Recursive genetic algorithm-finite element method technique for the solution of transformer manufacturing cost minimisation problem

The transformer manufacturing cost minimisation (TMCM), also known as transformer design optimisation, 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 TMCM problem. The main contributions of the proposed method are: (a) introduction of an innovative recursive GA with a novel external elitism strategy associated with variable crossover and mutation rates resulting in an improved GA, (b) 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 (c) combination of the innovative recursive GA with the two particular finite element models resulting in a proposed GA-FEM 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?5.8% more expensive than the optimal solution.     

Environmental cost of distribution transformer losses

Improvements in energy efficiency of electrical equipment reduce the greenhouse gas (GHG) emissions and contribute to the protection of the environment. Moreover, as system investment and energy costs continue to increase, electric utilities are increasingly interested in installing energy-efficient transformers at their distribution networks. This paper analyzes the impact of the environmental cost of transformer losses on the economic evaluation of distribution transformers. This environmental cost is coming form the cost to buy GHG emission credits because of the GHG emissions associated with supplying transformer losses throughout the transformer lifetime. Application results on the Hellenic power system for 21 transformer offers under 9 different scenarios indicate that the environmental cost of transformer losses can reach on average 34% and 8% of transformer purchasing price for high loss and medium loss transformers, respectively. That is why it is important to incorporate the environmental cost of transformer losses into the economic evaluation of distribution transformers.     

ICARUS: hybrId inCentive mechAnism for coopeRation stimUlation in ad hoc networkS

Mobile ad hoc networks rely on the cooperation of nodes for routing and forwarding. However, individual nodes may not always be willing to cooperate. In order thus to stimulate cooperation in ad hoc networks, several incentive mechanisms have been developed. In this paper we propose a new hybrid incentive mechanism, called ICARUS, which is an extension of DARWIN, a well-known reputation-based mechanism, combining advantages of both reputation-based and credit-based mechanisms. The objective of ICARUS is to detect and punish selfish nodes efficiently and at the same time motivate nodes to cooperate by rewarding the packet forwarding. Furthermore, ICARUS ensures fairness for distant nodes and prevents selfish nodes from corrupting the system using false information. The proposed scheme’s performance is tested through extended series of simulations and is compared with DARWIN. We show that ICARUS detects and isolates selfish nodes much faster, while at the same time improves the Quality of Service (QoS) received by non-selfish nodes, including distant ones.     

Spotlight on transformer design

This paper presents an integrated artificial intelligence technique to achieve an optimum design of a transformer. AI is used to reach an optimum transformer design solution for the winding material selection problem. To be more precise, decision trees (DTs) and adaptive trained neural networks (ATNNs) are combined with the aim of selecting the appropriate winding material (Cu or Al) to design an optimum distribution transformer. Both methodologies have emerged as important tools for classification     

A Systematic Stochastic Petri Net Based Methodology for Transformer Fault Diagnosis and Repair Actions

Transformer fault diagnosis and repair is a complex task that includes many possible types of faults and demands special trained personnel. Moreover, the minimization of the time needed for transformer fault diagnosis and repair is an important task for electric utilities, especially in cases where the continuity of supply is crucial. In this paper, Stochastic Petri Nets are used for the simulation of the fault diagnosis process of oil-immersed transformers and the definition of the actions followed to repair the transformer. Transformer fault detection is realized using an integrated safety detector, in case of sealed type transformer that is completely filled with oil, while a Buchholz relay and an oil thermometer are used, in case of transformer with conservator tank. Simulation results for the most common types of transformer faults (overloading, oil leakage, short-circuit and insulation failure) are presented. The proposed Stochastic Petri Net based methodology provides a systematical determination of the sequence of fault diagnosis and repair actions and aims at identifying the transformer fault and estimating the duration for transformer repair.     

Impact of wind power forecasting error bias on the economic operation of autonomous power systems

Many efforts have been presented in the literature for wind power forecasting in power systems and few of them have been used for autonomous power systems. In addition, some recent studies have evaluated the impact on the operation of power systems and energy markets that the improvement of wind power forecasting can have. In this paper, the value of the information provided to the operators of autonomous power systems about forecasting errors is studied. This information may vary significantly, e.g. it can be only the normalized mean absolute error of the forecast, or a probability density function of the errors for various levels of forecasted wind power, which can be provided either during the evaluation phase of the wind power forecasting tool or by online uncertainty estimators. This paper studies the impact of the level of detail provided about wind power forecasting accuracy for various levels of load and wind power production. The proposed analysis, when applied to the autonomous power system of Crete, shows significant changes among the various levels of information provided, not only in the operating cost but also in the wind power curtailment. Copyright © 2008 John Wiley & Sons, Ltd.     

Differential evolution solution to transformer no-load loss reduction problem

After the completion of core manufacturing and before the assembly of transformer active part, 2N small individual cores and 2N large individual cores are available and have to be optimally combined into N transformers so as to minimise the total no-load loss (NLL) of N transformers. This complex combinatorial optimisation problem is called transformer no-load loss reduction (TNLLR) problem. A new approach combining differential evolution (DE) and multilayer perceptrons (MLPs) to solve TNLLR problem is proposed. MLPs are used to predict NLL of wound core distribution transformers. An improved differential evolution (IDE) method is proposed for the solution of TNLLR problem. The modifications of IDE in comparison to the simple DE method are (i) the scaling factor F is varied randomly within some range, (ii) an auxiliary set is employed to enhance the population diversity, (iii) the newly generated trial vector is compared with the nearest parent and (iv) the simple feasibility rule is used to treat the constraints. Application results show that the performance of the proposed method is better than that of two other methods, that is, conventional grouping process and genetic algorithm. Moreover, the proposed method provides 7.3% reduction in the cost of transformer main materials.     

A novel iron loss reduction technique for distribution transformersbased on a combined genetic algorithm - neural network approach

The paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network/genetic algorithm approach. The originality of the work presented is that it tackles the iron loss reduction problem during the transformer production phase, while previous works concentrated on the design phase. More specifically, neural networks effectively use measurements taken at the first stages of core construction in order to predict the iron losses of the assembled transformers, while genetic algorithms are used to improve the grouping process of the individual cores by reducing iron losses of assembled transformers. The proposed method has been tested on a transformer manufacturing industry. The results demonstrate the feasibility and practicality of this approach. Significant reduction of transformer iron losses is observed in comparison to the current practice leading to important economic savings for the transformer manufacturer     

Core lamination selection for distribution transformers based on sensitivity analysis

In this paper, the sensitivity analysis is used to select the core lamination thickness of single-phase distribution transformers rated from 5 to 50 kVA. Three different magnetic materials (M2, M3 and M4) with thicknesses of 0.18, 0.23 and 0.27 mm are considered. Transformer designs are compared based on the total owning cost as well as on the transformer bid price. The impact of the different laminations on total owning cost and bid price is calculated for a total of 144 transformers (72 for each criterion). All transformers fulfill all the operating and construction constraints. The paper considers the impact on core losses of the space factor (core-assembling pressure) and of the building factor and also describes how core losses are affected by core design parameters such as the number of laminations per step, air gap and overlap. It is concluded that for the analyzed power range, M3 lamination is the best choice since all of the studied cases have smaller bid price and 79% of the studied cases have lower total owning cost. This paper gives guidelines to select the appropriate thickness and can help transformer manufacturers to select the optimal thickness for distribution transformers.     

GENETIC ALGORITHM MODEL FOR PROFIT MAXIMIZATION OF GENERATING COMPANIES IN DEREGULATED ELECTRICITY MARKETS

In deregulated and rapidly changing electricity markets, there is strong interest on how to solve the new price-based unit commitment (PBUC) problem used by each generating company to optimize its generation schedule in order to maximize its profit. This article proposes a genetic algorithm (GA) solution to the PBUC problem. The advantages of the proposed GA are: 1) flexibility in modeling problem constraints because the PBUC problem is not decomposed either by time or by unit; 2) smooth and easier convergence to the optimum solution thanks to the proposed variable fitness function which not only penalizes solutions that violate the constraints but also this penalization is smoothly increasing as the number of generations increases; 3) easy implementation to work on parallel computers, and 4) production of multiple unit commitment schedules, some of which may be well suited to situations that may arise quickly due to unexpected contingencies. The method has been applied to systems of up to 120 units and the results show that the proposed GA constantly outperforms the Lagrangian relaxation PBUC method for systems with more than 60 units. Moreover, the difference between the worst and the best GA solution is very small, ranging from 0.10% to 0.49%.