Performance evaluation of noise reduction method during on-line monitoring of MV switchgear for PD measurements by non-intrusive sensors

Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper.     

Pharmacotherapeutics and molecular docking studies of alpha-synuclein modulators as promising therapeutics for Parkinson’s disease

Parkinson’s disease (PD) is an age-related neurodegenerative ailment that affects dopamine-producing neurons in a specific area of the brain called the substantia nigra of the ventral midbrain. It is clinically characterized by movement disorder and marked with unusual synaptic protein alpha-synuclein accumulation in the brain. To date, only a few Food and Drug Administration (FDA) approved drugs are available on the market for the treatment of PD. Nonetheless, these drugs show parasympathomimetic related adverse events and remarkably higher toxicity; hence, it is important to find more efficacious molecules to treat PD. In our study, We chosen 22 natural compounds as inhibitors that potentially block the alpha-synuclein clump—the pathological hallmark of PD—and provide new avenues for its treatment. Most of these molecules exhibited good pharmacokinetic behaviors, making them decisively favorable drug candidates to cure PD. Molecular docking studies were performed to investigate the binding interactions between natural compounds and alpha-synuclein as anti-Parkinson drug targets. Among the examined compounds, curcumin and piperine emerged as promising phytochemicals with the highest binding affinity, key residual stable bindings and showed a good inhibitory features. Thus, the present study indicates that curcumin and piperine hold the potential to be developed as treatment options against PD. Experimental validations are needed for insights into their mechanism of action and potential clinical application.     

Optimal supplementary frequency controller design using the wind farm frequency model and controller parameters stability region

In most of the existing studies, the frequency response in the variable speed wind turbines (VSWTs) is simply realized by changing the torque set-point via appropriate inputs such as frequency deviations signal. However, effective dynamics and systematic process design have not been comprehensively discussed yet. Accordingly, this paper proposes a proportional-derivative frequency controller and investigates its performance in a wind farm consisting of several VSWTs. A band-pass filter is deployed before the proposed controller to avoid responding to either steady state frequency deviations or high rate of change of frequency. To design the controller, the frequency model of the wind farm is first characterized. The proposed controller is then designed based on the obtained open loop system. The stability region associated with the controller parameters is analytically determined by decomposing the closed-loop system's characteristic polynomial into the odd and even parts. The performance of the proposed controller is evaluated through extensive simulations in MATLAB/Simulink environment in a power system comprising a high penetration of VSWTs equipped with the proposed controller. Finally, based on the obtained feasible area and appropriate objective function, the optimal values associated with the controller parameters are determined using the genetic algorithm (GA).     

基于改进模糊自适应补偿的柔性关节机器人PD控制

针对柔性关节机器人具有不确定性、轨迹跟踪精度低和抖动的问题,提出一种改进模糊自适应补偿的PD控制方法。该方法在原有模糊自适应控制和PD控制的基础上进行改进,采用改进模糊自适应控制对PD控制进行补偿,以提高存在不确定性条件下的关节轨迹跟踪精度并抑制抖动。通过Lyapunov理论证明了系统的稳定性。仿真结果表明：新型控制器具有良好的自适应能力,与传统PD控制和模糊自适应控制相比,新型控制策略显著提高了关节的轨迹跟踪精度并在一定程度上抑制了关节抖动。     

反硝化除磷+短程反硝化厌氧氨氧化工艺的深度脱氮除磷效能

为经济高效地去除城市生活污水和硝酸盐废水中的氮磷元素,本研究在厌氧折流板反应器（ABR）和连续搅拌反应器（CSTR）一体式反应器中分别建立了反硝化除磷（DPR）和短程反硝化厌氧氨氧化（PDA）工艺。结果表明,反应器运行185天,在缺氧/厌氧和外加COD/NO{}_{\mathrm{3}}^{-}-N比仅为0.7条件下,PO{}_{\mathrm{4}}^{\mathrm{3}-}-P和TN的去除率高达96.91%和97.75%,最终出水PO{}_{\mathrm{4}}^{\mathrm{3}-}-P和TN的浓度低至0.22mg/L和3.30mg/L,意味着该系统极佳的脱氮除磷效果不依赖氧气和有机碳源量。DPR对系统PO{}_{\mathrm{4}}^{\mathrm{3}-}-P和TN的去除均占主体部分（99.07%和60.23%）,而PDA对总氮（TN）的去除占比呈现逐渐上升的趋势（4.53%→37.52%）。批次实验表明：①COD（300mg/L）显著抑制DPR菌活性,PO{}_{\mathrm{4}}^{\mathrm{3}-}-P主要是在缺氧状态下以NO{}_{\mathrm{3}}^{-}-N为电子受体,有机物为电子供体通过DPR途径去除;②高效短程反硝化过程（亚硝酸转化率92.25%）稳定为厌氧氨氧化供给电子受体（NO{}_{\mathrm{2}}^{-}-N）,DPR系统剩余NH{}_{\mathrm{4}}^{+}-N主要被NO{}_{\mathrm{2}}^{-}-N氧化去除,因此DPR+PDA系统实现了高效同步脱氮除磷效果。高通量测序表明,Accumulibacter（7.41%）是DPR系统功能性除磷菌,Thauera（7.24%）和Candidatus Brocadia（3.12%）为PDA系统关键脱氮菌。     

电站用主变压器局放共模故障成因分析

本文中作者针对一起电站主变压器局部放电共模故障进行了分析,根据分析结果,调整了结构布局参数,消除了缺陷。     

交流与直流电压比例对油纸绝缘内部气隙放电特性影响分析

为实现换流变压器故障诊断,研究交流与直流电压比例对油纸绝缘放电特性的影响对换流变压器的故障诊断判据选取具有实际工程意义。因此该文搭建了一套交直流复合电压作用下油纸绝缘气隙缺陷局部放电测量平台,通过多次试验得到了局部放电特征与交直流复合比例的试验关系;另外建立了油纸绝缘气隙缺陷的物理模型,从理论上分析了比例与放电特征关系的仿真结果。试验测量和理论仿真结果相同:随着交流和直流叠加电压中直流成分的增大,脉冲重复率和放电量不断减小;交流时的放电起始电压低于交直流复合电压作用时的起始电压,而且直流比例越高,起始电压越高;随着交流中直流成分的增加,放电起始相位从交流正负半周过零点附近向负半周期峰值附近移动,换流变压器不同位置的绝缘问题应该采用不同的诊断依据。     

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.