In this paper, a new high step-up current-fed LLC resonant DC-DC converter with a center-tapped transformer is proposed. By selecting the switching frequency to be lower than, but near to, the series resonant frequency of the LLC resonant tank, soft-switching operation of all semiconductors, i.e., zero VOLTAGE switching (ZVS) turn-on of power MOSFETs and zero current switching (ZCS) turn-off of diodes is achieved. This leads to lower electromagnetic interference (EMI) and lower switching LOSSES and improves the converter efficiency. An interleaved structure is used at the primary side. Thus, input current ripple is smaller, and its frequency is twice the switching frequency. Consequently, a smaller input filter is necessary in practice. The converter with 1.2 kW output power and 760 V regulated output VOLTAGE with 80-200 V input VOLTAGE variations is simulated. The output VOLTAGE is regulated by using asymmetric pulse width modulation (APWM) at 200 kHz switching frequency. Finally, a 700-W prototype has been implemented and experimental results are also presented to verify the simulation results.

Reactive power dispatch for VOLTAGE profile modification has been of interest to power utilities. Usually local bus VOLTAGEs can be altered by changing generator VOLTAGEs, reactive shunts, ULTC transformers and SVCs. Determination of optimum values for control parameters, however, is not simple for modern power system networks. Heuristic and rather intelligent algorithms have to be sought. In this paper a new algorithm is proposed that is based on a variant of a genetic algorithm combined with simulated annealing updates. In this algorithm a fuzzy multi-objective approach is used for the fitness function of the genetic algorithm. This fuzzy multi-objective function can efficiently modify the VOLTAGE profile in order to minimize transmission lines LOSSES, thus reducing the operating costs. The reason for such a combination is to utilize the best characteristics of each method and overcome their deficiencies. The proposed algorithm is much faster than the classical genetic algorithm and can be easily integrated into existing power utilities software. The proposed algorithm is tested on an actual system model of 1284 buses, 799 lines, 1175 fixed and ULTC transformers, 86 generators, 181 controllable shunts and 425 loads.

Transmission network expansion planning (TNEP) is an important component of power system planning. It determines the characteristics and performance of the future electric power network and influences the power system operation directly. Different methods have been proposed for the solution of the static transmission network expansion planning (STNEP) problem till now. But in all of them, STNEP problem considering the network LOSSES, VOLTAGE level and uncertainty in demand has not been solved by improved binary particle swarm optimization (IBPSO) algorithm. Binary particle swarm optimization (BPSO) is a new population-based intelligence algorithm and exhibits good performance on the solution of the large-scale and nonlinear optimization problems. However, it has been observed that standard BPSO algorithm has premature convergence when solving a complex optimization problem like STNEP. To resolve this problem, in this study, an IBPSO approach is proposed for the solution of the STNEP problem considering network LOSSES, VOLTAGE level, and uncertainty in demand. The proposed algorithm has been tested on a real transmission network of the Azerbaijan regional electric company and compared with BPSO. The simulation results show that considering the LOSSES even for transmission expansion planning of a network with low load growth is caused that operational costs decreases considerably and the network satisfies the requirement of delivering electric power more reliable to load centers. In addition, regarding the convergence curves of the two methods, it can be seen that precision of the proposed algorithm for the solution of the STNEP problem is more than BPSO.

One of the most suitable models for studing the electromagnetic transient behavior of the high VOLTAGE winding of the power transformers is the detailed model. This model consists of inductive and capacitive effects as well as the eddy current and dielectric LOSSES in the form of lumped parameters. The LOSSES have significant effects on the damping of transient over VOLTAGEs. In this paper, the eddy current LOSSES are expressed by a matrix which its elements satisfy the proximity effects and skin effects precisely. The matrix is calculated at low frequency by the finite element method (FEM) and then generalized to the high frequencies. simulation are presented for illustration and validation.

In the modern context of electricity market deregulation, the price of the kilowatt-hour must take both power injections and withdrawals of the multiple market participants into consideration, as well as their actual grid usage. The responsibility for causing transmission LOSSES and VOLTAGE drops therefore, needs to be fairly attributed. While grid power injections and withdrawals are unequivocally attributed, it remains to date impossibly to naturally share responsibility for transmission LOSSES. Relevant literature proposes a variety of methods. This paper proposes a new method for allocating transmission LOSSES to market participants by using the network. The overall grid LOSSES are obtained from summing the difference between injected and withdrawn power for all nodes. A set of allocation factors derived from the electrical distance between concerned buses and their VOLTAGE levels that is used to attribute active power loss to each bus, after the LOSSES which are arising from the mutual influencing between buses has been calculated. This method focuses on bus bar current injections and it assumes which there is a hypothetical power flow between nodes. For mutual influencing, one of the bus bars is considered a generator and the other is a load. A reference bus VOLTAGE is set and then the load side is penalized depending on how far its own VOLTAGE is lower than that reference value. Results from a sample network are compared to those of previous methods.