A multi-objective approach to optimal placement and sizing of multiple active power filters using a music-inspired algorithm

This paper proposes a new multi-objective framework for optimal placement and sizing of the active power filters (APFs) with satisfactory and acceptable standard levels. total harmonic distortion (THD) of voltage, harmonic transmission line loss (HTLL), motor load loss function (MLLF), and total APFs currents are the four objectives considered in the optimization, while harmonic distortions within standard level, and maximum allowable APF size, are modeled as constraints. The proposed model is one of non-convex optimization problem having a non-linear, mixed-integer nature. Since, a new modified harmony search algorithm (MHSA) is used and followed by a min–max technique in order to obtain the final optimal solution. The harmony search algorithm is a recently developed optimization algorithm, which imitates the music improvisation process. In this process, the Harmonists improvise their instrument pitches searching for the perfect state of harmony. The newly developed method has been applied on the IEEE 18-bus test system and IEEE 30-bus test system by different scenarios and cases to demonstrate the feasibility and effectiveness of the proposed method. The detailed results of the case studies are presented and thoroughly analyzed. The obtained results illustrate the sufficiency and profitableness of the newly developed method in the placement and sizing of the multiple active power filters, when compared with other methods.     

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.     

A new method for optimal location and sizing of capacitors in distorted distribution networks using PSO algorithm

This paper presents an optimization algorithm for simultaneous improvement of power quality (PQ), optimal placement and sizing of fixed capacitor banks in radial distribution networks in the presence of voltage and current harmonics. The algorithm is based on particle swarm optimization (PSO). The objective function includes the cost of power losses, energy losses and those of the capacitor banks. Constraints include voltage limits, number/size of installed capacitors at each bus, and PQ limits of standard IEEE-519. Using a newly proposed fitness function, a suitable combination of the objective function and relevant constraints is defined as a criterion to select a set of the most suitable buses for capacitor placement. This method is also capable of improving particles in several steps for both converging more readily to the near global solution as well as improving satisfaction of the power quality constraints. Simulation results for the 18-bus and 33-bus IEEE distorted networks using the proposed method are presented and compared with those of previous works. In the 18-bus IEEE distorted network, this indicated an improvement of 3.29% saving compared with other methods. Using the proposed optimization method and simulation performed on the 33-bus IEEE distorted network an annual cost reduction of 31.16% was obtained.     

An application of PSO technique for harmonic elimination in a PWM inverter

Harmonic elimination problem in PWM inverter is treated as an optimization problem and solved using particle swarm optimization (PSO) technique. The derived equation for computation of total harmonic distortion (THD) of the output voltage of PWM inverter is used as the objective function in the PSO algorithm. The objective function is minimized to contribute the minimum THD in the voltage waveform and the corresponding switching angles are computed. The method is applied to investigate the switching patterns of both unipolar and bipolar case. While minimizing the objective function, the individual selected harmonics like 5th, 7th, 11th and 13th can be controlled within the allowable limits by incorporating the constraints in the PSO algorithm. The results of the unipolar case using five switching angles are compared with that of a recently reported work and it is observed that the proposed method is effective in reducing the voltage THD in a wide range of modulation index. The simulated results are also validated through suitable experiments.     

An application of sequential neural-network approximation for sitting and sizing passive harmonic filters

This paper presents a method which is combined by sequential neural-network approximation and orthogonal arrays (SNAOA) for reducing harmonic distortion with passive harmonic filters and determining the optimal locations for harmonic filters among existent capacitor busses in the power network. An orthogonal array is first conducted to obtain the initial solution set. The set is then treated as the initial training sample. Next, a back-propagation sequential neural network is trained to simulate the feasible domain for seeking the optimal filter design. A restart strategy is also incorporated into the SNAOA so that the searching process may have a better opportunity to reach a near global optimum solution. In order to determine a set of weights of objective function to represent the relative importance of each term, the simplest and most efficient form of triangular membership functions has been considered. To illustrate the performance of the SNAOA, a practical harmonic mitigation problem in a 36-bus radial distribution system is studied. The results show that the SNAOA performs better than the original scheme and satisfies the harmonic limitations with respect to the objective of minimizing total harmonic distortion of voltages and the cost of commercially available discrete sizes for sitting and sizing passive harmonic filters.     

Minimizing harmonic distortion in power system with optimal design of hybrid active power filter using differential evolution

Hybrid active power filter (HAPF) is an advanced form of harmonic filter combining advantages of both active and passive filters. In HAPF, selection of active filter gain, passive inductive and capacitive reactances, while satisfying system constraints on individual and overall voltage and current harmonic distortion levels, is the main challenge. To optimize HAPF parameters, this paper proposes an approach based on differential evolution (DE) algorithm called L-SHADE. SHADE is the success history based parameter adaptation technique of DE optimization process for a constrained, multimodal non-linear objective function. L-SHADE improves the performance of SHADE with linearly reducing the population size in successive generations. The study herein considers two frequently used topologies of HAPF for parameter estimation. A single objective function consisting of both total voltage harmonic distortion (VTHD) and total current harmonic distortion (ITHD) is formulated and finally harmonic pollution (HP) is minimized in a system comprising of both non-linear source and non-linear loads. Several case studies of a selected industrial plant are performed. The output results of L-SHADE algorithm are compared with a similar past study and also with other well-known evolutionary algorithms.     

Comprehensive learning particle swarm optimization for reactive power dispatch

Reactive power dispatch (RPD) is an optimization problem that reduces grid congestion by minimizing the active power losses for a fixed economic power dispatch. RPD reduces power system losses by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks and provides better system voltage control, resulting in an improved voltage profile, system security, power transfer capability and over all system operation. In this paper, RPD problem is solved using particle swarm optimization (PSO). To overcome the drawback of premature convergence in PSO, a learning strategy is introduced in PSO, and this approach called, comprehensive learning particle swarm optimization (CLPSO) is also applied to this problem and a comparison of results is made between these two. Three different test cases have been studied such as minimization of real power losses, improvement of voltage profile and enhancement of voltage stability through a standard IEEE 30-bus and 118-bus test systems and their results have been reported. The study results show that the approaches developed are feasible and efficient.     

A multiobjective approach for optimal placement and sizing of distributed generators and capacitors in distribution network

Both active and reactive power play important roles in power system transmission and distribution networks. While active power does the useful work, reactive power supports the voltage that necessitates control from system reliability aspect as deviation of voltage from nominal range may lead to inadvertent operation and premature failure of system components. Reactive power flow must also be controlled in the system to maximize the amount of real power that can be transferred across the power transmitting media. This paper proposes an approach to simultaneously minimize the real power loss and the net reactive power flow in the system when reinforced with distributed generators (DGs) and shunt capacitors (SCs). With the suggested method, the system performance, reliability and loading capacity can be increased by reduction of losses. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is adopted to select optimal sizes and locations of DGs and SCs in large scale distribution networks with objectives being minimizing system real and reactive power losses. MOEA/D is the process of decomposition of a multiobjective optimization problem into a number of scalar optimization subproblems and optimizing those concurrently. Case studies with standard IEEE 33-bus, 69-bus, 119-bus distribution networks and a practical 83-bus distribution network are performed. Output results of MOEA/D method are compared with similar past studies and notable improvement is observed.     

混沌布谷鸟搜索算法在谐波估计中的应用

针对布谷鸟搜索（CS）算法存在后期收敛速度慢、计算精度不高和陷入局部最优等缺点,提出了混沌布谷鸟（CCS）算法。首先,通过混沌理论初始化种群来增加种群多样性;然后,对局部最优值引入混沌扰动算子来跳出早熟收敛,提高计算精度,进而完成全局优化。对4个单目标基准函数进行仿真测试,对比最优值、最差值、平均值、中位数值及标准差值,结果表明,基于CCS算法比CS算法有更快的收敛速度和更高的收敛精度。在电力系统中谐波问题成分引起电流波形畸变,电网不稳定。精确分析谐波成分是解决谐波污染的重要前提。将性能更好的CCS算法应用于谐波估计,通过比较估计均值及标准偏差,结果显示在分析谐波电流时CCS算法相比粒子群优化（PSO）算法具有更好的性能。     

Harmonics elimination in multilevel inverter with unequal DC sources by fuzzy-ABC algorithm

The harmonious appearance in multilevel inverter output voltage is more for the case of unequal DC sources. In this paper, a hybrid technique incorporating fuzzy inference system (FIS) and artificial bee's colony (ABC) algorithm is proposed. FIS is a rule-based artificial intelligent technique which is used for generating the data set in terms of switching angle, harmonic voltage and harmonic distortion. The data set is generated as per the behaviour of the multilevel inverter without using any harmonic elimination technique. In the generated data set, the switching angle and the harmonic voltage are categorised as SMALL, MEDIUM and LARGE. Then, the ABC algorithm is used to optimise the selection of switching angles from the training data set. The performance of the proposed hybrid technique is tested on a 7-level cascade H-bridge inverter for different voltage levels of unequal DC sources using MATLAB/SIMULINK platform. The effectiveness and superiority of the proposed technique is evaluated by comparing the reduction capacity of total harmonic distortion for different voltage levels of unequal DC sources with particle swarm optimisation (PSO) algorithm and fuzzy-PSO algorithm.     

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 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.     

Real-time particle swarm optimization based current harmonic cancellation

As a powerful optimization algorithm, particle swarm optimization (PSO) has been widely applied to power system researches. However, most existing applications of PSO can only be implemented offline. The difficulties of online implementation mainly come from the unavoidable lengthy simulation time to evaluate a candidate solution. Recently, PSO was implemented online that can identify parameters in a motor control systems. In this paper, the real-time PSO (RT-PSO) based identification technique is applied to cancel current harmonics in power systems. By transforming the identification problem to optimization problem, RT-PSO can simultaneously identify four parameters associated with fundamental current from measurement. In this way, there is no need to identify the fundamental frequency separately or construct fundamental signal from identified harmonic information. The identification algorithm can be applied to three-phases independently, even for unbalanced system or single-phase system. The identified fundamental signal is then used as the reference for current harmonics cancellation. The RT-PSO based harmonic cancellation is realized with an active filter and used to compensate harmonic current created by a nonlinear load. Simulation results demonstrate that the RT-PSO algorithm can provide accurate identification of the fundamental current which in turn will result in good harmonic cancellation performance. As a capable online optimization technique, RT-PSO can be extensively applied to many optimization and control problems.     

Application and comparison of computational intelligence techniques for optimal location and parameter setting of UPFC

Unified power flow controller (UPFC) is one of the most effective flexible AC transmission systems (FACTS) devices for enhancing power system security. However, to what extent the performance of UPFC can be brought out, it highly depends upon the location and parameter setting of this device in the system. This paper presents a new approach based on computational intelligence (CI) techniques to find out the optimal placement and parameter setting of UPFC for enhancing power system security under single line contingencies (N?1 contingency). Firstly, a contingency analysis and ranking process to determine the most severe line outage contingencies, considering lines overload and bus voltage limit violations as a performance index, is performed. Secondly, a relatively new evolutionary optimization technique, namely: differential evolution (DE) technique is applied to find out the optimal location and parameter setting of UPFC under the determined contingency scenarios. To verify our proposed approach and for comparison purposes, simulations are performed on an IEEE 14-bus and an IEEE 30-bus power systems. The results, we have obtained, indicate that DE is an easy to use, fast, robust and powerful optimization technique compared with genetic algorithm (GA) and particle swarm optimization (PSO). Installing UPFC in the optimal location determined by DE can significantly enhance the security of power system by eliminating or minimizing the number of overloaded lines and the bus voltage limit violations.     

Optimum placement of active power conditioner in distribution systems using improved discrete firefly algorithm for power quality enhancement

This paper presents an improved solution for optimal placement and sizing of active power conditioner (APC) to enhance power quality in distribution systems using the improved discrete firefly algorithm (IDFA). A multi-objective optimization problem is formulated to improve voltage profile, minimize voltage total harmonic distortion and minimize total investment cost. The performance of the proposed algorithm is validated on the IEEE 16- and 69-bus test systems using the Matlab software. The obtained results are compared with the conventional discrete firefly algorithm, genetic algorithm and discrete particle swarm optimization. The comparison of results showed that the proposed IDFA is the most effective method among others in determining optimum location and size of APC in distribution systems.     

Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.     

一种改进的谐波电流检测方法

针对传统的ip-iq谐波电流检测方法采用锁相环虽然能得到三相电流的基频和初相角,但当电网电压发生畸变时则存在检测精度较低、电路复杂的问题,提出了一种改进的无锁相环的谐波电流检测方法;详细分析了当电网电压发生畸变时,在三相电流对称和不对称的情况下该改进方法的检测原理,并给出了该改进方法应用于单相电路谐波电流检测的实现。实验结果表明,该改进方法能够准确、实时地检测谐波电流,且算法简单。     

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.     

Adaptive PSODV algorithm for OPF with non-smooth cost functions and statistical analysis

This paper presents a particle swarm optimization with differentially perturbed velocity hybrid algorithm with adaptive acceleration coefficient (APSO-DV) for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The APSO-DV employs differentially perturbed velocity with adaptive acceleration coefficient for updating the positions of particles for the particle swarm optimization. The feasibility of the proposed approach was tested on IEEE 30-bus and IEEE 118-bus systems with three different objective functions. Several cases were investigated to test and validate the robustness of the proposed method in finding the optimal solution. The effectiveness of the proposed approach was tested including contingency also. Simulation results demonstrate that the APSO-DV provides superior results compared to classical DE, PSO, PSO-DV and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

Group search optimizer based optimal location and capacity of distributed generations

This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system—the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.