温度波动下的锅炉汽包水位模糊控制方法

针对余热烟气温度波动引起锅炉汽包水位难以控制的问题,提出了温度波动下的锅炉汽包水位模糊控制方法。该方法通过分析余热烟气温度波动下的汽包水位动态特性,在已有PID三冲量控制算法的基础上,利用模糊控制方法将余热烟气温度偏差和偏差变化率引入到汽包水位控制算法中,以修正给水量设定值,抑制烟气温度波动造成的影响,稳定汽包水位。系统仿真和实际工程应用结果验证了所提方法的有效性、可行性。     

M701联合循环发电机组余热锅炉中压汽包水位控制的分析和改进

M7 0 1 F联合循环发电机组的余热锅炉由于中压汽包设计容量偏小,在机组启停阶段,中压汽包水位控制难以投入自动。本文对M701F联合循环两班制（DSS）运行机组的余热锅炉中压汽包水位控制的特点和难点进行了解析;同时对余热锅炉在汽包上水、启动升负荷和正常运行分三阶段的中压汽包水位控制进行了详细的分析。最后提出的改进和解决方案,可为同类兄弟电厂作参考和借鉴。     

模糊控制策略在火电厂汽包水位控制中的应用

对火电厂锅炉汽包水位的基本知识进行了介绍,目前汽包水位控制方式上基本采用传统PID,但因运行条件的复杂性,控制结果不尽人意。通过Simulink对汽包水位的模糊PID控制系统进行仿真分析。经过仿真实验,系统的实时性和稳定性都得到了提高,取得了良好的控制效果。     

Deltav控制系统与智能总线仪表在火法冶炼中的应用

余热锅炉汽包水位必须控制在一定的范围内,锅炉才能正常运行,水位三冲量控制系统克服了汽包的虚假水位,保证了锅炉的正常运行.     

基于模糊RBF神经网络控制器的锅炉汽包水位控制的实现

锅炉是典型的复杂热工系统。对蒸汽锅炉而言，维持汽包水位在一定的范围内是保证锅炉安全运行的首要条件。本文介绍了一种锅炉汽包水位控制器，采用基干模糊RBF神经网络整定的PID控制方法。通过对阶跃输入信号作用下系统动态性能的仿真分析，表明该控制器具有较好的适应性，控制效果得到明显改善。     

熔铸余热锅炉汽包水位的控制

针对熔铸余热锅炉汽包水位的特点,开发了基于汽包水位,蒸汽流量(微分)的双冲量控制方案.生产运行表明,该控制方案成功实现了使汽包水位平稳、波动小、给水稳定等目标.     

电站锅炉汽包水位系统的单神经元自适应PID控制

电站锅炉汽包水位间接地反映了锅炉负荷与给水平衡的关系。是电站发电机组运行的主要控制参数之一。本文提出了一种电站锅炉汽包水位系统的单神经元自适应PID控制策略。将蒸汽流量信号引入到单神经元网络中来考虑负荷变化对锅炉汽包水位的影响，使其具有前馈补偿能力，以消除“虚假水位”现象。仿真试验结果表明了该控制策略的有效性和实用性。     

曲靖锌厂焙烧余热锅炉汽包水位三冲量控制系统

余热锅炉汽包水位必须控制在一定的范围内,锅炉才能正常运行,水位三冲量控制系统克服了汽包的虚假水位,保证了锅炉的正常运行。     

T-S模糊模型在锅炉汽包水位建模中的应用

对实际对象进行数学建模来加以分析和处理是非常重要的,电厂锅炉汽包水位控制系统是火力发电中的一个重要组成部分,维持锅炉汽包水位在一定的范围内是机组安全运行的主要条件.本文将T S模糊神经网络模型应用到锅炉汽包水位对象的数学建模中,通过对模型的参数学习,得到锅炉汽包水位的仿真模型.     

自然循环锅炉汽包水位动态模型的研究

介绍了一种利用经典热量信号建立锅炉有效吸热量模型的方法.引入凝结比例系数,简化了汽包液面下汽容积的求取.建立一个300MW自然循环锅炉汽包水位动态模型,通过仿真验证能较好的反映汽包水位动态变化.     

Water Level Deviation Analysis of Levels Between Two Sides of the Boiler Drum

It is always quite difficult to accurately measure boiler drum water level in power plant. Though the effect of false measurement of water level can be reduced with some devices, the effect of deviation of boiler drum water level to the monitoring and alarm system, even to the control of drum water level, and so on, cannot be surmounted. Because of these reasons, the accurate water level alarm signal cannot be provided and the water level control measures cannot be applied. In order to solve this problem, a water level deviation analysis method is presented for analyzing boiler drum water level. Based on the analysis of boiler drum water level related running parameters, the relational model of water level deviation under different working conditions and its parameters is constructed. By analyzing this model, the specific impacts of the main factors can be fixed. Thus the drum water level deviation can be reduced by adjusting running parameters without changing unit load. And then the measurement of drum water level can be more accurate only if power plants have accurate measuring devices. Therefore, the boiler drum water level can be more accurately monitored and controlled. So, this innovation is important in ensuring the safe running of power plant.     

Noise Tracking Using DFT Domain Subspace Decompositions

All discrete Fourier transform (DFT) domain-based speech enhancement gain functions rely on knowledge of the noise power spectral density (PSD). Since the noise PSD is unknown in advance, estimation from the noisy speech signal is necessary. An overestimation of the noise PSD will lead to a loss in speech quality, while an underestimation will lead to an unnecessary high level of residual noise. We present a novel approach for noise tracking, which updates the noise PSD for each DFT coefficient in the presence of both speech and noise. This method is based on the eigenvalue decomposition of correlation matrices that are constructed from time series of noisy DFT coefficients. The presented method is very well capable of tracking gradually changing noise types. In comparison to state-of-the-art noise tracking algorithms the proposed method reduces the estimation error between the estimated and the true noise PSD. In combination with an enhancement system the proposed method improves the segmental SNR with several decibels for gradually changing noise types. Listening experiments show that the proposed system is preferred over the state-of-the-art noise tracking algorithm.     

基于∑-△M的五阶MFLR数字微加速度计闭环控制系统

为了进一步抑制加速度计信号带宽范围内的噪声,提出并设计了一种基于∑-△M的五阶多反馈谐振式（MFLR）微机械加速度计闭环控制系统,该系统通过增加额外的内部负反馈对量化噪声进行再一次整形.微机械加速度计结构为一种全差分式结构,在结构层厚度为60 μm、基底层厚度为400μm的SOI硅片上,经过光刻、溅射、深度反应离子刻蚀等工艺步骤加工而成.整个闭环控制系统的Matlab/Simulink模型首先被建立,然后采用“单位圆分析法”进行系统参数的设定,系统仿真显示：当输入幅值1gn、频率128Hz的加速度信号时,加速度计的噪声为-136.2 dB,与传统五阶MF结构的∑-△M闭环控制系统相比,在0 ～500 Hz信号带宽范围内的噪声降低了7.9dB.最后整个系统在四层PCB电路板上进行了功能性验证和测试.     

基于小波变换的电力热线预警信息盲检测方法研究

电力电网系统在电力传输控制中节点分布具有动态性,容易产生热线预警,为了提高电力热线预警检测能力,提出一种基于小波变换的电力热线预警信息盲检测方法。构建电力热线预警的信号模型,采用随机线性时间序列分析方法构建电力热线预警的统计信号分析模型,对电力热线预警信号采用相关性检测方法进行时频分解,采用小波分析方法将电力热线预警信号从时域向频域转换,根据接收到的两路电力热线预警信息的关联性进行特征匹配和自动分离,实现对电力热线预警信息的盲分离和噪声干扰抑制,采用小波变换和谱特征检测方法,实现对电力热线预警的盲检测。仿真结果表明,采用该方法进行电力热线预警检测的盲分离性能较好,检测准确概率较高,提高了电力热线预警能力。     

Distributed multivariable control system design for coal-fired boilers

This paper describes the multivariable characteristics of industrial coal-fired boilers and associated mathematical models, such as combustion model, a water drum level model and a superheated steam model. The purpose of the paper is to design multivariable control systems using multivariable system frequency domain design theory and the Smith-predictor technique. A specific application shows how to design a model-based distributed multivariable control system for coal-fired boilers in a small-scale power station.     

动车组司机室空调蒸发器气动噪声数值仿真

为研究动车组司机室空调蒸发器的噪声响应，建立某型司机室分体式空调蒸发器的计算流体动力学模型，采用FLUENT中的大涡模拟（large eddy simulation, LES）计算瞬态气动流场。对瞬态流场数据进行傅里叶变换，得到空气流场的频域数据。基于流场频域数据，采用Virtual.Lab的边界元法计算蒸发器的气动噪声，采用声压法计算蒸发器的辐射声功率，并与测试结果进行对比分析。结果表明：蒸发器出口位置气动噪声最高，最大声压级高于56 dB；最大声功率级出现在125~400 Hz的低频段；声功率级随着频率的增加逐渐降低，但在5 000 Hz的高频辐射中声功率级仍然超过55 dB，这表明空调蒸发器气动噪声属于宽频噪声；计算结果与测试结果吻合良好，验证声压法计算空调蒸发器气动声功率可行。     

锅炉汽包水位控制系统的研究

锅炉汽包水位是一种非线性、时变大、强耦合的多变量系统。在建直了锅炉汽包水位调节对象的数学模型的基础上，利用模糊控制理论设计锅炉水位控制系统，运用模糊控制与PID控制分别对汽包水位进行控制。介绍了控制器的设计和仿真分析，并应用MATLAB软件对汽包水位控制系统进行验证和仿真，给出了模糊控制方法与传统方法的比较结果，表明了模糊控制明显地改善了汽包水位控制系统的静、动态特性，从而实现了对锅炉汽包水位的最件实时控制。     

A novel fast nonstationary noise tracking approach based on MMSE spectral power estimator

Estimating the noise power spectral density (PSD) from the corrupted speech signal is an essential component for speech enhancement algorithms. In this paper, a novel noise PSD estimation algorithm based on minimum mean-square error (MMSE) is proposed. The noise PSD estimate is obtained by recursively smoothing the MMSE estimation of the current noise spectral power. For the noise spectral power estimation, a spectral weighting function is derived, which depends on the a priori signal-to-noise ratio (SNR). Since the speech spectral power is highly important for the a priori SNR estimate, this paper proposes an MMSE spectral power estimator incorporating speech presence uncertainty (SPU) for speech spectral power estimate to improve the a priori SNR estimate. Moreover, a bias correction factor is derived for speech spectral power estimation bias. Then, the estimated speech spectral power is used in “decision-directed” (DD) estimator of the a priori SNR to achieve fast noise tracking. Compared to three state-of-the-art approaches, i.e., minimum statistics (MS), MMSE-based approach, and speech presence probability (SPP)-based approach, it is clear from experimental results that the proposed algorithm exhibits more excellent noise tracking capability under various nonstationary noise environments and SNR conditions. When employed in a speech enhancement system, improved speech enhancement performances in terms of segmental SNR improvements (SSNR+) and perceptual evaluation of speech quality (PESQ) can be observed.     

基于深度学习的复杂观测条件下工频磁异常探测

复杂观测条件下使用工频磁场探测人员、车辆、飞行目标等多类型目标造成的磁场扰动时,受到复杂环境下电磁噪声、供电设备及外来物体扰动等影响,工频磁场扰动信号具有噪声多、干扰强等特征,为有效削弱噪声及干扰对工频磁场扰动信号的影响,实现工频磁场扰动探测,该文利用实验数据对复杂观测条件下的磁场扰动信号进行特征分析,提出了一种基于深度学习的工频磁场异常探测方法,通过提取正常状态与有扰动状态的信号序列,将该信号输入神经网络训练,得到准确检测工频磁场异常信号的网络模型。实验结果表明,该方法的识别准确率在80%以上。     

Online Noise Estimation Using Stochastic-Gain HMM for Speech Enhancement

We propose a noise estimation algorithm for single-channel noise suppression in dynamic noisy environments. A stochastic-gain hidden Markov model (SG-HMM) is used to model the statistics of nonstationary noise with time-varying energy. The noise model is adaptive and the model parameters are estimated online from noisy observations using a recursive estimation algorithm. The parameter estimation is derived for the maximum-likelihood criterion and the algorithm is based on the recursive expectation maximization (EM) framework. The proposed method facilitates continuous adaptation to changes of both noise spectral shapes and noise energy levels, e.g., due to movement of the noise source. Using the estimated noise model, we also develop an estimator of the noise power spectral density (PSD) based on recursive averaging of estimated noise sample spectra. We demonstrate that the proposed scheme achieves more accurate estimates of the noise model and noise PSD, and as part of a speech enhancement system facilitates a lower level of residual noise.