Interval prediction of wind power, which features the upper and lower limits of wind power at a given confidence level, plays a significant role in accurate prediction and stability of the power grid integrated with wind power. However, the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function, which neglects the correlations among various variables, leading to decreased prediction accuracy. Therefore, in this paper, we improve the multiobjective interval prediction based on the conditional copula function, through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function. We use the multi-objective optimization method of nondominated sorting genetic algorithm-II (NSGA-II) to obtain the optimal solution set. The particular best solution is weighted by the prediction interval average width (PIAW) and prediction interval coverage probability (PICP) to pick the optimized solution in practical examples. Finally, we apply the proposed method to three wind power plants in different Chinese cities as examples for validation and obtain higher prediction accuracy compared with other methods, i.e., relevance vector machine (RVM), artificial neural network (ANN), and particle swarm optimization kernel extreme learning machine (PSO-KELM). These results demonstrate the superiority and practicability of this method in interval prediction of wind power.

Existing reactive power systems do not readily provide support or anti-disturbance capabilities. This study was conducted to explore the predisposing factor and suppression measures of low-frequency oscillation in large-scale wind power cluster systems by establishing a wind farm cluster mode with wind power fluctuation in a DIgSILENT/power factory. Considering the multiple time scale and the operating characteristics of cluster system, a novel modified fruit fly optimisation algorithm (nMFOA) combined with probabilistic sensitivity indices is proposed to coordinate and optimise static VAR compensator (SVC) damping controller parameters to enhance the power system stability of the wind farm cluster. Adverse effects in the SVC damping controller are eliminated via the nMFOA with probabilistic eigenvalue, which can be used to effectively coordinate and optimise SVC parameters. The proposed scheme was tested on a certain wind farm cluster in Hami, Xinjiang Province. * damping constant expectation h total number of damping controllers n total number of oscillation models ξ¯i expectation of damping ratio λ i

Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

Due to the insufficient consideration of medium and long-term wind power contract power in short-term dispatch, long-term planning and real-time consumption of wind power cannot be effectively undertaken, resulting in a large amount of abandoned wind power. A way to improve the wind power absorption capacity has become an urgent problem to be studied. According to the characteristics of the market and dispatching in the process of wind-fire integration construction, this paper constructs a wind power consumption model that connects the mid- and long-term transaction power decomposition and short-term dispatch. Considering the unit output characteristics and maintenance, the monthly contract electricity is decomposed into daily electricity, and the nesting of medium and long-term transactions and short-term scheduling is realized; the second stage is a short-term multi-objective optimal scheduling model considering the decomposition of contract electricity and the output of non-bidding units to improve the real-time consumption of wind power. Finally, a province in northwest China is taken as an example to verify the effectiveness of the proposed method.

High-precision wind power prediction could reference the optimal dispatch and stable operation of the power system. This paper proposes an adaptive hybrid optimization algorithm that integrates decomposition and reconstruction to effectively explore the potential characteristics and related factors of wind power output and improve the accuracy of short-term wind power prediction. First, the extreme-point symmetric mode decomposition is used to analyze the periodicity, trend, and abrupt characteristics in the original wind power sequence and form multiple intrinsic mode functions with local time-domain characteristics. Then, considering the similarity of the feature sequence and the efficiency of the prediction algorithm, the permutation entropy is used to reconstruct the components with close time-domain characteristics to form subsequences that could reflect different spectral characteristics. Then, the improved maximum relevance minimum redundancy-the long short-term memory-the adaptive boosting algorithm model is used to determine the prediction model structure, parameters, and optimal feature factors of the subsequences. Finally, the prediction results of each subsequence are integrated to obtain the final wind power. Taking a wind farm in northern Shaanxi as the application object, the prediction accuracy and efficiency of the methods proposed in this paper are compared in terms of the decomposition method, prediction model, and prediction timeliness. The results show that in the 15 min to 3 h forecast periods, compared with other models, the mean absolute error and root mean square error of the proposed model are increased. At the same time, as the forecast period grows, the superiority of the proposed method is more prominent.