Showing papers on "Insulator (electricity) published in 2022"

Echo state network applied for classification of medium voltage insulators

Abstract: Insulators are components of electrical power grid that have the function of mechanically supporting cables and isolating electrical potential. The proper functioning of the insulators is essential for the continuity in the supply of electrical energy. When an insulator has its properties damaged, disruptive discharges may shut down the system and impairs the network’s reliability. For this reason, classifying adverse conditions is a critical task to keep the system running. In this paper, the echo state network is used for the classification of the insulators based on the ultrasound signal. Hypertuning is applied to automate the evaluation of the parameters and optimize the network to have a general application. The insulators are evaluated in the laboratory under controlled conditions from the application of 7.95 kV (phase-to-ground), under the same conditions that are found in the field. The assessment is made on perforated and contaminated insulators, which were removed from service due to defects. The echo state network achieves 87.36 % accuracy for the multiclassification and 99.99 % for the specific classification of drilling. For comparative analysis, the multilayer perceptron and support-vector machines are evaluated based on the fast fourier transform. The results show that echo state network is promising to classify the evaluated conditions, being more accurate than the multilayer perceptron and support-vector machines based on fast fourier transform.

Correlated interlayer exciton insulator in heterostructures of monolayer WSe2 and moiré WS2/WSe2

Abstract: Moiré superlattices in van der Waals heterostructures have emerged as a powerful tool for engineering quantum phenomena. Here we report the observation of a correlated interlayer exciton insulator in a double-layer heterostructure composed of a WSe2 monolayer and a WS2/WSe2 moiré bilayer that are separated by ultrathin hexagonal boron nitride. The moiré WS2/WSe2 bilayer features a Mott insulator state when the density of holes is one per moiré lattice site. When electrons are added to the Mott insulator in the WS2/WSe2 moiré bilayer and an equal number of holes are injected into the WSe2 monolayer, a new interlayer exciton insulator emerges with the holes in the WSe2 monolayer and the electrons in the doped Mott insulator bound together through interlayer Coulomb interactions. The interlayer exciton insulator is stable up to a critical hole density in the WSe2 monolayer, beyond which the interlayer exciton dissociates. Our study highlights the opportunities for realizing quantum phases in double-layer moiré systems due to the interplay between the moiré flat band and strong interlayer electron interactions. When independent layers of electrons and holes are in close proximity to each other, their Coulomb interaction allows them to pair into neutral bosons and form an insulating state. This phenomenon is reported in a heterostructure of 2D materials.

Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model

Abstract: Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on the Internet, combining with the image augmentation method based on GraphCut. The insulator images were preprocessed by Laplace sharpening method. To solve the problems of too many parameters and low detection speed of the YOLOv4 object detection model, the MobileNet lightweight convolutional neural network was used to improve YOLOv4 model structure. Combining with the transfer learning method, the insulator image samples were used to train, verify, and test the improved YOLOV4 model. The detection results of transmission line insulator and defect images show that the detection accuracy and speed of the proposed model can reach 93.81% and 53 frames per second (FPS), respectively, and the detection accuracy can be further improved to 97.26% after image preprocessing. The overall performance of the proposed lightweight YOLOv4 model is better than traditional object detection algorithms. This study provides a reference for intelligent inspection and defect detection of suspension insulators on transmission lines.

Electric control of a canted-antiferromagnetic Chern insulator

Abstract: The interplay between band topology and magnetism can give rise to exotic states of matter. For example, magnetically doped topological insulators can realize a Chern insulator that exhibits quantized Hall resistance at zero magnetic field. While prior works have focused on ferromagnetic systems, little is known about band topology and its manipulation in antiferromagnets. Here, we report that MnBi2Te4 is a rare platform for realizing a canted-antiferromagnetic (cAFM) Chern insulator with electrical control. We show that the Chern insulator state with Chern number C = 1 appears as the AFM to canted-AFM phase transition happens. The Chern insulator state is further confirmed by observing the unusual transition of the C = 1 state in the cAFM phase to the C = 2 orbital quantum Hall states in the magnetic field induced ferromagnetic phase. Near the cAFM-AFM phase boundary, we show that the dissipationless chiral edge transport can be toggled on and off by applying an electric field alone. We attribute this switching effect to the electrical field tuning of the exchange gap alignment between the top and bottom surfaces. Our work paves the way for future studies on topological cAFM spintronics and facilitates the development of proof-of-concept Chern insulator devices.

CRISPR and biochemical screens identify MAZ as a cofactor in CTCF-mediated insulation at Hox clusters

Abstract: CCCTC-binding factor (CTCF) is critical to three-dimensional genome organization. Upon differentiation, CTCF insulates active and repressed genes within Hox gene clusters. We conducted a genome-wide CRISPR knockout (KO) screen to identify genes required for CTCF-boundary activity at the HoxA cluster, complemented by biochemical approaches. Among the candidates, we identified Myc-associated zinc-finger protein (MAZ) as a cofactor in CTCF insulation. MAZ colocalizes with CTCF at chromatin borders and, similar to CTCF, interacts with the cohesin subunit RAD21. MAZ KO disrupts gene expression and local contacts within topologically associating domains. Similar to CTCF motif deletions, MAZ motif deletions lead to derepression of posterior Hox genes immediately after CTCF boundaries upon differentiation, giving rise to homeotic transformations in mouse. Thus, MAZ is a factor contributing to appropriate insulation, gene expression and genomic architecture during development.

In vivo dissection of a clustered-CTCF domain boundary reveals developmental principles of regulatory insulation

Abstract: Abstract Vertebrate genomes organize into topologically associating domains, delimited by boundaries that insulate regulatory elements from nontarget genes. However, how boundary function is established is not well understood. Here, we combine genome-wide analyses and transgenic mouse assays to dissect the regulatory logic of clustered-CCCTC-binding factor (CTCF) boundaries in vivo, interrogating their function at multiple levels: chromatin interactions, transcription and phenotypes. Individual CTCF binding site (CBS) deletions revealed that the characteristics of specific sites can outweigh other factors such as CBS number and orientation. Combined deletions demonstrated that CBSs cooperate redundantly and provide boundary robustness. We show that divergent CBS signatures are not strictly required for effective insulation and that chromatin loops formed by nonconvergently oriented sites could be mediated by a loop interference mechanism. Further, we observe that insulation strength constitutes a quantitative modulator of gene expression and phenotypes. Our results highlight the modular nature of boundaries and their control over developmental processes.

CRISPR and biochemical screens identify MAZ as a cofactor in CTCF-mediated insulation at Hox clusters

Abstract: CCCTC-binding factor (CTCF) is critical to three-dimensional genome organization. Upon differentiation, CTCF insulates active and repressed genes within Hox gene clusters. We conducted a genome-wide CRISPR knockout (KO) screen to identify genes required for CTCF-boundary activity at the HoxA cluster, complemented by biochemical approaches. Among the candidates, we identified Myc-associated zinc-finger protein (MAZ) as a cofactor in CTCF insulation. MAZ colocalizes with CTCF at chromatin borders and, similar to CTCF, interacts with the cohesin subunit RAD21. MAZ KO disrupts gene expression and local contacts within topologically associating domains. Similar to CTCF motif deletions, MAZ motif deletions lead to derepression of posterior Hox genes immediately after CTCF boundaries upon differentiation, giving rise to homeotic transformations in mouse. Thus, MAZ is a factor contributing to appropriate insulation, gene expression and genomic architecture during development.

Comparison of artificial intelligence techniques to failure prediction in contaminated insulators based on leakage current

Abstract: Contamination in insulators results in an increase in surface conductivity. With higher surface conductivity, insulators are more vulnerable to discharges that can damage them, thus reducing the reliability of the electrical system. One of the indications that the insulator is losing its insulating properties is its increase in leakage current. By varying the leakage current over time, it is possible to determine whether the insulator will develop an irreversible failure. In this way, by predicting the increase in leakage current, it is possible to carry out maintenance to avoid system failures. For forecasting time series, there are many models that have been studied and the definition of which model is suitable for evaluation depends on the characteristics of the data associated with the analysis. Thus, this work aims to identify the most suitable model to predict the increase in leakage current in relation to the time the insulator is outdoors, exposed to environmental variations using the same database to compare the methods. In this paper, the models based on linear regression, support vector regression (SVR), multilayer Perceptron (MLP), deep neural network (DNN), and recurrent neural network (RNN) will be analyzed comparatively. The best accuracy results for prediction were found using the RNN models, resulting in an accuracy of up to 97.25%.

An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery

Abstract: For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to improve the prediction accuracy of small target insulator position information. Then, three transposed convolution modules were added for upsampling, aiming to better restore the semantic information and position information of the image. Finally, the insulator dataset (ID) constructed by ourselves and the public dataset (CPLID) were used for model training and validation, aiming to improve the generalization ability of the model. The experimental results showed that compared with the CenterNet model, MobileNet_CenterNet improved the detection accuracy by 12.2%, the inference speed by 1.1 f/s for FPS-CPU and 4.9 f/s for FPS-GPU, and the model size was reduced by 37 MB. Compared with other models, our proposed model improved both detection accuracy and inference speed, indicating that the MobileNet_CenterNet model had better real-time performance and robustness.

Up to 300 K lasing with GeSn-On-Insulator microdisk resonators.

Abstract: GeSn alloys are the most promising direct band gap semiconductors to demonstrate full CMOS-compatible laser integration with a manufacturing from Group-IV materials. Here, we show that room temperature lasing, up to 300 K, can be obtained with GeSn. This is achieved in microdisk resonators fabricated on a GeSn-On-Insulator platform by combining strain engineering with a thick layer of high Sn content GeSn.

Polarity Reversal and Over Voltage Affecting Discharge Inception of Tri-Post Insulator in ±800 kV GIL

Abstract: Surface charge and electric field distributions of a tri-post insulator in ±800-kV gas-insulated transmission line (GIL) are calculated under the superimposed dc-impulse voltage and the polarity reversal voltage. The theoretical discharge inception voltage of the tri-post insulator under the abovementioned conditions is estimated by the volume-time theory. The accumulated homocharges on the insulator surface significantly relax the electric field at the dc steady state, thus improving the theoretical discharge inception voltage under dc superimposed homopolar impulse voltage but reducing that under dc superimposed heteropolar impulse voltage. When the voltage polarity is reversed in two minutes, the intensified electric field on the insulator surface shifts from the grounded (GND) electrode toward the vicinity of the high voltage (HV) electrode, in which the maximum electric field grows by 21.4% and increases as the reversal time shortens. The micro-discharge around the particle trap may induce the accumulation of heterocharges on the insulator surface between the lower two posts, aggravating the electric field distortion in the surrounding region and degrading its insulation performance.

CTCF is a DNA-tension-dependent barrier to cohesin-mediated loop extrusion

Abstract: In eukaryotes, genomic DNA is extruded into loops by cohesin1. By restraining this process, the DNA-binding protein CCCTC-binding factor (CTCF) generates topologically associating domains (TADs)2,3 that have important roles in gene regulation and recombination during development and disease1,4-7. How CTCF establishes TAD boundaries and to what extent these are permeable to cohesin is unclear8. Here, to address these questions, we visualize interactions of single CTCF and cohesin molecules on DNA in vitro. We show that CTCF is sufficient to block diffusing cohesin, possibly reflecting how cohesive cohesin accumulates at TAD boundaries, and is also sufficient to block loop-extruding cohesin, reflecting how CTCF establishes TAD boundaries. CTCF functions asymmetrically, as predicted; however, CTCF is dependent on DNA tension. Moreover, CTCF regulates cohesin's loop-extrusion activity by changing its direction and by inducing loop shrinkage. Our data indicate that CTCF is not, as previously assumed, simply a barrier to cohesin-mediated loop extrusion but is an active regulator of this process, whereby the permeability of TAD boundaries can be modulated by DNA tension. These results reveal mechanistic principles of how CTCF controls loop extrusion and genome architecture.

Insulator-Defect Detection Algorithm Based on Improved YOLOv7

Abstract: Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes of the insulator dataset are clustered based on K-means++ to generate more suitable anchor boxes for detecting insulator-defect targets. Secondly, the Coordinate Attention (CoordAtt) module and HorBlock module are added to the network. Then, in the channel and spatial domains, the network can enhance the effective features of the feature-extraction process and weaken the ineffective features. Finally, the SCYLLA-IoU (SIoU) and focal loss functions are used to accelerate the convergence of the model and solve the imbalance of positive and negative samples. Furthermore, to optimize the overall performance of the model, the method of non-maximum suppression (NMS) is improved to reduce accidental deletion and false detection of defect targets. The experimental results show that the mean average precision of our model is 93.8%, higher than the Faster R-CNN model, the YOLOv7 model, and YOLOv5s model by 7.6%, 3.7%, and 4%, respectively. The proposed YOLOv7 model can effectively realize the accurate detection of small objects in complex backgrounds.

Field-Dependent Pollution Model under Polluted Environments for Outdoor Polymeric Insulators

Abstract: In-depth understanding of the pollution problems such as dry bands and the polymeric aging process requires better determination of electric field strength and its distribution over the polymeric surface. To determine the electric field distribution over the insulator surface, this research proposes utilizing a novel approach model based on nonlinear electrical characteristics derived from experimental results for polluted polymer insulators. A case study was carried out for a typical 11 kV polymeric insulator to underline the merits of this new modeling approach. The developments of the proposed pollution model and the subsequent computational works are described in detail. The study is divided into two main stages; laboratory measurements and computer simulations. In the first stage, layer conductance tests were carried out to develop nonlinear field-dependent conductivity for the pollution modeling. In the second part, equipotential and electric field distributions along the leakage were computed using the finite element method (FEM). Comparative field studies showed that the simulation using the proposed dynamic pollution model results in more detailed and realistic field profiles around insulators. This may be useful to predict the formation of dry bands and the initiation of electrical discharges on the polymeric surface.

A Charge-Based Analytical Model for Gate All Around Junction-Less Field Effect Transistor Including Interface Traps

Abstract: This article proposes an analytical charge-based model that incorporates interface trapping. The model's applicability to all operating zones includes various interface trap charges with varying doping concentrations. Using the analytical model, the impact of interface traps on different electrical parameters, such as channel potential, surface potential, electric field, and drain current, is examined. The transconductance and cut-off frequency models are also developed from the drain current model. To validate our model, the analytical model results were compared with TCAD device simulation results and available experimental data from the literature. The Fermi level location of interface traps greatly influences surface potential in the bandgap, leading to subthreshold deterioration and flat band shifting in Junction-Less Field Effect Transistor (GAAJLFET) with SiO2 as a gate insulator, which leads to performance degradation of different device parameters. To decrease the impact of the interface trap on the device's characteristics without impairing the performance, a suitable device with SiO2 and high-k gate-stack as an insulator is designed and compared with GAAJLFET with SiO2 as a gate insulator. A GAAJLFET with SiO2 as an insulating material has very different device parameters than a GAAJLFET with SiO2 and high-k gate-stack as a gate insulating material.

Efficient Electronic Tunneling Governs Transport in Conducting Polymer-Insulator Blends

Abstract: Electronic transport models for conducting polymers (CPs) and blends focus on the arrangement of conjugated chains, while the contributions of the nominally insulating components to transport are largely ignored. In this work, an archetypal CP blend is used to demonstrate that the chemical structure of the non-conductive component has a substantial effect on charge carrier mobility. Upon diluting a CP with excess insulator, blends with as high as 97.4 wt % insulator can display carrier mobilities comparable to some pure CPs such as polyaniline and low regioregularity P3HT. In this work, we develop a single, multiscale transport model based on the microstructure of the CP blends, which describes the transport properties for all dilutions tested. The results show that the high carrier mobility of primarily insulator blends results from the inclusion of aromatic rings, which facilitate long-range tunneling (up to ca. 3 nm) between isolated CP chains. This tunneling mechanism calls into question the current paradigm used to design CPs, where the solubilizing or ionically conducting component is considered electronically inert. Indeed, optimizing the participation of the nominally insulating component in electronic transport may lead to enhanced electronic mobility and overall better performance in CPs.

Study on non‐uniformity and dynamic fracture characteristics of GIL tri‐post insulators considering Al 2 O 3 sedimentation

Abstract: Tri-post insulators in gas-insulated transmission line (GIL) are usually fabricated with high mass fraction of micron aluminum oxide (Al2O3), which will unavoidably settle under the action of gravity during the preparation process and seriously affect the uniformity. A model of filler sedimentation in epoxy resin composite materials was proposed based on the particle size analysis and Stokes' Law. Some scaled tri-post insulators were prepared and tested by slicing. It is determined that the position of density concentration is the lower side of the two lower posts and the upper interface between the insulator and the conductor. The density ranges from 2.144 to 2.346 g/cm3. The dynamic fracture simulation model of insulator was established and it is found that the insulator fracture occurs at the interface of upper post/insert under radial load, which is verified by experiments. By comparing the influence of sprue position on the density distribution, it is found that the uniformity of insulators is increased by 13.7% by forward pouring compared with reverse pouring. This research develops an accurate method for simulating the filler sedimentation and the fracture process in epoxy-based insulators, which is helpful for the improvement of mechanical reliability of GIL.

Isospin competitions and valley polarized correlated insulators in twisted double bilayer graphene

Abstract: New phase of matter usually emerges when a given symmetry breaks spontaneously, which can involve charge, spin, and valley degree of freedoms. Here, we report an observation of new correlated insulators evolved from spin-polarized states to valley-polarized states in twisted double bilayer graphene (TDBG) driven by the displacement field (D). At a high field |D | > 0.7 V/nm, we observe valley polarized correlated insulators with a big Zeeman g factor of ~10, both at v = 2 in the moiré conduction band and more surprisingly at v = -2 in the moiré valence band. Moreover, we observe a valley polarized Chern insulator with C = 2 emanating at v = 2 in the electron side and a valley polarized Fermi surface around v = -2 in the hole side. Our results demonstrate a feasible way to realize isospin control and to obtain new phases of matter in TDBG by the displacement field, and might benefit other twisted or non-twisted multilayer systems.

Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning

Abstract: Insulators are an important part of transmission lines; failure may threaten the operation of these transmission lines. For insulator defect detection, an optical image detection method based on deep learning and morphological detection is proposed. First of all, the Faster RCNN is used to locate the insulator and extract its target image from the detection image. In the second place, a segmentation method of insulator image is proposed to remove the background of the target image. In order to simplify insulator defect detection, an insulator shape transformation method is proposed to unify all types of insulator detection. Finally, a mathematical model is established in the binary image to describe the defect of the insulator. Experiments show that our proposed Faster RCNN can accurately detect the insulators in the image. Its AP is as high as 0.9175, and its Recall rate is as high as 0.98, which is higher than the common insulator recognition algorithm. The accuracy of the proposed defect detection method is 0.98, which can accurately locate the defect position of the insulator. In order to prove the efficiency of the proposed method, we compared several common detection methods.

Polymeric Insulator Conditions Estimation by Using Leakage Current Characteristics Based on Simulation and Experimental Investigation

Abstract: The current work contributes an estimate of the time-frequency characteristics of a leakage current in assessing the health condition of a polluted polymeric insulator. A 33 kV polymer insulator string was subjected to a series of laboratory tests under a range of environmental conditions, including pollution, wetting rate (WR), non-soluble deposit density (NSDD), and non-uniform distribution pollution (FT/B). The temporal and frequency features of the leakage current were then extracted and used as assessment indicators for insulator conditions based on laboratory test findings. Two indices were generated from the leakage current waveform in the time domain: the curve slope index (F1), which is determined by measuring the inclination of the curve between two successive time peaks of the leakage current, and the crest factor indicator (F2). The frequency domain of the leakage current signal was used to calculate the other two indices. These are the odd harmonic indicators derived from the odd frequency harmonics of the leakage current up to the 9th component (F3) and the 5th to 3rd harmonics ratio (F4). The findings showed that the suggested indicators were capable of evaluating insulator conditions. Finally, the confusion matrix for the experimental and prediction results obtained with the proposed indices was used to assess which indicator performed the best. Therefore, the analysis suggests an alternative and effective method for estimating the health condition of a polluted insulator through leakage current characteristics obtained in the time and frequency domains.

Adversarial Reconstruction Based on Tighter Oriented Localization for Catenary Insulator Defect Detection in High-Speed Railways

Abstract: The catenary insulator maintains electrical insulation between catenary and ground. Its defects may happen due to the long-term impact from vehicle and environment. At present, the research of defect detection for catenary insulator faces several challenges. 1) Localization accuracy is low, which causes the localized object to be incomplete or/and merge with unnecessary background. 2) Horizontal localization brings inevitable unnecessary information because horizontal box cannot fit well with the shape of insulator. 3) Supervised learning models for defects recognition are unreliable as the available defect samples are insufficient to train models well. To address these issues, this article proposes a novel two-stage defect detection method. In the localization stage, a novel localization network called TOL-Framework is constructed to reduce the background and realize tighter oriented localization. Compared with general basic framework Faster R-CNN, the TOL-Framework cascades a regression module inside basic framework and adds an external postprocess network, which is adversarially trained by standard insulators to refine the localization. These two novel steps greatly improve the oriented localization accuracy. In the defect detection stage, an adversarial reconstruction model that is trained only using normal samples is proposed to evaluate the defect states. A comparison with other methods is conducted using a dataset collected from a 60km section of the Changsha-Zhuzhou railway line in China. The results show the proposed method has the highest localization accuracy, and is effective for insulator defect detection.

Multi-Objective Optimization of 400 kV Composite Insulator Corona Ring Design

Abstract: The electric field distribution is one of the main factors governing the long-term reliability of high voltage composite insulators. However, under severe pollution conditions, electric field stresses, when exceeding thresholds and applying for long periods, could lead to degradation and deterioration of the housing materials and, therefore, to failures of the composite insulators. This paper is intended to improve the distributions of the electric field and potential by minimizing the corona ring on a 400 kV AC transmission line composite insulator. The performances of three powerful multi-objective meta-heuristic algorithms, namely Ant Lion Optimizer (MOALO), Particle Swarm Optimizer (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II) are established to achieve this goal. First, variations of electrical fields on the critical parts of the string are obtained using three-dimensional finite element method (FEM) software. Then, three objective functions are developed to establish the relationships between the electric field and the guard ring parameters. Finally, the optimization parameters consist of diameter, tube diameter, and installation height of the corona ring. The obtained results confirm the effectiveness of the three algorithms; the MOLAO is the better in terms of computing time and solution quality.

FINet: An Insulator Dataset and Detection Benchmark Based on Synthetic Fog and Improved YOLOv5

Abstract: The inspection of insulators and their defects is of great significance for ensuring the safety and stability of power system. Small sample is one of the main issues of insulator defect detection based on neural network. In this research, we release a dataset for insulators and self-explosive defects detection, and provide a benchmark based on improved YOLOv5, named Foggy Insulator Network (FINet). In this work, a synthetic fog algorithm is implemented and optimized. An insulator dataset (SFID) with 13000 images is constructed and released. The YOLOv5 network is improved into SE-YOLOv5 by introducing the channel attention mechanism, and a robust detection model with 96.2% F1 score for insulators and their defects is trained from scratch, and served as benchmark. The synthetic fog algorithm proposed in this paper can be widely used for data augmentation of various datasets. The trained model can be applied in the field of transmission line inspection. The source codes, datasets and tutorials are available on GitHub.

Alternating-Current Nonlinear Electrokinetics in Microfluidic Insulator-Decorated Bipolar Electrochemistry

Abstract: We proposed herein a unique method of insulator-decorated bipolar electrochemistry (IDBE), for realizing large-scale separation of bioparticles in microchannels driven by AC dielectrophoresis (DEP). In IDBE, a pair of planar driving electrodes (DE) is placed at the bottom of channel sidewalls, between which an array of the rectangular floating electrode (FE) strips without external Ohmic contact are evenly spaced along transversal direction, and a series of insulating dielectric blocks are periodically deposited above all the inter-electrode gaps and in full contact with the channel bottom surface. By creating local field maximum and minimum at multiple sites, IDBE extends well the actuating range of DEP force field from the immediate vicinity of electrode tips in traditional bipolar electrochemistry to current fluid bulk. Considering DEP force plays the dominant role around 1MHz, we utilize Lagrange particle tracing algorithm to calculate motion trajectories of incoming samples for testing the feasibility of microchip in continuous separation of live and dead yeast cells. By applying suitable voltage parameters, highly efficient DEP sorting is theoretically achievable under a moderate inlet flow rate, where most of the viable yeasts are trapped by positive-DEP to sharp dielectric edges, while all the incoming nonviable yeasts are repelled by negative-DEP to the top surface of both FE and insulating block to form multiple thin beams co-flowing into the channel outlet. The microfluidic device exploiting insulators on bipolar FE effectively expands the actuating range of nonlinear electrodynamics and provides invaluable guidelines for developing flexible electrokinetic frameworks in modern microfluidic systems.