Optimal coordination of directional overcurrent relays using a modified electromagnetic field optimization algorithm

The optimal coordination of Directional Overcurrent Relays (DOCRs) is of paramount importance for power systems protection. The optimization model of this problem is non-linear and highly constrained. The main objective of this paper is to develop a modified version of the Electromagnetic Field Optimization (EFO) algorithm referred to as MEFO for the optimal coordination of DOCRs. The EFO is inspired by the behaviour of particles of electromagnets with different polarities where attraction–repulsion forces among these electromagnets lead particles toward global minima. It uses also the golden ratio. The proposed algorithm has been applied to three test systems including the 8-bus, the 9-bus and the 15-bus test systems. Furthermore, the results obtained using the proposed MEFO are compared with those obtained using the traditional EFO and a number of well-known algorithms. The obtained results show the effectiveness of the proposed MEFO to minimize the relay operating time for the optimal coordination of DOCRs.     

Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique

In this paper, a newly surfaced nature-inspired optimization technique called moth-flame optimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature.     

基于改进蚁群算法的方向过电流保护整定优化

本文提出了基于蚁群优化算法的方向过电流保护整定配合的优化模型.首先说明了方向过电流保护的时间特性,然后建立了方向过电流整定优化模型.优化目标是所有主保护动作时间之和最小,考虑了主后备保护配合约束、保护动作时间约束、启动电流约束等.本文所提方向过电流保护为非线性优化问题,提出利用改进蚁群优化算法来求解该模型.最后本文利用...     

Optimal coordination of over-current relays using modified differential evolution algorithms

Optimization of directional over-current relay (DOCR) settings is an important problem in electrical engineering. The optimization model of the problem turns out to be non-linear and highly constrained in which two settings namely time dial setting (TDS) and plug setting (PS) of each relay are considered as decision variables; the sum of the operating times of all the primary relays, which are expected to operate in order to clear the faults of their corresponding zones, is considered as an objective function. In the present study, three models are considered namely IEEE 3-bus model, IEEE 4-bus model and IEEE 6-bus model. To solve the problem, we have applied five newly developed versions of differential evolution (DE) called modified DE versions (MDE1, MDE2, MDE3, MDE4, and MDE5). The results are compared with the classical DE algorithm and with five more algorithms available in the literature; the numerical results show that the modified DE algorithms outperforms or perform at par with the other algorithms.     

面向动态能源资源协调的分布式原对偶优化方法

分布式优化在电力系统中发挥着越来越重要的作用。本文研究一类包含分布式发电机（DGs）和储能设备（ESs）的动态能源资源（DERs）协调问题,其目标是在满足局部耦合物理约束的前提下,使得总成本（包括发电成本, 储能成本和环境成本）最小化。首先,本文将动态DERs协调问题等价转换为更具一般性的分布式复合约束优化模型,并利用拉格朗日对偶理论分析得到原问题的对偶形式。 其次,提出一种新的分布式原对偶优化算法。特别地,所提算法使用局部常数步长,同时采用基于边的通信方式,这本质上区别于基于节点的一致性优化方法。最后,利用基于IEEE 39-bus系统的仿真实验进一步验证了所提算法在求解DERs协调问题上的有效性与可行性。     

Application of improved invasive weed optimization technique for optimally setting directional overcurrent relays in power systems

This paper aims to study the application of a heuristic optimization technique namely, Invasive Weed Optimization (IWO) technique for optimal protection coordination in power systems. The optimal relay coordination problem is formulated as a nonlinear constrained optimization, which is solved using Improved IWO (IIWO). The proposed IIWO algorithm modifies the standard deviation expression of the weed population. The simulation results show that IIWO has faster and better convergence compared with standard IWO. To further improve the computational efficiency, a hybrid IIWO method is also proposed which is obtained by defining sequential quadratic programming (SQP) as a subroutine in IIWO for searching local solutions, thus eliminate weaker weeds in the colonization process. The proposed techniques are tested on both the 9-bus test system and IEEE- 30 bus systems and the performance is compared. Relay coordination algorithm is developed in MATLAB, and the results are found to be effective and reliable.     

Multiobjective evolutionary algorithms for electric power dispatch problem

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.     

Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market

A novel stochastic optimization approach to solve optimal bidding strategy problem in a pool based electricity market using fuzzy adaptive gravitational search algorithm (FAGSA) is presented. Generating companies (suppliers) participate in the bidding process in order to maximize their profits in an electricity market. Each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. The gravitational search algorithm (GSA) is tedious to solve the optimal bidding strategy problem because, the optimum selection of gravitational constant (G). To overcome this problem, FAGSA is applied for the first time to tune the gravitational constant using fuzzy “IF/THEN” rules. The fuzzy rule-based systems are natural candidates to design gravitational constant, because they provide a way to develop decision mechanism based on specific nature of search regions, transitions between their boundaries and completely dependent on the problem. The proposed method is tested on IEEE 30-bus system and 75-bus Indian practical system and compared with GSA, particle swarm optimization (PSO) and genetic algorithm (GA). The results show that, fuzzification of the gravitational constant, improve search behavior, solution quality and reduced computational time compared against standard constant parameter algorithms.     

Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers

This study presents a particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) for the solution of optimal reactive power dispatch (ORPD) problem. The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss and the total voltage deviations are to be minimized separately. In order to evaluate the performance of the proposed algorithm, it has been implemented on IEEE 30-, 57- and 118-bus test power systems and the optimal results obtained are compared with those of the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving the ORPD problem of power system.     

A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch

Management and scheduling of reactive power resources is one of the important and prominent problems in power system operation and control. It deals with stable and secure operation of power systems from voltage stability and voltage profile improvement point of views. To this end, a novel Fuzzy Adaptive Heterogeneous Comprehensive-Learning Particle Swarm Optimization (FAHCLPSO) algorithm with enhanced exploration and exploitation processes is proposed to solve the Optimal Reactive Power Dispatch (ORPD) problem. Two different objective functions including active power transmission losses and voltage deviation, which play important roles in power system operation and control, are considered in this paper. In order to authenticate the accuracy and performance of the proposed FAHCLPSO, it applied on three different standard test systems including IEEE 30-bus, IEEE 118-bus and IEEE 354-bus test systems with six, fifty-four and one-hundred-sixty-two generation units, respectively. Finally, outcomes of the proposed algorithm are compared with the results of the original PSO and those in other literatures. The comparison proves the supremacy of the proposed algorithm in solving the complex optimization problem.     

Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem

Solution of optimal power flow (OPF) problem aims to optimize a selected objective function such as fuel cost, active power loss, total voltage deviation (TVD) etc. via optimal adjustment of the power system control variables while at the same time satisfying various equality and inequality constraints. In the present work, a particle swarm optimization with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power systems. The proposed approach is examined and tested on modified IEEE 30-bus and IEEE 118-bus test power system with different objectives that reflect minimization of fuel cost or active power loss or TVD. The simulation results demonstrate the effectiveness of the proposed approach compared with other evolutionary optimization techniques surfaced in recent state-of-the-art literature. Statistical analysis, presented in this paper, indicates the robustness of the proposed ALC-PSO algorithm.     

单隐层神经网络输入权值的新算法

针对传统极端学习机输入权值与隐层阈值随机设定的问题,提出了输出值反向分配算法。算法在传统极端学习机的基础上,通过优化方法得到最优输出值分配系数,并利用最小二乘法确定网络输入参数。将该算法应用到常用数据集进行实验,并与其他极端学习机改进算法进行比较,显示该算法有良好的学习以及泛化能力,能够得到简单的网络结构,证明了算法的有效性。     

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.     

Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms.     

AFSA＋PSO混合算法在BP网络故障诊断中的应用

提出一种基于人工鱼群算法和粒子群算法混合训练BP网络的故障诊断系统.采用人工鱼群算法和粒子群算法结合算法训练神经网络权值,局部搜索速度快且保证全局收敛,有效克服了传统的BP神经网络收敛速度慢且容易陷入局部极值的缺点.将该网络用于齿轮箱故障诊断,并与传统BP模型用于故障诊断结果进行了比较,取得了较好的效果.     

双循环策略下岸桥与跨运车的联合调度

集装箱码头采用跨运车能够减少作业环节和码头机械设备的种类与数量,同时缓存区容量的设置至关重要。首先,为降低码头总完工时间、提高码头作业效率,并解决采用跨运车作为水平运输设备与岸桥进行联合装卸作业时产生的时空协调问题,引入了双循环操作策略,对岸桥与跨运车的联合作业序列优化问题进行研究。其次,建立以总完工时间最小化为目标,考虑岸桥与跨运车双循环操作的实际约束、岸桥缓存区容量限制、安全时间等约束的混合整数规划模型。然后,针对传统禁忌搜索（TS）算法的局限性,加入贪婪算法、多种邻域搜索方式、响应性策略,设计了基于贪婪算法的响应性TS算法,并进行了数值实验。实验结果验证了所提模型与算法的有效性。最后,通过对缓存区容量与跨运车数量、岸桥与跨运车配比的实验分析,得出了最优的跨运车数量和缓存区容量、岸桥与跨运车配比。结果表明：与传统码头设备配置相比,双循环策略可减少跨运车使用数量,提高岸桥与跨运车使用率。     

基于运行风险的电力系统预防-校正协调控制策略

将运行风险应用于预防控制与校正控制之间的协调问题中,提出一种新的控制策略.首先,利用二层规划理论建立预防控制和校正控制的二层优化模型;然后,采用一种将内点法嵌入粒子群算法的混合解法进行该二层优化模型的求解;最后,利用6节点系统进行所提出模型与传统模型的对比性仿真.仿真计算结果表明,所提出控制策略在经济效益和运行风险方面的综合性能优于传统的安全约束调度方法,从而验证了所提出策略的合理性.     

A Nonlinear Coordinated Approach to Enhance the Transient Stability of Wind Energy-Based Power Systems

This paper proposes a novel framework that enables the simultaneous coordination of the controllers of doubly fed induction generators (DFIGs) and synchronous generators (SGs). The proposed coordination approach is based on the zero dynamics method aims at enhancing the transient stability of multi-machine power systems under a wide range of operating conditions. The proposed approach was implemented to the IEEE 39-bus power systems. Transient stability margin measured in terms of critical clearing time along with eigenvalue analysis and time domain simulations were considered in the performance assessment. The obtained results were also compared to those achieved using a conventional power system stabilizer/power oscillation (PSS/POD) technique and the interconnection and damping assignment passivity-based controller (IDA-PBC). The performance analysis confirmed the ability of the proposed approach to enhance damping and improve system’s transient stability margin under a wide range of operating conditions.      

Chaotic krill herd algorithm for optimal reactive power dispatch considering FACTS devices

Conventionally, optimal reactive power dispatch (ORPD) is described as the minimization of active power transmission losses and/or total voltage deviation by controlling a number of control variables while satisfying certain equality and inequality constraints. This article presents a newly developed meta-heuristic approach, chaotic krill herd algorithm (CKHA), for the solution of the ORPD problem of power system incorporating flexible AC transmission systems (FACTS) devices. The proposed CKHA is implemented and its performance is tested, successfully, on standard IEEE 30-bus test power system. The considered power system models are equipped with two types of FACTS controllers (namely, thyristor controlled series capacitor and thyristor controlled phase shifter). Simulation results indicate that the proposed approach yields superior solution over other popular methods surfaced in the recent state-of-the-art literature including chaos embedded few newly developed optimization techniques. The obtained results indicate the effectiveness for the solution of ORPD problem of power system considering FACTS devices. Finally, simulation is extended to some large-scale power system models like IEEE 57-bus and IEEE 118-bus test power systems for the same objectives to emphasis on the scalability of the proposed CKHA technique. The scalability, the robustness and the superiority of the proposed CKHA are established in this paper.