Power system aggregate load area modelling by particle swarm optimization

This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.     

Application of firefly algorithm for multi-stage transmission expansion planning with adequacy-security considerations in deregulated environments

Electric energy is the most popular form of energy because it can be transported easily at high efficiency and reasonable cost. Nowadays the real-world electric power systems are large-scale and highly complex interconnected transmission systems. The transmission expansion planning (TEP) problem is a large-scale optimization, complicated and nonlinear problem that the number of candidate solutions increases exponentially with system size. Investment cost, reliability (both adequacy and security), and congestion cost are considered in this optimization. To overcome the difficulties in solving the non-convex and mixed integer nature of this optimization problem, this paper offers a firefly algorithm (FA) to solve this problem. In this paper it is shown that FA, like other heuristic optimization algorithms, can solve the problem in a better manner compare with other methods such genetic algorithm (GA), particle swarm optimization (PSO), Simulated Annealing (SA) and Differential Evolution (DE). To show the feasibility of proposed method, applied model has been considered in IEEE 24-Bus, IEEE 118-Bus and Iran 400-KV transmission grid case studies for TEP problem in both adequacy and security modes. The obtained results show the capability of the proposed method. A comprehensive analysis of the GA, PSO, SA and DE with proposed method is also presented.     

Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm

In this paper, a flexible power system planning strategy using a novel population-based metaheuristic algorithm inspired by the pollination process of flowers named adaptive flower pollination algorithm (APFPA) has been proposed. The proposed power system planning strategy implemented and successfully applied for solving the security optimal power flow (OPF) considering faults at critical generating unit. The main particularity of the proposed variant is that the control variables are optimized based on an adaptive and flexible structure. Also the performances of the standard FPA is improved by dynamically adjusting their control parameters, this allows creating diversity and balance between exploration and exploitation during search process. The robustness of the proposed planning strategy, is demonstrated on the IEEE 30-Bus, and IEEE 57-Bus tests power system for different objectives such as fuel cost, power losses, and voltage deviation. Considering the quality of the obtained results compared with various recent methods reported in the literature, the proposed strategy seems to be a competitive tool for solving with accuracy the security OPF considering critical situations.     

Security optimal power flow considering loading margin stability using hybrid FFA–PS assisted with brainstorming rules

This paper presents a new power system planning strategy which combines firefly algorithm (FFA) with pattern search algorithm (PS). The purpose is minimizing total fuel cost, total power loss and reducing total voltage deviation, with the objective of enhancing the loading margin stability and consequently the power system security. A new interactive and simple mechanism, inspired in brainstorming process, is proposed that allows FFA and PS algorithms to explore new regions of the search space. In this study the Static VAR compensator (SVC) is modeled and integrated in an efficient location which is chosen considering the voltage stability index. The proposed algorithm is interactive and tries to optimize a set of control variables at the same time, namely, active power generations, voltage of generators, tap transformers, and the reactive power of shunt compensators to optimize three objective functions such as: fuel cost, total power loss and total voltage deviation. These variables are optimized using a flexible interactive and competitive search mechanism. The proposed planning strategy has been examined and applied to two practical test systems IEEE 14-Bus and IEEE 30-Bus. Simulation results confirm the effectiveness of this hybrid strategy for solving the security optimal power flow.     

Differential evolution approach for optimal reactive power dispatch

Differential evolution based optimal reactive power dispatch for real power loss minimization in power system is presented in this paper. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts to be switched, for real power loss minimization in the transmission system. The problem is formulated as a mixed integer nonlinear optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling. The formulation also checks for the feasibility of the optimal control variable setting from a voltage security point of view by using a voltage collapse proximity indicator. The algorithm is tested on standard IEEE 14, IEEE 30, and IEEE 118-Bus test systems. To show the effectiveness of proposed method the results are compared with Particle Swarm Optimization and a conventional optimization technique – Sequential Quadratic Programming.     

光伏电站集成检测系统设计

采用电子电路对光伏电站环境参数进行检测,将采样得到的环境温度、组件温度和光辐照量转换成相应的电信号。利用单片机的实时控制和数据处理功能,完成系统对环境参数的检测,配置了485总线与上位机进行通信。充分考虑光辐照量测量的各种干扰,进行了温度校正和谱校正,确保测量的准确性,实现了基于光伏电池的低成本光伏电站环境参数检测系统。     

A computational intelligence-based suite for vulnerability assessment of electrical power systems

This paper discusses the feasibility of implementing computational intelligence algorithms for power system analysis in an open source environment. The scope is specially oriented to education, training and research. In particular, the paper describes a software package, namely Computational Intelligence Applications to Power System (CIAPS), that implements a variety of heuristic techniques for vulnerability assessment of electrical power systems. CIAPS is based on Matlab and suited for analysis and simulation of small to large size electric power systems. CIAPS is used for solving power flow, optimal power flow, contingency analysis based on artificial neural networks and fuzzy logic techniques. A variety of illustrative examples are given to show the features of the developed software tool.     

Multi-objective GA with fuzzy decision making for security enhancement in power system

Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.     

Classifier design for static security assessment using particle swarm optimization

Security is recognized as an important problem in planning, design and operation stages of electric power systems. Power system security assessment deals with the system’s ability to continue to provide service in the event of an unforeseen contingency. This paper proposes a particle swarm optimization (PSO) based classification for static security evaluation in power systems. A straightforward and quick procedure is used to select a small number of variables as features from a large set of variables which are normally available in power systems. A simple first order security function is designed using the selected features for classification. The training of weights in the classifier function (security function) is carried out by PSO technique. The PSO algorithm has minimized the error rate in classification. The procedure to determine the security function (classifier) is discussed. The performance of the algorithm is tested on IEEE 14 Bus, IEEE 57 Bus and IEEE 118 Bus systems. Simulation results show that the PSO classifier gives a fairly high classification accuracy and less misclassification rate.     

分布式最小二乘估计中隐匿FDI攻击策略的设计

虽然分布式坏值检测方法能够消除观测数据中坏值对分布式最小二乘估计性能的影响,但是现有的分布式坏值检测方法中依然存在安全漏洞.针对一类分布式最小二乘估计算法研究了相应隐匿虚假数据注入(FDI)攻击策略的设计问题,设计依赖于部分节点系统信息的分布式隐匿FDI攻击方法,这一方法不仅使得FDI攻击信号无法被现有分布式坏值检测方法检测到,而且可以以预设的偏移量改变估计结果.最后,通过IEEE 118-Bus电力系统模型验证所设计FDI攻击方法的隐匿性和有效性.     

基于拉普拉斯特征映射学习的隐匿FDI攻击检测

智能电网中的隐匿虚假数据入侵(False data injection, FDI)攻击能够绕过坏数据检测机制, 导致控制中心做出错误的状态估计, 进而干扰电力系统的正常运行. 由于电网系统具有复杂的拓扑结构, 故基于传统机器学习的攻击信号检测方法存在维度过高带来的过拟合问题, 而深度学习检测方法则存在训练时间长、占用大量计算资源的问题. 为此, 针对智能电网中的隐匿FDI攻击信号, 提出了基于拉普拉斯特征映射降维的神经网络检测学习算法, 不仅降低了陷入过拟合的风险, 同时也提高了隐匿FDI攻击检测学习算法的泛化能力. 最后, 在IEEE57-Bus电力系统模型中验证了所提方法的优点和有效性.     

改进的BT—SVM应用于电力系统SSA

随着电力系统的广泛发展,电力系统静态安全评估已变得越来越重要。文中比较了现在几种常用的人工智能方法,选择了支持向量机算法解决这一问题。由于解决大样本问题时,支持向量机所需训练时间显著增加,文中提出了约简样本的方法,并结合适合于电力系统的二叉树结构,提出了一种改进的简化二叉树支持向量机算法。将这种新的支持向量机算法应用于IEEE57节点电力系统,结果表明,文中提出的算法取得了比较好的结果,有效可行。     

Power control strategy for PV/Wind reverse osmosis desalination without battery

The goal of this paper is to develop and validate a novel power control strategy allowing to operate a Brackish Water Reverse Osmosis (BWRO) desalination test bench properly using isolated hybrid (PV/Wind) renewable sources without batteries. The proposed strategy uses Power Field Oriented Control (PFOC) in order to maintain DC bus voltage stability and to ensure power transfer from electrical sources to hydro-mechanical loads under variable power generation conditions. Experimental performances have been tested in different operating modes while respecting a Safe Operating Window (SOW) for desalination process. Power sharing is ensured by a basic Energy Management System (EMS).     

基于Nios Ⅱ的蓄电池组在线监测系统的设计

蓄电池组是一种常用的后备电源设施.为确保电力系统自动化设备不间断地运行,蓄电池组自身的运行状态必须得到有效监控.介绍了一种基于Nios Ⅱ软核的蓄电池组在线监测方法.系统在Cyclone系列EP1C12Q芯片上,采用了Nios Ⅱ来配置核心处理器,并提出了一种抗高共模电压的模拟量信号过程通道,实现了对蓄电池电压的在线检测.该系统具有高可靠性、高效率、低功耗和低成本等特点.     

无线局域网中时延感知的电源管理策略

因为移动需求,无线局域网的节点大多采用电池供电,节能对延长节点的工作时间极为重要。IEEE 802. 11 标准提出了电源管理策略,即允许无数据传递任务的节点进入休眠模式以节省能耗。对上述策略进行建模与分析,导 出了数据包传递时延、节点的连续休眠次数、活跃时间以及节点在活跃与休眠模式之间的切换次数等指标,提出了适 用于IEEE 802.n基础设施无线局域网的时延感知的电源管理策略并对之进行数值分析,这种方案可以让节点寻找 到最优的休眠时间长度以使能耗最低且满足给定的时延约束。     

面向对象的嵌入式系统电源管理模型

针对嵌入式系统的低功耗设计,提出一种面向对象的软件电源管理模型。在操作系统的基础上将相关API封装与扩展,抽象出操作系统电源管理类（OSPM）、CPU电源管理类（CPUPM）、设备驱动电源管理类（DDPM）、应用程序电源管理类（APM）。在操作系统层、驱动程序层、应用程序层之间形成电源管理接口,简化嵌入式系统电源管理的软件设计与维护。     

几种遗传算法在舰船电力系统网络重构中应用及比较

舰船电力系统(SPS)的网络重构是恢复系统故障、提高舰船生命力的重要途径之一。在满足各种运行约束条件下,快速地恢复因战斗受损的负荷供电是一典型的非线性整数规划问题。该文给出了大型舰船电力系统的网络拓扑结构,建立了综合考虑线路容量限制、节点电压约束及供电负荷优先性的重构数学模型,提出了全局遗传算法和免疫遗传算法来解决舰船电力系统网络重构问题。通过算例,对这几种遗传算法进行了比较,结果表明免疫遗传算法具有较高的收敛速度和精度,能够迅速求得全局最优解,避免了不成熟收敛,较好地实现了舰船电力系统的多目标故障恢复。     

Performance of IEEE 802.15.4 Clusters with Power Management and Key Exchange

The IEEE 802.15.4 specification is a recent low data rate wireless personal area network standard.While basic security services are provided for,there is a lack of more advanced techniques which are indispensable in modern personal area network applications.In addition,performance implications of those services are not known.In this paper,we describe a secure data exchange protocol based on the ZigBee specification and built on top of IEEE 802.15.4 link layer.This protocol includes a key exchange mechanism.We assume that all nodes are applying power management technique based on the constant event sensing reliability required by the coordinator.Power management generates random sleep times by every node which in average fairly distributes the sensing load among the nodes.Key exchange is initiated by a cluster coordinator after some given number of sensing packets have been received by the coordinator.We develop and integrate simulation model of the key exchange and power management technique into the cluster's reliable sensing function.We evaluate the impact of security function and its periodicity on cluster performance.     

Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer

In this paper, a novel two-archive Multi-Objective Grey Wolf Optimizer (2ArchMGWO) is proposed for solving Multi-Objective Optimal Reactive Power Dispatch (MORPD) problems. The optimizer has been improved from its original Multi-Objective Grey Wolf Optimizer (MGWO) by modifying the reproduction operator and adding the 2-archive concept to the algorithm. It is then implemented on solving MORPD with objective functions being active power loss minimization and voltage profile improvement (voltage deviation minimization). The generator bus voltages, tap setting transformers and shunt reactive power sources or flexible alternating current transmission systems are set as design variables. The proposed algorithm along with other existing multiobjective optimizers are applied to solve three test problems with the standard IEEE 30-bus, IEEE 57-bus, and the IEEE 118-bus power systems. The optimum results obtained from the various optimizers performance are compared based on the hypervolume indicator and they reveal that 2ArchMGWO is clearly superior to the others.     

Optimal reactive power dispatch for real power loss minimization and voltage stability enhancement using Artificial Bee Colony Algorithm

Reactive Power Dispatch (RPD) plays important role in power system reliability and security. This paper proposes the Optimal Reactive Power Dispatch (ORPD) for real power loss minimization, voltage deviation minimization and voltage stability enhancement using Artificial Bee Colony (ABC) Algorithm. ORPD is a mixed integer nonlinear optimization problem which includes both continuous and discrete control variables. The ABC algorithm is used to find the setting of control variables such as generator voltage magnitude, tap position of tap changing transformer and reactive power output of the compensation devices. The proposed algorithm is tested on IEEE 30 and 57 bus systems, Simulation results show that the proposed approach converges to better solutions and much faster than the earlier reported approaches in the literature. The optimization strategy is general and can be used to solve other power system optimization problems.