Designed damping controller for SSSC to improve stability of a hybrid offshore wind farms considering time delay

This paper presents the stability improvement results of hybrid doubly fed induction generator (DFIG)-based and permanent magnet generator (PMG)-based offshore wind farms (OWFs) using a static synchronous series compensator (SSSC). An adaptive-network-based fuzzy inference system (ANFIS) controller of the proposed SSSC is designed to render adequate damping characteristics to the studied system. A frequency-domain approach based on a linearized system model using eigenvalue technique analysis is performed. A time-domain scheme based on a nonlinear system model subject to a three-phase short circuit fault at infinite bus with variations in the signal transmission delays has also been investigated to compare the damping of the studied system in cases of with and without controller. The simulation results with MATLAB/SIMULINK toolbox have been presented. It can be concluded from the simulation results that the proposed SSSC joined with the designed ANFIS damping controller can offer adequate damping performance to the studied hybrid DFIG-based and PMG-based OWFs under severe disturbance.     

Stress distribution in thick-walled cylinder due to non-uniform radial pressure on the example of reinforcement corrosion in concrete

This paper presents an analytical solution to the non-uniform pressure on thick-walled cylinder. The formulation is based on the linear elasticity theory (plain strain) and stress function method. As an example, the proposed solution is used to model the stress distribution due to non-uniform steel reinforcement corrosion in concrete. The model is formulated considering different scenarios of corrosion pressure distribution. It is validated against the finite element model for different cases of non-uniform pressure distributions. The results show that the corrosion-induced cracks are likely to start just beyond the anodic zone. This is confirmed by the experimental tests on concrete cylinder exposed to non-uniform accelerated corrosion of steel reinforcement. The model can be effectively used to calculate the distribution of corrosion-induced stresses in concrete.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

Synthesis and Interfacial Tension Behavior of Heavy Alkyl Benzene Sulfonates

Heavy alkyl benzene sulfonates (HABS) were synthesized, and their interfacial tensions were measured and compared with those of petroleum sulfonates ORS-41. Effects of their coupling with lignosulfonate were also studied. The results showed that HABS exhibited good interfacial activities and the system of HABS blending with LS displayed better interfacial activities than ORS-41 and therefore reduced the prime cost. Their best proportions of synergism were from 3:1 to 4:1. HABS might be a more promising alkali-surfactant-polymer (ASP) flooding agent.     

一种单相串联混合电力有源滤波器的研究

文中对串联混合电力滤波器抑制谐波的原理进行了分析。为了检测单项系统的谐波电流,提出一种基于虚拟三相法和瞬时无功理论的谐波检测方法,并且对串联滤波逆变器的直流侧电压的控制进行了讨论。仿真结果证明了其有效性。     

宝浪油田宝北区块低渗透油藏层系优化研究

针对宝浪油田宝北区块低孔低渗油藏地质特征以及油层层数多、分布井段长、部分零散层动用难度大等问题 ,研究了宝北区有效厚度、油层层数、单井产能及单井控制地质储量等层系划分与组合的技术经济界限 ,并在此基础上进行了宝北区层系划分与组合优化论证 ,最终优选出宝北区开发层系     

声光效应拉曼-内斯方程矩阵级数解法及声光调制器优化设计

提出一种求解正常声光相互作用拉曼 内斯 (Raman Nath)方程的矩阵级数解法 ,该解法直观方便且具有普遍性。计算结果表明 ,对Q =4 1π ,Bragg衍射的效率只有 97 5 % ;对非对称入射 ,以往的Raman Nath近似解误差较大 ;指出提高Bragg衍射效率的有效途径在于提高声光频率比并给出计算声光器件最优长度的计算公式。     

Biodegradation of monoaromatic hydrocarbons in aquifer columns amended with hydrogen peroxide and nitrate

The ability of indigenous microorganisms to degrade benzene, toluene, ethylbenzene and xylenes (BTEX) in laboratory scale flow-through aquifer columns was tested separately with hydrogen peroxide (110 mg/l) and nitrate (330 mg/l as NO₃⁻) amendments to air-saturated influent nutrient solution. The continuous removal of individual components from all columns relative to the sterile controls provided evidence for biodegradation. In the presence of hydrogen peroxide, the indigeneous microorganisms degraded benzene and toluene (> 95%), meta- plus para-xylene (80%) and ortho-xylene (70%). Nitrate addition resulted in 90% removal of toluene and 25% removal of ortho-xylene. However, benzene, ethylbenzene, meta- and para-xylene concentrations were not significantly reduced after 42 days of operation. Following this experiment, low dissolved oxygen (< 1 mg/l) conditions were initiated with the nitrate-amended column influent in order to mimic contaminated groundwater conditions distal from a nutrient injection well. Toluene continued to be effectively degraded (> 90%), and more than 25% of the benzene, 40% of the ethylbenzene, 50% of the meta- plus para-xylenes and 60% of the ortho-xylene were removed after several months of operation.     

以齐聚副产物制备耐高温泡沫剂

以齐聚副产物为原料在实验室分别以AlCl3、AlCl3络合物和杂多酸为催化剂合成并经磺化制备了用于提高石油采收率的耐高温泡沫剂，试验结果说明烷基碳数为16的异构烷基苯磺酸盐常温发泡性能最佳，烷基碳数大于20的正构烷基苯磺酸盐高温发泡性能最佳，后者特别适合高温蒸汽驱油。加入适当的非离子助剂及无机盐能够提高磺酸盐的水溶性，改善发泡剂溶液的稳定性。