5 results about "Bayesian formulation" patented technology

This invention discloses an adaptive reclosing method, system, and electronic equipment for power transmission lines in winter based on evidence theory fusion. The method includes calculating the posterior probabilities of different subdivided fault causes under corresponding weather characteristics during winter power transmission line faults using a Bayesian formula model. These probabilities are then used as weights and incorporated into the fault cause identification and classification results output by a pre-constructed waveform image recognition model. Major fault categories are further subdivided to obtain the identification probabilities of different subdivided fault causes. The posterior probabilities and identification probabilities are fused using a D-S evidence theory fusion model to obtain the fused probabilities of different fault causes. Based on these probabilities, the reclosing operation mode, reclosing time, and number of reclosing operations are dynamically adjusted. Compared to existing technologies, this invention significantly improves the accuracy of fault cause identification and enables the reclosing system to dynamically adjust according to weather changes and fault analysis, providing effective decision support for adaptive reclosing of power transmission lines in winter.

The invention relates to a method and equipment for tracing and repairing water conservancy data quality abnormity, and a medium, and belongs to the technical field of intelligent water conservancy. Collecting multi-source water conservancy data, extracting data features and generating a standardized feature vector set; constructing a three-layer Bayesian network topological structure of circulation link-business influence factor-anomaly type, training to obtain a dynamic weight Bayesian network model adaptive to the water conservancy scene, inputting the standardized feature vector into the dynamic weight Bayesian network model obtained by training, outputting quality anomaly root cause probability distribution, and obtaining the quality anomaly root cause probability distribution. The posterior probability of each abnormal root cause is calculated through a Bayesian formula, and a root cause output traceability result is judged; performing graded repair and verification according to different traceability results; and the repaired qualified data is synchronized to a data asset module of the water conservancy credible data space for service output and generation of a visual data quality report. According to the method, the traceability precision is remarkably improved, and the service adaptability is higher.

The present application relates to the technical field of data verification, especially relates to a kind of distributed photovoltaic irradiation data verification and correction method.The method first obtains grid-connected power station operating data, and the actual irradiance of each point is deduced based on photovoltaic module physical model;Subsequently, the reference point is screened from multiple dimensions, and the total deviation of measured and original simulated irradiance is decoupled into regional system deviation factor, local shading deviation factor and installation equipment deviation factor.On this basis, combined with historical meteorological statistics experience, the prior distribution of candidate point correction factor is established, the posterior mean and variance of each point correction factor are calculated by using Bayes formula to fuse measured likelihood information.Finally, the original data is calibrated using the expected value of correction factor, and the confidence interval under the preset confidence level is output.

This invention relates to the field of coating process monitoring and analysis technology, specifically to a coating process anomaly prediction system and method. The method includes: acquiring the core processes of coating production; establishing a data flow model between the core processes based on a multiple linear regression model; optimizing the regression coefficients of the model using Gaussian process regression combined with particle swarm optimization to determine the optimal regression coefficients; performing anomaly propagation path inference based on a Bayesian network path probability inference model; determining the edge weights of directed edges between nodes based on the calculation results of the improved Bayesian formula, and determining the influence coefficients between processes; processing the influence coefficients between processes, determining the predicted abnormal process for each process when an anomaly occurs, and sending notification information. This invention achieves accurate anomaly prediction and efficient notification, enhancing the accuracy and targeting of anomaly prediction, and is more suitable for the anomaly prevention and control needs of complex coating production processes.

The small current grounding system single-phase grounding fault line selection method solves the problem of how to improve the line selection accuracy, and belongs to the field of small current fault line selection. The two power frequency period data before the fault are used as the reference, and the data after the fault are compared by difference, so that the inherent interference such as load imbalance, harmonic background and line parameter asymmetry during normal operation of the system is eliminated, and the fault characteristics are more prominent. Secondly, four criteria (differential steady-state phase, wavelet packet differential energy spectrum, differential instantaneous phase consistency and envelope weighted cumulative polarity) are used to describe the fault from different physical aspects. Each criterion independently outputs a 0~1 confidence level, forming a multi-dimensional complementary verification mechanism. Even if a certain criterion fails under certain conditions, other criteria can still provide reliable evidence. Finally, the prior probability and the four criterion likelihood values corrected by dynamic weight are fused through the Bayes formula to obtain the posterior probability of each branch and bus grounding, and the high and low threshold values are used for hierarchical decision-making.