The wavelet transform, the S-transform, the Gabor transform, and the Wigner distribution function are popular techniques for power quality (PQ) analysis in electrical power systems. They are mainly used to identify power harmonics and power disturbances and to estimate power quantities in the presence of nonstationary power components such as root-mean-square values and total harmonic distortions. Recently, the Hilbert-Huang transform has been also used in PQ analysis. These techniques have proven to be useful in PQ analysis; however, their performances depend on the types of PQ events. In this paper, a novel frequency-shifting wavelet decomposition via the Hilbert transform is introduced for PQ analysis. The proposed algorithm overcomes the spectra leakage problem in the discrete wavelet packet transform and can be used for estimating power quantities accurately and for detecting flickers. The effectiveness of the proposed algorithm was verified by computer simulations and experimental tests.

The widespread penetration of electric vehicles (EVs) will cause an increase in energy demand. To achieve an effective demand response approach, the participation of EV users is a crucial factor. Nevertheless, the discharging price found in the literature is generally set in accordance with the spot electricity price. A fair pricing strategy is thus desirable to motivate EV users to regulate their charging behavior for less bill payment, and at the same time to achieve load regulation as well as benefit for power companies. The charging and discharging prices should be cohesively equilibrated to achieve the task. This paper presents a new V2G (Vehicle-to-Grid) pricing policy by incorporating the system load condition, maximum power limit, and price rate for user load in a fair manner. The price setting follows a hierarchical optimization procedure between the operator and end users with the aim to maximize operator's profit and balance end users' bill and comfort level. The optimization for the operator is achieved via genetic algorithm whereas the energy management of each user is decomposed as a single power optimization problem. Simulation results have confirmed the benefit of liberating the discharging price from the user load tariff and have verified the effectiveness of the proposed method.

This paper presents a novel algorithm for real-time continuous electrical waveform compression and transmission. The proposed algorithm achieves a high data compression ratio by processing the stationary and non-stationary components of a power waveform separately. It works on integer data which simplifies the computation process, and can be run on a low-cost digital signal processor. The proposed algorithm has been implemented on a prototype meter successfully. Experimental and extensive field tests have confirmed the effectiveness of the algorithm. The prototype smart meter can communicate through a low-power, low-bit-rate sensor network protocol such as ZigBee.

This paper presents a novel coreless current sensor unit for measuring electrical current of circular conductors. The proposed sensor unit consists of three magnetic field sensors and a microcontroller unit (MCU). The new sensor unit does not need a bulky magnetic core necessary for magnetic field concentration, and can be used in the same way as conventional current transformers (CTs). An algorithm is developed to calibrate the outputs of the magnetic field sensors (e.g., Hall effect integrated circuits (ICs) with respect to the relative position of the current-carrying circular conductor. The algorithm can be implemented on a low-cost MCU for automatic real-time operation. Unlike other coreless magnetic field sensors that need to be fixed onto the conductor for accurate current measurement, the proposed sensor unit affords a more flexible installation option. A hardware prototype is constructed for the proposed coreless current sensor unit. The performance evaluation results show that the accuracy of the proposed sensor unit is comparable to the conventional magnetic core CTs.