A gbrt-based static voltage stability margin evaluation method and system
A technology for static voltage stabilization and power system, applied in system integration technology, information technology support system, AC network voltage adjustment, etc., to achieve the effect of high real-time performance and accurate prediction
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Embodiment 1
[0058] Such as figure 1 Shown is a functional architecture diagram of the on-line monitoring system of the GBRT-based voltage stability margin evaluation method of a preferred embodiment of the present invention. The on-line monitoring system includes the PMU device 1 of each substation, the phasor data concentrator 2 that summarizes the PMU data of each substation, and evaluates the static voltage stability margin of the current system based on the data of the phasor data concentrator 2 and the evaluation method Degree Control Center3.
[0059] The PMU device 1 of each substation collects the current PMU data of the corresponding node, and transmits the PMU data to the phasor data concentrator 2 for summary; the control center 3 improves the decision tree based on the current grid structure of the power system and the gradient ( Gradient Boosting Regression Tree (GBRT) voltage stability margin evaluation method is used to match the nonlinear relationship trained in advance t...
Embodiment 2
[0114] In order to further optimize the economic cost and index accuracy of PMU layout, the static voltage stability margin evaluation method based on GBRT in this embodiment further quantifies the importance of the input characteristics of each node, and sorts the importance of each node, so as to realize the importance of important nodes. filter. The static voltage stability margin evaluation method based on GBRT consists of a series of decision trees, and the input features will be used as the criteria for dividing the nodes of the decision tree. The closer it is to the root node, the higher the importance of the input features. By averaging the importance of each feature in all decision trees, its ranking can be obtained. The top ten input features with relative importance such as Figure 4 As shown, it is the result of importance normalization, that is, the relative importance of the feature with the highest degree of importance is 100. The importance of each node can b...
Embodiment 3
[0119] figure 1 The structure diagram of a static voltage stability margin evaluation system of a power system according to a preferred embodiment of the present invention is schematically shown. The evaluation system includes a PMU device 1 , a phasor data concentrator 2 and a control center 3 .
[0120] The PMU device 1 of each substation collects the current PMU data of the corresponding node, and transmits the PMU data to the phasor data concentrator 2 for summary; the control center 3 receives the data collected by the phasor data concentrator 2; the control center 3 according to The current grid structure of the power system is matched with the nonlinear relationship trained in advance by the voltage stability margin evaluation method based on GBRT, and the online evaluation of the static voltage stability margin is performed. When the control center 3 monitors that the stability margin is less than the specified threshold, it sends an alarm to the dispatching and opera...
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