Multi-Objective Optimal Planning for Distribution Network Considering the Uncertainty of PV Power and Line-Switch State.

With the construction of the smart grid, the distribution network with high penetration of the photovoltaic (PV) generator relies more and more on cyber systems to achieve active control; thus, the uncertainty of PV power and the line-switch state will inevitably affect the distribution network. To avoid the situation, a min-max multi-objective two-level planning model is proposed. Firstly, the uncertainty of PV power is considered, and a multi-time PV power model is established. Followed by the analysis of the line-switch state uncertainty in the distribution network, and according to Claude Shannon's information theory, the line-switch state uncertainty model is established under multiple scenarios. After the distribution network reconfiguration, the Latin hypercube sampling (LHS) method is used to determine the line-switch state when the uncertainty budget is different. Finally, considering the worstcase by the uncertainty of PV power and line-switch status, the control model is proposed to improve the stability of the distribution network with the minimal maintenance cost. The model feasibility is verified by the test system and the characteristics of PV power uncertainty, the line-switch state uncertainty is analyzed, and the influence of the scheduling strategy is discussed, thus providing practical technical support for the distribution network.

A New Denoising Method for UHF PD Signals Using Adaptive VMD and SSA-Based Shrinkage Method.

Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies.

[Research on Denoising Ultraviolet Spectrum Signal with An Improved Effective Singular Value Selection Method].

Spectrum denoising is an important part of spectrum detection. As we know, spectral signal is susceptible to thermal noise, mechanical vibration on site and random noise, etc. However, online monitoring systems require to reduce the impact of parameter selection caused by human operation on denoising, so a method based on singular value decomposition is proposed to denoise spectrum signal. An improved effective singular value selection method is also proposed. First, the author specify the maximum peak of the difference spectrum of singular value for the lower bound which named θ1, using the integrated information of singular value and its difference spectrum to select the upper bound, which is called θ2. The interval θ1～θ2 is defined as a fuzzy area. Then, the membership is obtained with Fuzzy C-means clusting and corresponding weight coefficients to the singular values in the fuzzy area are given. Finally, the proposed method is used to denoise UV spectrum signal with different signal to noise ratio. The signal to noise ratio, root mean square error, normalied correlation coefficient and smoothness radio are used to evaluate the result of denoising. The result shows that: based on data-driven, the proposed method has a good denoising effect, which can effectively restore the original signal.

[Early warning for various internal faults of GIS based on ultraviolet spectroscopy].

As the basis of accurate diagnosis, fault early-warning of gas insulation switchgear (GIS) focuses on the time-effectiveness and the applicability. It would be significant to research the method of unified early-warning for partial discharge (PD) and overheated faults in GIS. In the present paper, SO2 is proposed as the common and typical by-product. The unified monitoring could be achieved through ultraviolet spectroscopy (UV) detection of SO2. The derivative method and Savitzky-Golay filtering are employed for baseline correction and smoothing. The wavelength range of 290-310 nm is selected for quantitative detection of SO2. Through UV method, the spectral interference of SF6 and other complex by-products, e.g., SOF2 and SOF2, can be avoided and the features of trace SO2 in GIS can be extracted. The detection system is featured by compacted structure, low maintenance and satisfactory suitability in filed surveillance. By conducting SF6 decomposition experiments, including two types of PD faults and the overheated faults between 200-400 degrees C, the feasibility of proposed UV method has been verified. Fourier transform infrared spectroscopy and gas chromatography methods can be used for subsequent fault diagnosis. The different decomposition features in two kinds of faults are confirmed and the diagnosis strategy has been briefly analyzed. The main by-products under PD are SOF2 and SO2F2. The generated SO2 is significantly less than SOF2. More carbonous by-products will be generated when PD involves epoxy. By contrast, when the material of heater is stainless steel, SF6 decomposes at about 300 "C and the main by-products in overheated faults are SO2 and SO2F2. When heated over 350 degrees C, SO2 is generated much faster. SOz content stably increases when the GIS fault lasts. The faults types could be preliminarily identified based on the generation features of SO2.

[An ultraviolet spectroscopy method for rapid partial discharge detection in GIS].

By detecting the stable by-products of SF6 through ultraviolet spectroscopy, the present paper achieved the rapid detection of the GIS partial discharge fault. First derivative and the S-G filter were used for the spectral denoising and smoothing. The discharge experiment was used for validating feature selection. Principal component regression was used for the analysis of the concentration of SO2. The concentration of SO2 was used for fuzzy judge. By selecting the appropriate wavelength range (295~305 nm), ultraviolet spectrum can identify SO2 from the complex by-products of SF6. In this paper, firstly, the author reviewed the decomposition mechanism of SF6 under partial discharge, and then verified the rationality of detecting partial discharge by UV, and ultimately achieved the rapid detection of GIS partial discharge and fuzzy judgment of discharge time.