Marginal loss coefficient method is a loss allocation method based on the principle of microeconomics and the assigned power flow solutions. It is one of the most typical and prospective methods to allocate transmission losses in regional electricity market because it can give correct economic signals and improve economic results. However, the effect of node-injected reactive power and the allocation of the losses caused by node-injected reactive power are not considered in most practical methods. For this reason, the basic principle and mathematical model of marginal loss coefficient method considering the influence of node-injected reactive power on the allocation of losses in electricity market is presented, and the loss allocation formulae in which whether the node-injected reactive power is considered or not are deduced and analyzed. The results of simulations of IEEE 14- bus, 30-bus system and the simulation of the peak-, middle- and valley-loads in a certain practical 2243-bus system show that the effects of node-injected reactive power increase along with the enlargement of power system scale. In conclusion, the ignoring of node-injected reactive power may result in the comparative error of loss allocation, especially for large scale power system. Thus, the importance and necessity of considering the effects of node-injected reactive on loss allocation are explained.

The suppression of interference is crucial in onsite Partial Discharge (PD) monitoring. This paper presents a new algorithm to reduce narrow band noise coupled into PD measurements. The algorithm is based on wavelet packet analysis and is capable of automatically detecting narrow band interference without requiring prior knowledge of detailed noise characteristics. Simulations are provided as well as some results obtained during laboratory experiment and on-line PD measurements for a power transformer. The efficiency of reducing narrow band noises is measured by comparing the frequency spectrum before and after filtering with the new method.

The paper puts forward a new method based on local cosine transform (LCT) to measure the time-varying harmonics in power system. Since the time-frequency window of LCT is localized well and adjustable, the method can quantify the frequency, amplitude and phase of time-varying harmonics more accurately than DFT and STFT. The time-frequency window is adjusted conveniently by using optimum base searching method. Moreover, is not necessary to have stable wave assumption in the window. The simulations and field tests show that LCT is a powerful tool and suitable for practical use.

The paper put forwards a method for selecting signal-adapted wavelet in de-noising. The method is based on constructing compacted orthogonal wavelets with Wu elimination algorithm. A threshold information cost function is used as the objective function during choosing optimal wavelet. The approach is validated by its application to de-noising for on-line PD measurement. The physical experiments and field tests show that the method improves the accuracy of the partial discharge (PD) extraction with less deformation and stronger robustness.