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Abstract. High efficient and accurate wind speed prediction is the basis of the wind farm power prediction. So it is helpful for the control of the wind power and has great importance to the parallel operation of the wind farms. Wind speed time series has strong nonlinear and volatility. Besides, it is very difficult to accurately predict. A new method of short-term wind speed forecasting is proposed based on regularized extreme learning machine (regularized extreme learning machine, RELM). First of all, the autocorrelation function (ACF) is used to analyze the correlation of wind speed time series. After that the number embedded in the time dimension is gotten. And the forecast network parameters such as inputs, output and so on are determined. That is to say the RELM model is set up. Then, using the training set trains the network parameters to get the RELM prediction model trained. Finally, prediction results are obtained with the test set data. And the wind speed data from the American wind energy technology center is carried out on the experiment. It shows that the new method has better prediction precision compared with the standard ELM and the traditional neural network.

With the improvement of the penetration of power electronic equipment, a challenging scenario is arising in power‐frequency control system design of virtual synchronous generator (VSG). This affects the stability and control performance of the multi‐VSG grid‐connected system due to the undesirable dynamic coupling of the power‐frequency control loops. From this perspective, this paper proposes a systematic procedure for accurately evaluating the coupling of power‐frequency control loop, which is based on the relative gain array (RGA) and Prony analysis method. Firstly, the output admittance model of multi‐VSG grid‐connected system is established, and the transfer function matrices of output power and angular frequency are derived, respectively. Then, the variation law of coupling between control loops of VSG with different parameters is investigated by two methods: One is numerical analysis, and the other is RGA method which can determine the frequency band of coupling. Besides, the interaction between different VSG control strategies is compared and analyzed based on RGA. Subsequently, Prony analysis method is proposed to verify the results of RGA analysis. Finally, simulation and experimental results in a two‐machine grid‐connected system are presented to support the theoretical analysis and demonstrate the feasibility of the proposed method.

Abstract. Accurate wind speed is the basis of the wind power prediction. And it is of great significance to the parallel operation of the wind farm. So it is to the maintenance of the safety and stability of the power system. In the view of the strong volatility and randomness of the wind speed time series, it is difficult to be predicted. A new method of short-term wind speed prediction is established based on the weighted regular extreme learning machine (Weighted Regularized Extreme Learning Machine, WRELM). First, the wind speed time series and the wind direction time series which have high correlation with the wind speed are taken account. Besides the meteorological factors are also taken as the candidate sets such as temperature, pressure, humidity and so on. Then the maximal relevance minimal redundancy (mRMR) principle is used to select the maximum serial correlation properties. And those are taken as the prediction inputs. Afterwards, the train set and test set of the prediction network are fixed to establish WRELM. Then, the network parameters are trained with the train set data. And the WRELM prediction model is built. Finally, the WRELM network is adopted to predict the short-term wind speed and the future wind speed are obtained. The data from the wind farm is carried out to do the experiment. And it wants to prove the effectiveness of the new method.