基于光传感器的输电线路盐密在线监测系统设计

采用Mann Kendall检验法和重标极差法（R/S）分析了泗合水流域1965~2010年全年及年内各时期的降雨量、蒸发量和径流量时间序列变化趋势及其持续性,分离和估算了气候变化和人类活动对径流的影响。结果表明,径流变化受人类活动的影响,但不足以颠覆最大月和旱季两个极端时期的降雨变化趋势。以径流系数突变年份2003年为界,显著变化期与平顺变化期相比,多年平均降雨量和蒸发量分别减少了73、34 mm,径流量却减少200 mm;并根据平顺变化期的降雨—径流相关关系,估算出显著变化期气候变化与人类活动对径流减少的贡献率分别为33.5%、66.5%。     

Robust design of multimachine power system stabilizers usingsimulated annealing

Robust design of multimachine power system stabilizers (PSSs) using simulated annealing (SA) optimization technique is presented in this paper. The proposed approach employs SA to search for optimal parameter settings of a widely used conventional fixed-structure lead-lag PSS (CPSS). The parameters of the proposed simulated annealing based power system stabilizer (SAPSS) are optimized in order to shift the system electromechanical modes at different loading conditions and system configurations simultaneously to the left in the s-plane. Incorporation of SA as a derivative-free optimization technique in PSS design significantly reduces the computational burden. One of the main advantages of the proposed approach is its robustness to the initial parameter settings. In addition, the quality of the optimal solution does not rely on the initial guess. The performance of the proposed SAPSS under different disturbances and loading conditions is investigated for two multimachine power systems. The eigenvalue analysis and the nonlinear simulation results show the effectiveness of the proposed SAPSS's to damp out the local as well as the interarea modes and enhance greatly the system stability over a wide range of loading conditions and system configurations     

A systematic approach to performance weights selection in design ofrobust H∞ PSS using genetic algorithms

Selection of suitable performance weights is the main problem in design of a robust H^∞ power system stabilizer (PSS). In this paper, a systematic and automated approach based on genetic algorithms (GAs) is proposed. It gives rise to selection of optimal performance weights without any trial and error attempt. The resulting H ^∞ PSS performs quite satisfactorily under a wide range of turbogenerator operating conditions and is robust against unmodelled low-damped torsional modes. It also provides sufficient robustness against significant changes in transmission line configuration and parameters     

用于励磁控制器参数优化的发电机外部动态等值系统参数辨识

针对静态等值系统不能完全反映近端机组对被研究发电机的动态影响,提出了一种动态等值系统,即将远方复杂的大电网和近端同母线机组分别等值为无穷大母线和动态机组,通过Matlab编程搭建了动态等值系统,提出了穷举辨识算法,仅根据研究发电机机端数据即可辨识出动态等值系统的参数,并通过大电网数据辨识及PSS参数优化验证了动态等值系统参数辨识的有效性。     

Optimal design of power system stabilizers using evolutionary programming

The optimal design of power system stabilizers (PSSs) using evolutionary programming (EP) optimization technique is presented in this paper. The proposed approach employs EP to search for optimal settings of PSS parameters that shift the system eigenvalues associated with the electromechanical modes to the left in the s-plane. Incorporation of EP algorithm in the design of PSSs significantly reduces the computational burden. The performance of the proposed PSSs under different disturbances, loading conditions, and system configurations is investigated for a multimachine power system. The eigenvalue analysis and the nonlinear simulation results show the effectiveness and robustness of the proposed PSSs to damp out the local as well as the interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations.     

基于智能算法的多机PSS参数协调优化

针对大规模电力多机系统考虑多种运行方式变化下的PSS参数协调优化问题,提出一种将遗传算法寻优和BP神经网络计算阻尼比结合的智能算法。该算法根据机组对振荡模态参与因子的大小选择需要优化的机组,采用遗传算法进行不同振荡模态及运行方式下最小阻尼比最大化的寻优,并以BP神经网络获得阻尼比的时域仿真过程。在经典的四机两区域系统上仿真表明,该算法寻优效果良好,具有较好的鲁棒性。     

A fuzzy logic based power system stabilizer with learning ability

A fuzzy logic-based power system stabilizer (PSS) with learning ability is proposed in this paper. The proposed PSS employs a multilayer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of artificial neural networks (ANNs) and fuzzy logic control (FLC) schemes. Studies show that the proposed adaptive network-based fuzzy logic PSS (ANF PSS) can provide good damping of power systems over a wide range of operating conditions and improve the dynamic performance of the power system     

Design and analysis of an adaptive fuzzy power system stabilizer

Power system stabilizers (PSS) must be capable of providing appropriate stabilization signals over a broad range of operating conditions and disturbances. Traditional PSS rely on robust linear design methods. In an attempt to cover a wider range of operating conditions, expert or rule-based controllers have also been proposed. Fuzzy logic as a novel robust control design method has shown promising results. The emphasis in fuzzy control design centers around uncertainties in system parameters and operating conditions. Such an emphasis is of particular relevance as the difficulty of accurately modelling the connected generation is expected to increase under power industry deregulation. Fuzzy logic controllers are based on empirical control rules. In this paper, a systematic approach to fuzzy logic control design is proposed. Implementation for a specific machine requires specification of performance criteria. This performance criteria translates into three controller parameters which can be calculated off-line or computed in real-time in response to system changes. The robustness of the controller is emphasized. Small signal and transient analysis methods are discussed. This work is directed at developing robust stabilizer design and analysis methods appropriate when fuzzy logic is applied     

Optimal design of power-system stabilizers using particle swarm optimization

In this paper, a novel evolutionary algorithm-based approach to optimal design of multimachine power-system stabilizers (PSSs) is proposed. The proposed approach employs a particle-swarm-optimization (PSO) technique to search for optimal settings of PSS parameters. Two eigenvalue-based objective functions to enhance system damping of electromechanical modes are considered. The robustness of the proposed approach to the initial guess is demonstrated. The performance of the proposed PSO-based PSS (PSOPSS) under different disturbances, loading conditions, and system configurations is tested and examined for different multimachine power systems. Eigenvalue analysis and nonlinear simulation results show the effectiveness of the proposed PSOPSSs to damp out the local and interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations. In addition, the potential and superiority of the proposed approach over the conventional approaches is demonstrated.     

Modeling thermal behavior and work flux in finite-rate systems with radiation

We apply thermodynamic analysis in modeling, simulation and optimization of radiation engines as non-linear energy converters. We also perform critical analysis of available data for photon flux and photon density that leads to exact numerical value of photon flux constant. Basic thermodynamic principles lead to expressions for converter’s efficiency and generated work in terms of driving energy flux in the system. Steady and dynamical processes are investigated. In the latter, associated with an exhaust of radiation resource measured in terms of its temperature decrease, real work is a cumulative effect obtained in a system composed of a radiation fluid, sequence of engines, and an infinite bath. Variational calculus is applied in trajectory optimization of relaxing radiation described by a pseudo-Newtonian model. The principal performance function that expresses optimal work depends on thermal coordinates and a dissipation index, h, in fact a Hamiltonian of the optimization problem for extremum power or minimum entropy production. As an example of work limit in the radiation system under pseudo-Newtonian approximation the generalized exergy of radiation fluid is estimated in terms of finite rates quantified by Hamiltonian h. The primary results are dynamical equations of state for radiation temperature and work output in terms of process control variables. In the second part of this paper these equations and their discrete counterparts will serve to derive efficient algorithms for work optimization in the form of Hamilton–Jacobi–Bellman equations and dynamic programming equations. Significance of non-linear analyses in dynamic optimization of radiation systems is underlined.     

Design optimization under uncertainty of hybrid fuel cell energy systems for power generation and cooling purposes

Reliance on point estimates when developing hybrid energy systems can over/underestimate system performance. Analyzing the sensitivity and uncertainty of large-scale hybrid systems can be a challenging task due to the large number of design parameters to be explored. In this work, a comprehensive and efficient sensitivity/uncertainty methodology is applied on two fuel cell hybrid systems to help analysts to investigate hybrid systems more efficiently. This methodology also includes a step-by-step approach to perform design optimization under uncertainty of energy systems. The two hybrid systems are: molten carbonate fuel cell with thermoelectric generator (MCFC-TEG) and phosphoric acid fuel cell with refrigerator (PAFC-REF). Various sensitivity and uncertainty methods are utilized to analyze the design parameters and their effect on the performance of these two systems. These methods perform local and regression-based sensitivity analysis, Monte Carlo uncertainty propagation, parameter screening, and variance decomposition. Detailed approach is adopted to identify and rank the influential design parameters for each system. Results demonstrate that the optimum power output of the MCFC-TEG has 10% uncertainty, driven mainly by the operating temperature, cahtode activation energy, TEG figure of merit, and TEG thermal conductivity. However, PAFC-REF is more reliable with larger power output and 1.4% uncertainty, driven by the charge transfer coefficient, heat transfer in the refrigeration cycle, cold reservoir temperature, and operating temperature. Based on this identification, design optimization under uncertainty is performed using these sensitive parameters to improve the system performance through increasing the power output and reducing its uncertainty.     

新型电力系统稳定器模型分析及参数协调优化

针对新型电力系统稳定器(PSS4B)参数众多且整定优化困难的现状,提出一种基于相位补偿法的PSS4B参数优化方法。在详细分析PSS4B模型的基础上,将待优化参数划分为两部分,采用粒子群优化算法通过两次相位补偿实现参数协调优化,提高了优化效率,且优化后的PSS4B相频特性能更好地满足性能要求。通过Matlab编程建立单机无穷大系统,并改变发电机的转子惯性时间常数以实现PSS4B在不同频段上的仿真,结果表明优化后的PSS4B能兼顾多个频段,有效抑制了低频振荡。     

The need for exergy analysis and thermodynamic optimization in aircraft development

This paper outlines a newly emerging body of work that relies on exergy analysis and thermodynamic optimization in the design of energy systems for modern aircraft. Exergy analysis establishes the theoretical performance limit. The minimization of exergy destruction brings the design as closely as permissible to the theoretical limit. The system architecture springs out of this constrained optimization principle. A key problem is the extraction of maximum exergy from a hot gaseous stream that is gradually cooled and eventually discharged into the ambient. The optimal configuration consists of a heat transfer surface with a temperature that decays exponentially in the flow direction. This configuration can be achieved in a counterflow heat exchanger with an optimal imbalance of flow capacity rates. The same optimal configuration emerges when the surface is minimized subject to specified exergy extraction rate. Similar opportunities for optimally matching components and streams exist in considerably more complex systems for power and refrigeration. They deserve to be pursued, and can be approached first at the conceptual level, based on exergy analysis and thermodynamic optimization. The application of such principles in aircraft energy system design also sheds light on the “constructal” design principle that generates all the systems that use powered flight, engineered and natural, cf. constructal theory.     

面向用户冷、热、电负荷需求的多能源系统耦合运行优化策略

建立了由风能和太阳能作为驱动能源的冷热电联产多能互补系统(DCERs CCHP),包含发电、冷热电联产(CCHP)和辅助供热三个子系统,将该系统与以天然气作为驱动能源的冷热电联产系统(NG CCHP)相对比,建立了能源绩效、环境绩效和经济绩效三个维度评估体系,并以能效最高、成本最小和环境效益最大为目标,构建调度策略优化模型,进行实例仿真。结果表明,与传统的NG CCHP相比,DCERs CCHP具有更好的能源绩效、经济绩效和环境绩效;与其他单目标优化模式相比,综合优化模式下的系统运行效果最佳;NG占比增加将降低项目盈利能力,风电、光伏设备成本降低将提升项目的盈利能力。     

Optimization of distributed hybrid power systems employing multiple fuel-cell vehicles

This paper investigates the benefits of distributed hybrid power systems employing multiple fuel-cell vehicles. In earlier work, our optimization of hybrid power systems showed that a single fuel cell acting as backup power to guarantee energy sustainability operates for less than 3% of the time but incurs more than 16% of the system costs. Therefore, the system cost could be reduced when applying a fuel-cell vehicle to dynamically support twelve power stations. Here, we extend this idea by employing multiple fuel-cell vehicles to support more power stations. We develop a power management strategy and optimize the management parameters by the genetic algorithm. The results show a reduction of more than 21% by applying multiple fuel-cell vehicles in the distributed systems. Experiments also confirm the feasibility of using multiple fuel-cell vehicles. Based on the results, the proposed systems are deemed effective for reducing system costs while maintaining system sustainability.     

A methodology for thermo-economic modeling and optimization of solid oxide fuel cell systems

In the context of stationary power generation, fuel cell-based systems are being foreseen as a valuable alternative to thermodynamic cycle-based power plants, especially in small scale applications. As the technology is not yet established, many aspects of fuel cell development are currently investigated worldwide. Part of the research focuses on integrating the fuel cell in a system that is both efficient and economically attractive. To address this problem, we present in this paper a thermo-economic optimization method that systematically generates the most attractive configurations of an integrated system. In the developed methodology, the energy flows are computed using conventional process simulation software. The system is integrated using the pinch based methods that rely on optimization techniques. This defines the minimum of energy required and sets the basis to design the ideal heat exchanger network. A thermo-economic method is then used to compute the integrated system performances, sizes and costs. This allows performing the optimization of the system with regard to two objectives: minimize the specific cost and maximize the efficiency. A solid oxide fuel cell (SOFC) system of 50 kW integrating a planar SOFC is modeled and optimized leading to designs with efficiencies ranging from 34% to 44%. The multi-objective optimization strategy identifies interesting system configurations and their performance for the developed SOFC system model.The methods proves to be an attractive tool to be used both as an advanced analysis tool and as support to decision makers when designing new systems.     

Application of an inverse input/output mapped ANN as a power systemstabilizer

An artificial neural network (ANN), trained as an inverse of the controlled plant, to function as a power system stabilizer (PSS) is presented in this paper. In order to make the proposed ANN PSS work properly, it was trained over the full working range of the generating unit with a large variety of disturbances. Data used to train the ANN PSS consisted of the control input and the synchronous machine response with an adaptive PSS (APSS) controlling the generator. During training, the ANN was required to memorize the reverse input/output mapping of the synchronous machine. After the training, the output of the synchronous machine was applied as the input of the ANN PSS and the output of the ANN PSS was used as the control signal. Simulation results show that the proposed ANN PSS can provide good damping of the power system over a wide operating range and significantly improve the system performance     

Dispatch modeling of a regional power generation system – Integrating wind power

A modeling tool has been developed which can be used to analyze interaction between intermittent wind power generation and thermal power plant generation in a regional electricity grid system. The model uses a mixed integer programming (MIP) approach to determine the power plant dispatch strategy which yields the lowest systems costs. In the model, each large thermal plant is described separately, including properties such as start-up time, start-up cost and minimum load level. The model is evaluated using western Denmark as a case study.For western Denmark, it is found that the inclusion of start-up performance (i.e. start-up time and related costs) and minimum load level of the power generating units have a significant impact on the results. It is shown that the inclusion of these aspects influences the analysis of the effect of wind power variations on the production patterns of thermal units in the system. The model demonstrates how the introduction of wind power production and associated variations change the dispatch order of the large thermal power plants in the western Denmark system so that the unit with the lowest running costs no longer has the highest capacity factor. It is shown that this effect only is detected if start-up performance and minimum load level limitations are included in the optimization. It can also be concluded that start-up performance and minimum load level must be taken into account if the total system costs and emissions are not to be underestimated. The simulations show that if these aspects are disregarded, both total costs and total emissions of the power system are underestimated, with 5% in the case of western Denmark. Models such as the one developed in this work can be efficient tools to understand the effects of large-scale wind power integration in a power generation system with base load plants.     

汽车动力总成隔振悬置布置的设计思想论析

作者系统总结和分析了汽车动力总成一悬置系统的设计思想。应用刚体的撞击中心理论布置动力总成的前、后悬置，同时兼顾动力总成和车身的垂向弹性弯曲振动模态的影响以及发动机往复不平衡惯性力的作用中心，可有效提高汽车的舒适性。V型布置的悬置组能解除动力总成、悬置系统横移、侧倾自由度之间的弹性耦合，既有较大的横向刚度，又有足够的侧倾柔度；既有利于获得动力总成的第一阶刚体模态为绕扭矩轴做侧倾振动的模态，也便于该系统刚体模态频率与汽车其它子系统固有频率之间的合理分配，从而获得良好的怠速隔振性能。     

Optimal operation of autonomous microgrid including wind turbines

This paper gives a novel hybrid optimization method to find optimal sitting and operation of an autonomous MG at the same time. The operation is optimized via finding the optimal droop gain parameters of DGs. The optimization problem is formulated as a multi-objective problem where the objectives are applied to minimize the fuel consumption of DGs and to improve the voltage profile and stability of MG subject to operational and security constraints. A hybrid algorithm, named HS-GA, is developed to solve the paper optimization problem. A new formulation of power flow is derived to run the proposed algorithm where the steady state frequency of system, reference frequency, reference voltage and droop coefficients of DGs, based on a droop controller, are considered as optimization variables. The performance of the paper approach is compared with other optimization and non-optimization methods in MG with 33and 69 buses using MATLAB. The performance of the proposed method is compared with a method that the parameters of DGs are pre-determined without conducting any optimization process. The results show, which optimized droop parameters improves the operation of the MG.