Performance Comparison of Multi Design Method and Meta-Heuristic Methods for Optimal Preliminary Design of Core-Form Power Transformers (original) (raw)

Innovative Aspects of Power Transformer Design Optimization

Shodh Ganga, 2017

Power transformer is the utmost important and costliest component of the power system. The design of a transformer can be optimized for its cost, no load loss, efficiency, weight, etc. However, the respective customer specifications have to be taken into account. Generally, researchers find optimum design by fixing several of the parameters. In this proposed work, besides K-factor and flux density, the geometry of winding conductor, number of axial and radial conductors and number of discs of LV as well as HV winding are also considered as design variables. The constraints are imposed on total cost, load loss, percentage impedance, efficiency, Winding Gradient, Tank deflections and no load loss. As the numbers of design variables are twelve, the problem cannot be solved by traditional optimization methods. So, Genetic Algorithm (GA), an efficient evolutionary algorithm, is used to optimize. Stray loss is extremely challenging factor while optimizing design. So 3D-FEM based analysis is carried out on same GA based transformer design. The design obtained was compared with the actual industrial design and found effective in terms of cost as well as efficiency. This work comprises the excel based software development for Transformer design as per conventional mathematical formulas, Industrial code of practice and applicable national and international standards. The optimization methods like GA,PSO,TLBO and the engineering analysis method like FEM is considered for this research work. Self-developed Binary encoded GA,PSO and TLBO is applied to drive the transformer design optimization. The multi objective optimization technique is applied for this work to conclude transformer design which has low cost and high efficiency. 15 MVA 66/11 kV transformer design data is referred to develop software based program of Transformer design and respective TDO methodology. On the base of developed TDO, the real transformer of 10 MVA 66/11 kV has been developed and tested at NABL laboratory.

Discrete design optimization of distribution transformers with guaranteed optimum convergence using the cuckoo search algorithm

2017

Transformer design optimization methods presented in the literature rarely yield solutions directly applicable in production; the design engineer usually needs to convert the theoretical solution to a practical one. This problem is addressed in this paper, and a discrete transformer design optimization method is proposed that yields solutions with commercially available or productionally feasible dimensions, thus eliminating the need for further efforts of the design engineer to make the theoretical solution a feasible one. The cuckoo search, a nature-inspired metaheuristic algorithm, is used as the optimization algorithm in this study, and it is shown that the guaranteed global optimum solution is attained in a single run. Furthermore, a simple method is proposed to reduce the number of objective function and constraint calculations. The method is based on skipping calculations for design vectors recurring during the search process by use a caching technique. It is envisaged that t...

FEM Based Preliminary Design Optimization in Case of Large Power Transformers

Since large power transformers are custom-made, and their design process is a labor-intensive task, their design process is split into different parts. In tendering, the price calculation is based on the preliminary design of the transformer. Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Most of the published algorithms are using a copper filling factor based winding model to calculate the main dimensions of the transformer during this first, preliminary design step. Therefore, these cost optimization methods are not considering the detailed winding layout and the conductor dimensions. However, the knowledge of the exact conductor dimensions is essential to calculate the thermal behaviour of the windings and make a more accurate stray loss calculation. The paper presents a novel, evolutionary algorithm-based transformer optimization method which can determine the optimal conductor shape for the w...

Global transformer optimization method using evolutionary design and numerical field computation

This paper addresses the complex optimum transformer design problem, which is formulated as a mixed-integer nonlinear programming problem, by introducing an integrated design optimization method based on evolutionary algorithms and numerical electromagnetic and thermal field computations. The main contributions of this research are: i) introduction of a new overall transformer optimization method, minimizing either the overall transformer materials cost or the overall transformer materials and operating cost, ii) expansion of the solution space by innovative techniques that define the variation of crucial design variables such as the conductors' cross-section, ensuring global optimum transformer designs, and iii) incorporation of numerical field computation in order to validate the feasibility of the optimum designs. The proposed method is compared with a heuristic optimization method of the transformer manufacturing industry and the results demonstrate the robustness and the superiority of this new approach.

A Simple and Efficient Optimization Routine for Design of High Frequency Power Transformers

An efficient, meanwhile simple optimization routine is presented for design of high frequency power transformers. Minimizing the product of power loss, core cross section and winding area is chosen as the optimization goal. This object function leads to a closed form solution, which reduces the computation time and the number of iterations. A CAD tool has been developed according to this method, which is addressed in this paper. By this tool the optimized core which fulfills the temperature rise constraint is determined. If the area product of the selected core is bigger than the optimum area product, the user can modify his design further by minimizing the power loss. The validation of the method is verified by a sample design example.

A performance-oriented power transformer design methodology using multi-objective evolutionary optimization

Journal of Advanced Research, 2014

Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper.

An Evolutionary Computation Solution to Transformer Design Optimization Problem

7th International Conference on Electrical and Electronics Engineering Research (CIIIEE), 2010

The transformer design optimization (TDO) is a complex constrained mixed-integer non-linear programming problem with discontinuous objective function. This paper proposes an innovative method combining genetic algorithm (GA) and finite element method (FEM) for the solution of TDO problem. The main contributions of the proposed method are: (i) introduction of an innovative recursive GA with a novel external elitism strategy associated with variable crossover and mutation rates resulting in an improved GA, (ii) adoption of two particular finite element models of increased accuracy and high computational speed for the validation of the optimal design by computing the no-load loss and impedance, and (iii) combination of the innovative recursive GA with the two particular finite element models resulting in a proposed GAFEM model that finds the global optimum, as concluded after several tests on actual transformer designs, while other existing methods provided suboptimal solutions that are 3.1% to 5.8% more expensive than the optimal solution.

Design Optimization of High-Frequency Power Transformer by Genetic Algorithm and Simulated Annealing

International Journal of Electrical and Computer Engineering (IJECE), 2011

This paper highlights the transformer design optimization using genetic algorithm (GA) and simulated annealing (SA). Any optimization problem, a given objective function is to be minimized keeping in view the constraints. Similarly, transformer design optimization problem involves minimizing the total mass (or cost) of the core and wire material by satisfying constraints imposed by international standards and transformer user specification. The constraints include appropriate limits on efficiency, voltage regulation, temperature rise, no-load current and winding fill factor. The design optimizations seek a constrained minimum mass (or cost) solution by optimally setting the transformer geometry parameters and require magnetic properties. This paper solves the said design problem by using genetic algorithm (GA) and simulated annealing (SA) techniques. The results of geometric programming (GP) technique have been compared with the results obtained by applying GA and SA techniques. It is quite evident from the results that the dimensions as well as mass of copper and core have been reduced in comparison to GP using same set of constraints. Therefore, the paper presents improved design of power transformer by these two techniques. The results of GA and SA have been obtained using optimization tool box MATLAB Release 9.1 which have not been applied for power transformer design so far. First it provides efficient and reliable solution for the design optimization problem with several variables. Second, it guaranteed that the obtained solution is global optimum. Hence paper demonstrates a better and efficient solution to high frequency power transformer design.

Design optimization of distribution transformers with nature-inspired metaheuristics: a comparative analysis

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES

Many economies in the world have adopted energy-efficiency requirements or incentive programs mandating or promoting the use of energy-efficient transformers. On the other hand, increases in transformer efficiency are subject to increases in transformer weight and size, sometimes as much as 50% or more. The transformer manufacturing industry is therefore faced with the challenge to develop truly optimum designs. Transformer design optimization (TDO) is a mixedinteger nonlinear programming problem having a complex and discontinuous objective function and constraints, with the objective of detailed calculation of the characteristics of a transformer based on national and/or international standards and transformer user requirements, using available materials and manufacturing processes, to minimize manufacturing cost or total owning cost while maximizing operating performance. This paper gives a detailed comparative analysis of the application of five modern nature-inspired metaheuristic optimization algorithms for the solution of the TDO problem, demonstrated on three test cases, and proposes two algorithms, for which it has been verified that they possess guaranteed global convergence properties in spite of their inherent stochastic nature. A pragmatic benchmarking scheme is used for comparison of the algorithms. It is expected that the use of these two algorithms would have a significant contribution to the reduction of the design and manufacturing costs of transformers.

OPTIMIZATION ASPECTS OF TRANSFORMER DESIGN

Master of Engineering Thesis GTU India, 2015

Looking at the development in the construction, manufacturing process, variety of materials and application of transformer, it is necessary to focus on technology of transformer design. The transformer design proposed in the current work is capable to manage with the existing complex power system network. Also, the main aim of optimization of transformer is to fulfill all the design criteria and minimizing the manufacturing cost. The optimization of transformer using various tools and methods has been discussed in the report. In dissertation phase-I, two methods for optimization in active part design were approached. First is iterative programming method and second is using optimization toolbox in MATLAB. In the first method, some design variants are created and by varying them, based on application point of view the selection of optimum design was carried out. In second method, the optimization toolbox in MATLAB gives the final value of variable which satisfies the optimum design requirements. In dissertation Phase-II, the design of tank is included in design of active part. Here, also two methods are approached for optimization of transformer for minimization of cost. Multiobjective optimization is also used by considering two objective functions as cost minimization and No-load losses minimization by Non-Dominating Sorting Genetic Algorithm-II(NSGA-II).