探讨加强质量监督管理提高电网安全水平

电网的安全水平主要取决于其使用产品的质量是否过关。如果产品的质量较好，则能建造出优质的电网，电网的安全水平便会随之提高。因此，应加强对产品质量的监督管理，从而提高电网的安全水平。我们可建立质量监督管理系统，及时抽检出质量不过关的产品，从而消除电网运行过程中潜在的安全隐患。     

Group search optimization for economic fuel scheduling

The economical use of fuel available for the generation of power has become a major concern of electric utilities. This paper presents an approach for economic fuel scheduling problem by using group search optimization. This is a minimization technique that includes the standard load constraints as well as the fuel constraints. The generation schedule is compared to that which would result if fuel constraints were ignored. The comparison shows that fuel consumed can be adequately controlled by adjusting the power output of various generating units so that the power system operates within its fuel limitations and within contractual constraints. It has been found that small additional amount of fuel may be required to serve the same power demand but the additional cost of this fuel may well compensate for the penalty that might otherwise be imposed for not maintaining the fuel contract. Numerical results for two test systems have been presented and the test results obtained from group search optimization are compared with those obtained from particle swarm optimization and evolutionary programming.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

汽车总装线上远程激光焊接能量评估

通过分析激光源的类型和结构,建立参数数据库,并结合机器人运动的动力学分析,本研究提出了汽车总装线上远程激光焊接(RLW)的能量评估模型。通过对简单焊件进行焊接实验,将传统激光焊接和远程激光焊接进行了对比,结果发现,焊接过程中机器人运动能量逐渐减少,而激光能量逐渐增加。处理时间和激光功率是影响远程焊接能源效率的主要因素。     

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

Damage mapping with a degrading elastic modulus using piezospectroscopic coatings

The development of piezospectroscopic (PS) composites has enabled the creation of a non-destructive evaluation (NDE) technique which integrates piezospectroscopy, digital image correlation (DIC) and analytical multi-scale mechanics to map the elastic modulus of a coated material. The measured elastic modulus was represented as a normal distribution with a mean value (32.2 GPa) which is within 8% of the conventionally recorded modulus (35 GPa). Damage mechanics are applied to map elastic degradation in situ mechanical loading with an average uncertainty (~10 GPa) that was sufficient in observing subsurface, progressive damage patterns which are unique to the coated material.     

俄罗斯原油性质变化趋势及加工设想

原油的特性对制定合理的加工方案起着决定性作用,所以,必须详细了解原油的性质,掌握原油的变化趋势,并随时跟踪炼油技术进步及市场波动趋势,提出优化的原油加工规划设想。通过对俄罗斯原油(简称俄油)性质的分析,减压渣油采用渣油加氢-催化裂化工艺技术进行生产加工,虽然投资较大,但因其可生产高附加值的汽油、丙烯等产品,因此具有很高的经济效益,为企业的持续发展奠定扎实的基础。     

Analytical solution for interaction of an arbitrary frequency curved rectangular inducer with a transverse ring shaped groove surrounding a long conductive cylinder

Field distributions around a transverse flaw surrounding a conductive cylinder which is excited by a three-dimensional inducer at arbitrary frequency are presented analytically. The solution is obtained by developing a two-dimensional Fourier series model and using Bessel functions in the third dimension. The metal is assumed as a lossy material and all possible field components in the conductor are expanded. After applying the mode-matching technique, a linear system of AX=B is solved to obtain the unknown coefficients. The accuracy of the proposed modeling technique is confirmed by comparing our results with those obtained by CST finite integration code.     

Improving scenario decomposition algorithms for robust nonlinear model predictive control

This paper deals with the efficient computation of solutions of robust nonlinear model predictive control problems that are formulated using multi-stage stochastic programming via the generation of a scenario tree. Such a formulation makes it possible to consider explicitly the concept of recourse, which is inherent to any receding horizon approach, but it results in large-scale optimization problems. One possibility to solve these problems in an efficient manner is to decompose the large-scale optimization problem into several subproblems that are iteratively modified and repeatedly solved until a solution to the original problem is achieved. In this paper we review the most common methods used for such decomposition and apply them to solve robust nonlinear model predictive control problems in a distributed fashion. We also propose a novel method to reduce the number of iterations of the coordination algorithm needed for the decomposition methods to converge. The performance of the different approaches is evaluated in extensive simulation studies of two nonlinear case studies.     

An optimization model for evaluating the economic impact of availability and maintenance notions during the synthesis and design of a power plant

In this paper, we introduce an optimization strategy in order to comprehensively quantify the impact of availability and maintenance notions during the early stages of synthesis and design of a new natural gas combined cycle power plant. A detailed state-space approach is thoroughly discussed, where influence of maintenance funds on each component's repair rate is directly assessed.In this context, analysis of the reliability characteristics of the system is centered at two designer-adopted parameters, which largely influence the obtained results: the number of components which may fail independently at the same time, and the number of simultaneous failure/repair events.Then, optimal solutions are evaluated as the availability-related parameters and the amount of resources assigned for maintenance actions are varied across a wide range of feasible values, which enable obtaining more accurate and detailed estimations of the expected economic performance for the project when compared with traditional economic evaluation approaches.