含混合储能的互联电力系统传感器容错负荷频率控制

为了改善多区域互联系统电能质量的问题,在原有负荷频率控制系统(LFC)的基础上引入由超级电容和蓄电池两者组成的混合储能系统(HESS),并针对电力系统中难以避免的传感器故障问题设计传感器主动容错策略.首先,建立含HESS的LFC系统模型用以减少负荷扰动对系统频率带来的影响;其次,针对带HESS的互联电力系统设计传感器主...     

多区域互联电力系统的自适应滑模负载频率控制

本文研究了一类计及电动汽车的电力系统中的负荷频率控制问题, 首先, 将电动汽车模型与传统的负载频率控制模型相结合,在未知扰动波动范围的条件下设计了自适应滑模控制律. 其次, 分别考虑了电网调频中的匹配扰动和不匹配扰动问题, 并利用李亚普诺夫稳定性理论导出了匹配和不匹配条件下的系统稳定的充分条件. 最后, 两个区域电力系统的仿真结果表明, 电动汽车作为电源和负载都可以提高电网的频率稳定性, 所设计的控制器可以有效地调节电网的频率波动.     

风电介入下的多区域互联电力系统分布式经济模型预测负荷频率控制

针对风电介入下的多区域互联电力系统,提出一种分布式经济模型预测负荷频率控制策略.通过将大规模互联电力系统分解成若干个动态耦合的子系统,这些子系统能够利用网络交流并共享信息,使得各区域的控制器实现各自优化问题的求解.同时,在满足状态约束和控制输入约束的前提下,遵循传统火力发电优先、风力发电配合的原则,通过在线求解优化问题,实现风电介入下的多区域互联电力系统的负荷频率控制.为了提高系统整体运行经济性,所提出的分布式经济模型预测控制器将负荷调频成本、燃料消耗成本以及风力发电成本等经济性指标考虑在内.仿真结果表明,在阶跃负荷扰动下,所设计的控制器不仅可以满足调频要求,在降低计算负担和提高经济性能方面也具有一定优势.     

Non-linear sliding mode load frequency control in multi-area power system

This paper addresses non-linear sliding mode controller (SMC) with matched and unmatched uncertainties for load frequency control (LFC) application in three-area interconnected power system. In conventional LFC scheme, as the nominal operating point varies due to system uncertainties, frequency deviations cannot be minimized. These lead to degradation in the dynamic performance or even system instability. In this paper, an effective control law is proposed against matched and unmatched uncertainties.. The proposed controller has ability to vary closed-loop system damping characteristics according to uncertainties and load disturbances present in the system. The frequency deviation converges to zero with minimum undershoot/overshoot, fast settling time, significantly reduced chattering and ensures asymptotic stability. In addition, the controller is robust in the presence of parameter uncertainties and different disturbance patterns. It also guarantees high dynamic performance in the presence of governor dead band (GDB) and generation rate constraint (GRC). Simulations are performed to compare the proposed controller with linear SMC. Using proposed control strategy, undershoot/overshoot and settling time gets reduced by approximately 30% with respect to linear SMC. The computed performance indices and qualitative results establish the superiority as well as applicability of the proposed design for the LFC problem. Further, the proposed controller scheme is validated on IEEE 39 bus large power system.     

Decentralized load frequency control for two-area interconnected power system

This paper proposes a decentralized output feedback control scheme applied to two-area interconnected power system. The controller synthesis problem is formulated as the scaled H∞control problem and a new LMI-based algorithm is proposed to compute the decentralized controller. The proposed controller provides robustness with regard to parametric uncertainties and also attenuates bounded exogenous disturbances in the sense of L2-gain. Simulation results clearly show the effectiveness of developed decentralized output feedback control scheme.     

Performance investigation of ABC algorithm in multi-area power system with multiple interconnected generators

This paper presents an extensive study on the application of Artificial Bee Colony (ABC) algorithm for load frequency control (LFC) in multi-area power system with multiple interconnected generators. The LFC model incorporates various possible physical constraints and non-linearities such as generation rate constraint, time delay, dead zone and boiler. The ABC algorithm is used to find the optimum PID controller parameters. The tuning performance of the algorithm is comparatively investigated against different optimization technique such as evolutionary programming (EP), genetic algorithm (GA), gravitational search algorithm (GSA) and particle swarm optimization (PSO). The robustness analysis of the system is also evaluated by investigating the dynamic response of the controller with load demand at varying time step, tuning based on different performance criterion and by varying the load demand. The performance of the system is evaluated based on the settling time and maximum overshoot value of the frequency deviation response. The performance of ABC is also verified against an exhaustive search based on interval halving method. Despite employing a single controller for multiple interconnected generators, the optimized controller is able to successfully damp oscillations in the system response and regulate the area control error back to zero in minimal amount of time. The results indicate the superiority of the ABC algorithm’s search mechanism in finding the optimum set of PID controller’s gain.     

A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems

In this paper, a novel hybrid Differential Evolution (DE) and Pattern Search (PS) optimized fuzzy PI/PID controller is proposed for Load Frequency Control (LFC) of multi-area power system. Initially a two-area non-reheat thermal system is considered and the optimum gains of the fuzzy PI/PID controller are optimized employing a hybrid DE and PS (hDEPS) optimization technique. The superiority of the proposed controller is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as DE, Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI controllers for the same interconnected power system. Furthermore, robustness analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Additionally, the proposed approach is further extended to multi-area multi-source power system with/without HVDC link and the gains of fuzzy PID controllers are optimized using hDEPS algorithm. The superiority of the proposed approach is shown by comparing the results with recently published DE optimized PID controller and conventional optimal output feedback controller for the same power systems. Finally, Reheat turbine, Generation Rate Constraint (GRC) and time delay are included in the system model to demonstrate the ability of the proposed approach to handle nonlinearity and physical constraints in the system model.     

Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system

This paper presents a novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system. The advantage of this controller is that it can handle the non-linearities, and at the same time it is faster than other existing controllers. The effectiveness of proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in four area interconnected power system. Area-1 and area-2 consist of thermal reheat power plant whereas area-3 and area-4 consist of hydro power plant. Performance evaluation is carried out by using fuzzy, ANN, ANFIS and conventional PI and PID control approaches. The performances of the controllers are simulated using MATLAB/Simulink package. The result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.     

Grasshopper optimization algorithm for optimal load frequency control considering Predictive Functional Modified PID controller in restructured multi-resource multi-area power system with Redox Flow Battery units

Load frequency control (LFC) is a well-established issue in design and operation of power systems considering to the extension, restructuring, and complexity of the interconnected power systems and also the emergence utilization of renewable energy resources. This paper studies the frequency control of multi-area multi-source power system based on the importance of the LFC in the stability of the power system which includes various generation units of thermal, hydroelectric, wind, natural gas and diesel under the restructured environment. In this system, non-linear physical constraints, governor dead band (GDB) and generation rate constraint (GRC) are considered. In this paper, a new Predictive Functional Modified PID (PFMPID) controller is proposed that the effectiveness of this controller is verified compared to the traditional one. In order to optimize and demonstrate the superiority of the proposed control method, Grasshopper Optimization Algorithm (GOA) is proposed as a suitable solution. To further improve the performance of the under study system, the use of the Redox Flow Battery (RFB) energy storage unit has also been proposed. Since the operation evaluation of the proposed process is necessary in different system conditions, the performance of the proposed method is studied under various disturbances and simulation results are presented.     

Enhanced coyote optimizer-based cascaded load frequency controllers in multi-area power systems with renewable

Neural Computing and Applications - Recently, the renewable energy has been occupied a lot of attention around the world since it presents cheap and sustainable energy. Consequently, its presence...     

Application of mutation operators to flower pollination algorithm

Flower pollination algorithm (FPA) is a recent addition to the field of nature inspired computing. The algorithm has been inspired from the pollination process in flowers and has been applied to a large spectra of optimization problems. But it has certain drawbacks which prevents its applications as a standard algorithm. This paper proposes new variants of FPA employing new mutation operators, dynamic switching and improved local search. A comprehensive comparison of proposed algorithms has been done for different population sizes for optimizing seventeen benchmark problems. The best variant among these is adaptive-Lévy flower pollination algorithm (ALFPA) which has been further compared with the well-known algorithms like artificial bee colony (ABC), differential evolution (DE), firefly algorithm (FA), bat algorithm (BA) and grey wolf optimizer (GWO). Numerical results show that ALFPA gives superior performance for standard benchmark functions. The algorithm has also been subjected to statistical tests and again the performance is better than the other algorithms.     

多策略增强花授粉算法及其应用

针对经典花授粉算法容易陷入局部最优解和收敛速度慢的缺点,提出一种增强型透镜成像策略和随机邻域变异策略的花授粉算法。通过增强型透镜成像策略扩展花授粉算法的搜索空间,增加解的多样性,有助于算法跳出局部最优解。引入随机邻域变异策略,借助邻域内的信息指导算法搜索,增强算法的收敛精度和搜索速度。对改进后的花授粉算法和四种其他改进算法在CEC2013测试函数上进行比较,实验证明改进后的多策略花授粉算法不论是收敛精度还是搜索速度都比对比算法优秀。最后把多策略花授粉算法应用在汽车传动参数模型上研究该算法的实际效用,结果表明多策略花授粉算法在汽车传动参数优化问题上都优于对比算法。     

自适应多策略花朵授粉算法

针对花朵授粉算法极易陷入局部最优解且寻优精度不高的问题,提出自适应多策略花朵授粉算法(self-adaptive flower pollination algorithm with multiple strategies,SMFPA)。利用锚点策略提高种群的多样性,采用摄动策略改善全局勘探能力,采用局部搜索增强策略提升其开采最优解的能力。为验证SMFPA的性能,比较5种算法在解决12个测试问题上的寻优结果,实验结果表明,在寻优速度以及寻优精度方面,SMFPA算法表现更优。通过比较算法在管柱设计问题上的寻优结果,进一步评估SMFPA的寻优性能。     

计及时滞的互联电网负荷频率控制最优分数阶PID控制器设计

分数阶PID控制器相比于传统整数阶PID控制器,具有控制性能好、鲁棒性强等诸多优势,可应用于电网的负荷频率控制(load frequency control,LFC)中.针对网络化时滞互联电网的LFC问题,提出了一种基于计算智能的分数阶PID控制器参数优化整定方案.该方案选择时滞LFC系统时域输出响应构建优化目标函数,采用最近提出的灰狼优化算法获得最优的分数阶PID控制器参数,所设计的控制器能确保一定时滞区间内LFC系统的稳定性.仿真算例表明,所设计的LFC最优分数阶PID控制器比传统整数阶PID控制器的控制性能更优,时滞鲁棒性更强.     

基于滑模控制的含风储多域电力系统负荷频率控制

建立含风储多域互联电力系统负荷频率控制(LFC)模型,同时考虑系统参数不确定性、储能系统和传统机组控制信道延时问题.为提高系统鲁棒性,降低储能系统的容量配置,针对含风储的LFC模型,设计滑模负荷频率控制器,并提出滑模负荷频率控制器和储能协调的控制策略.算例分析表明,所提出的协调控制策略在新能源大规模渗透和系统负荷波动情况下能够有效减小系统频率偏差和区域控制偏差,同时降低储能系统的配置容量,提高电力系统安全稳定运行的经济性.     

PSO-scaled fuzzy logic to load frequency control in hydrothermal power system

This paper, presents the particle swarm optimization-based fuzzy logic controller (PSO FLC) design for load frequency control in a two-area interconnected hydrothermal power system. Flexible alternating current transmission system devices and energy storage devices are being installed to improve the reliability and stability of the system under dynamic conditions. One such devices namely thyristor-controlled phase shifter (TCPS) is connected in series with the tie-line to damp out the power swings and frequency oscillations. Similarly at the terminal of one control area, a fast acting energy storage device of superconducting magnetic energy storage (SMES) is connected to meet the sudden changes in demand. The existing conventional controllers are unable to provide the satisfactory performance over a wide range of operating conditions due to system nonlinearity and plant parameter variations. To improve the dynamic performance of the system, this work proposes an intelligent tuning approach using a combination of particle swarm optimization (PSO) and fuzzy logic technique. In this work, PSO algorithm is employed for the optimal selection of membership function parameters of the proposed fuzzy PI, TCPS and SMES controllers by minimizing the time domain objective function. The simulation study is performed by the proposed PSO FLC in a two-area interconnected power system. To show the effective performance of the proposed controller, a comparative study has been made with the conventional, genetic algorithm and fuzzy logic-based optimized controller under varying load conditions.     

基于萤火虫算法的自适应花授粉优化算法

花授粉算法是一种新的启发式算法,由于存在易陷入局部最优且演化后期收敛速度慢等缺陷,导致算法的寻优能力受到限制。针对该算法存在的不足,在局部授粉过程中引入自适应的变异因子,并对花授粉算法中的转换概率进行自适应调整后,将其与萤火虫算法相结合,提出了一种基于萤火虫算法的改进花授粉算法;最后,通过经典的标准测试函数对新提出的算法与DE-FPA、PSO-FPA做比较实验。实验结果表明,改进后的算法比基本花授粉算法具有更高的收敛精度和稳定性。     

A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system

In this paper, a hybrid gravitational search algorithm (GSA) and pattern search (PS) technique is proposed for load frequency control (LFC) of multi-area power system. Initially, various conventional error criterions are considered, the PI controller parameters for a two-area power system are optimized employing GSA and the effect of objective function on system performance is analyzed. Then GSA control parameters are tuned by carrying out multiple runs of algorithm for each control parameter variation. After that PS is employed to fine tune the best solution provided by GSA. Further, modifications in the objective function and controller structure are introduced and the controller parameters are optimized employing the proposed hybrid GSA and PS (hGSA-PS) approach. The superiority of the proposed approach is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as firefly algorithm (FA), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), particle swarm optimization (PSO), hybrid BFOA-PSO, NSGA-II and genetic algorithm (GA) for the same interconnected power system. Additionally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. Also, the proposed approach is extended to two-area reheat thermal power system by considering the physical constraints such as reheat turbine, generation rate constraint (GRC) and governor dead band (GDB) nonlinearity. Finally, to demonstrate the ability of the proposed algorithm to cope with nonlinear and unequal interconnected areas with different controller coefficients, the study is extended to a nonlinear three unequal area power system and the controller parameters of each area are optimized using proposed hGSA-PS technique.     

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.     

Synthesis of linear antenna array using flower pollination algorithm

Linear antenna array (LAA) design is a classical electromagnetic problem. It has been extensively dealt by number of researchers in the past, and different optimization algorithms have been applied for the synthesis of LAA. This paper presents a relatively new optimization technique, namely flower pollination algorithm (FPA) for the design of LAA for reducing the maximum side lobe level (SLL) and null control. The desired antenna is achieved by controlling only amplitudes or positions of the array elements. FPA is a novel meta-heuristic optimization method based on the process of pollination of flowers. The effectiveness and capability of FPA have been proved by taking difficult instances of antenna array design with single and multiple objectives. It is found that FPA is able to provide SLL reduction and steering the nulls in the undesired interference directions. Numerical results of FPA are also compared with the available results in the literature of state-of-the-art algorithms like genetic algorithm, particle swarm optimization, cuckoo search, tabu search, biogeography based optimization (BBO) and others which also proves the better performance of the proposed method. Moreover, FPA is more consistent in giving optimum results as compared to BBO method reported recently in the literature.