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High peak-to-average power ratio (PAPR) of transmitted signals is a major drawback for multi-carrier modulation (MCM) systems such as orthogonal frequency division multiplexing (OFDM) and wavelet packet division multiplexing (WPDM). Companding transform is an efficient and simple method to reduce the PAPR for MCM systems. In this paper, a novel adaptive companding transform scheme is proposed to efficaciously reduce the Peak-to-Average Power Ratio of OFDM and WPDM signals. In this technique, only the signals, whose amplitudes are higher than the threshold, are compressed in the transmitter, adaptively. We propose a new formula for the threshold level and send the threshold value to the receiver as side information. Using a simple judgment process, the expanding operation based on the transmitted side information will be executed to the compressed signals in the receiver. Computer simulation results show that the adaptive threshold companding (ATC) scheme effectively improves the performance of PAPR by introducing a simple adaptive threshold judgment process.

This paper proposes an Imperialist Competitive Algorithm (ICA) for optimal multiple distributed generations (DGs) placement and sizing in a distribution system. The objective is to minimize the total real power losses and improve the voltage profile within real and reactive power generation and voltage limits. Three types of DG are considered and the ICA is used to find the better sizes and locations of DGs for maximum real power losses reduction and voltage improvement for given number of DG units in each type. Both integer and continuous variables are considered in ICA, integer variable for locations and continues variable for sizes. The total real power losses and voltage profile evaluation are based on a power flow method for radial distribution system with the representation of DGs. The proposed method has been demonstrated on 33 bus radial distribution system. The efficiency of the ICA in reducing the total power losses and improving voltage is validated by comparing the obtained results with Particle Swarm Optimization (PSO) algorithm.

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Power companies world-wide have been restructuring their electric power systems from a vertically integrated entity to a deregulated, open-market environment. Previously, electric utilities usually sought to maximize the social welfare of the system with distributional equity as its main operational criterion. The operating paradigm was based on achieving the least-cost system solution while meeting reliability and security margins. This often resulted in investments in generating capacity operating at very low capacity factors. Decommissioning of this type of generating capacity was a natural outcome when the vertically integrated utilities moved over to deregulated market operations. This study proposes an optimizing base and load demand relative binding strategy for generating power apprises of different units in the investigated system. Afterwards, congestion effect in this biding strategy is investigated. The described systems analysis is implemented on 5 and 9 bus systems and optimizing technique in this issue is the Invasive Weed Optimization algorithm; the results are then compared by GA. Finally, examined systems is simulated by using the Power World software; experimental results show that the proposed technique (Invasive Weed Optimization) is a high performance by compared GA for the congestion management purposes.