Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer

In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi Sugeno fuzzy logic (SFL) based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer variables.     

Intelligent particle swarm optimized fuzzy PID controller for AVR system

In process plants like thermal power plants, biomedical instrumentation the popular use of proportional-integral-derivative (PID) controllers can be noted. Proper tuning of such controllers is obviously a prime priority as any other alternative situation will require a high degree of industrial expertise. So in order to get the best results of PID controllers the optimal tuning of PID gains is required. This paper, thus, deals with the determination of off-line, nominal, optimal PID gains of a PID controller of an automatic voltage regulator (AVR) for nominal system parameters and step reference voltage input. Craziness based particle swarm optimization (CRPSO) and binary coded genetic algorithm (GA) are the two props used to get the optimal PID gains. CRPSO proves to be more robust than GA in performing optimal transient performance even under various nominal operating conditions. Computational time required by CRPSO is lesser than that of GA. Factors that have influenced the enhancement of global searching ability of PSO are the incorporation of systematic and intelligent velocity, position updating procedure and introduction of craziness. This modified from of PSO is termed as CRPSO. For on-line off-nominal system parameters Sugeno fuzzy logic (SFL) is applied to get on-line terminal voltage response. The work of SFL is to extrapolate intelligently and linearly, the nominal optimal gains in order to determine off-nominal optimal gains. The on-line computational burden of SFL is noticeably low. Consequently, on-line optimized transient response of incremental change in terminal voltage is obtained.     

Velocity relaxed and craziness-based swarm optimized intelligent PID and PSS controlled AVR system

This paper explores a comparative performance study of two new classes of particle swarm optimization techniques, one with velocity update relaxation (VURPSO) and the other based on novel position, velocity updating strategy and craziness (CRPSO). Both VURPSO and CRPSO highly enhance searching ability. Genetic algorithm (GA) is considered for the sake of comparison. Finally, it is revealed that while applying in two power systems applications (PID controlled AVR system, PSS controlled AVR system), VURPSO exhibits better transient performance than CRPSO/GA. For on-line, off-nominal conditions, Takagi Sugeno fuzzy logic is applied to obtain on-line responses for both the system models.     

Optimal Power System Stabilizers design via Cuckoo Search algorithm

Cuckoo Search (CS) algorithm is introduced in this paper for optimal Power System Stabilizers (PSSs) design in a multimachine power system. The PSSs parameter tuning problem is formulated as an optimization problem which is solved by CS Algorithm. An eigenvalues based objective function involving the damping ratio, and the damping factor of the lightly damped electromechanical modes is considered for the PSSs design problem. The performance of the proposed CS based PSSs (CSPSS) has been compared with Genetic Algorithm (GA) based PSSs (GAPSS) and the Conventional PSSs (CPSS) under different operating conditions and disturbances. The results of the developed CSPSS are verified through time domain analysis, eigenvalues and performance indices. Also, the effectiveness of the proposed algorithm in providing good damping characteristics is confirmed.     

Parameter optimization of multimachine power system stabilizers using genetic local search

A genetic local search (GLS) algorithm for optimal design of multimachine power system stabilizers (PSSs) is presented in this paper. The proposed approach hybridizes the genetic algorithm (GA) with a heuristic local search in order to combine their strengths and overcome their shortcomings. The potential of the proposed approach for optimal parameter settings of the widely used conventional lead–lag PSSs has been investigated. Unlike the conventional optimization techniques, the proposed approach is robust to the initial guess. The performance of the proposed GLS-based PSS (GLSPSS) under different disturbances, loading conditions, and system configurations is investigated for different multimachine power systems. Eigenvalue analysis and simulation results show the effectiveness and robustness of the proposed GLSPSS to damp out local as well as interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations.     

Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control

In this paper, various novel heuristic stochastic search techniques have been proposed for optimization of proportional–integral–derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants. The techniques are classical particle swarm optimization, hybrid particle swarm optimizations and hybrid genetic algorithm simulated annealing. Numerical results show that all optimization techniques are more or less equally very effective in yielding optimal transient responses of area frequency and tie-line power flow deviations. The gains obtained by particle swarm optimization are more optimal than those obtained by GA/hybrid GA-simulated annealing. Particle swarm optimizations take the least time to achieve the same optimal gains. These gains are for nominal system parameters. For varying off-nominal on-line system parameters, fast acting Sugeno fuzzy logic manipulates the nominal gains adaptively to determine transient responses.     

Power system stabilizer design using Strength Pareto multi-objective optimization approach

Power system stabilizers (PSSs) are the most well-known and effective tools to damp power system oscillation caused by disturbances. To gain a good transient response, the design methodology of the PSS is quite important. The present paper, discusses a new method for PSS design using the multi-objective optimization approach named Strength Pareto approach. Maximizations of the damping factor and the damping ratio of power system modes are taken as the goals or two objective functions, when designing the PSS parameters. The program generates a set of optimal parameters called Pareto set corresponding to each Pareto front, which is a set of optimal results for the objective functions. This provides an excellent negotiation opportunity for the system manager, manufacturer of the PSS and customers to pick out the desired PSS from a set of optimally designed PSSs. The proposed approach is implemented and examined in the system comprising a single machine connected to an infinite bus via a transmission line. This is also done for two familiar multi-machine systems named two-area four-machine system of Kundur and ten-machine 39-bus New England system. Parameters of the Conventional Power System Stabilizer (CPSS) are optimally designed by the proposed approach. Finally, a comparison with famous GAs is given.     

Tuning of power system stabilizers using genetic algorithms

Several techniques exist for developing optimal controllers. This paper investigates the tuning of power system stabilizers (PSS) using genetic algorithms (GA). A digital simulation of a linearized model of a single-machine infinite bus power system at some operating point is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the time-multiplied absolute value of the error performance indices are considered in the search for the optimal PSS parameters. In order to have good damping characteristics over a wide range of operating conditions, the PSS parameters are optimized off-line for a selected set of grid points in the real power (P)-reactive power (Q) domain. The optimal settings thus obtained can then be stored and retrieved on-line to update the PSS parameters based on measurements of the generator real and reactive power. Time domain simulations of the system with GA-tuned PSS show the improved dynamic performance under widely varying load conditions.     

A fuzzy logic-based self tuning power system stabilizer optimized with a genetic algorithm

This paper presents an approach for designing power system stabilizers (PSS) with a fuzzy logic based parameter tuner. In the initial design step, Prony analysis is used to identify linear models for the synchronous generator at a large number of operating points, consisting of various power outputs and machine terminal voltages. Next, optimal parameter settings for a conventional PSS are generated using the linearized models. From the operating point settings, a selection of fuzzy rules is used to tune the stabilizer parameters online according to real-time measurements. The membership functions of the fuzzy parameter tuner are optimized using a genetic algorithm (GA). Simulation studies show that the proposed stabilizer performs well over a wide range of operating conditions and provides better dynamic performance than a fixed parameter PSS.     

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.     

基于TMS320F28335的电力系统稳定器的研究

目前国内普遍采用以电功率作为输入信号的单输入电力系统稳定器（PSS）,然而在原动机功率发生变化时, PSS本身不能区分系统波动和原动机功率波动,容易引起“反调”。设计了一种基于TMS320F28335的双输入PSS,发电机的电功率和转子角速度作为该PSS的输入信号。论述了双输入PSS的模型,硬件设计部分主要包括总体结构的设计,软件设计部分包括总体设计及流程图。运用附加双输入PSS励磁调节器进行了阶跃和反调实验,结果表明该PSS能够有效抑制低频振荡,很好地解决“反调”问题。     

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.     

Effects of various power system stabilizers on improving power system dynamic performance

To ensure the small-signal stability of a power system, power system stabilizers (PSSs) are extensively applied for damping low frequency power oscillations through modulating the excitation supplied to synchronous machines, and increasing interest has been focused on developing different PSS schemes to tackle the threat of damping oscillations to power system stability. This paper examines four different PSS models and investigates their performances on damping power system dynamics using both small-signal eigenvalue analysis and large-signal dynamic simulations. The four kinds of PSSs examined include the Conventional PSS (CPSS), Single Neuron based PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). A steep descent parameter optimization algorithm is employed to seek the optimal PSS design parameters. To evaluate the effects of these PSSs on improving power system dynamic behaviors, case studies are carried out on an 8-unit 24-bus power system through both small-signal eigenvalue analysis and large-signal time-domain simulations.     

Optimal locations and tuning of robust power system stabilizer using genetic algorithms

Optimal locations and design of robust multimachine power system stabilizers (PSSs) using genetic algorithms (GA) is presented in this paper. The PSS parameters and locations are computed to assure maximum damping performance under different operating conditions. The efficacy of this technique in damping local and inter-area modes of oscillations in multimachine power systems is confirmed through nonlinear simulation results and eigenvalues analysis.     

基于遗传序优化算法的配电网规划

针对传统遗传算法存在收敛过早、终止条件难以确定等缺陷,将序优化理论与遗传算法相结合,用序优化的思想来指导遗传进化操作,通过算法的混合集成了序优化理论和遗传算法的优良特性,从而实现以较高的概率高效地搜索到全局最优解。对于2个IEEE配电网规划算例,以综合变电站和馈线的年投资费用、折旧费用、运行费用之和为目标函数,得到了最优的配电网拓扑结构,验证了该遗传序优化算法的有效性和实用性。     

Three-dimensional power system stabilizer

Power system stabilizers (PSSs) are used to enhance damping of power system oscillations through excitation control of synchronous generator. The objective of the PSS is to generate a stabilizing signal, which produces a damping torque component on the generator shaft. Conventional PSSs are designed with the phase compensation technique in the frequency domain and include the lead-lag blocks whose parameters are determined according to a linearized power system model. The performance of conventional PSSs (CPSSs) depends upon the generator operating point and the system parameters, but a reasonable level of robustness can be achieved depending on the tuning method. This paper presents a new three-dimensional PSS (3D PSS), which uses rotor speed deviation, rotor acceleration and load angle deviation as input signals. The 3D PSS attempts to return the generator to the state-space origin, based on the generator’s trajectory in state-space and the achievement of torque equilibrium. The 3D PSS is robust to system parameters changes. The proposed algorithm was implemented in a digital control system, tested in a laboratory environment on a synchronous generator connected to the power system, and then compared with CPSS. Experimental results show that the proposed PSS achieves better performance than the CPSS in damping oscillations.     

基于GATS混合算法的PSS与SVC控制器参数设计

随着电力网络规模的扩大,电力系统优化问题日益复杂,故提出了一种采用遗传禁忌GATS混合优化策略对电力系统稳定器PSS和静止无功补偿器SVC附加线性稳定控制器进行参数协调优化的设计方法。该方法结合遗传算法GA和禁忌搜索算法TS各自的优点,将禁忌搜索引入到遗传算法的变异操作,改进了遗传算法的变异算子,具有比常规遗传算法更强的局部搜索能力。在10机新英格兰电力系统上对该优化方法进行了测试。特征值分析表明,该设计方法能有效地将多种不同运行方式下系统的特征根移到复平面目标函数限定的区域内,保证了小扰动稳定性控制的鲁棒。同时还对不同优化方法的收敛性及计算时间进行了比对,结果表明遗传禁忌混合策略的性能优于常规遗传算法以及遗传模拟退火混合优化策略。     

Optimization and coordination of damping controls for improvingsystem dynamic performance

This paper presents a global tuning procedure for FACTS device stabilizers (FDS) and power system stabilizers (PSS) in a multi-machine power system using a parameter-constrained nonlinear optimization algorithm implemented in a simulation program. This algorithm deals with such an optimization problem by solving a sequential quadratic programming using the dual algorithm. The main objective of this procedure is to simultaneously optimize pre-selected parameters of the FDSs and PSSs having fixed parameters in coping with the complex nonlinear nature of the power system. By minimizing a nonexplicit target function in which the oscillatory rotor modes of the generators involved and suing characteristics between areas are included, interactions among the FACTS controls under transient conditions in a multimachine power system are improved. A multimachine power system equipped with a TCSC and an SVC as well as three PSSs is applied to demonstrate the efficiency and robustness of the tuning procedure presented. The results obtained from simulations validate the improvement in damping of overall power oscillations in the system in an optimal and globally coordinated manner. The simulations also show that the stabilizers tuned are robust in providing adequate damping for a range of conditions in the system     

多智能体搜寻者优化算法在电力系统无功优化中的应用

针对无功优化这个典型的非线性问题,提出了一种基于多Agent系统的搜寻者优化算法MASOA (Multi-agent Seeker Optimization Algorithm)来求解.该算法针对SOA算法邻域划分随意性较大,融入智能体技术,在改进SOA算法邻域划分合理性的同时,提高粒子寻优的准确度;利用SOA算法的进化机制,引入自适应思想,使新算法具有良好的非线性搜索能力,更好地适应无功优化问题.以网损最小为目标函数,在IEEE 30节点系统上进行测试,并与四种智能算法进行比较,结果表明,MASOA在算法计算精度、收敛稳定性、寻优时间等方面都具有普遍优势,能有效地应用于电力系统无功优化中.     

多智能体搜寻者优化算法在电力系统无功优化中的应用

针对无功优化这个典型的非线性问题,提出了一种基于多Agent系统的搜寻者优化算法MASOA (Multi-agent Seeker Optimization Algorithm)来求解。该算法针对SOA算法邻域划分随意性较大,融入智能体技术,在改进SOA算法邻域划分合理性的同时,提高粒子寻优的准确度;利用SOA算法的进化机制,引入自适应思想,使新算法具有良好的非线性搜索能力,更好地适应无功优化问题。以网损最小为目标函数,在IEEE 30节点系统上进行测试,并与四种智能算法进行比较,结果表明,MASOA在算法计算精度、收敛稳定性、寻优时间等方面都具有普遍优势,能有效地应用于电力系统无功优化中。