Bidishna Bhattacharya

Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique

Nonconvex emission constrained economic dispatch (NECED) is a complex optimization problem.Many optimization techniques and algorithm have been applied to solve it.A new improved particle swarm optimization technique is proposed for the same.The effectiveness of the proposed method is tested on two practical systems.The results are compared with classical as well as other heuristic technique. This paper presents a novel parameter automation strategy for particle swarm optimization algorithm for solving non-convex emission constrained economic dispatch (NECED) problems. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for non-convex emission constrained economic dispatch (NECED) problems to avoid premature convergence. Generator ramp rate limits and prohibited operating zones are taken into account in problem formulation. Non-convex emission constrained economic dispatch (NECED) problem is obtained by considering both the economy and emission objectives. The performance of the proposed method is demonstrated on two sample test systems. The results of the proposed method are compared with other methods. It is found that the results obtained by the proposed method are superior in terms of fuel cost, emission output and losses.

A novel population-based optimization algorithm for optimal distribution capacitor planning

Optimal distribution capacitor planning is an important task for economic operation of power systems. The optimal distribution capacitor planning is a combinatorial optimization problem which considers both power losses and cost of capacitor installation subject to bus voltage constraints. Discrete nature of capacitors, different load are considered in the problem formulation. This paper presents a novel reliable and efficient algorithm based on biogeography-based optimization technique for the solution of capacitor placement problem. Biogeography-based optimization technique is inspired by geographical distribution of biological organisms. The performance of the proposed method is demonstrated on a sample test system. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing comparable results.

Reactive power optimization using hybrid cultural algorithm

Optimal reactive power dispatch is an important task to achieve secure and economic operation of power systems. A well-organized allocation of reactive power in an electric network can minimize the system losses. This paper presents a Cultural Algorithm (CA) with a single point crossover to minimize the real power loss subjected to limits on generator real and reactive power outputs. In this hybrid approach, CA is used to give a good direction to the optimal global region, and a domain knowledge is used as a fine tuning to determine the optimal solution at the final for better convergence. The solution can be achieved by varying the bus voltages, the on-load tap changer positions of transformers and by switching of shunt capacitors. The performance of the proposed method is demonstrated on IEEE 14-bus system to find the optimal reactive power control variables subjected to various equality and inequality constraints. It is found that the results obtained by the proposed method are comparable in terms real power losses.

Logistic map adaptive differential evolution for optimal capacitor placement and sizing

This paper presents a new adaptive differential evolution technique based on logistic map for optimal distribution placement and sizing. The parameters of differential evolution that need to be selected by the user are the key factors for successful operation DE. Choosing suitable values of parameters are difficult for DE, which is usually a problem-dependent task. Unfortunately, there is no fix rule for selection of parameters. The trial-and-error method adopted generally for tuning the parameters in DE requires multiple optimization runs. Even this method can not guarantee optimal results every time and sometimes it may lead to premature convergence. The proposed method combines differential evolution with chaos theory for self adaptation of DE parameters. The performance of the proposed method is demonstrated on a sample test system. It is seen that the proposed method can avoid premature convergence and provides better convergence characteristics. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing comparable results.

Real and reactive power optimization using hybrid cultural algorithm

This paper presents hybrid cultural algorithm to solve multiobjective real and reactive power optimization problem. The main aim of this paper to minimize the real power loss, while, the cost of real power is also taken as an objective to make the model of P-Q multiobjective optimization problem on optimal power flow. Here sequential operation of real power optimization and reactive power optimization is done to reduce the cost of production followed by real power loss. By P-optimization problem generation cost is first minimized. Then by optimized generator real power outputs, bus voltages, tap settings of transformer and shunt compensation devices, the combined objective function is satisfied subject to some equality and inequality constraints by Q-optimization. IEEE 30 bus system is applied here to prove the efficacy of the proposed method. A comparative study shows that this proposed algorithm yields an optimum result.

Multiobjective optimal placement and sizing of SVC using cultural algorithm

This paper presents a cultural algorithm based technique to achieve the optimal size and placement of Static Var Compensators (SVCs) in a power system. A multiobjective reactive power problem is chosen here to establish the feasibility of the algorithm. Two objective functions are proposed for reducing the SVC investment cost while minimizing the active power loss but maintaining voltage stability. The complexity of the multiobjective problem is solved using Pareto optimization. The results of IEEE 30-bus test system show that the proposed algorithm gives a lower power loss and greater voltage stability in comparison to base value.

A New Improved Knowledge Based Cultural Algorithm for Reactive Power Planning

This paper proposes a novel hybrid method for the planning of reactive power problem RPP. The objective of this paper is to determine the optimum investment required to satisfy suitable reactive power constraints for an acceptable performance level of a power system. Due to the discrete nature of reactive compensation devices, the given objective function leads to a nonlinear problem with combined distinct and constant variables. It is solved by a hybrid procedure, aiming to develop the best search features of an iterative algorithm. The performance of the proposed procedure is shown by presenting the numerical results obtained from its application to the IEEE 30-bus test network. The results obtained are compared with evolutionary programming EP and Broyden method to determine the efficacy of the proposed method.

A new improved self adaptive particle swarm optimization technique for economic load dispatch

This paper presents a new improved self adaptive particle swarm optimization technique to avoid premature convergence for economic load dispatch problem. Many evolutionary techniques such as particle swarm optimization (PSO), differential evolution (DE) have been applied to solve this problem and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. In this method, the inertia weight is made self adaptive depending on the population size and the fitness rank of the particle along with time variant acceleration coefficients. A thirteen-unit test system is considered to demonstrate the effectiveness of the proposed method. The results obtained by the proposed algorithm are compared with other classical as well as modern heuristic techniques. It is found that the proposed method can produced improved results.

Knowledge Based Evolutionary Programming: Cultural Algorithm Approach for Constrained Optimization

A cultural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper. The practical problems of economic load dispatch have non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. Our approach is based on the concept of a cultural algorithm and is applied to constrained optimization problems in which a map of the feasible region is used to guide the search more efficiently. It combines cultural algorithm with evolutionary programming technique in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a domain knowledge (using the concept of cultural algorithm) is used as a fine tuning to determine the optimal solution at the final. The effectiveness and feasibility of the proposed method is tested on a practical thirteen generator system. Results obtained by the proposed method are compared with the other evolutionary methods. It is seen that the proposed method can produce comparable results.