Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization

In this paper, a new method for optimal locating multi-type FACTS devices in order to optimize multi-objective voltage stability problem is presented. The proposed methodology is based on a new variant of particle swarm optimization (PSO) specialized in multi-objective optimization problem known as non-dominated sorting particle swarm optimization (NSPSO). The crowding distance technique is used to maintain the Pareto front size at the chosen limit, without destroying its characteristics. To aid the decision maker choosing the best compromise solution from the Pareto front, the fuzzy-based mechanism is employed for this task. NSPSO is used to find the optimal location and setting of two types of FACTS namely: Thyristor controlled series compensator (TCSC) and static var compensator (SVC) that maximize static voltage stability margin (SVSM), reduce real power losses (RPL), and load voltage deviation (LVD). The optimization is carried out on two and three objective functions for various FACTS combinations considering. For ensure the robustness of the proposed method and gives a practical sense of our study, N − 1 contingency analysis and the stress of power system is considered in the optimization process. The thermal limits of lines and voltage limits of load buses are considered as the security constraints. The proposed method is validated on IEEE 30-bus and realistic Algerian 114-bus power system. The simulation results are compared with those obtained by particle swarm optimization (PSO) and non-dominated sorting genetic algorithms (NSGA-II). The comparisons show the effectiveness of the proposed NSPSO to solve the multi-objective optimization problem and capture Pareto optimal solutions with satisfactory diversity characteristics.     

Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability

This paper presents the application of particle swarm optimization (PSO) technique to find the optimal location of flexible AC transmission system (FACTS) devices with minimum cost of installation of FACTS devices and to improve system loadability (SL). While finding the optimal location, thermal limit for the lines and voltage limit for the buses are taken as constraints. Three types of FACTS devices, thyristor controlled series compensator (TCSC), static VAR compensator (SVC) and unified power flow controller (UPFC) are considered. The optimizations are performed on the parameters namely the location of FACTS devices, their setting, their type, and installation cost of FACTS devices. Two cases namely, single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC and UPFC) are considered. Simulations are performed on IEEE 6, 30 and 118 bus systems and Tamil Nadu Electricity Board (TNEB) 69 bus system, a practical system in India for optimal location of FACTS devices. The results obtained are quite encouraging and will be useful in electrical restructuring.     

Genetic algorithm-based fuzzy multi-objective approach to congestion management using FACTS devices

This paper investigates a novel optimization-based methodology for placement of Flexible AC Transmission Systems (FACTS) devices in order to avoid congestion in the transmission lines while increasing static security margin and voltage profile of a given power system. The optimizations are carried out on the basis of location, size, and number of FACTS devices. Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC) are two FACTS devices which are implemented in this investigation to achieve the determined objectives. The problem is formulated according to Sequential Quadratic Programming (SQP) problem in the first stage to accurately evaluate static security margin with congestion alleviation constraint in the presence of FACTS devices and estimated annual load profile. In the next stage a Genetic Algorithm (GA)-based fuzzy multi-objective optimization approach is used to find the best trade-off between conflicting objectives. The IEEE 14-bus test system is selected to validate the proposed approach.     

考虑多种无功补偿装置协同优化的电压控制方法

为了应对电力系统扰动带来的稳态及暂态电压波动问题,以静止无功补偿器（static var compensator,SVC）、电容器组（capacitor bank,CB）、有载调压变压器（on load tap changing,OLTC）和同步调相机（synchronous compensator,SC）为综合性无功补偿装置,提出了一种考虑多种无功补偿装置协同优化的电压控制方法。建立多目标电压控制模型,引入多目标权重系数计算方法,并基于水循环算法（water cycle algorithm,WCA）进行模型求解。通过仿真算例验证了所提方法的有效性。     

Planning of multi-type FACTS devices in restructured power systems with wind generation

Many electrical power systems are changing from a vertically integrated entity to a deregulated, open-market environment. This paper proposes an approach to optimally allocate multi-type flexible AC transmission system (FACTS) devices in restructured power systems with wind generation. The objective of the approach is to maximize the present value of long-term profit. Many factors like load variation, wind generation variation, generator capacity limit, line flow limit, voltage regulation, dispatchable load limits, generation rescheduling cost, load shedding cost, and multilateral power contracts are considered in problem formulation. The proposed method accurately evaluates the annual costs and benefits obtainable by FACTS devices in formulating the large-scale optimization problem under both normal condition and possible contingencies. The overall problem is solved using both Particle Swarm Optimization (PSO) for attaining optimal FACTS devices allocation as main problem and optimal power flow as sub optimization problem. The efficacy of the proposed approach is demonstrated for modified IEEE 14-bus test system and IEEE 118-bus test system.     

Optimal sizing of SSSC controllers to minimize transmission loss and a novel model of SSSC to study transient response

In this paper, based on steady-state models of flexible AC transmission system (FACTS) devices, the sizing of static synchronous series compensator (SSSC) controllers in transmission network is formed as an optimization problem. The objective of this problem is to reduce the transmission losses in the network. The optimization problem is solved using particle swarm optimization (PSO) technique. The Newton–Raphson load flow algorithm is modified to consider the insertion of the SSSC devices in the network. Numerical examples, illustrating the effectiveness of the proposed algorithm, are introduced. In addition, a novel model of a three-phase voltage source converter (VSC) that is suitable for series connected FACTS a controller is introduced. The model is verified by simulation using Power System Blockset (PSB) and Simulink software.     

Constraints violation handling of SSSC with multi‐control modes in Newton–Raphson load flow algorithm

The static synchronous series compensator (SSSC ) is an important component of flexible AC transmission systems (FACTS ) devices. SSSC can be used to control the active and reactive power flow in transmission lines. This paper presents a simplified model for SSSC in Newton–Raphson (NR ) load flow algorithm. It also presents strategies for handling the operating constraints of SSSC including the series‐injected voltage and current passing through this device. The presented strategies are based on modifying the specified active and reactive powers with the maximum limits of the operating constraints. However, the SSSC is simply implemented in NR load flow algorithm based on the power injection approach. In this model, the SSSC is represented as injected loads as a function of the desired power flow through the transmission line. The main advantages of this model are avoiding the modification of Jacobin matrix and reducing the complexities of incorporating SSSC in the load flow algorithm. Moreover, the resistance of SSSC is considered in this model. Standard IEEE 14‐bus and 30‐bus test systems are used to verify the performance of the developed model and strategies handling the constraints of the SSSC model. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Assessment of techno-economic contribution of FACTS devices to power system operation

In contemporary power system studies, the optimal allocation and utilization of Flexible AC Transmission System (FACTS) devices are important issues primarily due to their cost. In this study four types of FACTS devices (Static VAr compensator (SVC), Thyristor-Controlled Series Capacitor (TCSC), Thyristor-Controlled Voltage Regulator (TCVR), and Thyristor-Controlled Phase Shifting Transformer (TCPST)) are optimally placed in a multi-machine power system to reduce the overall costs of power generation. The placement methodology considers simultaneously the cost of generated active and reactive powers and cost of selected FACTS devices for a range of operating conditions. The optimal power flow (OPF) and genetic algorithm (GA) based optimization procedure are employed to solve the allocation task. The net present value (NPV) method is used to assess the economic value of the proposed methodology. In addition to net reduction in generation cost allocated FACTS devices increased power transfer across the network and improved damping of electromechanical oscillations.     

潮流和最优潮流分析中FACTS控制器的建模

综述了最近FACTS控制器的数学模型在电力系统潮流和最优潮流分析中的新进展.不仅讨论了单换流器FACTS控制器,如静止同步并联补偿器(STATCOM)和静止同步串联补偿器(SSSC);而且也讨论了多换流器FACTS控制器,如统一潮流控制器(UPFC)、相间功率控制器(IPFC)、通用统一潮流控制器(GUPFC)和电压源型直流输电(VSC HVDC).此外还讨论了基于电压源换流器技术的HVDC的数学模型.不仅涵盖FACTS控制器的单相数学模型,而且也涉及FACTS控制器的三相数学模型.此外,还探讨了多换流器FACTS控制器的电流、电压以及功率等不等约束在潮流计算中的数学模型及计算机实现.     

静止同步补偿器与传统静止无功补偿器的比较与分析

随着灵活交流输变电(FACTS)技术的不断发展,出现了很多新型的FACTS装置。比如基于全控型电力电子器件(如GTO晶闸管与IGBT等)的STATCOM就是其中之一,它与传统的补偿器SVC相比在技术上有着很多的优势。介绍FACTS装置中的STATCOM与SVC,并对它们在电压支撑、动态仿真、控制方法、谐波和经济性等方面进行综合分析与比较。     

Brainstorm optimisation algorithm (BSOA): An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems

In electric power systems, finding optimal location and setting of flexible AC transmission system (FACTS) devices represents a difficult optimisation problem. This is due to its discrete, multi-objective, multi-modal and constrained nature. Finding near-global solutions in such a problem is very demanding. Brainstorm optimisation algorithm (BSOA) is a novel promising heuristic optimisation algorithm inspired by brainstorming process in human beings. In this paper, BSOA is employed to find optimal location and setting of FACTS devices. Static var compensators (SVC’s) and thyristor controlled series compensators (TCSC’s) are used as FACTS devices. FACTS allocation problem is formulated as a multi-objective problem whose objectives are voltage profile enhancement, overload minimisation and loss minimisation. The results of applying BSOA to FACTS allocation problem in IEEE 57 bus system demonstrate its high efficacy in solving this problem both with TCSC and SVC units. BSOA leads to better voltage profile and lower losses than particle swarm optimisation (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), hybrid of genetic algorithm and pattern search (GA–PS), backtracking search algorithm (BSA), gravitational search algorithm (GSA) and asexual reproduction optimisation (ARO). The findings of this research can be used by power system decision makers in order to establish a better voltage profile and lower voltage deviations during contingencies.     

TCSC allocation in power systems considering switching loss using MOABC algorithm

This paper is dedicated to investigate the influence of switching losses on Thyristor Controlled Series Compensator (TCSC) optimum allocation in power systems by using a Multi-Objective optimization technique. This allocation is performed with respect to minimization in system cost and voltage deviation as the objectives. For this purpose, cost of active power losses, investment cost of devices and active power generation cost in the peak load are considered to constitute the cost function as the economical objective. In addition, the paper considers interest rate in cost calculations to avoid impractical allocation results and imprecise solutions. The operational, technical and controlling constraints as well as load constraints are regarded in the allocation procedure. Also, the simulations are utilizing three unequal annual load levels to find optimum location and size of TCSC. The IEEE-14 Bus test system is used to validate the proposed method and show the importance of TCSC switching loss. Here, a novel optimization algorithm, Multi-Objective Artificial Bee Colony, is presented to provide Pareto optimal solutions. Also, an approach based on the goal attainment method combined with Genetic Algorithm is used to compromise between contradictory objectives and approach to the global optimum.     

A new coordination strategy of SSSC and PSS controllers in power system using SOA algorithm based on Pareto method

Along with the development of power grids and increasing the use of Flexible AC Transmission System (FACTS) devices, complex and unexpected interactions will be increased in power system. With considering to the non-linearity of power system, operating point changes and reaction between power system and FACTS devices, using of linear methods are not suitable for controller design. Therefore, the nonlinear model to design of Power System Stabilizer (PSS) and Static Synchronous Series Compensator (SSSC) coordinated controllers is considered here. In this paper, a new multi-objective function as an optimization problem is proposed for this coordination process. Also a beneficial strategy to solve this optimization problem using Seeker Optimization Algorithm (SOA) based on Pareto optimum method with high convergence speed is presented. In order to evaluate the performance of the proposed method, coordination strategy is applied on a four-machine system under different contingencies. The results of the proposed multi-objective function are obtained and compared with others in this system and finally, superior ability of the proposed method is observed.     

Performance Comparison of Distance Protection Schemes for Shunt-FACTS Compensated Transmission Lines

This paper presents a comparative study of the performance of distance relays for transmission lines compensated by shunt connected flexible ac transmission system (FACTS) controllers/devices. The objective of this study is to evaluate the performance of various distance protection schemes on transmission lines with shunt-FACTS devices applied for midpoint voltage control. The impact of two types of shunt FACTS devices, static var compensator (SVC) and static synchronous compensator (STATCOM) on the transmission line distance protection schemes is studied for different fault types, fault locations and system conditions. The power system elements and the shunt-FACTS devices with their associated controllers are modeled using RSCAD/RTDS software. The results are based on the performance of commercial distance relays using a real time digital simulator (RTDS).     

柔性交流输电系统交直流潮流可靠性评估模型

根据串联补偿器(TCSC)、晶闸管控制移相器(TCPST)和统一潮流控制器(UPFC)的工作原理,从交流和直流潮流的视角建立了潮流计算和最优负荷削减模型,采用状态枚举法在RBTS可靠性测试系统中进行分析和验证,结果表明采用交流潮流模型能更准确评估FACTS元件对系统可靠性的影响;而直流潮流模型计算耗时较少,且当节点电压约束不是刚性约束时,所得结果与交流潮流模型结果相近.     

Steady-state optimization in power systems with series FACTS devices

This paper presents an optimization-based methodology to identify key locations in the AC network where placement of a series-connected FACTS device increases the maximum megawatt power transfer the most. The models used for the thyristor-controlled series capacitor (TCSC) and unified power-flow controller (UPFC) include the nonlinear constraints of voltage limitation, zero megawatt active-power exchange, voltage control, and reactive-power exchange. This article describes briefly the steady-state flexible AC transmission systems (FACTS) models and their integration in an existing optimal power-flow (OPF) software package designed and implemented by the authors. A reduced real-life network is used for the case studies. The optimization results help in evaluating the effectiveness of the series FACTS devices in maximizing the network transfer capability and deliver a measure of the FACTS ratings.     

计及负荷随机性含风电电力系统TCSC多目标优化配置

实现可用输电能力和电压稳定的双重改善，提出一种考虑风电和负荷随机性的灵活交流输电系统(flexible AC transmission system,?FACTS)多目标优化配置方法。首先推导基于拉丁超立方、k-means聚类和蒙特卡洛抽样三者相结合的系统场景生成技术。然后以区域间可用输电能力和电压稳定指标L为目标，建立晶闸管控制串联电容器(thyristor-controlled series capacitor, TCSC)多目标优化配置模型。最后通过增加混沌初始化和变惯性权重设置改进多目标粒子群算法以求解所建模型。基于改进的IEEE30节点系统，对比了最可能发生的系统场景配置TCSC前后的非劣解集和模糊最优解，分析了极端系统场景配置TCSC前后的优化结果。仿真结果表明，所提场景处理方法、多目标优化模型和改进算法在解决相关问题上具有有效性。     

基于多目标进化算法的TCSC与SVC控制器协调设计

以可控串补(TCSC)与静止无功补偿器(SVC)两种FACTS装置为研究对象,通过理论分析 指出了TCSC与SVC联合运行时不同控制目标间的矛盾,并通过一多机系统时域仿真实例,介绍 了TCSC与SVC控制器之间存在的交互影响。仿真表明:单独设计且运行良好的SVC与TCSC 控制器,并不能保证两者同时投运工况下控制器的性能。将FACTS功能控制器的协调问题转化 为一多目标优化问题,采用多目标进化算法(MOEA)优化控制器参数,得到一组Pareto优化解集, 时域仿真验证了协调控制器的控制效果良好。     

Probabilistic and Multi-Objective Placement of D-STATCOM in Distribution Systems Considering Load Uncertainty

Advanced technologies such as distribution static compensator (D-STATCOM) are broadly deployed in distribution systems (DSs) to tackle different issues. Successful deployment of such technologies requires their adaption to stochastic nature of DS. This paper proposes a new method for allocation of D-STATCOM in DSs considering load uncertainty. Monte Carlo simulation (MCS) technique is utilized to cope with uncertain attribute of loads. To do so, output of MCS is considered as a deterministic case which is solved by backward-forward sweep power flow approach. The objective functions of the proposed model include total voltage deviation, total active losses, and voltage stability metrics. These metrics are combined in a multi-objective optimization formulation and solved by non-dominated sorting genetic algorithm-II (NSGA-II). Lastly, the best compromise solution is determined by Pareto front technique taking advantage of fuzzy decision making (FDM) engine. Comprehensive simulation studies are carried out on IEEE 33-bus standard distribution system as well as on Portuguese 94-bus real-world distribution system. Simulation studies confirm the accuracy of the proposed method in determining proper location of D-STATCOM in DSs by fulfilling the objectives.     

Power system with multi-type FACTS devices states estimation based on predictor–corrector interior point algorithm

This paper addresses a state estimation problem of power systems incorporating various flexible alternating current transmission system (FACTS) devices. Static var compensators, thyristor controlled series compensators, and unified power flow controllers are considered since they represent various kinds of FACTS devices. By considering constraints of system buses and devices, the estimation problem can be formulated as a nonlinear optimization with constraints. An algorithm based on predictor–corrector interior point is applied to solve the problem. The IEEE 14- and 118-bus systems modified by incorporating FACTS devices are used as test systems to verify and demonstrate the effectiveness of the proposed algorithm. Numerical results indicate that the proposed algorithm performs better than the primal dual interior point based method even the presence of bad measurement data.