Multi-machine power system stabilizer design by rule based bacteria foraging

Several power system stabilizers (PSS) connected in number of machines in a multi-machine power systems, pose the problem of appropriate tuning of their parameters so that overall system dynamic stability can be improved in a robust way. Based on the foraging behavior of Escherichia coli bacteria in human intestine, this paper attempts to optimize simultaneously three constants each of several PSS present in a multi-machine power system. The tuning is done taking an objective function that incorporates a multi-operative condition, consisting of nominal and various changed conditions, into it. The convergence with the proposed rule based bacteria foraging (RBBF) optimization technique is superior to the conventional and genetic algorithm (GA) techniques. Robustness of tuning with the proposed method was verified, with transient stability analysis of the system by time domain simulations subjecting the power system to different types of disturbances.     

Bacteria foraging optimization algorithm based load frequency controller for interconnected power system

Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations.     

Fuzzy Genetic Algorithm Approach for the Optimal Placement of Flexible AC Transmission Systems Devices in a Power System

Abstract—This article presents a novel approach for optimal flexible AC transmission systems devices planning in an interconnected power system under different loading conditions. The static VAR compensator and thyristor-controlled series capacitor are two types of flexible AC transmission systems devices considered for optimal power system operation. In the proposed approach, a fuzzy membership function is used to determine weak nodes in the power system for the placement of static VAR compensators as a flexible AC transmission systems device. The thyristor-controlled series capacitor is the other type of flexible AC transmission systems devices for which its positions are determined by the reactive power flow in lines. The genetic algorithm is used for the optimal setting of the power system variables, including flexible AC transmission systems devices. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes or point of VAR compensation is determined by eigenvalue analysis, and the amount of flexible AC transmission systems devices is determined by evolutionary techniques, such as the genetic algorithm, differential evolution, and particle swarm optimization. The superiority of the proposed fuzzy-based optimization approach is established by the results and comparative analysis with other methods.     

A Gravitational Search Algorithm Based Static VAR Compensator Switching Function Optimization Technique for Minimum Harmonic Injection

Switching function optimization for minimum source harmonic injection for a static VAR compensator is presented. The static VAR compensator is configured with a fixed capacitor and insulated-gate bipolar transistor controlled reactor. The switching function is optimized for minimum source harmonic injection considering the desired fundamental voltage across load terminals. A gravitational search algorithm is employed for this purpose. It is observed that the proposed switching scheme with two different switching angles per half-cycle provides lower source harmonic injection compared to conventional switching, hence improving the source current harmonics. The switching angles are computed off-line using the gravitational search algorithm for varying modulation indices considering the minimum total harmonic distortion of the reactor voltage. The switching angles are stored in a processor as a function of the modulation index for on-line application using a piecewise mixed model approximation technique for low memory usage. It is observed that the proposed switching improves the source current total harmonic distortion on an average of 4 to 5% over most of the operating range compared to conventional switching without optimization. Various simulation and experimental results are presented on different loads to validate the proposed concept.     

Imperialist competitive algorithm for optimal STATCOM design in a multimachine power system

A new optimization technique, Imperialist Competitive Algorithm (ICA), which is inspired from socio-political phenomenon of imperialistic competition, is proposed in this paper for optimal design of static synchronous compensator (STATCOM) in a multimachine environment. PV curves are illustrated to deduce the best location of STATCOM. The STATCOM parameter tuning problem is converted to an optimization problem which is solved by ICA. The performance of the proposed ICA based STATCOM (ICASTATCOM) is compared with Genetic Algorithm (GA) based STATCOM (GASTATCOM) and open loop STATCOM under various operating conditions and disturbances. The superiority of the proposed technique in damping oscillations and enhancing voltage profiles over a wide range of operating conditions and system configurations is confirmed through eigenvalues and time domain simulation results.     

Application of Firefly Algorithm for Load Frequency Control of Multi-area Interconnected Power System

Abstract—In this article, a firefly algorithm is proposed for load frequency control of multi-area power systems. Initially a two equal area non-reheat thermal system is considered and the optimum gains of the proportional integral/proportional integral derivative controller are optimized employing the firefly algorithm technique. The superiority of the proposed approach is demonstrated by comparing the results with some recently published techniques such as genetic algorithm, bacteria foraging optimization algorithm, differential evolution, particle swarm optimization, hybrid bacteria foraging optimization algorithm-particle swarm optimization, and Ziegler–Nichols-based controllers for the same interconnected power system. Further, the proposed approach is extended to a three-unequal-area thermal system considering generation rate constraint and governor dead-band. Investigations reveal on comparison that proportional integral derivative controller provides much better response compared to integral and proportional integral controllers. Additionally, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, and inertia constant in the range of +50 to –50% from their nominal values as well as the size and position of step load perturbation to demonstrate the robustness of the proposed firefly algorithm optimized proportional integral derivative controller.     

Dynamic Reactive Power Compensation and Cost Analysis for Isolated Hybrid Power System

The wind-diesel-based isolated hybrid power system requires dynamic reactive power compensation for the fast recovery of voltage under load and input changes. Dynamic compensation techniques give a better voltage response but at a high cost; in contrast, the static compensator reduces the compensation cost by compromising on the voltage response. The use of, both, static as well as dynamic compensators together may give cost-effective reactive power compensation for predefined transient limits of voltage response. This paper presents the pricing of reactive power compensation under steady-state and dynamic conditions of a system with fixed capacitor (FC) and STATCOM. The main contributions of the paper are: (1) evaluation of reactive power compensation using FC as a static and STATCOM as a dynamic compensator, (2) fast recovery of voltage response using genetic algorithm-based tuning of the STATCOM controller's gain constant, (3) evaluation of reactive power compensation cost for steady and dynamic conditions due to change in load and/or input demand, and (4) comparison of responses obtained for the optimized case with a pre-existing reference compensation method.     

基于改进离散花授粉算法的继电保护定值优化方法的研究

提出一种基于改进花授粉算法的继电保护定值优化方法,利用约束区间编码,以整定时间最短和灵敏度最优为目标,以满足继电保护选择性、灵敏性、速动性、可靠性要求为约束,寻找得到使电力系统稳定运行的最优整定结果。通过与传统的继电保护整定方法和基于遗传算法的整定值寻优方法进行比较,结果表明改进花授粉算法收敛速度快,寻优效率高,适用于继电保护全局优化整定。     

Optimal controllers designs for automatic reactive power control in an isolated wind-diesel hybrid power system

The paper presents the bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) algorithm based robust controllers for voltage deviations due to the variation of reactive power in an isolated wind-diesel hybrid power system. The isolated wind-diesel system consists of wind energy conversion system (WECS) utilizing a permanent magnet induction generator (PMIG). Further, a synchronous generator (SG) is used with the diesel engine set for power generation. The mismatch between generated and consumed reactive power in the system causes voltage fluctuations, which will occur at generator terminals. These oscillations further causes reduction in the stability and quality of the power supply. The static synchronous compensator (STATCOM) and an automatic voltage regulator (AVR) are used to suppress voltage fluctuations in an isolated wind-diesel hybrid power system. The STATCOM is used as a reactive power compensator and the AVR is used to keep the terminal voltage constant for the synchronous generator. Both STATCOM and AVR are having proportional and integral (PI) controllers with single input. In modeling for the system, a normalized co-prime factorization is applied to show the possible unstructured uncertainties in the power system such as variation of system parameters and generating and loading conditions. The performance and robust stability conditions of the control system are formulated as the optimization problem, which is based on the Hα loop shaping. BFOA and PSO algorithms are implemented to solve this optimization problem and to achieve PI control parameters of STATCOM and AVR simultaneously. In order to show the efficiency of the proposed controllers, the performance of the proposed controllers is compared with the performance of the conventional controller and genetic algorithm (GA) based PI controllers for the same wind-diesel system. The dynamic responses of the system for four different small-disturbance case studies has been carried out in MATLAB environment.     

Coordinated design of PSSs and SVC via bacteria foraging optimization algorithm in a multimachine power system

This paper develops a novel algorithm for simultaneous coordinated designing of power system stabilizers (PSSs) and static var compensator (SVC) in a multimachine power system. The coordinated design problem of PSS and SVC over a wide range of loading conditions is formulated as an optimization problem. The Bacterial Foraging Optimization Algorithm (BFOA) is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the bacterial foraging based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BFOA (BFPSS) and optimized SVC controller based on BFOA (BFSVC). Moreover, a statistical T test is performed to ensure the effectiveness of coordinated controller versus uncoordinated one.     

基于改进FPA算法的含分布式光伏配电网选址定容多目标优化方法

近年来配电网分布式光伏数量不断增加,不合理的分布式光伏接入位置和容量给配电网带来了极大的冲击.针对分布式光伏接入位置和容量不合理给配电网带来的影响,提出了一种以投资成本最低、网损最小、电压质量最优为优化目标的选址定容模型.结合遗传算法、混沌序列和花授粉算法求解优化模型.通过混沌序列对花粉位置进行初始化,保证种群的多样性...     

基于改进生物地理学优化算法的SVC次同步阻尼控制器设计

针对常用的次同步振荡控制器不能较好地适应电力系统时变非线性的特点,提出了一种引入余弦迁移模型、早熟判断机制、变尺度混沌变异策略及排重操作的改进生物地理学优化算法。基于该算法结合静止无功补偿器(SVC)抑制次同步振荡的机理,对次同步阻尼控制器进行优化设计,并采用特征值分析和时域仿真验证了控制系统的有效性。锦界电厂算例分析表明:经改进生物地理学算法优化的SVC次同步阻尼控制器能较好地提高机组扭振的模态阻尼,可有效抑制次同步振荡,进而保证机组和电网的安全稳定运行;与传统的生物地理学优化算法、粒子群算法及遗传算法相比,改进生物地理学优化算法在搜索最优控制参数时具有较快的搜索速度和较高的搜索精度。     

基于BBO算法优化设计SVC阻尼控制器

电力系统中因次同步振荡(SSO)问题,会危害发电机组的安全运行。为了抑制这一不正常的现象,提出将生物地理学优化算法(BBO)和静止无功补偿器(SVC)相结合,利用BBO算法优越的寻优能力和SVC能够自动补偿无功功率,设计了一种阻尼控制器。在PSCAD仿真平台上,搭建IEEE第一标准模型,验证了经BBO算法优化后的SVC阻尼控制器能够有效抑制SSO。     

电力系统无功优化算法综述

总结了无功优化算法的研究现状 ,介绍了求解无功优化问题的常规方法和人工智能方法 ,同时综合评述了现有优化方法的优缺点。提出利用混沌搜索全局最优解的混合遗传算法用于无功优化的新探索 ,并对以后的研究动态进行了预测     

采用混合智能算法的含风电电力系统多目标优化调度

为了增强含风电电力系统的安全性和稳定性,提出一种计及运行风险及备用成本的含风电电力系统环境经济调度新模型。在目标函数中加入了系统运行风险指标和正、负旋转备用成本;增加了系统可靠性约束条件,确保了较低的系统运行风险,并同时获取正、负旋转备用量。采用一种新型高效的场景生成技术来描述风电功率的随机性。基于花授粉算法及差分进化算法提出一种具有时变模糊选择机制的多目标优化算法。将所提模型及求解方法在具有一个并网风电场的4机组系统中进行仿真。分析了各参数变化对系统运行的影响,并与其他两种启发式智能算法进行比较,验证了所提模型及算法的可行性和有效性。     

Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

Conventional proportional integral derivative (PID) controllers are being used in the industries for control purposes. It is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the systems. Therefore, in this study, a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the system. This paper focuses upon the stability improvement of a wind-diesel hybrid power system model (HPSM) using a static synchronous compensator (STATCOM) along with a secondary PID controller with derivative filter (PIDF). Under any load disturbances, the reactive power mismatch occurs in the HPSM that affects the system transient stability. STATCOM with PIDF controller is used to provide reactive power support and to improve stability of the HPSM. The controller parameters are also optimized by using soft computing technique for performance improvement. This paper proposes the effectiveness of symbiosis organisms search algorithm for optimization purpose. Binary coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.     

免疫遗传算法在多机电力系统PSS参数优化中的运用

电力系统稳定器参数的优化对于抑制多机电力系统低频振荡具有非常重要的意义。运用免疫遗传算法对多机系统PSS参数进行优化,将免疫操作融入基本的遗传算法中,运用免疫二次应答原理有效抑制了参数抗体在交叉、变异过程中的退化现象,提高了寻优过程的收敛性和稳定性。算例表明,采用免疫遗传算法对PSS参数优化其收敛性、计算时间和稳定性方面都优于传统遗传算法,将经过参数优化后的PSS加装到系统中,不反可以克服低频振荡现象,而且使系统的稳定性大幅度增强。     

基于模糊混合进化算法的多个FACTS元件协调控制

针对多个FACTS装置的控制器之间存在的负交互影响,通过将模糊理论、协同进化算法和免疫算法相结合,提出一种模糊混合多目标进化算法求解FACTS元件的协调控制问题。在求解过程中,利用模糊集理论建立模糊多目标优化模型,确定整体的优化目标。采用协同进化算法对各FACTS元件控制器的参数进行种群划分,以克服常规进化算法在问题规模扩大时易于出现的早收敛现象。在优化过程中,结合多种群合作策略和免疫算法保持解的多样性,最终得到控制参数的Pareto最优解集。对静止同步串联补偿器与静止同步补偿器2种FACTS装置的控制器进行了协调设计,仿真结果表明,与常规进化算法相比,所提方法能同时实现影响分析与参数优化功能,且收敛快速,稳定性好。     

基于改进花授粉算法的永磁同步电机参数辨识

针对传统花授粉算法辨识永磁同步电机参数迭代后期易陷入局部最优导致收敛速度慢和寻优精度低的缺陷,提出了一种基于t-分布扰动和高斯扰动的改进花授粉算法（tGFPA）,以实现永磁同步电机参数的高精度辨识。首先利用混沌Logistic映射对花朵个体位置进行初始化,然后在全局授粉过程中引入t-分布扰动,提高搜索空间的多样性。在局部授粉过程中加入高斯扰动,增强跳出局部最优解的能力。最后,对比仿真结果表明：基于双扰动策略的改进花授粉算法收敛速度更快、辨识精度更高,对于永磁同步电机控制性能改善具有重要意义。     

Automatic Generation Control in an Interconnected Power System Incorporating Diverse Source Power Plants Using Bacteria Foraging Optimization Technique

This article presents automatic generation control (AGC) of a two-area interconnected power system with diverse energy sources using the bacteria foraging optimization technique. The control areas of interconnected power systems consist of hydro, thermal, and gas power plants. In this study, the proportional-integral-derivative (PID) structures of AGC regulators are designed for various case studies identified herein. An artificial intelligent optimization algorithm using the modeling behavior of E. Coli bacteria present in human intestines, is applied to tune the gains of PID structured AGC regulators. The closed-loop system dynamic response plots are obtained with designed AGC regulators for various power system models. The effectiveness of the proposed AGC regulators is demonstrated in the wake of a 1% step load disturbance in one of the control areas. It has been shown that the system dynamic responses subject to a step load disturbance are superior over other power plant combinations in a control area with only thermal and gas power plants participating in the AGC schemes, and it is sluggish/poor when only hydro power plants participated in the AGC scheme as one of the diverse sources in the power system.