Sequence Components Research Articles

Fault detection and classification scheme for power islands with inverter interfaced distributed generators

The low fault current contribution from inverter-interfaced distributed generators (IIDGs) and bidirectional flow of current in the power networks introduces protection challenges when islanded. The existing fault detection scheme based on the transient monitoring function (TMF) fails under such scenarios. A new decentralised machine learning (ML) based fault detection and classification scheme is proposed in this paper using terminal voltage information of IIDG (vo−abc). TMF of vo−abc, along with the zero sequence component of vo−abc, is utilised for training and testing of ensemble bagged trees ML algorithm. The proposed scheme is implemented inside each IIDG, and hence, it does not require communication. It is validated on a modified CIGRE test benchmark microgrid for all shunt faults at various locations with different loading, fault resistances, and inception angles. The accuracy of detection and classification of fault (DCF) during fault cases with unbalanced loads, noise, harmonic loads, and no-fault scenarios such as load and DG switching proves the effectiveness of the proposed method with varying scenarios. The performance of the proposed scheme is found to be superior when compared with the existing threshold based TMF and Hilbert–Huang transform (HHT) based techniques considering accuracy, dependability, security, and response time.

Control of DFIG-based wind power generation system under unbalanced grid voltage conditions

Doubly Fed Induction Generator (DFIG)-based wind turbines have become increasingly popular in recent years due to their capacity to operate at varying speeds. Weaknesses in the DFIG system can arise from issues with the power grid due to the stator's direct connection and the excitation converter's power rating limitation. Under situations of unbalanced grid voltage, this study aims to explore the efficacy of the Direct Power Control (DPC) approach in managing wind turbine systems based on DFIG. Throughout the experimental investigation, we evaluated the system in standard and unbalanced grid voltage settings. MATLAB/SIMULINK simulations implement DPC, specifically tailored for a 9 MW DFIG-based wind farm. The results of these simulations show that the changed control method effectively reduces torque oscillations by making it possible to create active and reactive power references for the rotor-side converter. This eliminates the requirement for sequence component excitation, which was previously necessary. Furthermore, the research highlights the intrinsic link between control techniques and grid circumstances, showing this connectivity's crucial role in improving wind energy systems' stability and operational efficiency based on DFIG.

Signal-based fault location method using time and frequency domain

This research introduces an innovative approach to fault detection in high-voltage alternating current (HVAC) systems by integrating temporal and frequency domain characteristics of signals. It utilizes Traveling Wave (TW) detection and analysis, focusing on a precise window of just 100 microseconds surrounding the TW's arrival at the measurement point. Mathematical Morphology is applied to identify time-domain properties, while the Stationary Wavelet Transform is employed to extract frequency-domain features. Additionally, the integration of a Dynamic Mode Decomposition-based technique enhances TW detection accuracy. The efficacy of this approach is extensively assessed for fault site classification and regression tasks, demonstrating superior performance compared to utilizing time or frequency-domain features separately. The study also examines the method's resilience to noisy measurements and the sufficiency of training data. Comparative analysis against existing signal-based approaches within the IEEE 34 node system underscores its heightened accuracy. Furthermore, the proposed technique showcases minimal fault location estimate errors along the feeder length and competes favorably with slower phasor-based fault detection methods in performance. Regarding the distance of the fault, its proximity to the generator, transmission line, or load significantly influences the parameters of positive, negative, and zero sequence components. When a fault occurs, these parameters undergo distinct changes, serving as indicators of the fault's severity and location. If the fault occurs near the generator or substation, there may be alterations in the voltage and current magnitudes, affecting the sequence components accordingly. In the case of a fault in the transmission line, impedance changes can lead to variations in sequence parameters. Similarly, a fault near the load can cause disturbances in the voltage and current profiles, impacting the sequence components. Analyzing these changes in positive, negative, and zero sequence parameters during a fault provides valuable insights into the fault's severity and location, aiding in effective fault detection and mitigation strategies. Key Words – fault detection, high-voltage alternating current (HVAC) systems, Traveling Wave (TW), temporal domain, frequency domain, Mathematical Morphology, Stationary Wavelet Transform, Dynamic Mode Decomposition, fault site classification,

A hybrid islanding detection method using wavelet transform for hybrid systems with zero non-detection zone

Purpose Islanding detection has become a serious concern due to the extensive integration of renewable energy sources. The non-detection zone (NDZ) and system-specific applicability, which are the two major issues with the islanding detection methods, are addressed here. The purpose of this paper is to devise an islanding detection method with zero NDZ and, which will be applicable to all types of renewable energy sources using the sequence components of the point of common coupling voltage. Design/methodology/approach Here, a parameter using the sequence components is derived to devise an islanding detection method. The parameter derived from the sequence components of point of common coupling voltage is analysed using wavelet transform. Various operating conditions, such as islanding and non-islanding, are considered for several test systems to evaluate the performance of the proposed method. All the simulations are carried out in Simulink/MATLAB environment. Findings The results showed that the proposed method has zero NDZ for both inverter- and synchronous generator-based renewable energy sources. In addition, the proposed method works satisfactorily as per the IEEE 1547 standards requirement. Originality/value Performance of the proposed method has been tested in several test systems and is found to be better than some conventional methods.

Comprehensive profiling of endocrine metabolism identifies a novel signature with robust predictive value in ovarian cancer.

The cell endocrine pathway is a critical physiological process composed of the endoplasmic reticulum, Golgi apparatus and associated vesicles. Loss of enzymes or proteins can cause dysfunction of endoplasmic reticulum and Golgi apparatus and affect secretion pathways leading to a variety of human diseases, including cancer. The single-cell RNA sequencing and single nucleotide variant principal component analysis data of ovarian cancer were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Eighty-four genes from SECRETORY_PATHWAYs were obtained from the gene set enrichment analysis (GSEA) website. Univariate cox regression analyses and ConsensusClusterPlus were used to identify prognostic genes and molecular subtypes, which were validated using the tumor immune dysfunction and exclusion (i.e. TIDE) analysis and gene mutation analysis. A prognosis model was established by randomForestSRC. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ssGSEA, ESTIMATE, MCP-counter and GSEA. The drug sensitive analysis was performed using pRRophetic package. Immunotherapy datasets and pan-carcinoma analysis were used to examine the performance of prognostic model. Eighteen prognostic genes from SECRETORY_PATHWAYs were found in both TCGA and GEO datasets. Next, two clusters (C1 and C2) were determined, for which C1 with a poor prognosis had higher immune infiltration. Tumor-related pathways, such as PATHWAYS_IN_CANCER and B_CELL_RECEPTOR_SIGNALING_PATHWAY, were enriched in C1. Moreover, C2 was suitable for immunotherapy. A four-gene (DNAJA1, NDRG3, LUZP1 and ZCCHC24) signature was developed and successfully validated. RiskScore of higher levels were significantly associated with worse prognoses. An enhanced immune infiltration, increased pathways score and inappropriate immunotherapy were observed in the high RiskScore group. The high- and low-RiskScore groups had different drug sensitivities. Immunotherapy datasets and pan-carcinoma analysis indicated that the low RiskScore group may benefit from immunotherapy. Based on the perspective of the secretory signaling pathway, a robust prognostic signature with great performances was determined, which may provide clues for clinical precision treatment of ovarian cancer.

Neural Network Quadrature Signal Generator-Based Virtual Flux Estimation for Predictive Control of PWM Converters

In this paper, a frequency-adaptive neural network-based virtual flux (FANN-VF) estimator is developed for sensorless control of a pulse-width modulation converter under unbalanced and distorted grid conditions. This method exploits two parallel FANNs configured as quadrature signal generators. The VF fundamental positive and negative sequence components (PNSCs) are inherently separated in the estimator without employing any cascaded filters. A frequency-locked loop is introduced to accurately track the frequency variations. The estimated VF PNSCs are directly exploited to compute the power references in VF-based predictive direct power control scheme. The developed strategy ensures constant active power and sinusoidal current waveforms under nonideal grid conditions. Simulation and experimental tests are performed to verify the effectiveness of the proposed method.

Two-Way Power Flow Balancing in Three-Phase Three-Wire Networks by Unbalanced Capacitive Shunt Compensation

Developed to achieve the balancing of three-phase loads and improve their power factor, the BCC (Balancing Capacitive Compensator) is presented in this paper as having a broader capability, namely that of balancing the two-way power flow. BCCs are becoming useful in today’s distribution networks with a high content of DERs (Distributed Energy Resources), where unbalanced power transfers to the higher voltage network occur more and more frequently as a result of the excess power generated. The article contains a case study in which, by means of Matlab-Simulink 2021 modelling, such a network is studied by considering two regimes corresponding to the two-way power flow. The numerical analysis of phase components and sequence components confirms the validity of the mathematical model concerning the BCC and also for the case of changing the way of power flow in the section controlled by the compensator. This demonstrates the possibility of extending the load balancing function of the BCC to that of balancing the two-way power flow and is an additional argument in support of replacing, in the more or less near future, conventional shunt capacitive compensators with capacitive balancing compensators.

Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.

LogGT: Cross-system log anomaly detection via heterogeneous graph feature and transfer learning

Automated system log anomaly detection plays a crucial role in ensuring service reliability. Existing methods incompletely utilize structured log entries, resulting in the loss of key information such as components and time. Besides, due to the limitations of labeled data, models trained by a single system are difficult to apply to other systems. Therefore, we propose a cross-system log anomaly detection method named LogGT, which simultaneously models log events, components and time, leveraging labeled system knowledge to achieve anomaly detection in unlabeled systems. Specifically, we firstly design a heterogeneous graph to accurately represent the interactions between different events and components in the log sequence. Then, in order to avoid noise interference and conduct cross-system semantic analysis, we employ BERT to extract log sentence vectors, and Normalizing Flow is used to optimize them for smoother node embedding. A Graph Transformer Architecture with Time Intervals (GTAT) is proposed to model heterogeneous graphs by integrating time feature, allowing for a comprehensive analyze of execution order and time anomalies. Additionally, we design a semantic weighting method and utilize a novel domain-adapted transfer learning technology to effectively transfer the heterogeneous graph features of the source system to the target system. Experimental results demonstrate that LogGT outperforms five log anomaly detection methods, achieving an average anomaly detection F1-score higher than 0.95. Moreover, the AUC value of GTAT exceeds the sequence model by more than 2.3%.

Grid synchronization method based on three novel types of third-order generalized integrators

Phase-locked loop (PLL) is widely used to estimate synchronous information such as amplitude and phase angle of grid voltage. It plays a crucial role in distributed renewable energy grid-connected power generation. However, the presence of grid harmonics and DC offset voltage (DCOV) can affect the accurate extraction of the fundamental voltage component, leading to phase-locked deviation. This paper introduces three novel or improved third-order generalized integrators (TOGI) with DCOV rejecting function to address this issue. A unified dual third-order generalized integrator (DTOGI) structure is designed based on the three TOGI structures. This DTOGI structure is further combined with a moving average filter (MAF) to propose three innovative three-phase PLLs. Unlike other PLL filtering methods that inadequately consider DCOV, the proposed PLL methods not only reject DCOV but also eliminate the fundamental voltage negative sequence component (FVNSC) and each order harmonic component. They demonstrate strong robustness and excellent filtering performance even under abnormal grid conditions. Among the three methods, the DII-TOGI based PLL exhibits superior dynamic performance compared to the other two methods, it can improve dynamic characteristics by about 10 % compared to the other two methods. The effectiveness of the proposed methods is verified through extensive Matlab/Simulink simulations and experiments.

A novel sequence component based fault detection index for microgrid protection

The paper introduces a novel protective relaying method using symmetrical components of current to detect asymmetrical faults in microgrid. This approach is focused on utilizing the complex magnitude-phase plane (MPP) of the fault detection index (FDI), which is computed using the positive and negative sequence components of the current signal retrieved at one end of the faulty line. The MPP obtained from the settling values of the FDI's magnitude (MFDI) and FDI's angle (AFDI) after the fault inception, is considered as the key indicator for fault detection. The scheme is comprehensively evaluated for a wide range of fault situations including variations in fault types, fault locations, fault resistances and DG penetrations in grid-connected (GC) and non-grid-connected (NGC) modes of operation. Several critical no fault cases are also tested to show the efficacy of the proposed logic for false operation. The fault study is conducted extensively on a modified low voltage standard Consortium for Electric Reliability Technology Solutions (CERTS) microgrid and a standard IEEE-34 bus distribution system including DGs. Furthermore, the proposed FDI-based scheme is tested on MATLAB/Simulink platform and validated on Typhoon HIL platform to assess the real-time performance. The test results show that this FDI-based scheme using the MPP concept can be a potent solution for the protection of microgrid.

The Prediction of Medium- and Long-Term Trends in Urban Carbon Emissions Based on an ARIMA-BPNN Combination Model

Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a novel model based on Lasso regression, an ARIMA model, and a BPNN is proposed. Lasso regression is used to screen the key factors affecting carbon emissions, and the ARIMA model is used to extract the linear components of the carbon emission sequences, while the BPNN is used to predict the residuals of the ARIMA model. The final result is the sum of that from the ARIMA model and the BPNN. The carbon peak, carbon neutralization time, and emissions were analyzed under different scenarios. Taking Suzhou City as an example, the results show that the electricity consumption of the whole population is one of the key drivers of carbon emissions; the carbon emission prediction accuracy and stability of the ARIMA-BPNN combined model are better than those of the single model, which improves the reliability as well as the accuracy of the model’s prediction. However, under the constraints of the current policies, the goal of achieving carbon peaking by 2030 in Suzhou City may not be realized as scheduled. This novel carbon emission prediction model built was validated to provide a scientific basis for low-carbon urban development. This study presents an important reference value for predicting carbon emissions and formulating emission reduction measures in other cities.

Study of Special Unbalanced Load Modes of New Double-Autotransformer of the Flexible Alternating Current Link

The purpose of the work is study of special asymmetric modes of new three-phase autotransformer device for flexible connection of alternative current power systems. The universal model was developed for the device, containing two autotransformers - the main phase-shifting and the additional regulating autotransformer. The model allows studying both symmetric and almost any asymmetric modes of the considered circuit. It became possible to connect asymmetrically the loads in different phases of the regulating autotransformer, as well as arbitrary connect the regulating autotransformer itself to the hexagon taps. According to the calculated values of currents, voltages and magnetic fluxes of the device the visual vectorial diagrams were constructed for the main and the regulating autotransformer, explaining specific of considered operating modes. It was shown that device’ currents in such modes contain symmetrical components of both zero and negative sequences. Also, at asymmetric connection of the regulating autotransformer, the relative magnetic flux of the legs of the main and regulating autotransformers are asymmetrical, but in main autotransformer their sum is approximately equal to zero and magnetic flux practically does not escape into the surrounding space. Radically different situation is observed in the regulating autotransformer. The significant zero-sequence fluxes arise with the release of significant (about 50%) magnetic flux into the surrounding space. This re-quires special measures to be taken to limit its harmful effects on the operation of the autotransformer.

The Current Sequence Components Extraction for Dual Synchronous Rotation Frame Current Control in Unbalanced Grid Conditions

The Current Sequence Components Extraction for Dual Synchronous Rotation Frame Current Control in Unbalanced Grid Conditions

A spatiotemporal motion prediction network based on multi-level feature disentanglement

The prediction task is significantly challenged by the intricate scene information and motion variations present in spatiotemporal data. Existing prediction methods struggle to accurately forecast long-term outcomes, particularly for transient motions characterized by notable trends, such as hand lifts, jumps, or vehicle turns. To address these challenges, we introduce a Spatiotemporal Motion Prediction Network based on Multi-level Feature Disentanglement (FDPNet). The model delineates spatiotemporal prediction into two distinct stages: feature disentanglement and motion prediction. We first devise a Multi-level Feature Disentanglement (MFD) model to disentangle the multilayer features of motion within the temporal sequence, encompassing period, trend, and residual components. This disentanglement is based on disentangling spatiotemporal coupling, enabling the network to comprehensively grasp the genuine laws governing motion in the spatiotemporal evolution process. Second, to enhance the prediction accuracy of the network over extended periods, we introduce the Motion Differential Self-Attention LSTM unit (MDSA-LSTM). This unit employs differential operations to extract inter-frame motion trends, elevating the network's proficiency in capturing spatiotemporal correlations over long distances through an enhanced self-attention mechanism. FDPNet attains state-of-the-art performance on the Moving MNIST, UCF101, KITTI, and Caltech pedestrian datasets. These outcomes substantiate the substantial potential of this research within the realm of spatiotemporal prediction.

Generating Synthetic Electricity Load Time Series at District Scale Using Probabilistic Forecasts

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems.

Sequence optimisation for compressed sensing CDMA MIMO radar via mutual coherence minimisation

AbstractThe authors focus on the waveform design for Code Division Multiple Access Multiple Input Multiple Output (CDMA‐MIMO) radar systems, with a specific emphasis on Compressed Sensing (CS) based target estimation. The selection of an appropriate waveform is a critical determinant in the effectiveness of estimation algorithms. Recent studies show the possibilities of optimising waveform parameters to improve the efficiency of CS based estimation. The authors introduce an optimisation framework designed to modify the phase components of code sequences used in CS‐CDMA MIMO radar systems. The objective of this optimisation is to minimise the l∞ norm of off‐diagonal elements within the Gramian matrix of the underlying sensing matrix, focusing on phase modulation of the waveform. Solving this optimisation problem requires dealing with a non‐convex, combinatorial and non‐linear scenario. Simulated Annealing is employed as the solution technique. To assess the effectiveness of the proposed optimisation approach, the resulting optimised sequence is rigorously compared against well‐established Hadamard and Gold sequences across various performance metrics. These metrics encompass correlation properties, ambiguity function behaviour, recovery percentage and recovery error. The study demonstrates that the generated poly‐phase sequences outperform existing sequences, leading to significantly improved target reconstruction results in the context of CDMA‐MIMO radar systems with CS‐based estimation.

A Brief Intervention to Teach Parents Naturalistic Language Facilitation Strategies.

This proof of concept study assessed the feasibility of a novel approach to teaching parents naturalistic language facilitation strategies in a single session. We investigated whether parents could learn to use the See and Say Sequence, which integrated responsive and language modeling strategies and measured the impacts that this intervention had on features of their input. Fourteen parent-child dyads participated in the study. Children ranged from 15 to 23 months of age and produced between three and 135 words. Five parents had concerns about their children's rate of language development. Parents were taught the See and Say Sequence during a brief single session (M = 18.98 min, SD = 2.65 min) using the Teach-Model-Coach-Review instructional process. We analyzed parents' use of the three See and Say Sequence components, total number of utterances, and mean turn length, as well as responsive and linguistic features of parent input before and after the brief intervention. Following intervention, parents significantly increased their use of the three See and Say Sequence components and decreased their total number of utterances and mean turn length. In addition, the use of the See and Say Sequence components substantially altered the overall composition of parent input. The results of this preliminary study demonstrate the feasibility of the See and Say Sequence in teaching responsive and language modeling strategies in a single session. We discuss the potential use and future evaluation of the See and Say Sequence as an option for early intervention service delivery.

An Adaptive Reclosing Scheme for Cross-Line Faults on Double-Circuit Wind Power Outgoing Lines with Shunt Reactors

Wind turbines are vulnerable to negative sequence current injection, the conventional automatic reclosing scheme for wind power outgoing lines, since we may inject negative sequence components into the system and reclose it without distinguishing the nature of the fault through rectification. Moreover, reclosing in permanent faults could induce a secondary impact on the system. To solve the above problems, an adaptive reclosing scheme for cross-line faults on double-circuit wind power outgoing lines with shunt reactors is proposed. Firstly, a new tripping strategy and a single-side partial-phase reclosing method are proposed for multiple types of outgoing line faults, while, simultaneously, phase-to-phase coupling loops are established. Secondly, the criteria of fault nature are established based on the fault phase shunt reactor current and fault phase voltage characteristics, and transient faults are rapidly and accurately distinguished from permanent ones according to the criteria. Finally, the theoretical derivation and simulation experiments are conducted on the PSCAD/EMTDC platform to demonstrate that the proposed adaptive reclosing method is applicable to avoid the injection of negative sequence currents into wind turbines. Meanwhile, the success rate of reclosing for wind power outgoing line is significantly improved and the continuity of power transmission on the wind farm(s) is ensured.

Model Predictive Control on Transient Flux Linkage and Reactive Power Compensation of Doubly Fed Induction Wind Generator

For weak grid scenario with high new energy proportion, large fluctuations of load are prone to cause low-voltage ride through. Moreover, stator transient magnetic flux will cause overvoltage and overcurrent problems in the rotor of doubly fed induction generator. Based on model predictive control, a control strategy for transient flux linkage and reactive power compensation is proposed. Firstly, regarding the issue of reactive power allocation of grid side converters (GSC) and rotor side converters (RSC), an allocation strategy is derived under minimizing winding energy loss on case of low-voltage ride through, enabling the wind energy conversion system to provide reactive power support during grid voltage recovery process. Meanwhile, an improved mixed second- and third-order generalized integrator (MSTOGI) phase-locked loop (PLL) is used to extract the positive and negative sequence components of the power grid voltage, further for RSC control. Secondly, in response to power grid faults, considering the influence of stator DC transient flux and negative sequence flux components on rotor current, by injecting rotor transient compensation current and stator flux feedforward compensation into RSC, the rotor impulse voltage and loop current are reduced. Moreover, combined with model predictive control algorithm, a control strategy of rotor current is designed. Finally, a simulation platform is built to validate the effectiveness of the proposed method based on comparing with several traditional vector control low-voltage ride through methods.