An evaluation of model-based stator resistance estimation forinduction motor stator winding temperature monitoring

In this paper, the feasibility of using an estimate of the stator resistance (R_s) as an indicator of stator winding temperature (T_s) is evaluated. The advantages of resistance-based temperature monitoring over conventional thermal model-based methods are presented. Since obtaining an accurate estimate of R_s is critical for this approach, an investigation of existing R_s estimation schemes, and an analysis showing the sensitivity of model-based R_s estimation due to the uncertainties in motor parameters and variables, are given. It is shown that R_s estimation is difficult during high-speed operation, because estimated R _s becomes sensitive to errors in motor electrical parameters and variables, as the input excitation frequency (speed) increases. A new R_s estimation scheme for the purpose of monitoring the temperature in steady state is proposed. Experimental results on a line-connected induction machine verify the validity of the proposed method and the analysis     

Energy-efficient control strategy for variable speed-driven parallel pumping systems

The article aims to find a solution for the energy efficiency improvements in variable speed-controlled parallel pumping systems with lesser initial data and without additional flow metering and start-up measurements. This paper introduces a new control strategy for variable speed-controlled parallel pumps based on flow rate estimation and pump operation analysis utilizing variable speed drives. The energy-saving potential of the new control strategy is studied with simulations and laboratory measurements. The energy consumption of the parallel pumps using the new control strategy is compared with the traditional rotational speed control strategy of parallel pumps. The benefit of the new control strategy is the opportunity to operate variable speed-controlled parallel pumps in a region which suggests improved energy efficiency and lower risk of mechanical failure of the controlled pumps compared with traditional control. The article concludes by discussing the implications of the findings for different applications and varying system conditions.     

Neural network based estimation of maximum power generation from PVmodule using environmental information

This paper presents an application of an artificial neural network for the estimation of maximum power generation from PV module. The output power from a PV module depends on environmental factors such as irradiation and cell temperature. For the operation planning of power systems, the prediction of the power generation is inevitable for PV systems. For this purpose, irradiation, temperature and wind velocity are utilized as the input information to the proposed neural network. The output is the predicted maximum power generation under the condition given by those environmental factors. The efficiency of the proposed estimation scheme is evaluated by using actual data on daily, monthly and yearly bases. The proposed method gives highly accurate predictions compared with predictions using the conventional multiple regression model     

Power signature analysis

Nonintrusive load monitoring (NILM) can determine operating schedule of electrical loads in a target system from measurements made at a centralized location, such as the electric utility service entry. NILM is an ideal platform for extracting useful information about any system that uses electromechanical devices. It has a low installation cost and high reliability because it uses a bare minimum of sensors. It is possible to use modem state and parameter estimation algorithms to verify remotely the "health" of electromechanical loads by using NILM to analyze measured waveforms associated with the operation of individual loads. NILM can also monitor the operation of the electrical distribution system itself, identifying situations where two or more otherwise healthy loads interfere with each other's operation through voltage waveform distortion or power quality problems. Strategies for nonintrusive monitoring have developed over the last 20 years. Advances in computing technology make a new wealth of computational tools useful in practical, field-based NILM systems. This article reviews techniques for high-performance nonintrusive load and diagnostic monitoring and illustrates key points with results from field tests.     

Oxidative steam reforming of ethanol on rhodium catalyst – I: Spatially resolved steady-state experiments and microkinetic modeling

Oxidative steam reforming of ethanol is an important process for on board production of hydrogen in fuel cell based auxiliary power systems. Although the process has been extensively studied from a catalyst perspective, accurate models that capture species and temperature information required by model-based control algorithms during operation have not yet been developed adequately. In this work, we develop a reduced micro-kinetic model for ethanol oxidative steam reforming, which can be used in computational fluid dynamics (CFD) studies and subsequently to develop model-based control strategies. We experimentally study cordierite monolith based reactors in which Rh/CeO₂ catalysts are prepared by the solution-combustion method. The catalyst system is characterized by X-ray diffraction (XRD), Scanning Electron Microscope (SEM), temperature programmed reduction and temperature programmed desorption analyses. The experimental reformer design enables measurement of species concentrations at various points along the reactor length, along with radial temperature profiles. A micro-kinetic model is adapted from the literature and validated against these experiments, with good agreement. The model results suggest a linear activation pathway for ethanol over rhodium catalysts by forming ethoxide, acetyl and acetate intermediates. After formation of single carbon species, the methane reforming pathway is followed. We expect that these studies, when coupled with transient studies will help in formulating model-based control strategies for ethanol reformers in complex fuel cell systems.     

电站风机故障智能预警技术的应用研究

针对电站风机状态监测和故障预警问题,提出了一种基于密度峰聚类的多元状态估计方法。首先,利用密度峰聚类算法对风机正常运行工况下的历史数据进行分析,提取包含设备正常运行特征信息的数据,构建记忆矩阵;然后通过相关性原理分析观测向量与记忆矩阵之间的相似程度,使用多元状态估计技术对该观测向量进行估计。计算估计值与实测值之间的统计残差和相似度,确定风机的运行状态。最后,以南京某电厂一次风机为监测对象进行研究,建立动态故障预警模型,并结合故障实例分析验证。结果表明:该方法能够实时准确预测风机运行状态,提前发现故障征兆,指导设备运行和维护。     

Performance prediction of proton exchange membrane fuel cell engine thermal management system using 1D and 3D integrating numerical simulation

The cooling system of proton exchange membrane fuel cell (PEMFC) engine was simulated by 1D and 3D collaborative simulation method. Firstly, the resistance characteristics of the flow channel are obtained by simulating the airside flow model. A three-dimensional simulation model including dual fans and radiator is also established to simulate the airflow distribution. The one-dimensional simulation model of 30 kW PEMFC engine cooling system that are mainly composed of a thermostat, water pump, and fan and radiator model is established. Secondly, the heat dissipation performance of the cooling system is calculated by using the coupled simulation model. It is found that the simulation results of the amount of heat transferred are in good agreement with the experimental data by compromising, which proves that the model is reasonable. Finally, the thermal performance of the extreme operating conditions of the PEMFC system is simulated by means of a simulation model. By monitoring the flow of the pump and the fan speed, we can maintain the stack internal heat balances, so that the stack efficient and stable operation. The results demonstrate that the 3D simulation can get the distribution of fluid flow more accurately, while the simulation time of 1D thermal system is short and can guide the matching of heat transfer parts quickly.     

Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden

The development of a model for any energy system is required for proper design, operation or its monitoring. Models based on accurate mathematical expressions for physical processes are mostly useful to understand the actual operation of the plant. However, for large systems like combined heat and power (CHP) plants, such models are usually complex in nature. The estimation of output parameters using these physical models is generally time consuming, as these involve many iterative solutions. Moreover, the complete physical model for new equipment may not be available. However, artificial neural network (ANN) models, developed by training the network with data from an existing plant, may be very useful especially for systems for which the full physical model is yet to be developed. Also, such trained ANN models have a fast response with respect to corresponding physical models and are useful for real-time monitoring of the plant. In this paper, the development of an ANN model for the biomass and coal cofired CHP plant of Västhamnsverket at Helsingborg, Sweden has been reported. The feed forward with back propagation ANN model was trained with data from this plant. The developed model is found to quickly predict the performance of the plant with good accuracy.     

泵或风机并联运行偶合器同步调速控制方法研究

本文分析了并联运行系统的工作特性,指出了泵或风机非同步运行存在的问题;大功率应用领域采用偶合器调速是一类适宜的调节方式,在分析偶合器调节特性的基础上,给出了一种实用的同步调速控制方法。     

Model-based condition monitoring of PEM fuel cell using Hotelling T2 control limit

Although a variety of design and control strategies have been proposed to improve the performance of polymer electrolyte membrane (PEM) fuel cell systems, temporary faults in such systems still might occur during operations due to the complexity of the physical process and the functional limitations of some components. The development of an effective condition monitoring system that can detect these faults in a timely manner is complicated by the operating condition variation, the significant variability/uncertainty of the fuel cell system, and the measurement noise. In this research, we propose a model-based condition monitoring scheme that employs the Hotelling T² statistical analysis for fault detection of PEM fuel cells. Under a given operating condition, the instantaneous load current, the temperature and fuel/gas source pressures of the fuel cell are measured. These measurements are then fed into a lumped parameter dynamic fuel cell model for the establishment of the baseline under the same operating condition for comparison. The fuel cell operation is simulated under statistical sampling of parametric uncertainties with specified statistics (mean and variance) that account for the system variability/uncertainty and measurement noise. This yields a group of output voltages (under the same operating condition but with uncertainties) as the baseline. Fault detection is facilitated by comparing the real-time measurement of the fuel cell output voltage with the baseline voltages by employing the Hotelling T² statistical analysis. The baseline voltages are used to evaluate the output T² statistics under normal operating condition. Then, with a given confidence level the upper control limit can be specified. Fault condition will be declared if the T² statistics of real-time voltage measurement exceeds the upper control limit. This model-based robust condition monitoring scheme can deal with the operating condition variation, various uncertainties in a fuel cell system, and measurement noise. Our analysis indicates that this scheme has very high detection sensitivity and can detect the fault conditions at the early stage.     

液粘调速装置在节能减排中的应用

针对水泵风机采用恒速节流调节运行时存在浪费大量电能的问题,吉林油田水厂选用液粘调速装置改造水泵机组后,通过改变水泵和风机转速调整水量、风量,使轴功率大幅度降低,节约大量电能,经济效益可观。     

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

Box-Jenkins-based multivariate stochastic modelling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures—at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modelling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modelling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimise energy savings. An approach to system simulation is suggested.     

水轮机节能技术在石化行业循环水冷却塔中的应用

水轮机利用循环水泵的富余扬程带动风机运转,以取代风机电机,实现节能的目的。以高效反击混流式水轮机在青岛石化循环水冷却塔中的应用为例,通过监测单开水轮机风机或电动风机时循环水的温降程度,来对比两种风机的运行效果,并在维持系统管网压力、流量不变的条件下,监测水轮机风机正常运行、转速减半、停运至系统稳定,以及重开风机系统、恢复稳定状态下的运行数据,测试水轮机风机运行对循环水系统的影响,从而验证水轮机节能技术的应用效果及经济效益。结果证明,循环水系统存在富余能量,水轮机利用该富裕能量带动风机运转,不增加循环水系统新的能耗;且水轮机风机降温效果与电动风机相当,可替代电机风机运行,满足精细化操作需要。采用水轮机运转风机,可优化循环水系统配置,减少维护保养费用,经济效益较高,具有较好的推广价值。     

Identification and monitoring of a PEM electrolyser based on dynamical modelling

To improve the efficiency and the safety of hydrogen electrolysis stations, some technological studies are still under investigation both on methods and materials. As methods, control, monitoring and diagnosis algorithms are relevant tools. This work focuses on the dynamical modelling and the monitoring of Proton Exchange Membrane (PEM) electrolyser. Our contribution consists of three parts: to propose a model of an analytical–dynamical PEM electrolyser, dedicated to control and monitoring; to identify the model parameters and to propose adequate monitoring tools. The proposed model is deduced from physical laws and electrochemical equations and consists of a steady-state electric model coupled with a dynamic thermal model. The estimation of the model parameters is achieved using identification and data fitting techniques based on experimental measurements. Taking into account the information given by the proposed analytical model and the experimentation data (temperature T, voltage U and current I) given by a PEM electrolyser, the model parameters are identified. After estimating the dynamical model, model-based diagnosis is used to monitor the PEM electrolyser and to ensure its safety. We illustrate how our algorithm can detect and isolate faults on actuators, on sensors or on electrolyser system.     

Analytic science for geospatial and temporal variability in renewable energy: A case study in estimating photovoltaic output in Arizona

To assess the electric power grid environment under the high penetration of photovoltaic (PV) generation, it is important to construct an accurate representation of PV power output for any location in the southwestern United States at resolutions down to 10-min time steps. Existing analyses, however, typically depend on sparsely spaced measurements and often include modeled data as a basis for extrapolation. Consequentially, analysts have been confronted with inaccurate analytic outcomes due to both the quality of the modeled data and the approximations introduced when combining data with differing space/time attributes and resolutions. This study proposes an accurate methodology for 10-min PV estimation based on the self-consistent combination of data with disparate spatial and temporal characteristics. Our Type I estimation uses the nearby locations of temporally detailed PV measurements, whereas our Type II estimation goes beyond the spatial range of the measured PV incorporating alternative data set(s) for areas with no PV measurements; those alternative data sets consist of: (1) modeled PV output and secondary cloud cover information around space/time estimation points, and (2) their associated uncertainty. The Type I estimation identifies a spatial range from existing PV sites (30-40 km), which is used to estimate accurately 10-min PV output performance. Beyond that spatial range, the data-quality-control estimation (Type II) demonstrates increasing improvement over the Type I estimation that does not assimilate the uncertainty of data sources. The methodology developed herein can assist the evaluation of the impact of PV generation on the electric power grid, quantify the value of measured data, and optimize the placement of new measurement sites.     

Nonlinear system identification for model-based condition monitoring of wind turbines

This paper proposes a data driven model-based condition monitoring scheme that is applied to wind turbines. The scheme is based upon a non-linear data-based modelling approach in which the model parameters vary as functions of the system variables. The model structure and parameters are identified directly from the input and output data of the process. The proposed method is demonstrated with data obtained from a simulation of a grid-connected wind turbine where it is used to detect grid and power electronic faults. The method is evaluated further with SCADA data obtained from an operational wind farm where it is employed to identify gearbox and generator faults. In contrast to artificial intelligence methods, such as artificial neural network-based models, the method employed in this paper provides a parametrically efficient representation of non-linear processes. Consequently, it is relatively straightforward to implement the proposed model-based method on-line using a field-programmable gate array.     

Capillary pumping systems for solar heating application

Capillary pumping two-phase loops have been continuously investigated for electronic cooling systems, satellite thermal control and other space applications. Most tests were performed in capillary evaporators using plastic or metallic porous wick as capillary structure and anhydrous ammonia as the working fluid. In this work, capillary pumping system assisting flat solar collectors is proposed as an alternative to residential and commercial water heating systems, using fine circumferential grooves as capillary structure and acetone as the working fluid. The experimental results are obtained for a small-scale solar heating system, using one capillary pump attached to a flat copper plate of 46 cm in length and 6 cm in width. The capillary pump consists of a 19 mm OD and 500 mm long aluminium tube, with fine internal circumferential groves as the capillary structure. The working fluid is pumped from a condenser designed to delivery heat to the water storage. Heat inputs up to 14 W (507 W/m²) were estimated for heating purpose. The system presented reliable start-ups and good performance in continuous operation. The measurements were found to be in good agreement with theoretical results for the temperature field and solar power absorbed by the system.     

采用喷射泵疏水方式的回热系统热经济性分析

介绍了目前凝汽式汽轮机组回热系统的基本疏水方式,分析了其优缺点。介绍了喷射泵的工作原理,建立了喷射泵工作流体流量的计算模型。提出了采用喷射泵的回热系统疏水方式,拟定了采用喷射泵疏水方式后机组的原则性回热系统简图。基于常规热平衡法,确定了喷射泵出口混合焓,并建立了机组热经济性指标的计算模型。以某300MW机组为例,计算了喷射泵的喷射系数,并对采用不同疏水方式机组的热经济性进行了比较。结果表明,采用喷射泵疏水方式机组的热经济性明显高于采用逐级自流疏水方式机组的热经济性。     

Unsteady operation of new type turbofan engine with aerodynamic torque converter reducing front fan speed

Introduction In turbofan engine technology of the 21st century, it is desired to select the best possible operating conditions for each engine section, to reduce the operating costs, fuel burn, and noise levels. The conventional mechanism in which the front fan is directly connected to the rest of the core engine arrests the optimization of the spool speed in order to avoid the flow blockage at the sonic speed. In such circumstances, the rotational speed of the front fan can be reduced using …     

汽车发动机冷却系仿真优选方法的研究

介绍了汽车发动机冷却系散热器、风扇和水泵的仿真优选方法。建立了散热器性能计算模型,提出了散热器优选的方法。通过收集不同结构形式的风扇和水泵的试验数据,建立风扇性能数据库,应用相似理论,对风扇和水泵进行优选,给出了应用实例。