英文摘要 | The security and stability of power grid is highly related to the national economy and people's livelihood. With the increasing social demand for electricity, the coverage and complexity of modern power grid increase continuously, characteristics of high-dimensional nonlinearity and coupling randomness are respectively presented at the macro level and the micro level of the system and the components, resulting in the increasing difficulty of modeling and simulation analysis as well as the upgrading risk of voltage instability accident in the power grid. Traditional mechanism/model-based power grid static stability analysis approaches are flawed in their poor accuracy and real-time performance, thus facing urgent transformation. On the one hand, the critical elements of voltage instability cannot be accurately located and intervened in advance, which leads to blindness and passivity of the power grid prevention and control operation; on the other hand, due to the complex and changeable overall voltage stability situation of power grid, the hysteresis of situational assessment delays the prime time of prevention and control decision-making, leading to the deterioration of stability situation or even large-scale blackouts, which may cause serious social and economic losses.In view of the above defects, this dissertation focuses on the problem of static voltage stability situational assessment of complex power grid. From the perspective of information driven theory, the key information in the massive measured data is deeply excavated, the feasible laws contained in the mechanism model and operation experience are integrated. We studies the approaches of power grid static voltage stability state determination, margin estimation and critical element identification so as to improve the prevention and control ability of power grid, reduce the risk of voltage instability and collapse and prevent large-scale blackout effectively. The main work and innovation achievements are summarized as follows:
(1)An approach of voltage stability margin estimation with consideration of the grid topology information is proposed. Based on the similarity among nodes, a local structure containing topology information is designed. The estimation of voltage stability margin is compared with image understanding. The unstructured topology information is transformed into structured vector in local structure by graph embedding technology, and the operational information of node is integrated as the input of deep convolution neural network. Then, the "local" and "global" features are fused hierarchically to form a high-order representation for building the real-time voltage stability margin estimation model based on measured information. Experimental results show that the introduction of local structure can effectively reduce the estimation error at multiple aspects, which proves the excellent performance of the proposed approach. Facing a single normal or fault state, this study realizes the static voltage stability situational assessment driven by the "model+information" from the perspective of quantitative analysis.
(2)An approach of voltage stability state determination based on regional information integration is proposed. Aiming at the problem that the network topology changes caused by line fault affect the stability state as well as the stability state has multiple load distribution subtypes due to the fault propagation characteristics, the proposed approach divides the network into sub-regions according to the correlation of energy transmission paths of the source nodes and introduces the quantitative representation of topology change based on the betweenness centrality. A multi-level deep neural network, which integrates the sub-region state information, topology change information and fault description information, realizes the multi-classification of voltage stability state based on the measured information. Experimental results show that the proposed approach can improve the accuracy of stability state determination obviously. Facing multiple fault states, this study realizes the static voltage stability situational assessment driven by "model+information" from the perspective of qualitative analysis.
(3)An empirical information driven unsupervised voltage stability state evaluation approach is proposed. Aiming at the problem of low modeling accuracy caused by the increasing complexity and randomness of power grid system and component, an approach for dimensionality reduction-based clustering guided by empirical rules is proposed. The optimal latent space for voltage stability state evaluation is constructed based on Siamese autoencoder, and further combined with clustering analysis to realize the effective division of voltage stability level without mechanism modeling. Experimental results show that the proposed approach can effectively approximate the theoretical true value. Based on unsupervised learning, this study explores an effective mode for information driven qualitative static voltage stability situational assessment.
(4)An approach of critical nodes and regions identification based on spatial-temporal information is proposed. The change degree of node voltage phase trajectory is used as the index for node importance and correlation degree measurement. The kinematic and morphological attribute information of voltage phase trajectory is extracted from the aspect of time and space without the need of grid component model and topological structure parameters. The critical nodes and regions are identified dynamically based on a two-stage information fusion and clustering analysis. Experimental results show that the identification results of the proposed method can accurately reflect the changes of voltage stability situation. From the perspective of traceability analysis, this study realizes the information driven search and positioning of critical elements of static voltage stability situation, forming a closed-loop of the overall research.
(5)Integrating the above key technologies, an on-line voltage stability situational assessment prototype verification system for smart grid is designed and implemented. Its overall architecture and core functions are described and the effectiveness of the proposed approaches are verified. |
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