Jet formation and penetration performance of a double-layer charge liner with chemically-deposited tungsten as the inner liner

This paper proposes a type of double-layer charge liner fabricated using chemical vapor deposition(CVD)that has tungsten as its inner liner.The feasibility of this design was evaluated through penetration tests.Double-layer charge liners were fabricated by using CVD to deposit tungsten layers on the inner surfaces of pure T2 copper liners.The microstructures of the tungsten layers were analyzed using a scanning electron microscope(SEM).The feasibility analysis was carried out by pulsed X-rays,slug-retrieval test and static penetration tests.The shaped charge jet forming and penetration law of inner tungsten-coated double-layer liner were studied by numerical simulation method.The results showed that the double-layer liners could form well-shaped jets.The errors between the X-ray test results and the numerical results were within 11.07%.A slug-retrieval test was found that the retrieved slug was similar to a numerically simulated slug.Compared with the traditional pure copper shaped charge jet,the penetration depth of the double-layer shaped charge liner increased by 11.4% and>10.8% respectively.In summary,the test results are good,and the numerical simulation is in good agreement with the test,which verified the feasibility of using the CVD method to fabricate double-layer charge liners with a high-density and high-strength refractory metal as the inner liner.

High-speed penetration of ogive-nose projectiles into thick concrete targets:Tests and a projectile nose evolution model

The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.

Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency

Shaped charge liner(SCL)has been extensively applied in oil recovery and defense industries.Achieving superior penetration capability through optimizing SCL structures presents a substantial challenge due to intricate rate-dependent processes involving detonation-driven liner collapse,high-speed jet stretching,and penetration.This study introduces an innovative optimization strategy for SCL structures that employs jet penetration efficiency as the primary objective function.The strategy combines experimentally validated finite element method with machine learning(FEM-ML).We propose a novel jet penetration efficiency index derived from enhanced cutoff velocity and shape characteristics of the jet via machine learning.This index effectively evaluates the jet penetration performance.Furthermore,a multi-model fusion based on a machine learning optimization method,called XGBOOST-MFO,is put forward to optimize SCL structure over a large input space.The strategy's feasibility is demonstrated through the optimization of copper SCL implemented via the FEM-ML strategy.Finally,this strategy is extended to optimize the structure of the recently emerging CrMnFeCoNi high-entropy alloy conical liners and hemispherical copper liners.Therefore,the strategy can provide helpful guidance for the engineering design of SCL.

The penetration depth of atomic radicals in tubes with catalytic surface properties

Catalysis of molecular radicals is often performed in interesting experimental configurations.One possible configuration is tubular geometry.The radicals are introduced into the tubes on one side,and stable molecules are exhausted on the other side.The penetration depth of radicals depends on numerous parameters,so it is not always feasible to calculate it.This article presents systematic measurements of the penetration depth of oxygen atoms along tubes made from nickel,cobalt,and copper.The source of O atoms was a surfatron-type microwave plasma.The initial density of O atoms depended on the gas flow and was 0.7×10^(21)m^(-3),2.4×10^(21)m^(-3),and 4.2×10^(21)m^(-3)at the flow rates of 50,300,and 600 sccm,and pressures of 10,35,and 60 Pa,respectively.The gas temperature remained at room temperature throughout the experiments.The dissociation fraction decreased exponentially along the length of the tubes in all cases.The penetration depths for well-oxidized nickel were 1.2,1.7,and 2.4 cm,respectively.For cobalt,they were slightly lower at 1.0,1.3,and 1.6 cm,respectively,while for copper,they were 1.1,1.3,and 1.7 cm,respectively.The results were explained by gas dynamics and heterogeneous surface association.These data are useful in any attempt to estimate the loss of molecular fragments along tubes,which serve as catalysts for the association of various radicals to stable molecules.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Contemporary strategies and approaches for characterizing composition and enhancing biofilm penetration targeting bacterial extracellular polymeric substances

Extracellular polymeric substances(EPS)constitutes crucial elements within bacterial biofilms,facili-tating accelerated antimicrobial resistance and conferring defense against the host's immune cells.Developing precise and effective antibiofilm approaches and strategies,tailored to the specific charac-teristics of EPS composition,can offer valuable insights for the creation of novel antimicrobial drugs.This,in turn,holds the potential to mitigate the alarming issue of bacterial drug resistance.Current analysis of EPS compositions relies heavily on colorimetric approaches with a significant bias,which is likely due to the selection of a standard compound and the cross-interference of various EPS compounds.Considering the pivotal role of EPS in biofilm functionality,it is imperative for EPS research to delve deeper into the analysis of intricate compositions,moving beyond the current focus on polymeric materials.This ne-cessitates a shift from heavy reliance on colorimetric analytic methods to more comprehensive and nuanced analytical approaches.In this study,we have provided a comprehensive summary of existing analytical methods utilized in the characterization of EPS compositions.Additionally,novel strategies aimed at targeting EPS to enhance biofilm penetration were explored,with a specific focus on high-lighting the limitations associated with colorimetric methods.Furthermore,we have outlined the challenges faced in identifying additional components of EPS and propose a prospective research plan to address these challenges.This review has the potential to guide future researchers in the search for novel compounds capable of suppressing EPS,thereby inhibiting biofilm formation.This insight opens up a new avenue for exploration within this research domain.

Physics-data coupling-driven method to predict the penetration depth into concrete targets

The projectile penetration process into concrete target is a nonlinear complex problem.With the increase ofexperiment data,the data-driven paradigm has exhibited a new feasible method to solve such complex prob-lem.However,due to poor quality of experimental data,the traditional machine learning(ML)methods,whichare driven only by experimental data,have poor generalization capabilities and limited prediction accuracy.Therefore,this study intends to exhibit a ML method fusing the prior knowledge with experiment data.The newML method can constrain the fitting to experimental data,improve the generalization ability and the predic-tion accuracy.Experimental results show that integrating domain prior knowledge can effectively improve theperformance of the prediction model for penetration depth into concrete targets.

Error field penetration in J-TEXT tokamak based on two-fluid drift-MHD model

An externally generated resonant magnetic perturbation can induce complex non-ideal MHD responses in their resonant surfaces.We have studied the plasma responses using Fitzpatrick's improved two-fluid model and program LAYER.We calculated the error field penetration threshold for J-TEXT.In addition,we find that the island width increases slightly as the error field amplitude increases when the error field amplitude is below the critical penetration value.However,the island width suddenly jumps to a large value because the shielding effect of the plasma against the error field disappears after the penetration.By scanning the natural mode frequency,we find that the shielding effect of the plasma decreases as the natural mode frequency decreases.Finally,we obtain the m/n=2/1 penetration threshold scaling on density and temperature.

Mechanism of acupoint penetration acupuncture therapy regulating chondrocyte autophagy via the PI3K/Akt-mTOR pathway in KOA rats

Objective:To investigate whether acupoint penetration acupuncture(APA)could regulate chondrocyte autophagy and apoptosis via the PI3K/Akt-mTOR signaling pathway to reduce cartilage degeneration in knee osteoarthritis(KOA)rats.Methods: KOA was induced in rats via intra-articular injection of sodium iodoacetate resolution.Forty male Sprague-Dawley rats were randomly assigned to blank control,model,APA,electro-acupuncture(EA),and sham model groups(n=8)and those in the APA and EA groups received their respective therapies.Following completion of the treatment course,histological examinations of cartilage and muscle were conducted.Levels of apoptosis-and autophagy-related factors,including Bax,Bcl-2,mTOR,ULK-1,and Beclin-1 protein,and mRNAs were assessed.Additionally,β-endorphin(β-EP)concentrations in the brain and serum were measured.Results: Histological analysis revealed that APA alleviated cartilage and muscle damage compared with the model group.APA inhibited cartilage degeneration by modulating the expression of apoptosis-and autophagy-related proteins and mRNA,thus preventing chondrocyte apoptosis.In the APA group,Bax and mTOR protein levels were significantly lower than those in the model group(both P=.024).Conversely,the Bcl-2 expression level was significantly higher than that in the EA group(P=.035).Additionally,ULK-1 expression was significantly lower than that in the EA group(P=.045).The mRNA level of Bax was significantly higher than that in the blank control group(P<.001).However,Beclin-1 levels were significantly higher than those in both the model and EA groups(both P<.001).ELISA results showed a significant decrease in the concentration ofβ-EP in the brains of the rats in the APA group compared with those in the model group(P=.032).Conclusions: APA reduced osteoarthritis-related pain and alleviated cartilage damage by upregulating chondrocyte autophagy and down-regulating apoptosis via signaling pathways involving PI3K/Akt-mTOR in KOA rats.

Association between intercellular adhesion molecule-1 to depression and blood-brain barrier penetration in cerebellar vascular disease

BACKGROUND Cerebral small vessel disease(CSVD)is a prevalent cerebrovascular disease in clinical practice that is often associated with macrovascular disease.A clear understanding of the underlying causes of CSVD remains elusive.AIM To explore the association between intercellular adhesion molecule-1(ICAM-1)and blood-brain barrier(BBB)penetration in CSVD.METHODS This study included patients admitted to Fuyang People’s Hospital and Fuyang Community(Anhui,China)between December 2021 and March 2022.The study population comprised 142 patients,including 80 in the CSVD group and 62 in the control group.Depression was present in 53 out of 80 patients with CSVD.Multisequence magnetic resonance imaging(MRI)and dynamic contrast-enhanced MRI were applied in patients to determine the brain volume,cortical thickness,and cortical area of each brain region.Moreover,neuropsychological tests including the Hamilton depression scale,mini-mental state examination,and Montreal cognitive assessment basic scores were performed.RESULTS The multivariable analysis showed that age[P=0.011;odds ratio(OR)=0.930,95%confidence interval(CI):0.880-0.983]and ICAM-1 levels(P=0.023;OR=1.007,95%CI:1.001-1.013)were associated with CSVD.Two regions of interest(ROIs;ROI3 and ROI4)in the white matter showed significant(both P<0.001;95%CI:0.419-0.837 and 0.366-0.878)differences between the two groups,whereas only ROI1 in the gray matter showed signi-ficant difference(P=0.046;95%CI:0.007-0.680)between the two groups.ICAM-1 was significantly correlated(all P<0.05)with cortical thickness in multiple brain regions in the CSVD group.CONCLUSION This study revealed that ICAM-1 levels were independently associated with CSVD.ICAM-1 may be associated with cortical thickness in the brain,predominantly in the white matter,and a significant increase in BBB permeability,proposing the involvement of ICAM-1 in BBB destruction.

Enhancing Mobile Security through Comprehensive Penetration Testing

In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.

Fundamental Study on Alkali-Activated Slag System with Sodium Carbonate or Calcium Hydroxide

Cement as a building material, has high fluidity, compressive strength, and durability, but carbon dioxide emissions during cement production are a major problem. As one of the countermeasures, alkali-activated cement using blast furnace slag powder with alkaline stimulants is considered to be a very promising solution for reducing carbon dioxide emissions, but there is a lack of information about the fundamental properties of alkali-activated materials. This study presents an experimental investigation of the fundamental properties of an alkali-activated slag system with sodium carbonate (NC) and calcium hydroxide (CH). The effects of calcium sulfo-aluminate (CSA) and shrinkage reducing agent (SRA) on the properties of blast furnace slag (BFS) based alkali-activated mixture were also investigated. In the experiments, fundamental characteristics including compressive strength, drying shrinkage, and water penetration tests of mortar were evaluated. Porosity, pH, and ignition loss were measured to verify the effectiveness of the materials. The experimental investigation revealed that the compressive strength was increased with the increasing replacement rates of NC in the BFS mortar, and in the case of water to BFS ratio of 0.45 with sodium carbonation addition contents 10 wt.%, the compressive strength for 28 days of curing reaches more than 50 MPa. Low water to BFS ratio and higher addition ratio of NC had a positive effect on the compressive strength development of mortar. Incorporating NC into BFS would affect the decrease in porosity and increase in ignition loss, leading to higher compressive strength. There was a negligible change to the compressive strength, porosity, pH, and ignition loss of BFS samples made with CH, thus, the addition rates of CH to BFS have no or little significant effect on the fundamental properties of alkali-activated cement. From the results of drying shrinkage and water penetration tests, the addition of NC and CH only to BFS exhibited poor drying shrinkage and water penetration characteristics. However, these problems may be overcome due to the use of CSA or SRA in the alkali-activated system made with NC or CH.

Power system planning with high renewable energy penetration considering demand response

Electric system planning with high variable renewable energy(VRE)penetration levels has attracted great attention world-wide.Electricity production of VRE highly depends on the weather conditions and thus involves large variability,uncertainty,and low-capacity credit.This gives rise to significant challenges for power system planning.Currently,many solutions are proposed to address the issue of operational flexibility inadequacy,including flexibility retrofit of thermal units,inter-regional transmission,electricity energy storage,and demand response(DR).Evidently,the performance and the cost of various solutions are different.It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source.In this study,the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed.Two types of DRs,namely interrupted DR and transferred DR,were modeled.Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization.Clustered unit commitment constraints for accommodating variability of renewables were incorporated.Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) performance.Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering.This study aims to predict TBM performance(i.e.FPI) by an efficient and improved adaptive neuro-fuzzy inference system(ANFIS) model.This was done using an evolutionary algorithm,i.e.artificial bee colony(ABC) algorithm mixed with the ANFIS model.The role of ABC algorithm in this system is to find the optimum membership functions(MFs) of ANFIS model to achieve a higher degree of accuracy.The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index(BI),fracture spacing,α angle between the plane of weakness and the TBM driven direction,and field single cutter load were assigned as model inputs to approximate FPI values.According to the results obtained by performance indices,the proposed ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model.In terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC model,respectively,which confirm its power and capability in solving TBM performance problem.The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.

SEM Comparison of Penetration in Artificial White Spots Lesion between an Infiltrant Resin and Two Adhesive Systems

White spot infiltration emerged as an alternative of non-invasive treatment to halt progression of the lesion, through the use of low viscosity resins that would permeate the porous enamel and form a physical barrier that would prevent the acid diffusion produced by micro-organisms. Purpose: To compare penetration levels in artificial white spot lesions, of infiltrant resin ICON&trade;and 2 conventional adhesives systems, XP-Bond&trade;and Single Bond 2&trade;. Methodology: White spot lesions (ICDAS code 2) were caused in 75 premolars or third molars were extracted in good conditions, by immersion in a 0.1 M lactic acid solution (pH 4.5) at 37℃ for 8 weeks. They were divided randomly into 3 groups of 25 samples and applied the following resins, Group A: ICON&trade;, B: XP-Bond&trade;and C: Single Bond 2&trade;. Subsequently, the enamel was removed with hydrochloric acid to expose resin saturated area and the samples were metalized with Au-Pd for SEM observation. The resin tags lengths were measured on microphotographs through software, and the values were analyzed with the statistics ANOVA and Scheffé post-test. Results: There were significant differences (p &trade;(82.7 μm ± 26.8 μm) compared to adhesive systems XP-Bond&trade;(58.5 μm ± 29.3 μm) and Single Bond 2&trade;(44.8 μm ± 32.5 μm). We found no significant differences between the two adhesive systems (p > 0.05). Conclusion: Under the conditions tested, the penetration of infiltrant ICON was significantly higher than the adhesive systems;however, it removes the surface layer of the enamel.

Prediction of rotary drilling penetration rate in iron ore oxides using rock engineering system

Prediction of the drilling penetration rate is one of the important parameters in mining operations. This parameter has a direct impact on the mine planning and cost of mining operations, Generally, effective parameters on the penetration rate is divided into two classes： rock mass properties and specifications of the machine, The chemical components of intact rock have a direct effect in determining rock mechan- ical properties, Theses parameters usually have not been investigated in any research on the rock drill- ability, In this study, physical and mechanical properties of iron ore were studied based on the amount of magnetite percent, According to the results of the tests, the effective parameters on the pen- etration rate of the rotary drilling machines were divided into three classes： specifications of the machi- nes, rock mass properties and chemical component of intact rock, Then, the rock drillahility was studied using rock engineering systems, The results showed that feed, rotation, rock mass index and iron oxide percent have important effect on penetration rate, Then a quadratic equation with 0,896 determination coefficient has been obtained, Also, the results showed that chemical components can he described as new parameters in rotary drill penetration rate,

Accommodating High PV Penetration on the Distribution System of Kinmen Island

A large-scale PV installation has a profound effect on the power quality and operation of the grid. Therefore, as installations of PV systems increase, changes to both technologies and interconnection standards are needed to address these high PV penetration systems. This paper investigates the main technical impacts of large-scale PV generations on power grids and associated interconnection standards. A case study by using the PSS/E software in the Kinmen power system was implemented to study the steady-state and transient characteristics for the high penetration PV systems.