Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory

Reliability assessment of composite power systems is a critical and important part of power investigations especially in the market-driven environments. Therefore, the reliability indices as criteria for the comparison of the reliability of the power systems should be evaluated precisely and carefully. Because of the nonlinear behavior of the systems as the effect of different parameters like weather conditions, load pattern changes and some others, reliability indices always contain much uncertainty. In this paper a neuro-fuzzy based method is proposed to reduce the degree of the uncertainty in the reliability indices and therefore to evaluate the reliability of the composite power systems precisely. Fuzzy logic theory makes it possible to make use of the human experts knowledge in the reliability evaluations. Also by the use of RBFNN and its powerful characteristic to learn any nonlinear mapping between two states it would be possible to evaluate the reliability indices for every short time interval needed so that reliability evaluation in real time would be achievable and feasible.In this paper the RBFNN is trained by the training patterns that are achieved by the use of fuzzy logic theory, then the results are examined on a standard Reliability Test System (RTS-96).     

The composite fuzzy reliability index of power systems

This paper describes a method for fuzzy calculation of power system reliability. It introduces a composite fuzzy security and fuzzy adequacy indices encompassing the power system segments of generation, transmission, and the segment of load nodes. The fuzzy security index is determined by the security membership grades of the power system components which are found to be the worst in their segments. The fuzzy adequacy index is based on the adequacy indices of generation, transmission, and transmission integration segments. The composite fuzzy reliability index enables the system operator to have a continuous insight into the distance between the power system's actual state and the closest unreliable state by tracking the system's components with the lowest security and adequacy membership grades of the respective segments. The algorithm has been tested on the models of the IEEE Three Area RTS-96 and Bosnia-Herzegovina power systems.     

基于复合幂函数修正G1法的商户综合评价

针对多指标综合评价问题中主客观权重相悖时客观权重淹没主观权重的问题,以G1法和客观赋权法为基础,提出了复合幂函数修正G1法的组合赋权模型。首先,建立指标体系并通过G1法确定各指标主观排序和主观初始向量;然后,利用客观赋权法计算各指标客观向量;其次,在不改变主观排序的情况下利用复合幂函数算出主客观结合的综合权重;最后,利用各指标标准化后的值和综合权重计算综合评价值。采用大众点评网的商户数据进行综合评价实验：该模型的均方根误差（RMSE）为3.891,均低于G1-熵权法的8.818和标准差修正G1法的4.752,且覆盖率优于两种对比方法;分别修改主观初始向量和主观排序进行对比实验,修改主观排序的均方根误差为5.430,高于修改主观初始向量的1.17。实验结果表明,该模型得到的评价值与大众点评网官方的评分的一致性较高,且该模型弱化了主观初值对评分结果的影响,体现了主观排序的基础作用。     

Hopf bifurcation index for large scale power systems

In this paper, we propose a novel index for the prediction of the Hopf bifurcation (HB) in power systems. The proposed index has strictly less processing load and less computation time in comparison to former HB indices. It also uses modern control properties of the power system such as system matrix and critical eigenvalue. Therefore, stochastic subspace system identification (SSSI) is used as a tool for estimation and prediction of the proposed HB index. Combining beneficial properties of SSSI methods and the processing advantages of the proposed index, we project a new algorithm for power system monitoring. The algorithm is easy and straight-forward. We conduct several test conditions for the proposed materials using 2-area 4-machine system, New England 10-Machine and IEEE 50-machine system. Simulation outcome expresses good performance of proposed index in comparison to former HB indices. The proposed index has fairly linear behavior, without discontinuities with respect to increases of system load. It also has less computation load.     

Optimal maintenance scheduling of generators using multiple swarms-MDPSO framework

In this paper, a challenging power system problem of effectively scheduling generating units for maintenance is presented and solved. The problem of generator maintenance scheduling (GMS) is solved in order to generate optimal preventive maintenance schedules of generators that guarantee improved economic benefits and reliable operation of a power system, subject to satisfying system load demand, allowable maintenance window, and crew and resource constraints. A multiple swarm concept is introduced for the modified discrete particle swarm optimization (MDPSO) algorithm to form a robust algorithm for solving the GMS problem. This algorithm is referred to by the authors as multiple swarms-modified particle swarm optimization (MS-MDPSO). The performance and effectiveness of the MS-MDPSO algorithm in solving the GMS problem is illustrated and compared with the MDPSO algorithm on two power systems, the 21-unit test system and 49-unit Nigerian hydrothermal power system. The GMS of the two power systems are considered and the results presented shows great potential for utility application in their area control centers for effective energy management, short and long term generation scheduling, system planning and operation.     

采用迭代求解的电动汽车优化充电算法

电动汽车优化充电对配电网的安全经济运行具有十分重要的意义.利用配电网辐射状运行结构采用迭代修正节点电压的方法能够大幅降低优化变量的维数,提高计算速度.该方法已被应用于以网损最小为目标函数、常规负荷模型为恒功率负荷、不计及节点电压、支路功率约束的三相平衡配电网中.该文提出了将其推广至一般情形的方法,目标函数为配电网供电能量最小、运行费用最小或利润最大,不再局限于网损最小,负荷模型不再局限于恒功率负荷.而且计及了配电网的三相不平衡、节点电压、支路功率约束.文中分析了负荷模型对优化计算结果的影响.研究发现,当负荷模型只包含恒功率、恒阻抗负荷时,每次迭代中优化模型仍为线性约束凸二次规划模型;当负荷模型包含恒电流负荷时,每次迭代中优化模型不再为线性约束凸二次规划模型,但仍为线性约束凸规划模型.3个算例的仿真结果表明,所提方法收敛性好、计算精度高、速度快,能够改善电压,提高配电网的经济效益.     

Reliability analysis for k-out-of-n systems with shared load and dependent components

Many structural systems require a minimal number of components to be operational, and predicting the reliability of such systems is a challenge because surviving components share the original system workload with higher component loads after the failure of some components. The states of all the components are also dependent. Such dependence, however, is generally neglected in many existing methods. In this study, we develop a new reliability method for systems with dependent components that share the system load equally before and after other components have failed. The components are also subjected to other loads, such as a preload. The new method is based on limit-state functions that predict the states of components, and the First Order Reliability Method is used. The advantage of the proposed method is that it can directly link the system reliability with design variables and random parameters because of the use of a physics-based approach. High accuracy is maintained with the consideration of dependent component states. Two examples are used to demonstrate the good accuracy and efficiency of the proposed method.     

风电介入下的多区域互联电力系统分布式经济模型预测负荷频率控制

针对风电介入下的多区域互联电力系统,提出一种分布式经济模型预测负荷频率控制策略.通过将大规模互联电力系统分解成若干个动态耦合的子系统,这些子系统能够利用网络交流并共享信息,使得各区域的控制器实现各自优化问题的求解.同时,在满足状态约束和控制输入约束的前提下,遵循传统火力发电优先、风力发电配合的原则,通过在线求解优化问题,实现风电介入下的多区域互联电力系统的负荷频率控制.为了提高系统整体运行经济性,所提出的分布式经济模型预测控制器将负荷调频成本、燃料消耗成本以及风力发电成本等经济性指标考虑在内.仿真结果表明,在阶跃负荷扰动下,所设计的控制器不仅可以满足调频要求,在降低计算负担和提高经济性能方面也具有一定优势.     

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control (LFC) is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC) based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints (GRCs), load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints.      

气制动整车模拟试验系统设计

研制了一套整车气制动综合模拟试验系统。采用精密伺服电机加快速气缸的加载模式,在一套加载机构上实现了快速制动和缓慢制动两种制动工况的模拟;应用伺服控制与高速数据采集技术,对制动系统的动态响应特性、关键制动部件的静特性和制动部件之间的匹配性进行检测。根据《专用检测设备评定方法指南》(JB/T 10633-2006),对制动模拟系统的测试项目进行了多次试验测试。试验数据表明,该制动模拟系统动态响应时间小于0.6 s,测量能力指数均大于2.0,满足汽车行业标准和测试评定要求。     

FACTOR SYSTEM RECONSTRUCTION ANALYSIS METHOD AND ITS APPLICATION IN ANTIBIOTICS FERMENTATION

Medium composite is studied based on experimental data in antibiotics fermentation. The steps and methods of setting up the system reconstruction aggregate state distribution table of factor-criteria system are given. Then the related parts of the computational results of the closeness distance (edc values) between the inspected factor-criteria level formed hypothesis behavior functions and overall system behavior function are shown. The sequence of edc values shows the order of importance between related factor states and criteria states and secondary indirect impacts of those states are demonstrated. By using the preference order method of multiple criteria decision making, a comprehensive evaluation process is introduced. This process gives an evaluation result for every factor state and the optimum medium composite states for the improvement of the production rate of studied antibiotics.     

Synergic predator-prey optimization for economic thermal power dispatch problem

This paper introduces a synergic predator-prey optimization (SPPO) algorithm to solve economic load dispatch (ELD) problem for thermal units with practical aspects. The basic PPO model comprises prey and predator as essential components. SPPO uses collaborative decision for movement and direction of prey and maintains diversity in the swarm due to fear factor of predator, which acts as the baffled state of preys’ mind. In the SPPO, the decision making of prey is bifurcated into corroborative and impeded parts. It comprises four behaviors namely inertial, cognitive, collective swarm intelligence, and prey's individual and neighborhood concern of predator. The prey particle memorizes its best and not-best positions as experiences. In this research work, to improve the quality of prey swarm, which influence convergence rate, opposition based initialization is used. To verify robustness of proposed algorithm general benchmark problems and small, medium, and large power generation test power system are simulated. These test systems have non-linear behavior due to multi-fuel options and practical constraints. The constraints of prohibited operating zone and ramp rate limits of power generators’ are handled using heuristics. Newton–Raphson procedure is exploited to attain the transmission losses using load flow analysis. The outcomes of SPPO are compared with the results described in literature and are found satisfactory.     

The scenario approach for Stochastic Model Predictive Control with bounds on closed-loop constraint violations

Many practical applications in control require that constraints on the inputs and states of the system are respected, while some performance criterion is optimized. In the presence of model uncertainties or disturbances, it is often sufficient to satisfy the state constraints for at least a prescribed share of the time, such as in building climate control or load mitigation for wind turbines. For such systems, this paper presents a new method of Scenario-Based Model Predictive Control (SCMPC). The basic idea is to optimize the control inputs over a finite horizon, subject to robust constraint satisfaction under a finite number of random scenarios of the uncertainty and/or disturbances. Previous SCMPC approaches have suffered from a substantial gap between the rate of constraint violations specified in the optimal control problem and that actually observed in closed-loop operation of the controlled system. This paper identifies the two theoretical explanations for this gap. First, accounting for the special structure of the optimal control problem leads to a substantial reduction of the problem dimension. Second, the probabilistic constraints have to be interpreted as average-in-time, rather than pointwise-in-time. Based on these insights, a novel SCMPC method can be devised for general linear systems with additive and multiplicative disturbances, for which the number of scenarios is significantly reduced. The presented method retains the essential advantages of the general SCMPC approach, namely a low computational complexity and the ability to handle arbitrary probability distributions. Moreover, the computational complexity can be adjusted by a sample-and-remove strategy.     

基于GaBP算法的快速潮流计算方法

研究在潮流迭代求解过程中雅可比矩阵方程组的迭代求解方法及其收敛性。首先利用PQ分解法进行潮流迭代求解,并针对求解过程中雅可比矩阵对称且对角占优的特性,对雅可比矩阵方程组采用高斯置信传播算法(GaBP)进行求解,再结合Steffensen加速迭代法以提高GaBP算法的收敛性。对IEEE118、IEEE300节点标准系统和两个波兰互联大规模电力系统进行仿真计算后结果表明：随着系统规模的增长,使用Steffensen加速迭代法进行加速的GaBP算法相对于基于不完全LU的预处理广义极小残余方法(GMRES)具有更好的收敛性,为大规模电力系统潮流计算的快速求解提供了一种新思路。     

有源电力滤波器多内模复合控制策略控制分析

为消除单一通过矢量谐振(VR)进行控制时面临的缺陷,采用重复控制(RC)并合理设置校正因子,设计了多内模控制器,通过对RC进行充分控制的方式达到跟踪与控制谐波分量的作用.采用MATLAB软件建立了上述系统模型并完成仿真测试.研究结果表明:电压与电流总谐波失真(THD)都低于1％,并没有发生明显畸变,呈现正弦波的特征.以...     

Suboptimal control of linear time-invariant systems subject to control structure constraints

A method is presented for designing controllers for linear time-invariant systems whose states are not all available or accessible for measurement and where the structure of the controller is constrained to be a linear time-invariant combination of the measurable states of the system. Two types of structure constraints are considered: 1) each control channel is constrained to be a linear, time-invariant combination of one set of measurable states; 2) each control channel is constrained to he a linear, time-invariant combination of different sets of measurable states. The control system, subject to these constraints is selected such that the resulting closed-loop system performs as "near" to some known optimal system as is possible, i.e., suboptimal. The nearness of the optimal system to the suboptimal system is defined in two ways and thus, two types of suboptimal controllers are found.     

Decentralized querying of topological relations between regions monitored by a coordinate-free geosensor network

Geosensor networks present unique resource constraints to spatial computation, including limited battery power, communication constraints, and frequently a lack of coordinate positioning systems. As a result, there is a need for new algorithms that can efficiently satisfy basic spatial queries within those resource constraints. This paper explores the design and evaluation of a family of new algorithms for determining the topological relations between regions monitored by such a resource-constrained geosensor network. The algorithms are based on efficient, decentralized (in-network) variants of conventional 4-intersection and intersection and difference models, with in-network data aggregation. Further, our algorithms operate without any coordinate information, making them suitable applications where a positioning system is unavailable or unreliable. While all four algorithms are shown to have overall communication complexity O(n) and optimal load balance O(1), the algorithms differ in the level of topological detail they can detect; the types of regions they can monitor; and in the constant factors for communication complexity. The paper also demonstrates the impact of finite granularity observations on the correctness of the query results. In the conclusions, we identify the need to conduct further fundamental research on the relationship between topological relations between regions and limited granularity sensor observations of those regions.     

A pre-run-time scheduling algorithm for object-based distributed real-time systems

The most important goal in hard real-time systems is to guarantee that all timing constraints are satisfied. Even though object-based techniques (which contain reusable software components) are used to manage the complexity in the software development process of such systems, execution efficiency may have to be sacrificed, due to the large number of procedure calls and contention for accessing software components. These issues are addressed by the following parallelizing techniques: (a) converting potentially inefficient procedure calls to a source of concurrency via asynchronous remote procedure calls (ARPC) (b) replicating (or cloning) software components to reduce the contention. The existing object-based scheduling algorithms construct an initial schedule and apply incremental parallelization techniques to modify the initial schedule till a feasible schedule is generated. But these algorithms are applicable for scheduling only multiple independent tasks. This paper describes a pre-run-time scheduling algorithm for a set of periodic object-based tasks having precedence constraints among them. The algorithm allocates the components of object-based periodic real-time tasks to the sites of a distributed system based on a clustering heuristic which takes into account the ARPC parallelism and load balancing, and schedules them on respective sites. The algorithm also finds a schedule for communication channel(s). Further, it clones the components of object-based periodic tasks, if contention occurs in accessing them. In addition to the above (periodicity and precedence) constraints, the tasks handled by our algorithm can have resource constraints among them. The experimental evaluation of the algorithm shows that the combination of the proposed clustering heuristic and cloning enhances schedulability.     

FILESPPA: Fast Instruction Level Embedded System Power and Performance Analyzer

In the low power embedded systems design, it is important to analyze and optimize both the hardware and the software components of the system. The power consumption evaluation of the embedded systems is very slow procedure using the instruction-level power models into the simulator. Moreover, a huge number of simulations are needed to explore the power consumption in the instruction memory hierarchy to find the best cache parameters of each hierarchy’s level. In this paper we present a methodology which is aiming to estimate the system power consumption in short time, without simulation. The proposed methodology is based on the fast instruction analysis using instruction level power models, cache memory and memory power models. Based on the proposed methodology a software tool was developed named FILESPPA in order to automate the methodology’s steps for the MIPS processor architectures. The experimental results show the efficiency of the proposed methodology and tool in term of estimation accuracy, reducing the system power estimation time of the simulation technique.     

基于大数据的电网规划精益分析平台研究与应用

随着新能源的大量接入和用户的广泛参与,电网企业的数据呈指数级增长,电网规划部门迫切需要运用大数据的分析手段提高规划决策的精准性。本文以地市供电公司的实体业务为切入点,基于地理位置信息,在数据融合贯通的基础上,运用数据可视化技术多维度动态展示地区电网负荷分布和电网供给能力,辅助规划人员快速发现负荷分布规律和电网薄弱点。运用大数据分析挖掘方法构建基于负荷特性分析的负荷预测模型,支撑电网项目和运行方式安排。打破电网负载问题的单一评价方法,建立可度量的电网综合评价体系及模型,为电网项目统筹提供量化依据。基于大数据的电网规划精益分析平台通过电网负荷可视化展示,有效提升了电网诊断分析效率,运用大数据分析方法提高了地区最大负荷的预测精度,实现了电网问题的数字化评估,提高了电网规划投资决策的精准性。