故障限流器在新能源并网中最佳安装位置和参数配置研究

当电力系统发生短路故障时,为了防止并网容量逐年增加的分布式电源提供过大的短路电流,该文提出安装故障限流器(FCL)来限制短路电流突增的问题.FCL的安装主要考虑安装位置和参数配置,利用PSCAD/EMTDC搭建典型仿真模型,分别进行有/无新能源情况下,不同故障点的短路电流变化情况,对比得出新能源并网端变化最大,即FCL...     

基于改进PSO算法的微能源网优化配置研究

微能源网优化配置的求解算法存在易陷入局部搜索和收敛速度低两大缺陷,如何同时解决这两个缺陷是一直以来的研究难点。针对这一问题,提出了基于时变压缩因子和自适应变异的改进粒子群算法。针对经济优化配置,建立了包括多种分布式设备的微能源网架构模型和以年经济成本最低为目标,计及可靠性并含多种约束的优化配置模型。最后,结合具体算例,将改进粒子群算法运用于模型中,得到各分布式设备的配置方案和最优年经济成本,对比验证改进粒子群算法性能。实验结果表明：改进粒子群算法较好地提高了算法的收敛速度和全局收敛能力;微能源网优化配置模型实现了低经济性和高可靠性的有效结合。     

基于改进的PSO算法的电力系统无功优化研究

电力系统无功优化是保证电力系统安全、经济运行的重要措施,粒子群优化算法（PSO）具有模型简单、收敛速度快、参数简洁等优点,但用于求解高维复杂优化问题时易陷入局部最优,针对此缺陷,在PSO算法的基础上提出了自适应随机变异粒子群优化算法（AMPSO）,将该算法用于求解电力系统无功优化问题,并以IEEE30标准节点系统为算例进行验证。结果表明,与PSO算法相比,AMPSO算法有效降低了系统网损,显现出良好的全局收敛特性。     

基于多目标粒子群算法的水资源优化配置研究

在分析阿拉尔市水资源利用现状的基础上,构建了以区域供水系统总缺水量最小、水库损失水量最小为目标的水资源优化配置模型,应用多目标粒子群算法,对阿拉尔市2015、2020和2025年三个水平年,二种不同保证率(P=50%、P=75%)下的水资源优化配置方案进行求解。结果表明,该方法有效解决了阿拉尔市水资源系统不同水平年、多水源、多用户、多受水区、多工程、多目标的求解问题,得到了阿拉尔市各受水区及其各用水部门的分配水量,实现了阿拉尔市水资源的综合配置。     

基于文化粒子群算法的三亚市水资源优化配置

为解决三亚市水资源时空分布不均、部分地区缺水现象严重的问题,建立了系统总缺水量最小、供水效益最大的多目标水资源优化配置模型;并应用新型智能文化粒子群优化算法,对2010年为现状水平年,2020、2030年为规划水平年的多年平均情况下的水资源配置方案进行了模型求解。结果表明,规划水平年缺水率不断下降,基本实现了总缺水量最小的目标,可见优化配置有效缓解了三亚市用水紧张的问题,具有一定的借鉴意义。     

基于粒子群算法的水资源优化配置研究

为了合理分配引大济湟工程从大通河调出的7.5×108 m3水量,以满足引大济湟工程受水区的生活、生产及生态用水需求,通过建立以供水净效益最大、供水系统总缺水量最小为目标函数的水资源优化配置模型,运用粒子群算法得到了受水区4类水源、5类用水户在2015、2020、2030三个规划水平年的多年平均情况下的水资源优化配置方案。结果表明,2015、2020、2030年净外调水量分别为3.10×108、6.05×108、7.34×108 m3,缺水量分别为0.69×108、0.64×108、2.15×108 m3,部分区域农业用水存在缺口,仍需采取必要的节水措施。     

基于PSO的水库群联合供水优化调度应用研究

针对水库群优化调度为水资源系统优化调度中的难点,分析了水库群优化调度存在的不足,提出了基于粒子群优化算法的水库群联合供水优化调度模型,并以余姚市陆埠-梁辉水库群为例进行应用研究.结果表明,粒子群算法能获得高性能的优化调度结果,可更好地发挥库群的综合效益,科学可行.     

基于PSO算法的水库防洪优化调度模型及应用

针对传统的交互式洪水调度方案生成方式存在的缺陷,由基于PSO算法的洪水优化调度模型,提出了有条件限制的随机自动生成方式,并利用罚函数法处理边界条件和其他非等式约束.实例应用结果表明,较之常规的动态规划法,改进后的PSO算法计算速度快、结果合理,为流域防洪优化调度提供了有效途径.     

基于Prony算法的风光分布式高压电网故障检测技术研究

针对传统的Prony算法运算效率较为低下,抗噪性能较弱的问题,文章提出一种改进Prony算法的高电压电网故障检测方法。该方法将粒子群算法与Prony算法进行结合,首先基于Prony算法可以建立包含谐波分量幅度值、衰减因子和频率等一系列的参数的模型,然后利用粒子群优化算法对谐波幅值、相位和频率进行辨识,避免了传统Prony算法中由于噪声问题造成的信号频率难以提取的弊端。通过仿真证明了文章算法具有极强的可靠性和较高的运算效率。     

基于粒子群混合算法的风力发电机齿轮箱故障诊断

针对传统方法在风力发电机齿轮箱故障诊断中存在精度不高的问题,引入了一种改进粒子群算法优化神经网络的方法。该算法的惯性权重可进行自适应调整,以平衡全局和局部搜索能力。同时,收缩因子可加快算法的收敛速度,以更快收敛到全局最优。仿真结果表明,该方法能较好地识别故障模式,具有一定的实用性。     

Placement optimization of Multi-Type fault current limiters based on genetic algorithm

The fault current limiter (FCL) is an effective measure for improving system stability and suppressing short-circuit fault current. Because of space and economic costs, the optimum placement of FCLs is vital in industrial applications. In this study, two objectives with the same dimensional measurement unit, namely, the total capital investment cost of FCLs and circuit breaker loss related to short-circuit currents, are considered. The circuit breaker loss model is developed based on the attenuation rule of the circuit breaker service life. The circuit breaker loss is used to quantify the current-limiting effect to avoid the problem of weight selection in a multi-objective problem. The IEEE 10-generator 39-bus system in New England is used to evaluate the performance of the proposed genetic algorithm (GA) method. Comparative and sensitivity analyses are performed. The results of the optimized plan are validated through simulations, indicating the significant potential of the GA for such optimization.     

Adaptive restarting method for LCC-HVDC based on principle of fault location by current injection

The existing LCC-HVDC transmission project adopts the fixed-time delay restarting method. This method has disadvantages such as non-selectivity, long restart process, and high probability of restart failure. These issues cause a secondary impact on equipment and system power fluctuation. To solve this problem, an adaptive restarting method based on the principle of fault location by current injection is proposed. First, an additional control strategy is proposed to inject a current detection signal. Second, the propagation law of the current signal in the line is analyzed based on the distributed parameter model of transmission line. Finally, a method for identifying fault properties based on the principle of fault location is proposed. The method fully considers the influence of the long-distance transmission line with earth capacitance and overcomes the influence of the increasing effect of the opposite terminal. Simulation results show that the proposed method can accurately identify the fault properties under various complex fault conditions and subsequently realize the adaptive restarting process.     

Improvement of estimation of surge arrester parameters by using Modified Particle Swarm Optimization

Metal Oxide Surge Arrester (MOSA) accurate modeling and its parameter identification are very important aspects for arrester allocation, system reliability determination and insulation coordination studies. In this paper, Modified Particle Swarm Optimization (MPSO) algorithm is used to estimate the parameters of surge arrester models. The convergence to the local optima is often a drawback of the Particle Swarm Optimization (PSO). To overcome this demerit and improve the global search capability, Ant Colony Optimization (ACO) algorithm is combined with PSO algorithm in the proposed algorithm. The suggested algorithm selects optimum parameters for the arrester model by minimizing the error among simulated peak residual voltage values given by the manufacturer. The proposed algorithm is applied to a 120 kV MOSA. The validity and the accuracy of estimated parameters are assessed by comparing the predicted residual voltage with experimental results.     

基于改进的GRNN的固体氧化物燃料电池辨识模型研究

固体氧化物燃料电（SOFC）作为一种新的能源形式,日益受到重视．针对SOFC系统过于复杂,现有的理论电压模型存在明显不足的特点,绕开了SOFC的内部复杂性,利用经过粒子群算法（PSO）优化的广义回归神经网络（GRNN）对SOFC系统进行辨识建模．以氢气流速为神经网络辨识模型的输入量,电流／电压为输出量,建立SOFC在不同氢气流速下的电池电流／电压动态响应模型．仿真结果表明所建模型能基本表示出SOFC系统的电流／电压的动态响应,说明利用GRNN建模的有效性,所建模型精度也较高．     

基于粒子群法的母管制锅炉负荷优化分配算法

母管制运行锅炉的特性差别很大,如何实现锅炉间负荷的优化分配从而提高节煤率是母管制电厂运行管理中经常遇到的难题,本文提出了一种不同于常规等微增率法的负荷分配新算法--粒子群法,实例计算表明该方法可以取得比等微增率法更好的节煤效果.     

Techno-economical optimization based on swarm intelligence algorithm for a stand-alone wind-photovoltaic-hydrogen power system at south-east region of Mexico

Renewable generating systems are alternative to produce electric energy in a clean manner. However, the high costs of the constituents limit their broad use. Thus, sizing is an important issue in the renewable generating systems design, in order to reach an efficient relationship between cost and benefit. Likewise, the random nature of the sources makes the sizing a complex task with regard to a conventional system. This paper is focused on calculate the optimal size of a wind-photovoltaic-fuel cell system to meet the power demand of an isolated residential load located in the south-east region of Mexico (Chetumal city 18°31′21.4″N 88°16′11.3″W), with a solar radiation range from 0 to 0.75 kW/m² and wind speed range from 5 to 7.8 m/s. Swarm intelligence techniques have been successfully applied in solving many combinatorial optimization problems in which the objective space possesses many local optimal solutions. This work employs the Particle Swarm Optimizer (PSO) algorithm to search the optimal sizing for the power plant minimizing the total costs of the system; as a metaheuristic procedure, the PSO was able to find the best configuration regardless the lack of a deep knowledge of the problem. Compared against the Differential Evolution (DE) technique, the PSO performance is faster and able to provide a configuration that saves around 10% of the total cost of the hybrid system.     

The influence of carbon dioxide trading scheme on economic dispatch of generators

This paper aims at probing into the topic of power units’ operation and dispatch based on Carbon Dioxide (CO₂) trading scheme. The trading cost of CO₂ emission is embedded into the traditional economic dispatch model, which will be solved by the New Particle Swarm Optimization (NPSO). By considering the CO₂ trading scheme, the influences of the various strategies for unit’s dispatch are simulated and analyzed in this paper. The proposed method, NPSO is developed in such a way that PSO with Constriction Factor (PSO-CF) algorithm is applied as a based level search. NPSO introduces two operators, “Random Particles” and “Fine-Tuning” into the PSO-CF algorithm to improve the drawback of searching global optimum and make the search method more efficient at the end of search. The efficiency and ability of NPSO is demonstrated by the six generating units. Simulation results indicated that reasonable solutions provide a practical and flexible framework for power sectors. They can be also used for generating alternatives and thus help decision makers to obtain the goals of minimal operation cost under their desired emission’s policies.     

基于PSO算法的供热系统锅炉集群运行参数优化

针对供热系统调峰热水锅炉集群的参数优化问题,应用基于数据驱动的神经网络算法,接入DCS系统锅炉数据,辨识建立锅炉的运行性能模型,以锅炉运行参数为决策变量、锅炉集群运行经济性最优为目标函数,应用粒子群算法优化获得最优的锅炉集群运行方案。以三河新源供热调峰锅炉集群的运行数据为实例,对3台和4台锅炉运行参数进行了优化,结果表明:通过优化获得的方案,与实际运行方案对比后,可产生约7%的节能量。     

地下管道群中安装电缆的热状态及载流量评估

为了保证地下电缆可靠运行,电力部门通常的做法是在电缆表面安装温度传感器,对电缆的热状态进行直接监测。但是对于管道群中安装的电缆,如在安装时打碎已有管道既不安全也不经济,使温度传感器不得不安装在管道群的外表面,带来如何从远离电缆安装的温度传感器的数据中推测出电缆绝缘温度的问题。对此,提出了一种通过土壤热参数的估算来评估电缆绝缘热状态的方法,主要涉及有限元法和优化方法。通过实际应用于220 kV变电站入线的3条220 kV／250 MVA地下管道群电缆线路,对热状态进行精确评估,预测结果与实际测量值具有很好的一致性,证明了所提方法的有效性。