Wind power parallel operation is an effective way to realize the large scale use of wind power, but the fluctuations of power output from wind power units may have great influence on power quality, hence a new method of power smoothing and capacity optimized allocation based on hybrid energy storage technology is proposed in terms of the uncontrollable and unexpected characteristics of wind speed in wind farms. First, power smoothing based on a traditional Inertial Filter is introduced and the relationship between the time constant, its smoothing effect and capacity allocation are analyzed and combined with Proportional Integral Differential (PID) control to realize power smoothing control of wind power. Then wavelet theory is adopted to realize a multi-layer decomposition of power output in some wind farms, a power smoothing model based on hybrid energy storage technology is constructed combining the characteristics of the Super Capacitor (SC) and Battery Energy Storage System (BESS) technologies. The hybrid energy storage system is available for power fluctuations with high frequency-low energy and low frequency-high energy to achieve good smoothing effects compared with a single energy storage system. The power fluctuations filtered by the Wavelet Transform is regarded as the target value of BESS, the charging and discharging control for battery is completed quickly by Model Algorithm Control (MAC). Because of the influence of the inertia and the response speed of the battery, its actual output is not completely equal to the target value which mainly reflects in high-frequency part, the difference part uses SC to compensate and makes the output of battery and SC closer to the target value on the whole. Compared with the traditional Inertial Filter and PID control method, the validity of the model was verified by simulation results. Finally under the premise of power grid standards, the corresponding capacity design had been given to reduce the size of the energy storage devices as far as possible, which has a certain practical engineering value.

At operated voltage, the movement behavior, partial discharge, and harmfulness of linear particle in different positions of GIS (high-voltage bus, shield ring, and insulator) are still not clear now. In order to study and solve this urgent engineering problem, the discharge signals, ultrasonic signal, and movement locus of linear particles were recorded and observed. The movement behavior, partial discharge characteristics, and harmfulness of metallic particle in different positions are analyzed and compared. The results suggest that: 1) at operated voltage, the movement behavior of linear particle has a great difference in different positions of GIS. The particle jumps with small height under the high-voltage bus, but the jumping frequency is very high and the time series of discharge signal has one-to-one correspondence with that of the ultrasonic signal. When the particle moves to the shielding ring, particle instantly jumps to the high-voltage electrode. At this time, the consistency between the discharge signal and the ultrasonic signal becomes worse. In addition, when the particle touches the insulator, it can attach to the insulator, never move, and become a fixed metallic particle. 2) When the metallic particle freely jumps under the high voltage bus and the shielding ring, the breakdown voltage of the GIS is still very high. However, when the particle attaches to the insulator, the creeping discharge of the particle will make it has serious harmfulness.

In leak noise correlation surveys, time delay estimation (TDE) is of great importance in pinpointing a suspected leak. Conventional TDE methods involve pre-filtering processes prior to performing cross-correlation, based on a priori knowledge of the leak and background noise spectra, to achieve the desired performance. Despite advances in recent decades, they have not proven to be capable of tracking changes in sensor signals as yet. This paper presents an adaptive phase transform method based on least mean square (LMS) algorithms for the determination of the leak location to overcome this limitation. Simulation results on plastic water pipes show that, compared to the conventional LMS method, the proposed adaptive method is more robust to a low signal-to-noise ratio. To further verify the effectiveness of the proposed adaptive method, an analysis is carried out on field tests of real networks. Moreover, it has been shown that by using the actual measured data, improved performance of the proposed method for pipeline leakage detection is achieved. Hence, this paper presents a promising method, which has the advantages of simple implementation and ability to track changes in practice, as an alternative technique to the existing correlation-based leak detection methods.