the power grid. In this study, the relationship between the wind speed and wind power over mountain area is firstly investigated using the observations in Hunan Baiguoshan Mountain, and the fitting equation is proposed to predict the wind power with wind speed. Using the simulation of the WRF model with a 3-kilometer horizontal resolution, its prediction performance for short-term wind power is further analyzed. The results show that a sixth power relationship exists between wind speeds and wind powers over the mountain area. Also, when the wind speed reaches up to about 9.5 m/s (half of the cut-out wind speeds), the wind power is almost up to its rated power (2200 KW). The evolution characteristics of the wind powers predicted by the WRF model resemble that in observations, but the predicted wind powers are larger than that as observations in most time, which results from the overestimated predicted wind speeds like that in observations.

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“…The voltage stability and control are two of the most important phenomena in operation of DCMG, which are achieved through control strategies. A control scheme of a DCMG can either be local, global, or both (Dragičević et al, 2016; Liang et al, 2019). Each scheme may have different control parameters and be designed to achieve certain objectives or functions.…”

Section: Introductionmentioning

confidence: 99%

Output-feedback based robust controller for uncertain DC islanded microgrid

Mehdi

Saad

Jamali

et al. 2019

Transactions of the Institute of Measurement and Control

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The integration of renewable energy resources to DC microgrid has captured the attention of the researchers in recent years. One of the active field of application of DC distribution is the islanded DC microgrid (DC ImG). The DC ImG present numerous challenges to researchers. Among many challenges, the regulation of voltage and stability of the system is indispensable to efficient operation. The voltage stability problem becomes more prominent when the system is exposed to disturbances and possess uncertainties in parameters. However, challenges can be overcome by skilful design of a robust controller for the system. Therefore, in this paper, an output-feedback based centralized robust control scheme is proposed. The proposed controller is designed to maintain good control performance in the presence of parametric uncertainties and exogenous disturbances. The uncertainties of the DC microgrid is modelled as a linear time-varying state-space system. The upper and the lower bounds of the time-varying parameters are determined by a Lebesque-measurable matrix. To attenuate the exogenous disturbances of the system [Formula: see text] based output-feedback controller is designed. The system stability is assured by the Lyapunov function candidate. The output-feedback controller needs only the voltage measurement; therefore, it requires less communication bandwidth as compared to the state-feedback. To obtain the controller parameters linear matrix inequality constraints are formulated and solved. The performance of the proposed controller is verified via simulations and compared with the existing schemes.

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Stabilization Method Considering Disturbance Mitigation for DC Microgrids with Constant Power Loads

Cited by 10 publications

References 49 publications

Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

Wind power short-term prediction over mountain area using a high-resolution WRF model

Output-feedback based robust controller for uncertain DC islanded microgrid

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