Influence of canopy ratio of powered roof support on longwall working stability——A case study

The case study describes longwall coal seam A in a hard coal mine,where longwall coal face stability loss and periodic roof fall occurrences had been registered.The authors have attempted to explain the situation based on in-situ measurements and observations of the longwall working as well as numerical simulation.The calculations included several parameters,such as powered roof support geometry in the form of the canopy ratio,which is a factor that influences load distribution along the canopy.Numerical simulations were realized based on a rock mass model representing realistic mining and geological conditions at a depth of 600 m below surface for coal seam A.Numerical model assumptions are described,while the obtained results were compared with the in-situ measurements.The conclusions drawn from this work can complement engineering knowledge utilized at the stage of powered roof support construction and selection in order to improve both personnel safety and longwall working stability,and to achieve better extraction.

Mathematical model of electric hydraulic and powered support control system at a plough mining face

Given the actual working of a fully mechanized plough at a mining face, we have proposed a formula for running constraints between powered supports and a coal plough under assumed geological conditions of the coal face and, on this basis, established an automatic control model of powered supports for the coal plough face. We introduced the working principle of the powered support control system of the plough at the mining face. We established three advanced characteristics of this control system： response speed, reliability and easy maintenance of the system. As well, we briefly introduced, the principal function of primary and subordinate controllers and the realization of the communication system by a Single Bus. Ten controllers were constructed and tested in our laboratorium. The results show that the control model is practical and meets actual conditions. It provides a theoretical basis for designing a comouter control system for a oowered support system of a plough at a mining face.

Consideration of the influence of supports in modeling the electromagnetic fields of 25 kV traction networks under emergency conditions

Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits exist between the contact wire and rails or ground.Despite the short duration of exposure,they can adversely affect electronic devices and induce significant voltages in adjacent power lines,which is dangerous for operating personnel.Although numerous investigations have focused on modeling the EMF of traction networks and power lines,the challenge of determining the three-dimensional electromagnetic fields near metal supports during the flow of a short-circuit current through them is yet to be resolved.In this case,the field has a complex spatial structure that significantly complicates the calculations of intensities.This study proposes a methodology,algorithms,software,and digital models for determining the EMF in the described emergency scenarios.During the modeling process,the objects being studied were represented by segments of thin wires to analyze the distribution of the electric charge and calculate the intensities of the electric and magnetic fields.This approach was implemented in the Fazonord software,and the modeling results show a substantial increase in EMF levels close to the support,with a noticeable decrease in the levels as the distance from it increases.The procedure implemented in the commercial software Fazonord is universal and can be used to determine electromagnetic fields at any electrical power facility that includes live parts of limited length.Based on the proposed procedure,the EMF near the supports of overhead power lines and traction networks of various designs could be determined,the EMF levels at substations can be calculated,and the influence of metal structures located near traction networks,such as pedestrian crossings at railway stations,can be considered.

Methods for measuring and calculating the external load of powered supports

Taking the chock shield supports as the object of study, methods formeasuring and calculating the external load of powered supports were discussed. Selecting the angleparameters as variables, the simple formulae of interactive computation with respect to the workangles of a powered support were deduced and verified by an example. Furthermore, the formulaedetermining the magnitude, direction and action point of the external load were put forward. Theinvestigation results have provided a sound basis for the software design of the intelligentinstrument for load measuring of powered supports.

Improved AVOA based on LSSVM for wind power prediction

Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.

Experimental study of a technique for load measurement of powered supports

Despite the increasing popularity of mechanized coal mining, there are no convenient and accurate means available to measure the loads of powered supports. The measurement of such loads is important for monitoring mine pressure and ensuring production safety. The load-carrying features of a powered support were used to develop a method for load measurement using the mag-netoelastic principle. A cross bridge-type magnetoelastic stress sensor was designed for the support structures to measure the different parts of the supports. Tests on single-body hydraulic cylinders and simulated linkages showed that an approximately linear relationship between the values of the sensor output signal and the loads borne by the hydraulic cylinders or linkages. The results were used to analyze the load-carrying measurements of powered supports with the cross bridge-type magnetoelastic stress sensor.

FLUID INDUCED HYDRAULIC SYSTEM VIBRATIONOF POWERED SUPPORT

Fluid and solid interaction analysis of hydraulic support under the coming pressure of roof rocks was presented. The mathematical model of the system was proposed and numerical studies by the character line method were carried out.

Automatic lifting system design and application of large load powered support test rig

This paper presented a design of an automatic lifting system. It is used for large load powered support and improves the old method wherein powered support lifting depends on manual control. This system applies a high accuracy gear shunt motor to match the flow for 4 lifting cylinders, and also allocates bypass throttles to realize automatic lifting. Through the dis- placement sensor feedback the height deviation among 4 lifting cylinders during the whole lifting process, when the deviation is up to the sitting value, the corresponding bypass throttle is operated immediately to reduce the deviation, so that the moving platform of the powered support would not be stuck. Through real application, it is shown that this system can realize automatic lifting of powered support; the lifting speed is controlled between 5 and 10 mm/s, and the final aligning accuracy is up to 1 mm.

Maximal loads acting on legs of powered roof support unit in longwalls with bumping hazards

In the article the results of measurements of the resultant force in the legs of a powered roof support unit, caused by a dynamic interaction of the rock mass, are discussed. The measurements have been taken in the longwalls mined with a roof fall, characterized by the highest degree of bumping hazard. It has been stated that the maximal force in the legs F m, recorded during a dynamic interaction of the rock mass, is proportional to the initial static force in the legs F st,p . Therefore a need for a careful selection of the initial load of the powered roof support, according to the local mining and geological conditions, results from such a statement. Setting the legs with the supporting load exceeding the indispensable value for keeping the direct roof solids in balance, deteriorating the operational parameters of a longwall system also has a disadvantageous influence on the value of the force in the legs and the rate of its increase, caused by a dynamic interaction of the rock mass. A correct selection of the initial load causes a decrease in the intensity of a dynamic interaction of the rock mass on powered roof supports, which also has an advantageous influence on their life. Simultaneously with the measurements of the resultant force in the legs, the vertical acceleration of the canopy was also recorded. It has enabled to prove that the external dynamic forces may act on the unit both from the roof as well as from the floor. The changes of the force in the legs caused by dynamic phenomena intrinsically created in the roof and changes of the force in the legs caused by blasting explosives in the roof of the working, have been analyzed separately. It has been stated that an increase in the loads of legs, caused by intrinsic phenomena is significantly higher than a force increase in the legs caused by blasting. It means that powered roof supports, to be operated in the workings, where the bumping hazard occurs, will also transmit the loads acting on a unit during blasting. The majority of recorded force changes in the legs has been caused by a dynamic interaction of the roof. They are characterized by a load increase coefficient K d, satisfying the inequality 1 06<K d =F m /F st,p <1 24. A much smaller number of cases, when the external load acted on the bases, was recorded. Individual, recorded results of measurements indicate that changes of the force in the legs, caused by external loads of this type, run more intensively due to roof loads (1 08< K d<1 80),particularly in these cases when the near the roof layer of the seam is under mining. A determination of more precise relations among the changes of forces in the legs, caused by a dynamic interaction of the floor and the bases and the mining and geological conditions requires a performance of additional underground tests.

Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.

Classification of power quality combined disturbances based on phase space reconstruction and support vector machines

Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.

Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine

An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.

Application of Slantlet Transform Based Support Vector Machine for Power Quality Detection and Classification

Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfunction and production stoppages which lead to huge financial loss for the production company. The deregulation of electricity markets has made the industry become more competitive and distributed. Thus, a higher demand on reliability and quality of services will be required by the end customers. To ensure the power supply is at the highest quality, an automatic system for detection and localization of PQ activities in power system network is required. This paper proposed to use Slantlet Transform (SLT) with Support Vector Machine (SVM) to detect and localize several PQ disturbance, i.e. voltage sag, voltage swell, oscillatory-transient, odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus odd-harmonics, voltage sag plus transient and pure sinewave signal were studied. The analysis on PQ disturbances signals was performed in two steps, which are extraction of feature disturbance and classification of the dis- turbance based on its type. To take on the characteristics of PQ signals, feature vector was constructed from the statistical value of the SLT signal coefficient and wavelets entropy at different nodes. The feature vectors of the PQ disturbances are then applied to SVM for the classification process. The result shows that the proposed method can detect and localize different type of single and multiple power quality signals. Finally, sensitivity of the proposed algorithm under noisy condition is investigated in this paper.

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.

A Decision Support System for Selection of Solar Power Plant Locations by Applying Fuzzy AHP and TOPSIS: An Empirical Study

The objective of this research is to propose a decision support system for avoiding flood on solar power plant site selection. Methodologically, the geographic information system (GIS) is used to determine the optimum site for a solar power plant. It is intended to integrate the qualitative and quantitative variables based upon the adoption of the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. These methods are employed to unite the environmental aspects and social needs for electrical power systematically. Regarding a case study of the choice of a solar power plant site in Thailand, it demonstrates that the quantitative and qualitative criteria should be realized prior to analysis in the Fuzzy AHP-TOPSIS model. The fuzzy AHP is employed to determine the weights of qualitative and quantitative criteria that can affect the selection process. The adoption of the fuzzy AHP is aimed to model the linguistic unclear, ambiguous, and incomplete knowledge. Additionally, TOPSIS, which is a ranking multi-criteria decision making method, is employed to rank the alternative sites based upon overall efficiency. The contribution of this paper lies in the evolution of a new approach that is flexible and practical to the decision maker, in providing the guidelines for the solar power plant site choices under stakeholder needs: at the same time, the desirable functions are achieved, in avoiding flood, reducing cost, time and causing less environmental impact. The new approach is assessed in the empirical study during major flooding in Thailand during the fourth quarter of 2011 to 2012. The result analysis and sensitivity analysis are also presented.

基于Power BI的数据分析在三级公立医院绩效考核管理中的研究与应用

介绍了在国家三级公立医院绩效考核的大背景下,针对考核指标从院级到临床科室的分解,以及科室快速迭代的数据分析需求,探寻了一种基于Power BI工具的数据分析方式,该方式对科室的管理决策起到辅助作用,并在国考指标的PDCA闭环管理上取得成效。实践证明,Power BI是医院数据分析工作的优选工具,在医院数据分析业务中具有广泛应用前景。

Short-Term Wind Power Prediction Using Fuzzy Clustering and Support Vector Regression

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly, wind energy is unlimited in potential. However due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. In this paper, an SVR （support vector regression） using FCM （Fuzzy C-Means） is proposed for wind speed forecasting. This paper describes the design of an FCM based SVR to increase the prediction accuracy. Proposed model was compared with ordinary SVR model using balanced and unbalanced test data. Also, multi-step ahead forecasting result was compared. Kernel parameters in SVR are adaptively determined in order to improve forecasting accuracy. An illustrative example is given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

An LCC-HVDC Adaptive Emergency Power Support Strategy Based on Unbalanced Power On-Line Estimation

HVDC auxiliary power control can significantly improve the transient stability of AC/DC power grid.An HVDC adaptive emergency power support method based on unbalanced power on line estimation is proposed in this paper.By establishing the extended state equation of the system,the on line dynamic estimation of unbalanced power of the system was realized.On this basis,power support was realized based on the principle of the ladder increment.The optimal DC was selected by the power support factor,and the emergency power support controller was installed on the DC.This emergency power support method can realize dynamic optimal power support with minimized control cost.The three infeed HVDC system was built on PSCAD.The simulation results show the effectiveness of the proposed method.

EMS Supporting System for Dispatching Automation in Northeast Power Network

The EMS supporting system of thesecond phase extension project for NortheastElectric Network dispatching automationsystem, co-developed by Electric PowerResearch Institute. Ministry of ElectricPower, and Northeast China Electric PowerGroup, is an open type, object oriented CC-2000 EMS/DMS supporting system withChinese proprietary copyright and anadvanced international level. It passed thetechnical appraisal of Ministry of ElectricPower in Oct. 1996. This EMS supportingsystem includes four parts f managementenvironment of the system operation.database management system. big object forpower system, man-machine interface. Theexperience of developing and operationindicates that the EMS supporting system haspowerful functions, the performance index isvery high, the operation is stable and reliable.the ability of supporting EMS/DMS is morepowerful than the first phase system. Thedevelopment work of the super applicationsoftware of Northeast Power Network EMS(Energy Management System) will befinished in 1997 on the supporting system.thereby an integrated and open EMS of ourcountry’s own copyright will be completed.

Fault diagnosis of power-shift steering transmission based on multiple outputs least squares support vector regression

A method of multiple outputs least squares support vector regression （LS-SVR） was developed and described in detail, with the radial basis function （RBF） as the kernel function. The method was applied to predict the future state of the power-shift steering transmission （PSST）. A prediction model of PSST was gotten with multiple outputs LS-SVR. The model performance was greatly influenced by the penalty parameter γ and kernel parameter σ2 which were optimized using cross validation method. The training and prediction of the model were done with spectrometric oil analysis data. The predictive and actual values were compared and a fault in the second PSST was found. The research proved that this method had good accuracy in PSST fault prediction, and any possible problem in PSST could be found through a comparative analysis.