Suli Ren

Bio: Suli Ren is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Signal processing & Compressed sensing. The author has an hindex of 1, co-authored 1 publications receiving 16 citations.

New Modeling of Metal Oxide Surge Arresters

Abstract: This paper describes simplified modeling of metal oxide surge arrester (MOSA) to operate analysis. This model is a new model proposed (P-K Model) to verify the accuracy in order to compare with IEEE and Pinceti Model. The simulations are performed with the Alternative Transients Program version of Electromagnetic Transient Program (ATP-EMTP). In the present paper, the MOSA models were verified for several medium voltages which consist of 18 kV and 21 kV, which 18 kV arrester was used in 22 kV system of Provincial Electricity Authority (PEA) and 21 kV arrester was used in 24 kV system of Metropolitan Electricity Authority (MEA) in Thailand. The P-K model was evaluate from different manufacturing, it is based on the General Electric (GE), Siemens and Ohio Brass as well as IEEE and Pinceti Model. The tests are performed by applying a fast front current surge with front time of up to 0.5μs and the standard impulse current surge (8/20μs). The results were compared between three models in order to calculate the error operation of the MOSA in the ATP-EMTP Program. The relative error of arrester models show that the P-K model can be used to simulate and calculate in ATP-EMTP program as well as IEEE and Pinceti model. In the case of fast front current surge, the P-K model has a maximum error of 5.39% (Ohio Brass, 10 kA, 21 kV) and has a minimum error of 0.24% (GE, 10 kA, 18 kV). Also, the standard impulse current surge, P-K model has a maximum error of 2% (Ohio Brass, 10 kA, 18 kV) and has a minimum error of 0.32% (Siemens, 10 kA, 21 kV) in the voltage response.

An improved pipeline leak detection and localization method based on compressed sensing and event-triggered particle filter

Abstract: This paper proposes an improved model based pipeline leak detection and localization method based on compressed sensing (CS) and event-triggered (ET) particle filter (ET-PF). First, the state space model of the pipeline system is established based on the characteristic line method. Then, the CS method is used to preprocess the sensor signals to recover the potentially lost leak information which is caused by the low sampling frequency of the industrial pipeline sensors, and an event based beetle antennae search (BAS) particle filter (BAS-PF) is proposed to improve the accuracy and efficiency of the pipeline state estimation. Finally, a pipeline leak detection and localization method is developed based on the proposed signal processing, and state estimation algorithms, as well as a pipeline partition strategy. Experiment results show that the proposed method can accurately detect and locate the leak of the pipeline system with a localization error of about 1.4%.

Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

Abstract: Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.

Construction of Measurement Matrix Based on Cyclic Direct Product and QR Decomposition for Sensing and Reconstruction of Underwater Echo

Abstract: Compressive sensing is a very attractive technique to detect weak signals in a noisy background, and to overcome limitations from traditional Nyquist sampling. A very important part of this approach is the measurement matrix and how it relates to hardware implementation. However, reconstruction accuracy, resistance to noise and construction time are still open challenges. To address these problems, we propose a measurement matrix based on a cyclic direct product and QR decomposition (the product of an orthogonal matrix Q and an upper triangular matrix R). Using the definition and properties of a direct product, a set of high-dimensional orthogonal column vectors is first established by a finite number of cyclic direct product operations on low-dimension orthogonal “seed” vectors, followed by QR decomposition to yield the orthogonal matrix, whose corresponding rows are selected to form the measurement matrix. We demonstrate this approach with simulations and field measurements of a scaled submarine in a freshwater lake, at frequencies of 40 kHz–80 kHz. The results clearly show the advantage of this method in terms of reconstruction accuracy, signal-to-noise ratio (SNR) enhancement, and construction time, by comparison with Gaussian matrix, Bernoulli matrix, partial Hadamard matrix and Toeplitz matrix. In particular, for weak signals with an SNR less than 0 dB, this method still achieves an SNR increase using less data.