Northeastern University

Other

Huaguang Zhang

Qiuye Sun

Zhenwei Liu

Tieyan Zhang

Xiangping Meng

Qingqi Zhao

Jianguo Zhou

Jun Yang

Dazhong Ma

Yan Zhao

Xinrui Liu

Jing Guo

Gang Wang

Jian Feng

Jinhai Liu

Fei Teng

Bin Jiang

Lei Wang

Guotao Hui

Bonan Huang

Jiayue Sun

Zhanshan Wang

Rui Wang

Lianzhi Wang

Senxiang Lu

Xintong Li

Zhiqiang He

Heng Du

Lei Chen

Xiaoyu Li

Zhenning Wu

Jingwei Hu

Zhenao Sun

Yushuai Li

Junsheng Liang

Jin Cui

Provided is an intelligent inversion method for pipeline defects based on heterogeneous field signals. The method includes the following steps: firstly, acquiring heterogeneous field signals, performing an abnormality judgement, then correcting base values of the heterogeneous field signals, and performing denoising treatment; padding the denoised heterogeneous field signals corresponding to the pipeline defects, unifying the heterogeneous field signals of different sizes into the heterogeneous field signals of same sizes, and performing a nonlinear transformation on signal amplitudes; designing a sparse autoencoder with an axisymmetric structure, and obtaining primary characteristics of the heterogeneous field signals; classifying the pipeline defects according to lengths, widths and depths to obtain category labels of the pipeline defects; designing a multi-classification neural network to classify the heterogeneous field signals, and extracting deep characteristics containing defect size information; and constructing a random forest regression model to realize intelligent inversion for sizes of the pipeline defects.

Intelligent inversion method for pipeline defects based on heterogeneous field signals

The present invention relates to an intelligent adaptive system and method for monitoring leakage of oil pipeline networks based on big data. The present invention effectively analyzes a large amount of data collected on site within a reasonable time period and obtains a state of a pipeline network by an intelligent adaptive method, thereby obtaining a topological structure of a pipeline network. The present invention specifically adopts a flow balance method in combination with information conformance theory to analyze whether the pipeline network has leakage; small amount of leakage and slow leakage can be perfectly and accurately alarmed upon detection; as a generalized regression neural network is adopted to locate a leakage of the pipeline network, an accuracy of a result is increased. Therefore, the present invention adopts a policy and intelligent adaptive method based on big data to solve problems of detecting and locating leakage of the pipeline network.

Intelligent adaptive system and method for monitoring leakage of oil pipeline networks based on big data

The present invention relates to an energy router for an energy internet, which comprises a three-phase three-level bi-directional rectifying unit, a six-phase interleaved DC/DC bi-directional conversion unit, a self-excitation soft start push-pull full-bridge DC/DC bi-directional conversion unit, a three-phase resonant soft switching bi-directional inversion unit, a single-phase full-bridge bi-directional inversion unit, a high-voltage DC bus and a low-voltage DC bus. The three-phase three-level bi-directional rectifying unit, the six-phase interleaved DC/DC bi-directional conversion unit, the self-excitation soft start push-pull full-bridge DC/DC bi-directional conversion unit, the three-phase resonant soft switching bi-directional inversion unit and the single-phase full-bridge bi-directional inversion unit each have three energy flow operating modes: a forward conduction, a reverse conduction and a non-conduction. According to the energy flow operating mode of each unit, different operating modes of the energy router for the energy internet are formed.

Energy router for energy internet

Disclosed is a transformer control device and method based on a three-dimensional zone diagram policy. The method includes: setting transformers, including a transformer A and a transformer B; collecting three-phase voltage data and three-phase current data in a power system; rectifying the collected three-phase voltage data and the three-phase current data; transferring the data to the latches in a time-transfer method; transferring the data to an A/D converter of a processor to convert analog signals to digital signals; constructing a three-dimensional zone diagram in the processor; determining transformer operating conditions according to positions of points formed by voltage, reactive power factor and substation load; and adjusting the transformers. This invention adopts a simple, easy and clear three-parameter control policy including voltage, power factors and substation loads, so as to simplify analysis of transformer operating conditions, realize optimal substation operation, reduce transformer loss, and increase economic benefits.

Transformer control device and method based on three-dimensional zone diagram policy

An automatic three-phase unbalanced load compensation experimental device includes an automatic compensation device, a load simulation part, and detection, display and control parts. The automatic compensation device includes power capacitors and intelligent grouping compound switches. The detection, display and control part includes a plurality of current transformers, molded case circuit breakers, a three-phase digital display ammeter, a three-phase electric power monitoring instrument, a protocol conversion module, RS-485 buses, Ethernet cables and an upper computer. The load simulation part includes phase A, B and C loads which are in star connection and are same in component and circuit connection, an analog output module and a relay output module. A power monitoring instrument and digital ammeter are connected to the grid by circuit breaker and the plurality of current transformers. The automatic compensation device and load simulation part are connected to the grid in parallel.

Automatic three-phase unbalanced load compensation experimental device and its control method

Growing community of inventors

Shenyang, China

Dazhong Ma