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Doubly Fed Induction Generator (DFIG) and Permanent Magnet Synchronous Generator (PMSG) are converter-interfaced wind turbines, which have much more complex short circuit behaviour after grid voltage drops. The research on short circuit current contributions provided by the wind turbines is very important for power system planning and relay protection's setting. Here, the Low Voltage Ride Through (LVRT) test data in north China are used to study the short circuit behaviour of DFIGs and PMSGs. The characteristics of two featured quantities, fundamental frequency component and peak current, are investigated. Unlike simulation or theoretical analysis, the field test data of real wind turbines from different manufacturers help utility operators to better understand the fault characteristics of converter-interfaced wind turbines.

According to the character of active distribution network that it could actively control and manage distributed energy resources, this paper puts forward a distribution network reconfiguration method for active distribution network. Because of the random power of distributed generation driven by renewable energy, this paper regards power of controllable load and electrical energy storage as controllable value and takes the effect of V2G(Vehicle-to-grid)into consideration, thus establishes a multi-objective optimization model. Based on knowledge of Graph Theory, this paper proposes a reconfiguration strategy containing island partition, and uses a hybrid algorithm of Prim algorithm and chaos particle swarm optimization to solve the model and optimize reconfiguration project. The validity of the proposed method of active distribution network reconfiguration is verified by simulation results of IEEE 33-node distribution system.

In China, large-scale wind power are usually integrated to the power system via long transmission lines, which is one of the most important development and utilization mode. With the increasing of installed capacity in wind power integration area, power quality problems, especially voltage unbalance rise. In this paper, an improved automatic voltage control (AVC) framework is proposed and the three-phase reactive power optimization model is developed considering wind power integration. Therefore, the goals of voltage profile and negative-sequence voltage depression are achieved. Finally, the efficiency are verified via a case study in North China.

Wind power forecasting (WPF) is an effective way to eliminate the negative impacts and enhance the market competitiveness of wind power. Probabilistic WPF gives the prediction intervals of possible output power, which may largely improve the confidence level and accuracy of the wind power forecasts. Considering the fluctuation characters of wind power, a combination scheme of different Gaussian kernels is used to establish the Gaussian Process Regression model, which is used to predict the mean value and the confidence interval of output power by taking wind speed and wind direction as input variates. Via the case analysis of a real wind farm in North China, results show that, the proposed Gaussian kernel scheme can well describe the fluctuation process of wind power, and the uncertain intervals produced by the established probabilistic WPF model basically able to cover the measured power. Compared with a typical statistical WPF model based on neural network, the improvement of monthly forecasting precision is up to 1.8 %.

An updated survey and statistical analysis were conducted about the petrographic compositions of Chinese commercial coals nationwide. 727 commercial coal samples were systematically collected from 25 provinces in China and the spatial distributions of the samples were consistent with the resources and capacities of the coal mines in China. The petrographic characteristics of different coal-forming ages, and coal-accumulating areas were analyzed in detail. The average petrographic compositions based on reserves weight were put forward. The weighted average contents of vitrinite, inertinite, liptinite were 61.3%, 36.5%, and 2.2%, respectively. Relatively high content of inertinite and low content of liptinite were found of Chinese commercial coals. The average contents of vitrinite were the highest in Cenozoic coals (93.5%) and Late Jurassic and Early Cretaceous coals (87.1%) while that was the lowest in Early and Middle Jurassic coals (52.4%). The average contents of vitrinite were the highest in Northeast (87%) and lowest in Shanxi-Shaanxi-Inner Mongolia-Ningxia (58.8%) and Northwest (59.9%). The coals in Northwest and Shanxi-Shaanxi-Inner Mongolia-Ningxia were characterized by high content of inertinite, which were mainly Early and Middle Jurassic coals. Diverse distributions of vitrinite and inertinite were found of coals in Northwest. Considering the layout of coal production, the vitrinite content will be decreased in the western areas and increased in the east areas.

Automatic identification of coal macerals has been a long-term pursuit for coal petrologists. As the rapid developments of computational processing capacities and algorithms in recent years, deep learning has been experiencing a considerable progress and successfully applied to image analysis. Semantic segmentation algorithms based on deep learning were introduced to make a pixel-level classification of maceral groups in Chinese bituminous coals. A high quality dataset with 739 representative petrographic images was established based on the characteristics of coal macerals in Chinese bituminous coal. The petrographic images were annotated into six classes, i.e., vitrinite, inertinite, liptinite, dark minerals (clay minerals), bright minerals (pyrite), and epoxy (background). The unbalanced proportions of annotated classes were calibrated by mathematical methods. Three classic semantic segmentation models, U-Net, SegNet, and DeepLab V3+, were used to make a pixel-level identification of the coal macerals. Pixel Accuracy (PA), Intersection over Union (IOU) and BFScore were introduced to evaluate the performances of the segmentation. The results have shown that the loss curves and accuracy curves of the three models in the training process rapidly converged after iterations. The training time of U-net, SegNet, and DeepLab V3 + was 172 min, 637 min, and 135 min respectively. The average pixel accuracy of the three models was 73%, 73%, and 92% respectively. The IoU and BFScore of DeepLab V3 + were also higher than those of U-Net and SegNet. The DeepLab V3 + was the preferred method for maceral group identification considering the training efficiency and segmentation accuracy. The maceral group compositions determined by DeepLab V3 + model achieved similar results as the manual point-counting method. The semantic segmentation method based on deep learning meet the accuracy requirements of quantitative analysis of coal maceral groups.

In weak power system with large-scale wind power integration, three-phase unbalance occurs frequently. Voltage unbalance may lead to more losses and heat to power system and deteriorate operation performance of wind turbine generators. In this paper, the voltage unbalance mechanism is investigated based on a field test case in North China. The technical features of three-phase voltage are extracted from field test data. An equivalent three-phase model is proposed, based on which, the theoretical derivation of voltage unbalance factor(VUF) is investigated and impacts of wind power on VUF are analyzed. Finally, the field practice model is built on matlab/simulink platform and the simulated three-phase voltage and VUF are compared with field test data.

Backgroud: Rheumatoid arthritis (RA), as an autoimmune disease, can eventually lead to joint deformity and loss of function, seriously reduce the quality of life of patients and increase economic burden. As a traditional Chinese therapy, warming acupuncture and moxibustion is safe, economical and has few side effects. At present, some studies have shown that warming acupuncture and moxibustion has a certain effect on RA, but there is no evidence of evidence-based medicine. The purpose of this study was to evaluate the efficacy and safety of warming acupuncture and moxibustion in the treatment of rheumatoid arthritis. Method: Randomized controlled trials of warming acupuncture and moxibustion treating RA will be searched in the databases including PubMed, EMBASE, the Cochrane library, Web of science, China National Knowledge Infrastructure(CNKI), WanFang, the Chongqing VIP Chinese Science and Technology Periodical Database (VIP), and China biomedical literature database(CBM) from inception to July, 2020. In addition, Baidu, Google Scholar, International Clinical Trials Registry Platform, and Chinese Clinical Trials Registry will be searched to obtain the gray literature and relevant data that have not yet been published. Two qualified researchers will extract data and assess the risk of bias from included studies independently. Statistical analysis is performed in RevMan 5.3 software. Results: The primary outcome is symptom evaluation including morning stiffness, pain and joint swelling. The number of joints affected by RA, Rheumatoid factor (RF), erythrocyte sedimentation rate (ESR), C reactive protei (CRP), anti-cyclic peptide containing citrulline (A-CCP), and adverse effects, will be evaluated as secondary outcomes. Conclusions: This study will compare the efficacy and safety of warming acupuncture and moxibustion with common acupuncture in the treatment of RA, providing reliable evidence for clinical application. Ethics and dissemination: The private information from individuals will not be published. This systematic review also will not involve endangering participant rights. Ethical approval is not required. The results may be published in a peer-reviewed journal or disseminated in relevant conferences. Keywords: warming acupuncture and moxibustion; rheumatoid arthritis; systematic review; meta-analysis; protocol

In order to ensure the micro-grid with distributed power to operate in a more economical, flexible and environmental way as well as the superiority of distributed power can be given full play. Aimed at the scheduling of the optimization of micro-grid, the paper propose the multi-objective optimization model that electric vehicles will be both a load and power generation unit at the same time, which aims to maximize the economic benefit and minimize the environmental cost. Using the non-objective sorting genetic algorithm NSGA-to solve the multi-objective optimization problems and obtain the Pareto front-end methods, finally, getting the best dispatch strategy of micro-grid. The validity of the proposed model, dispatch strategy and algorithm is proved by the examples.

New energy grid integration has become the main application field of large scale energy storage. The controllability of new energy power output can be effectively improved by using energy storage device with quick response capability. Aiming at the current status that the control target of wind-energy storage combined generation system is single and the reactive power control ability is not fully played, a coordinated control strategy of active power and voltage for large scale wind-energy storage combined generation system is proposed in this paper. The priority of active power and reactive power regulation is determined by the deviation severity of active power output and voltage. Active power feedback control strategy based on SOC interval reduction technique is also considered to avoid occurrence of overcharge and over discharge. Based on simulation model of the actual power system in DIgSILENT, multiple case simulation results show that the proposed control strategy can coordinate the regulation requirement of active power and voltage. The qualified rate of PCC's voltage and the ability of tracking the active power plan can be improved at the same time, which achieve a win-win goal of economic and security.

PD electromagnetic signal attenuation occurs when electromagnetic waves pass through a switchgear gap and propagate outward. Installing a sensor at the gap is a method of analyzing PD characteristics, as the sensor can receive the electromagnetic signals radiated from the switchgear. In order to study the impact of switchgear gaps on the propagation of UHF signals, we adopted the finite difference time domain method to conduct a modeling analysis of the attenuation characteristics of UHF signals and study the variation characteristics of the time domain parameters of radiation field intensity for UHF signals at the switchgear gap, as well as before and after the UHF signals propagate through the gap. The results show that the components of field intensity in each direction are different when the electromagnetic waves pass through the gap and the amplitude of electromagnetic wave attenuation is significant when the electromagnetic waves pass through the gap and propagate outward.

Ultra high frequency (UHF) and high frequency (HF) current partial discharge (PD) detection technology can detect PD phenomenon caused by internal defects of transformers, gas-insulated switchgear (GIS) and cable terminals and avoid insulation failure of the electrical equipment. Meanwhile, the Medical CT Imaging Technology provides reliable precise and intuitive measures for detecting human internal disease. Based on several real cases, this article raises the method to detect PD signal, diagnose and locate defects under the successful application of UHF, HF and Medical CT Imaging Technology in detecting and positioning internal gap defects of 110kv cable terminals and GIS. It also calculates PD distortion caused by internal gap defects using the Finite Field Simulation. All simulation results, field testing consequences, and the assembling of equipment with defects verify this method of diagnosis and detection efficient and scientific.

The paper establishes a defect simulation platform for multi-sensor 1100kVGIS, simulating four types of typical partial discharge defects within the GIS capsule and obtaining detection spectra of different types of defects as well as detection signal spectra of diversified position sensors. The attenuation characteristics of different position sensors were acquired under diversified voltage signals by utilizing steep pulse signal sources, as well as the relationship of equivalence between steep pulse injection signal amplitude and the discharge capacity of the discharge model, which provides a theoretical basis for field verification technology for a1100kVGIS on-line monitoring system.

In recent years, as a clean energy, wind power developed quickly. And the influence on the system of wind power is becoming more and more important; When the capacity of wind power system reaches a certain size, the wind power fluctuations will run out, and it could lead to a dynamic unbalance between energy output and load. Besides it could increase the system frequency modulation(FM) requirements, seriously it could lead to the system frequency out of limit, which endangers the safe operation of the power grid; In order to solve these problems, this paper puts forward a method which can calculate quantificationally. According to the historical load data sequence, power generation data sequence and error data, this paper builds the model of future study year's load and power generation, which based on the sequence of production simulation method, and the method can calculate the system Automatic Generation Control (AGC) capacity. Differ from the empirical method or rough statistical approach, the proposed method has advantages of high calculation precision and comprehensive consideration.

This paper presents a method of using nonlinear decision function to improve the performance of AdaBoost with SVM based weak learners. Compared with the existing AdaBoostSVM methods,this method, named ERBF-AdaBoostSVM, has advantages of higher hate rate and better generalization performance. This method also provides non-linear separator in the weak learner space and classifies accurately more examples. Experimental results demonstrated that ERBF-AdaBoostSVM achieve better generalization performance and higher hate rate than the existing SVM and AdaBoostSVM methods.