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Fault trip of overload line transfers the power flow to other lines in the associated transmission section, which results in interlocking tripping accident. The key to prevent cascading tripping accidents is to quickly formulate a reasonable and effective power flow control strategy to eliminate line overload. In view of the defects of low accuracy of sensitivity method and too much calculation of optimization method, a double-layer optimal power flow control strategy of transmission section based on positive and negative comprehensive sensitivity is proposed. Considering the overall regulation effect of nodes on overload and heavy load line power flow, the positive and negative comprehensive sensitivity index is proposed, and then the power flow control strategy is quickly formulated by double-layer optimization method. In order to prevent the surge of computation caused by too large search space, a multi-objective selecting optimization model of the first layer scheduling nodes is established to optimize the scheduling nodes involved in the adjustment. On this basis, the second layer power flow regulation optimization model is constructed to obtain the accurate output adjustment of the scheduling nodes. The above model realizes the accurate dispatching of power flow out of limit on the transmission section. The simulation results of IEEE 39-bus system and a provincial power grid verify the rapidity and accuracy of the proposed method.

Aiming at the problem that traditional convolutional neural network (CNN) continuously stacks convo-lutional layers and pooling layers in order to obtain high accuracy, resulting in complicated model structure, long training time, and single data processing method, a optimized layered convolutional sub-health recognition algorithm of improved capsule network is proposed. Firstly, the original vibration data are transformed by wavelet denoising and wavelet packet denoising to better retain the useful information in the original signal for sub-health recognition. Secondly, CNN adopts the idea of layered convolution, parallelizes three convolution kernels of different scales, and carries on multi-angle feature extraction. Finally, the features extracted by the convolution kernels are input into the improved capsule network with pruning strategy for sub-health recognition. The improved capsule network can not only guarantee the accuracy, but also accelerate the sub-health recognition time, thus the problems of too compli-cated CNN structure and poor recognition effect are solved. Experimental results show that the proposed algorithm has high recognition accuracy and less recognition time.

Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment. [ABSTRACT FROM AUTHOR]

The growing penetration of wind power in a power system brings great challenges to system operation flexibility. For generation planning in presence of high wind power penetration, it is essential to take the operation flexibility of the system into account. Firstly, this paper developed the system operation flexibility metrics through considering the flexibility contribution of thermal generating units (TGUs) by operational state transition. Secondly, a planning model for the bundled wind-thermal-storage generation system (BWTSGS) that considers the operation flexibility constraints is proposed. The planning model is used to determine the power and energy rating of an energy storage system (ESS) as well as the type and number of TGUs. A daily scheduling simulation model of a BWTSGS is proposed to calculate the operation cost for the planning model and consider the sequential operation characteristics of the BWTSGS. Further, in order to accelerate the computation, a wind power sequential clustering technique based on the discrete Fourier transform (DFT) method is developed for improving the computational efficiency. Case studies have been conducted on a 1000-MW wind farm to demonstrate the validity and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]