-It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

Aiming at the problems of a low convergence speed, low accuracy and poor generalization ability of traditional power disturbance identification and classification methods, a new deep convolutional network structure is presented, and a power quality disturbance identification and classification method for microgrids based on the new network structure is proposed. The network consists of a five-layer one-dimensional modified Inception-residual network (ResNet) (1D-MIR) and a three-layer full-connection tier, which is a deep convolutional network. The idea of the method can be described as follows: First, power disturbance signals are expressed by an n-dimensional unit vector, and a database of these power disturbance signals is established. Second, the disturbance signals in the database are randomly sampled, and the power quality disturbances are calibrated with the n-dimensional unit vector to form both data and test sets. Finally, the gradient descent method and the adaptive moment estimation method (Adam) are adopted to train and optimize the network, respectively, and the trained and optimized network is applied to power quality disturbance identification and classification. A large number of experiments has been conducted, and the obtained results show that the constructed network can quickly extract the characteristics of the various disturbance signals, including single and composite disturbances, and identify and classify them. A comparison of the results obtained by the proposed method with those obtained by several other methods reveals that the proposed method attains a higher accuracy, higher convergence speed and stronger generalization ability. INDEX TERMS New network structure, power quality disturbance detection, deep convolution neural network, deep learning.

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Considering uncertain wind power and dispatchable load, a mixed probabilistic and interval optimal power flow model is proposed, and Monte Carlo sampling and affine arithmetic method are used to solve it. First, an uncertain optimal power flow model with mixed probabilistic variables and interval variables is established by expressing the uncertain dispatchable load as the interval model and the uncertain wind speed and node load as the probabilistic model. Then Monte Carlo sampling is used to sample the probabilistic variables in the proposed model. By this way, the mixed probabilistic and interval optimal power flow can be transformed into interval optimal power flow with sampling points, and the interval optimal power flow of each sampling point can be solved by the affine arithmetic method. Finally, a maximum probability density function and a minimum probability density function are synthesized based on the interval extremum of unknown variables in optimal power flow for each sampling point. The numerical results obtained by the IEEE-118 and IEEE-300 bus systems show that the mixed probabilistic and interval optimal power flow model has the merits of handling the problem including both probabilistic variables and interval variables at the same time, obtaining the probabilistic interval of optimal power flow with any value and learning the maximum probability and minimum probability of the power system's possible operating status. The proposed algorithm has the advantage of high solution efficiency.