V. Mukherjee

Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem

Graphical abstract for application of ALC-PSO algorithm for solution of OPF problem. ALC-PSO is applied for the solution of OPF problem of power systems.OPF is formulated as a non-linear constrained optimization problem.The study is implemented on two IEEE standard test power systems.The presented results demonstrate the potential of the proposed approach. Solution of optimal power flow (OPF) problem aims to optimize a selected objective function such as fuel cost, active power loss, total voltage deviation (TVD) etc. via optimal adjustment of the power system control variables while at the same time satisfying various equality and inequality constraints. In the present work, a particle swarm optimization with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power systems. The proposed approach is examined and tested on modified IEEE 30-bus and IEEE 118-bus test power system with different objectives that reflect minimization of fuel cost or active power loss or TVD. The simulation results demonstrate the effectiveness of the proposed approach compared with other evolutionary optimization techniques surfaced in recent state-of-the-art literature. Statistical analysis, presented in this paper, indicates the robustness of the proposed ALC-PSO algorithm.

A new design method using opposition-based BAT algorithm for IIR system identification problem

BAT algorithm BA is a meta-heuristic algorithm, based on the echolocation behaviour of bats. In this paper, optimal set of filter coefficients is searched by the modified optimisation methodology called opposition-based BAT algorithm OBA for infinite impulse response IIR system identification problem. Opposition based numbering concept is embedded into the primary foundation of BA metaphorically to enhance the convergence speed and performance for finding better near-global optimal solution. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters of OBA technique. When tested against standard benchmark examples, for same and reduced order models, the simulation results establish the OBA as a more competent candidate to other evolutionary algorithms as real coded genetic algorithm RGA, differential evolution DE and particle swarm optimisation PSO in terms of accuracy and convergence speed.

A novel quasi-oppositional harmony search algorithm for automatic generation control of power system

For single-area NRTWD (a) comparative frequency deviation and (b) QOHS-based convergence profile of FOD. AGC study of single- and multi-area power systems by using a novel QOHS algorithm.Effects of GRC in the studied three- and five-area power system model.Incorporation of PID controller in single- and three-area power systems.Investigation of IDD controller performance in five-area power system.Comparative analysis of the QOHS algorithm over other methods. This paper presents the significances of a novel quasi-oppositional harmony search (HS) (QOHS) algorithm in the context of automatic generation control (AGC) of power system. The proposed QOHS algorithm is framed by utilizing the quasi-oppositional concept in the pre-available basic HS algorithm. Also, the proposed algorithm houses both the characters of two guesses i.e. opposite-point and its mirror point (quasi-opposite point) to converge rapidly toward the optimal solution(s). The proposed QOHS algorithm is, individually, applied to single-, three- and five-area interconnected test power systems (considering suitable cases) for its survival in AGC domain. The single- and three-area test systems are supplemented with the proportional-integral-derivative (PID) controller installed in each control area. In the second phase of investigation, the proposed QOHS based integral-double derivative (IDD) controller is also examined in AGC mechanism of five-area test power system. Initially, integral of square error based objective function is minimized and, further, two performance indices (such as integral of time absolute error and integral of time square error) are also calculated to test the AGC performance offered by the proposed QOHS based PID/IDD controller. To add some degree of non-linearities, appropriate generation rate constraint (GRC) is also considered for both three- and five-area test power systems. The simulated results, as obtained by the proposed QOHS algorithm, are compared to those offered by other optimization algorithms reported in the recent state-of-the-art literature. The extensive results, as presented in this paper, reveal that the proposed QOHS algorithm may be, effectively, imposed to boost the AGC performance of power system having various degrees of complexities (like model uncertainties) and non-linearities (like GRC).

Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers

Application of ALC-PSO algorithm for solution of ORPD problem. ORPD is a non-linear constrained optimization problem.ALC-PSO algorithm is applied for the solution ORPD problem of power systems.ORPD problem is formulated for real power loss and voltage deviation minimization.The study is implemented on IEEE 30-, 57- and 118-bus test power systems.The results presented demonstrate the potential of the proposed approach. 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.

Solution of economic dispatch problems by seeker optimization algorithm

Seeker optimization algorithm (SOA), a novel heuristic population-based search algorithm, is utilized in this paper to solve different economic dispatch (ED) problems of thermal power units. In the SOA, the act of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the algorithm has been tested on four different small, as well as, large scale test power systems to solve the ED problems. The outcome of the present work is to establish the SOA as a promising alternative approach to solve the ED problems in practical power systems. Both the near-optimality of the solution and the convergence speed of the algorithm are promising. The results obtained are compared with those published in the recent literatures.

A novel design method for optimal IIR system identification using opposition based harmony search algorithm

Abstract In this paper a population based evolutionary optimization methodology called Opposition based Harmony Search Algorithm (OHS) is applied for the optimization of system coefficients of adaptive infinite impulse response (IIR) system identification problem. The original Harmony Search (HS) algorithm is chosen as the parent one and opposition based approach is applied to it with an intention to exhibit accelerated near global convergence profile. During the initialization, for choosing the randomly generated population/solution opposite solutions are also considered and the fitter one is selected as apriori guess for having faster convergence profile. Each solution in Harmony Memory (HM) is generated on the basis of memory consideration rule, a pitch adjustment rule and a re-initialization process which gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic HS algorithm results in balancing of exploration and exploitation of search space. The proposed OHS based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.

A maiden application of gravitational search algorithm with wavelet mutation for the solution of economic load dispatch problems

Gravitational search algorithm (GSA) is one of the new optimisation algorithms based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a novel GSA with wavelet mutation (WM) (GSAWM) is proposed. It utilises the wavelet theory to enhance the GSA in exploring the solution space more effectively for a better solution. This algorithm is utilised for the optimal solutions of different economic load dispatch (ELD) problems of power systems. The obtained results are compared with those of the other state-of-the-art heuristic optimisation techniques published in the literature. Both the near-optimality of the solution and the convergence speed of the algorithm are promising.

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

Abstract In today’s competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool-based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population-based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

Modeling and seeker optimization based simulation for intelligent reactive power control of an isolated hybrid power system

Abstract Seeker optimization algorithm (SOA) is a novel heuristic population-based search algorithm based on the concept of simulating the act of human searching. In SOA, the acts of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, effectiveness of the SOA has been tested for optimized reactive power control of an isolated wind–diesel hybrid power system model. In the studied power system model, a diesel engine based synchronous generator (SG) and a wind turbine based induction generator (IG) are used for the purpose of power generation. IG offers many advantages over the SG but it requires reactive power support for its operation. So, there is a gap between reactive power demand and its supply. To minimize this gap between reactive power generation and its demand, a variable source of reactive power such as static VAR compensator (SVC) is used. The SG is equipped with IEEE type-I excitation system and dual input power system stabilizer (PSS) like IEEE-PSS3B. The performance analysis of a Takagi–Sugeno fuzzy logic (TSFL)-based controller for the studied isolated hybrid power system model is also carried out which tracks the degree of reactive power compensation for any sort of input perturbation in real-time. In time-domain simulation of the investigated power system model, the proposed SOA–TSFL yields on-line, off-nominal coordinated optimal SVC and PSS parameters resulting in on-line optimal reactive power control and terminal voltage response. The performance of the proposed controller, with the influence of signal transmission delay, has also been investigated.

Artificial Bee Colony Algorithm for Transient Performance Augmentation of Grid Connected Distributed Generation

In this paper, a conventional thermal power system equipped with automatic voltage regulator, IEEE type dual input power system stabilizer (PSS) PSS3B and integral controlled automatic generation control loop is considered. A distributed generation (DG) system consisting of aqua electrolyzer, photovoltaic cells, diesel engine generator, and some other energy storage devices like flywheel energy storage system and battery energy storage system is modeled. This hybrid distributed system is connected to the grid. While integrating this DG with the onventional thermal power system, improved transient performance is noticed. Further improvement in the transient performance of this grid connected DG is observed with the usage of superconducting magnetic energy storage device. The different tunable parameters of the proposed hybrid power system model are optimized by artificial bee colony (ABC) algorithm. The optimal solutions offered by the ABC algorithm are compared with those offered by genetic algorithm (GA). It is also revealed that the optimizing performance of the ABC is better than the GA for this specific application.