Sequence Components Research Articles

A Novel Microgrid Islanding Detection Algorithm Based on a Multi-Feature Improved LSTM

Islanding detection is one of the conditions necessary for the safe operation of the microgrid. The detection technology should provide the ability to differentiate islanded operations from power grid disturbances effectively. Given that it is difficult to set the fault threshold using the passive detection method, and because the traditional active detection method affects the output power quality, a microgrid islanding detection method based on the Sliding Window Discrete Fourier Transform (SDFT)-Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) network optimized by an attention mechanism is proposed. In this paper, the inverter output current and voltage at the point of common coupling (PCC) are transformed by the SDFT. The positive sequence, zero sequence, and negative sequence components of voltage and current harmonics are calculated and reconstructed by adopting the symmetrical component method (SCM). Meanwhile, the current and voltage are decomposed into a mono intrinsic mode function (IMF). The symmetric components of voltage, current, and IMFs are used as inputs to the deep learning algorithm. An LSTM with the features extracted to classify islanding and grid disturbance is proposed. By using the attention mechanism to set the weight values of the features of hidden states obtained by the LSTM network, the proportion of important features increases, which improves the classification effect. MATLAB/Simulink simulation results indicate that the proposed method can effectively classify the islanding state under different working conditions with an accuracy level of 98.4% and a loss value of 0.0725 with a maximal detection time of 66.94 ms. It can also reduce the non-detection zone (NDZ) and detection time and has a certain level of noise resistance. Meanwhile, the problem whereby the active method affects the microgrid power quality is avoided without disturbing the current or power of the microgrid.

An enhanced fast fundamental frequency estimator for three-phase electric aircraft grid

This article proposes a robust and enhanced three-phase fundamental frequency estimation algorithm for the electric aircraft grid. Unlike conventional frequency-locked loops, the digital signal processing technique-based frequency estimator proposed relies on the storage of five consecutive samples of the fundamental grid voltage signal. Furthermore, the ability to successfully operate at a low sampling frequency, i.e. 8 kHz, makes it sufficiently attractive as regards reducing the memory storage in a low-cost real-time controller. The proposed frequency estimator can additionally eliminate the negative effects of the DC-offset and the fundamental negative sequence components present in the grid signal without any additional effort. Also, the tuning efforts are reduced for the frequency variation range of 350–900 Hz owing to the existence of a single tuning gain parameter. The proposed algorithm has also been found to have a fast dynamic response, which has been experimentally validated through the use of a real-time digital controller.

Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO

To improve the accuracy of park load forecasting, a combined forecasting method for short-term park load is proposed based on complementary ensemble empirical mode decomposition (CEEMD), sample entropy, the satin bower bird optimization algorithm (SBO), and the least squares support vector regression (LSSVR) model. Firstly, aiming at the random fluctuation of park load series, the modes with different characteristic scales are divided into low-frequency and high-frequency according to the calculation of sample entropy, which is based on the decomposition of historical park load data modes by CEEMD. The low-frequency is forecast by multiple linear regression (MLR), and the high-frequency component is the training input of the LSSVR forecasting model. Secondly, the SBO algorithm is adopted to optimize the regularization parameters and the kernel function width of LSSVR. Then, the park load forecasting model of each sequence component is built. The forecast output of each sequence component is superimposed to get the final park load forecast value. Finally, a case study of a park in Liaoning Province has been performed with the results proving that the proposed method significantly outperforms the state-of-art in reducing the difficulty and complexity of forecasting effectively, also eliminating the defect of large reconstruction error greatly through the decomposed original sequence by the ensemble empirical model.

Long-read sequencing of 111 rice genomes reveals significantly larger pan-genomes.

The concept of pan-genome, which is the collection of all genomes from a population, has shown a great potential in genomics study, especially for crop sciences. The rice pan-genome constructed from the second-generation sequencing (SGS) data is about 270 Mb larger than Nipponbare, the rice reference genome (NipRG), but it is still disadvantaged by incompleteness and loss of genomic contexts. The third-generation sequencing (TGS) with long reads can help to construct better pan-genomes. In this paper, we report a high-quality rice pan-genome construction method by introducing a series of new steps to deal with the long-read data, including unmapped sequence block filtering, redundancy removing, and sequence block elongating. Compared to NipRG, the long-read sequencing-based pan-genome constructed from 105 rice accessions, which contains 604 Mb novel sequences, is much more comprehensive than the one constructed from ∼3000 rice genomes sequenced with short reads. The repetitive sequences are the main components of novel sequences, which partially explain the differences between the pan-genomes based on TGS and SGS. Adding six wild rice accessions, there are about 879 Mb novel sequences and 19,000 novel genes in the rice pan-genome in total. In addition, we have created high-quality reference genomes for all representative rice populations, including five gapless reference genomes. This study has made significant progress in our understanding of the rice pan-genome, and this pan-genome construction method for long-read data can be applied to accelerate a broad range of genomics studies.

Data-based model with EMD and a new model selection criterion for dam health monitoring

Data-based models are extensively applied in concrete dam health monitoring. In this paper, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the irreversible component of deformation monitoring sequence. Consequently proper mathematical formulation of the irreversible component of deformation monitoring model for concrete dams can be established, and the problem of improperly assuming the mathematical form of irreversible component when constructing data-based models is well addressed. The periodic component of the deformation time series is modelled by regression and three machine learning algorithms (i.e., Extreme learning machine, Support Vector Machine based on Grid search and Cross-Verification, and Back Propagation Neural Networks optimized by Genetic Algorithm and Simulated Annealing) respectively. The data-based models with the EMD are able to effectively improve the performance of formulating irreversible component of concrete dam deformation and decrease the over-fitting levels. A new model selection criterion, namely Over-fitting coefficient, is proposed for evaluating and selecting the optimum model among candidate monitoring models. The Over-fitting coefficient can better reflect over-fitting levels of individual monitoring models compared to traditional model selection criteria. As a case study, the displacement and strain monitoring data from the YL gravity dam are modeled by data-based models with the EMD and machine learning algorithms. It is shown that data-based models with the EMD are of higher prediction accuracy and lower rate of false alarm in deformation monitoring than traditional models. The Over-fitting coefficient can better reflect over-fitting levels of monitoring models compared to the error measurement criteria and information criteria. The case study highlights the merits of the proposed data-based models and the model selection criterion for health monitoring of concrete dams.

Separate modeling technique for deformation monitoring of concrete dams

Deformation monitoring is an important aspect of safety control for concrete dams. Deformation monitoring models (such as statistical models and hybrid models) are extensively applied to predict concrete dam deformation and derive confidence interval of normal deformation for anomaly detection. Deformation monitoring models for concrete dams mainly consist of hydrostatic component, temperature component, and aging component. The optimum parameters of individual components are simultaneously determined by least square method for monitoring data fitting. Thus, significant over-fitting of deformation monitoring models may be induced by mutual compensation among the parameters of model components. In this paper, the Separate Modeling Technique (SMT) is proposed for mitigating the over-fitting problem of deformation monitoring models for concrete dams. Firstly, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the aging component of displacement sequence. Proper mathematical formulation of the aging component can be established, and the problem of improperly presupposing the mathematical form of aging component in the process of constructing traditional deformation monitoring models is well addressed. In this study, the hydrostatic component is represented by the Hybrid Response Surface (HRS), which is formulated using numerical simulation with varying water levels and material parameters. The displacement variation caused by water level fluctuation is identified in terms of isothermal conditions and is used to calibrate the material parameters in the HRS. The temperature component is separated through subtracting the hydrostatic and aging components from displacement time series and then is expressed with proper mathematical formulations. Finally, hybrid models for displacement monitoring of concrete dams are established by combining the separately formulated components. The Separate modeling technique is applied to formulate crest displacement of the YL concrete gravity dam. The false alarm rate of displacement monitoring and a new model selection criterion (namely over-fitting coefficient) are adopted to compare various deformation monitoring models. It is shown that the over-fitting levels of deformation monitoring models can be effectively reduced using the SMT. The deformation monitoring models constructed with the SMT are of better accuracy in displacement prediction and present no false alarm of displacement monitoring for the tested period.

Three-phase unbalanced voltage sag detection method based on morphological filtering

In dynamic voltage restorer (DVR), accurate and fast voltage sag detection method is the key. However, in the case of three-phase imbalance, the traditional voltage sag detection method still has shortcomings in real-time and accuracy. Therefore, a voltage sag detection method based on morphological filter is proposed. That is, based on the commonly used dq transformation method, the morphological filter is used to replace the Butterworth low-pass filter to suppress the second harmonic generated by the negative sequence component after dq transformation in the case of three-phase imbalance, reduce the system delay and improve the real-time and accuracy of the detection method. The morphological filter is designed, and the appropriate structural elements and morphological transformation algorithm are selected. The results show that the morphological filter has good filtering effect and small delay, and can better meet the detection requirements of DVR.

Evaluation of suitable rice (Oryza sativa) based cropping systems under rainfed medium land situation of Assam for enhancing farmers income and soil health

Rice is the predominant kharif crop grown in low and medium land situation of Assam. As the monocropping of rice is practiced by most of the farmers, the cropping intensity of the state is not upto the mark. In recent years, the farmers of the state have started growing different rabi pulses and oilseeds in sequence which helps in increasing the cropping intensity of the state as well as the farmers income. Diversification and intensification of rice-based cropping systems with remunerative and efficient crops like pulses, oilseeds and vegetables has great scope to generate maximum profit and soil health. Therefore, this study was conducted during 2016–17 and 2017–18 at farmers field to assessed 6 rice-based cropping sequences under rainfed medium land situation for their productivity, profitability and soil health. The treatments consisted of 6 rice-based cropping sequences viz. rice - rapeseed - fallow; rice - rapeseedblackgram; rice - garden pea -blackgram; rice -lentil -blackgram; rice - garden pea– Sesbania and rice - lentil-Sesbania. The results revealed that the performance of individual crops contributing the components of different cropping sequences were more or less identical in both the years of experimentation. The higher land utilization Index (92.33), employment generation (265 man days), relative equivalent yield (117.29 q/ha)), production efficiency (39.90 kg/ha), rain water productivity (8.14(kg/ha/mm), nutrientuse productivity (50.70 kg/ha/kg of nutrients applied), sustainable yield index (0.64) alongwith economic parameters like gross return (134.89103 Rs/ha), net return (66.27103 Rs/ha), benefit:cost ratio (2.16), economic efficiency (236.09 Rs/ha/day) and relative economic efficiency (149.38%) were calculated based on standard formula. The higher Soil organic carbon (0.71 & 0.64% in 0–15 and 15–30 cm depth), available Nitrogen (278.12 kg/ha), Phosphorous (36.08 kg/ha) and Potash (158.18 kg/ha) content after completion of the experiment were recorded under the sequences diversified with pulses. So, it can be concluded from the present investigation that, rice-garden pea –black gram and ricegarden pea-sesbania (GM) were found to be suitable rice-based cropping sequences in respect of farmers income is concerned. But the rice-based cropping sequences diversified with food legumes and green manuring crop contributed to more soil organic carbon and NPK status of soil.

Analysis of Fault Characteristics of Distribution Network with PST Loop Closing Device under Small Current Grounding System

With the rapid development of cities and the increasing complexity of distribution network systems, the cableization of transmission lines and the diversification of power users have brought new challenges to the supply reliability of distribution networks. Short-time outages caused by power outages and maintenance are one of the factors that affect the reliability of the power supply in the distribution network. The non-stop load transfer through a phase-shifting transformer (PST) operation can effectively improve the reliability of power supply to complex distribution systems. Considering the operation mode and structure form and fault type of the urban distribution network in China, comparing and analyzing the applicable scenarios of different neutral grounding methods, and based on the structure and zero sequence path characteristics of PST, an improved PST-based phase shifting and grounding transformer loop closing device with low power consumption is proposed. The fault characteristics of the PST-based loop closing device under the small current grounding system are also analyzed by the sequence component method, and, finally, the effectiveness of the phase-shifting and grounding transformer device is verified by simulation under PSCAD/EMTDC for fault routing and protection configuration of the urban distribution network in China.

Massively parallel reporter perturbation assays uncover temporal regulatory architecture during neural differentiation

Gene regulatory elements play a key role in orchestrating gene expression during cellular differentiation, but what determines their function over time remains largely unknown. Here, we perform perturbation-based massively parallel reporter assays at seven early time points of neural differentiation to systematically characterize how regulatory elements and motifs within them guide cellular differentiation. By perturbing over 2,000 putative DNA binding motifs in active regulatory regions, we delineate four categories of functional elements, and observe that activity direction is mostly determined by the sequence itself, while the magnitude of effect depends on the cellular environment. We also find that fine-tuning transcription rates is often achieved by a combined activity of adjacent activating and repressing elements. Our work provides a blueprint for the sequence components needed to induce different transcriptional patterns in general and specifically during neural differentiation.

Evaluating the Risk of Exceeding the Normal Operating Conditions of a Low-Voltage Distribution Network due to Photovoltaic Generation

Connecting photovoltaic micro-installations to a low-voltage network changes the operating conditions of the network. As a result, in certain situations, the permissible operating limits may be periodically exceeded. The risk of exceeding the normal operating conditions of the network depends on multiple factors, including the installed capacity of the photovoltaic sources. In this article, we use a time-series method to determine the annual risks of exceeding the bus voltage limits, the rated current of the lines and transformer, and the acceptable limit of the negative sequence component of bus voltage, as well as the risk of a reverse flow occurring, and the risk of energy losses increasing. We calculate these risks for different levels of penetration of the photovoltaic sources, different divisions of the rated power of the photovoltaic sources between individual phases, and different consumer load profiles. We perform calculations on a CIGRE test network using OpenDSS and statistical meteorological data for the Katowice (Poland) weather station. The results obtained indicate that connecting photovoltaic micro-installations to a low-voltage network has the greatest impact on the risk of reverse flow occurring and the risk of energy losses increasing. In addition, the risk of overvoltage and branch overload increases substantially. The method we present allows one to determine the value of the hosting capacity of a given low-voltage network, ensuring that the assumed risk of exceeding the normal operating conditions of the network is retained.

An Improved Current Detecting Method for STATCOM Considering Unbalanced Compensation

Background: Static Synchronous Compensator plays an essential role in governing the reactive power and harmonics of power systems. The accuracy and real-time performance of reactive power and harmonic detection are necessary to ensure balancing compensation effectiveness. The traditional dq current detection method can only extract positive sequence components in the positive sequence rotational reference frame and extract negative sequence components in the negative sequence rotational reference frame. Objective: This paper aims to give a practical and straightforward current detection solution to enhance the compensator performance and simplify the control system by extracting the positive sequence, negative sequence, and harmonics in the positive sequence rotational reference frame. Methods: Firstly, a grid-connected four-quadrant converter with an Inductance-Capacitance- Inductance (LCL) filter balancing compensation topology is constructed. Then an improved current detection algorithm is proposed to extract the positive sequence, negative sequence, and harmonics in the positive rotational reference frame separately. Finally, the Direct Current (DC) side voltage design method is discussed to avoid the overmodulation effects. Results: Matlab simulation and a prototype 500kVA Static Synchronous Compensator (STATCOM) experiment are implemented to demonstrate the proposed method. Conclusion: The proposed strategy can effectively improve the compensation accuracy, and the system structure is simplified while the stability and reliability of the whole system are ensured.

CrisprVi: a software for visualizing and analyzing CRISPR sequences of prokaryotes

BackgroundClustered regularly interspaced short palindromic repeats (CRISPR) and their spacers are important components of prokaryotic CRISPR-Cas systems. In order to analyze the CRISPR loci of multiple genomes more intuitively and comparatively, here we propose a visualization analysis tool named CrisprVi.ResultsCrisprVi is a Python package consisting of a graphic user interface (GUI) for visualization, a module for commands parsing and data transmission, local SQLite and BLAST databases for data storage and a functions layer for data processing. CrisprVi can not only visually present information of CRISPR direct repeats (DRs) and spacers, such as their orders on the genome, IDs, start and end coordinates, but also provide interactive operation for users to display, label and align the CRISPR sequences, which help researchers investigate the locations, orders and components of the CRISPR sequences in a global view. In comparison to other CRISPR visualization tools such as CRISPRviz and CRISPRStudio, CrisprVi not only improves the interactivity and effects of the visualization, but also provides basic statistics of the CRISPR sequences, and the consensus sequences of DRs/spacers across the input strains can be inspected from a clustering heatmap based on the BLAST results of the CRISPR sequences hitting against the genomes.ConclusionsCrisprVi is a convenient tool for visualizing and analyzing the CRISPR sequences and it would be helpful for users to inspect novel CRISPR-Cas systems of prokaryotes.

Recognition of Islanding and Operational Events in Power System With Renewable Energy Penetration Using a Stockwell Transform-Based Method

Integration of RE sources to the utility grid offers technical and operational challenges causing problems of PQ, stability, identification of operational events, etc. This article presents an algorithm to identify events including islanding, grid integration, and outage of the solar PV and WG plants in grid using a ST. Islanding event may occur in the presence of any kind of plant. Processing of negative sequence component of voltage is performed by utilizing ST based multiresolution analysis at the test node and the output matrix is evaluated. The features (F1–F4), VI and STD indexes are obtained from this matrix. These features are utilized for identifying the events and transient phenomenon. The VI and STD indexes are used to recognize the type of RE source present during the islanding and outage events. Moreover, for recognizing the type of RE source at the time of synchronization event, an SI is proposed. This is computed by the ST depended processing of voltage signals. Performance of the algorithm is found satisfactory for all incidence angles and complete voltage cycle under the noisy conditions of 10 dB SNR. As compared to the time–frequency transform based coefficients of the voltage signal, the proposed technique is found to be superior in terms of small NDZ and low computation time and least affected by noise. Further, the developed technique is also efficient to detect various events stated above and the type of RE source. Study is performed using MATLAB/Simulink software and validated in real time using RTDS.

Active Disturbance Rejection Control of Three-Phase LCL Grid-Connected Inverter under Unbalanced Grid Voltage

When the three-phase LCL grid-connected inverter operates under the condition of unbalanced grid voltage, the influence of the negative sequence component in the grid voltage will cause the grid-connected inverter system to produce active and reactive dual-frequency interference. This will cause the grid-side current to have many low-order harmonics, mainly the third harmonic, which does not conform the IEEE-Std. 929–2000 standard. In order to solve this problem, this paper designs a current auto-disturbance-rejection controller, which speeds up the tracking response speed of the system and reduces the harmonic content of the grid-side current under unbalanced conditions. At the same time, optimize the phase-locked loop structure and introduce a notch filter in the modulation link to eliminate the third low-frequency harmonic components and further improve the quality of the grid-side current waveform. Finally, the correctness of the control strategy suggested in this paper is verified by MATLAB/Simulink simulation.

A Comprehensive Virtual Synchronous Generator Control Strategy for Harmonic and Imbalance Voltage Suppression of Multi-Inverter Parallel Microgrid

To reduce the impact of the imbalance of mixed non-linear loads on an inverter voltage output in the microgrid, we improve the disadvantage of the lack of damping and inertia for traditional droop control. This paper proposes a comprehensive virtual synchronous generator (VSG) control strategy for harmonic suppression and imbalance suppression of a multi-inverter parallel microgrid. On one hand, an improved VSG control strategy is proposed to increase the damping and inertia of distributed generations (DGs) in the microgrid, and secondary control is introduced to improve system stability. On the other hand, the frequency division suppression control strategy is used to eliminate the influence of harmonics, and the negative sequence component is compensated to eliminate the influence of imbalance. Then small-signal analysis is used for analysis of the stability of the strategy. Finally, we verify the comprehensive control strategy proposed in this paper through experiments. The experimental results suggest a significant improvement on the voltage, frequency, power optimization, handling of non-linear load and capacity distribution precision, as well as providing inertia support for the system.

PLL-Free Voltage Oriented Control Strategy for Voltage Source Converters Tied to Unbalanced Utility Grids

Asymmetrical working conditions of the utility grid introduce the large-amplitude negative-sequence component to the output current of the voltage source converter (VSC), causing power semiconductor devices to suffer from thermal fatigue and thermal damage. Though the conventional phase-locked loop (PLL) based voltage oriented control (VOC) solution can suppress the steady-state negative-sequence current effectively, it has a weak suppression ability of transient overload current, and even aggravates the transient current asymmetry and causes more severe transient impact to the VSC. This paper first analyzes the transient performance of the conventional VOC strategy, especially its dynamic response time and the main factors for performance limitation. On this basis, the PLL-free VOC strategy for VSCs tied to unbalanced grids is proposed, and its critical parts, namely, the reference current calculation and the fast detection of the grid voltage sequence components, are implemented. Besides, to improve the frequency adaptability, a high-performance grid frequency detection strategy is developed based on the difference-frequency phase caused by the frequency variation. Finally, experiments are performed to verify the effectiveness and advancement of the proposed method. Specifically, the results proved the rapidity, accuracy, and frequency adaptability of the proposed method in suppressing the VSC negative-sequence current, both in transient and steady-state conditions.

Distribution Network Voltage Arc Suppression Method Based on Flexible Regulation of Neutral Point Potential of the New Grounding Transformer

Aiming at the problems that the existing arc suppression cabinet technology cannot adjust the zero sequence voltage flexibly and the control and hardware implementation of active arc suppression equipment based on power electronics are difficult, a new method of voltage arc suppression in the distribution network based on the flexible regulation of the neutral point potential of the new grounding transformer is proposed. From the perspective of the sequence component, the variation law of the neutral point voltage before and after the closing of the tapped grounding branch of the new grounding transformer is analyzed. By selecting the appropriate initial tap position of the tap-to-ground switch, the fault phase voltage is flexibly controlled so that the fault phase voltage is reduced to below the reignition voltage to quickly solve the influence of the single-phase grounding fault, and the risk of non-fault phase insulation breakdown is reduced to ensure the stable operation of the distribution network. According to the ratio of the fault phase current to the zero sequence voltage before and after the gear inputs, whether the arc suppression of the single-phase grounding fault is completed is judged. Compared with power electronic products, the new grounding transformer has the advantages of high reliability, long service life, and low cost and has strong economy and applicability in engineering. The single-phase grounding fault model of the 10kV distribution network is built in the PSCAD/EMTDC simulation environment, and the new method of the distribution network voltage arc suppression based on the flexible regulation of neutral point potential of the new grounding transformer is verified. The simulation results show that the proposed method can effectively reduce the fault phase voltage to achieve the reliable arc suppression of the single-phase grounding fault.

Harmonic and Sequence Component Estimation by a Novel Method

Harmonic and Sequence Component Estimation by a Novel Method by Abinash Rath 1,* and Rumpa Saha 2 1 National Institute of Technology, Department of Electrical Engineering, Rourkela, 769008, India 2 Aliah University, Department of Electrical Engineering, Kolkata, 700156, India * Author to whom correspondence should be addressed. Journal of Engineering Research and Sciences, Volume 1,

Bacterial diversity and competitors for degradation of hazardous oil refining waste under selective pressures of temperature and oxygen

Oil refining waste (ORW) contains complex, hazardous, and refractory components, causing more severe long-term environmental pollution than petroleum. Here, ORW was used to simulate the accelerated domestication of bacteria from oily sludges and polymer-flooding wastewater, and the effects of key factors, oxygen and temperature, on the ORW degradation were evaluated. Bacterial communities acclimated respectively in 30/60 °C, aerobic/anaerobic conditions showed differentiated degradation rates of ORW, ranging from 5% to 34%. High-throughput amplicon sequencing and ORW component analysis revealed significant correlation between bacterial diversity/biomass and degradation efficiency/substrate preference. Under mesophilic and oxygen-rich condition, the high biomass and abundant biodiversity with diverse genes and pathways for petroleum hydrocarbons degradation, effectively promoted the rapid and multi-component degradation of ORW. While under harsh conditions, a few dominant genera still contributed to ORW degradation, although the biodiversity was severely restricted. The typical dominant facultative anaerobes Bacillus (up to 99.8% abundance anaerobically) and Geobacillus (up to 99.9% abundance aerobically and anaerobically) showed oxygen-independent sustainable degradation ability and broad-spectrum of temperature adaptability, making them promising and competitive bioremediation candidates for future application. Our findings provide important strategies for practical bioremediation of varied environments polluted by hazardous ORW.