Sequence Components Research Articles

Identification of ancestral gnathostome Gli3 enhancers with activity in mammals.

Abnormal expression of the transcriptional regulator and hedgehog (Hh) signaling pathway effector Gli3 is known to trigger congenital disease, most frequently affecting the central nervous system (CNS) and the limbs. Accurate delineation of the genomic cis-regulatory landscape controlling Gli3 transcription during embryonic development is critical for the interpretation of noncoding variants associated with congenital defects. Here, we employed a comparative genomic analysis on fish species with a slow rate of molecular evolution to identify seven previously unknown conserved noncoding elements (CNEs) in Gli3 intronic intervals (CNE15-21). Transgenic assays in zebrafish revealed that most of these elements drive activities in Gli3 expressing tissues, predominantly the fins, CNS, and the heart. Intersection of these CNEs with human disease associated SNPs identified CNE15 as a putative mammalian craniofacial enhancer, with conserved activity in vertebrates and potentially affected by mutation associated with human craniofacial morphology. Finally, comparative functional dissection of an appendage-specific CNE conserved in slowly evolving fish (elephant shark), but not in teleost (CNE14/hs1586) indicates co-option of limb specificity from other tissues prior to the divergence of amniotes and lobe-finned fish. These results uncover a novel subset of intronic Gli3 enhancers that arose in the common ancestor of gnathostomes and whose sequence components were likely gradually modified in other species during the process of evolutionary diversification.

Analysis of power grid operation modes considering the influence of tooth harmonics

A harmonic analysis of the magnetic field of an asynchronous motor was carried out, taking into account bilateral gearing and magnetic asymmetry of the air gap. The spectrum is obtained in the form of infinite series of spatio-temporal harmonic components. Magnetic asymmetry of an asynchronous motor takes into account static and dynamic eccentricity, magnetic asymmetry of steel. 27 different spatial harmonics of the field in the air gap of the induction motor were obtained. The considered spectrum of harmonics of the magnetic field will be induced in the stator windings by the electromotive force with the corresponding frequencies. The fields with γр ± κ pairs of poles rotate asynchronously with the rotor and are damped by the fields created by the currents of the rotor cage. Thus, the magnetic asymmetry affects the spatial harmonics of the magnetic field, changing the angular velocities of their rotation in the air gap. The appearance of magnetic asymmetry causes a redistribution of the spatio-temporal distribution of the harmonics of the magnetic field of the tooth frequency, which leads to a change in the amplitude and phase relationships between the quantities. This leads to the appearance of an asymmetric system of electromotive force of tooth frequency in the stator windings, and, therefore, to a different effect of the asymmetry of the air gap at the level of symmetrical components. In the general case, at the level of symmetric components, the electromotive force of the gear frequency is influenced by two groups of factors that determine asymmetric or different from the nominal operating modes of an asynchronous motor of the same size. An increase in the magnetic asymmetry of the air gap causes a nonlinear, close to parabolic character of the growth of the existing symmetrical components. Symmetric components of the electromotive force of the toothed frequency cause currents of the corresponding components to flow through the closed circuits of the stator windings of the asynchronous motor and the entire power supply system. The calculation scheme for replacing a three-phase network for toothed harmonics of electromotive force is given. The phase resistances of the windings of rotating electric machines for asymmetric three-phase networks are not the same for different sequences. The main method of calculating the considered network is the method of symmetrical components. The calculation is made for one main phase according to the substitution schemes, which corresponds to the symmetrical components of the electromotive force of the tooth frequency. As a result of theoretical studies, functional dependences of the influence of magnetic asymmetry on the level and distribution of phase currents of the toothed frequency were obtained. The nature of the influence at the level of symmetric components is close to parabolic, and the most sensitive to changes in magnetic asymmetry is the minor symmetric component of the zero sequence. The method of calculating the currents and voltages of the toothed frequencies of asynchronous motors in the conditions of power consumption has been developed. Theoretical propositions are confirmed by experimental studies. It has been established that the levels of toothed harmonics in power supply systems, as a rule, do not exceed 10–15 % of the level of the fundamental frequency. This leads to a decrease in the reliability of the operation of electrical networks and a shortening of the service life of the main energy equipment at the enterprise. However, frequency-amplitude resonance at tooth frequencies causes the greatest threat to the power plants of industrial enterprises. This is due to the specific nature of gear harmonics of asynchronous motors (parity, dependence on load and various technological factors). The biggest threat arises when using the same type of asynchronous motors with similar operating modes. Oscillograms of experimental studies are given.

Simulation-based post-fault analysis in WECS using DFIG systems under an unbalanced fault scenario

Fault analysis in an integrated Wind Energy Conversion System (WECS) is the highest priority factor to be taken into account for distributed generation (DG). The contribution of renewable energies (like solar and wind) to the power system is increasing continuously. New challenges are being faced while integrating such systems because of the no inertial response provided by renewable resources. So, for a reliable integration further exploration, development, and even new strategies are needed to improve the reliability, power quality, control and protection of renewable energy generation. Disintegration in distributed generation is still a huge question mark on the reliability of modern power systems. Disintegration has many causes and faults are one of them, that cause severe voltage profile distortions, which makes WECS a complex system in terms of control and protection of power system. In this work, focus has been paid to the analysis of asymmetrical faults in wind farms and their impacts are evaluated on the overall generation system. A 30 MW wind power plant has been developed in MATLAB to study the faults. Since wind farms are Low Voltage (LV) networks, therefore, fault impedance method (IEC 60909) and the Fort-cue Method have been used for the fault analysis. The fault current response and behaviour of the DG system under asymmetrical fault conditions at different locations in and around the wind farm have been obtained. It is found that fault loop impendence, transformer grounding type, and fault location largely affect the dynamic behaviour of the co-generation system under asymmetrical faults. In this work dynamic behaviour of the wind generation system has been analysed for the different asymmetrical faults induced at different locations of the system. The response of the wind generator has been evaluated based on the different fault simulations considering the sequence component method.

Forecasting of Solar Power Using GRU–Temporal Fusion Transformer Model and DILATE Loss Function

Solar power is a clean and sustainable energy source that does not emit greenhouse gases or other atmospheric pollutants. The inherent variability in solar energy due to random fluctuations introduces novel attributes to the power generation and load dynamics of the grid. Consequently, there has been growing attention to developing an accurate forecast model using various machine and deep learning techniques. Temporal attention mechanisms enable the model to concentrate on the critical components of the input sequence at each time step, thereby enhancing the accuracy of the prediction. The suggested GRU–temporal fusion transformer (GRU-TFT) model was trained and validated employing the “Daily Power Production of Solar Panels” Kaggle dataset. Furthermore, an innovative loss function termed DILATE is introduced to train the proposed model specifically for multistep and nonstationary time series forecasting. The outcomes have been subjected to a comparative analysis with alternative algorithms, such as neural basis expansion analysis for interpretable time series (N-BEATS), neural hierarchical interpolation for time series (N-HiTS), and extreme gradient boosting (XGBoost), using several evaluation metrics, including the absolute percentage error (MAE), mean square error (MSE), and root mean square error (RMSE). The model presented in this study exhibited significant performance improvements compared with traditional statistical and machine learning techniques. This is evident from the achieved values of MAE, MSE, and RMSE, which were 1.19, 2.08, and 1.44, respectively. In contrast, the machine learning approach utilizing the Holt–Winters method for time series forecasting in additive mode yielded MAE, MSE, and RMSE scores of 4.126, 29.105, and 5.3949, respectively.

Local positive sequence component based protection of series compensated parallel lines connecting solar photovoltaic plants

Local positive sequence component based protection of series compensated parallel lines connecting solar photovoltaic plants

An Accelerated Distance Protection Scheme for the Lines Connected to Inverter-Based Resources

Distance relay based pilot protection schemes are commonly employed in industrial relays to provide fast tripping of the faulty transmission lines. However, the probable communication link failure or interruption can make these schemes ineffective. Therefore, some non-pilot methods are addressed in the technical literature which cannot offer an appropriate performance in the presence of inverter-based resources (IBRs) owing to the unconventional fault behavior of IBRs as compared with that of synchronous generators. Thus, an analytical non-pilot method is established for the accelerated tripping (AT) of the second zone of distance relay in the presence of IBRs. At the first place with the proposed method, the faulted phase is identified through a fault classification scheme. Within the second phase, using the strength of the variations existing in the negative and zero sequence components of both voltage and current signals, the remote breaker status is realized. Finally, according to the remote breaker status, the AT or delayed tripping decision is decided. Performance of the established methodology is examined for different simulation scenarios, such as different fault locations as well as fault resistances, employing the PSCAD/EMTDC software. The obtained promising results obviously approve the efficiency of the proposed framework.

An Enhanced Filtering Generalized Integrator-Based Control for Improved Performance of a Grid-Tied PV System at Adverse Grid Voltages

This paper deals with a single stage photovoltaic (PV) system with improved resilience against adverse grid conditions. This is majorly achieved in two ways. First, it presents an enhanced filtering generalized integrator (EFGI) based filtering stage, to process the prevalent grid voltages and derive their positive sequence components free of harmonics components, unbalance, and DC offset. The presented EFGI delivers a considerable improvement over the conventional filters and helps to improve the power quality (PQ) in case of unequal or distorted voltage conditions. Second, to address large voltage dip or rise scenarios, a dual mode mechanism is applied for the selection of the DC link voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> ) reference and the corresponding generated PV array power. At regular voltage scenario and at availability of PV power, the reference <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> is governed by the necessity to keep the PV array operating at its peak generation levels. However, at loss of PV power or divergent grid voltages, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> control is quickly transferred to an alternate approach, varying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> as per the existing grid voltages. Using this, the injected currents are limited, and system de-synchronization is prevented at voltage dips. The increased <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> at grid voltage rise avoids deterioration in the grid PQ. The grid PQ is also assured despite the presence of local non-ideal loads. The EFGI benefits and the real time performance of the presented system, are assessed using test results.

Localization of HIFS in primary distribution networks using voltage and current sequence components

In the power system protection when a conductor accidentally makes contact with a highly resistive medium, a HIF problem arises. Because of their lowest current amplitude and incredibly varied features, the HIF are challenging to detect. To detect the fault type this work proposes Support Vector Machine (SVM) classifier and Convolution Neural Network (CNN) classifier to localize the detected fault based on the trained statistical features attained from current and voltage sequence components at distribution feeders. The weight parameters of the SVM classifier and the CNN classifiers are tuned by a novel Hybrid AqSea Optimization Algorithm. The performance of the presented HIFD method is compared to assess proficiency. The adopted model at 70 % learning attains an accuracy of 93 %, which is 0.89 %, 7.69 %, 4.60 %, 1.21 % and 30.37 %, better than the previous models like SVM, SVM + GWO, SVM + DFA, SVM + AQO, SVM + SOA, and Aqsea + SVM, respectively.

A Current Differential Protection Scheme for Distribution Networks with Inverter-Interfaced Distributed Generators Considering Delay Behaviors of Sequence Component Extractors

The high-level proliferation of inverter-interfaced distributed generators (IIDGs) in modern distribution networks (DNs) has changed system topologies and fault current signatures, which compromises the protective relays in DNs. Investigating IIDG fault behaviors-based protection scheme will benefit the grid’s safety and stability. This paper proposes a novel current differential protection (CDP) scheme that considers the delay behaviors of positive- and negative-sequence component extractors for IIDGs in DNs. A frequency-domain analytical model of the fault current for a grid-connected IIDG with the PQ control strategy and a low-voltage ride-through (LVRT) capability is investigated. The dynamic behavior of the IIDGs considering the sequence-component extractor based on the Pade approximation is presented, where the T/4 delay extractor of the IIDGs causes a two-stage behavior in the fault transient process. It is found that a 5 ms error between the measured and actual values after the fault will affect the transient characteristics of the IIDGs. The transient current generated by the IIDGs during grid faults contains a large number of low-order harmonic components within the range of 0–200 Hz, which is significantly different to the current provided by the power grid. Therefore, the proposed CDP scheme uses protective relays at both terminals to obtain the required transient electric quantity using the Prony method. By constructing the frequency-characteristics ratio (FCR) and the exchanging FCR between two terminal relays, the developed protection criteria are implemented. The accuracy of the fault analysis method, whose maximum computational error is below 0.1%, and the feasibility of the proposed protection scheme are demonstrated by using a 10 kV DN in a PSCAD/EMTDC simulation, which can be applied to various fault conditions and traditional DNs without IIDGs.

Eukaryotic RNA-guided endonucleases evolved from a unique clade of bacterial enzymes.

RNA-guided endonucleases form the crux of diverse biological processes and technologies, including adaptive immunity, transposition, and genome editing. Some of these enzymes are components of insertion sequences (IS) in the IS200/IS605 and IS607 transposon families. Both IS families encode a TnpA transposase and a TnpB nuclease, an RNA-guided enzyme ancestral to CRISPR-Cas12s. In eukaryotes, TnpB homologs occur as two distinct types, Fanzor1s and Fanzor2s. We analyzed the evolutionary relationships between prokaryotic TnpBs and eukaryotic Fanzors, which revealed that both Fanzor1s and Fanzor2s stem from a single lineage of IS607 TnpBs with unusual active site arrangement. The widespread nature of Fanzors implies that the properties of this particular lineage of IS607 TnpBs were particularly suited to adaptation in eukaryotes. Biochemical analysis of an IS607 TnpB and Fanzor1s revealed common strategies employed by TnpBs and Fanzors to co-evolve with their cognate transposases. Collectively, our results provide a new model of sequential evolution from IS607TnpBs to Fanzor2s, and Fanzor2sto Fanzor1s that details how genes of prokaryotic origin evolve to give rise to new protein families in eukaryotes.

45 Differential Clinical Utility of Forward, Backward and Sequencing Components of Digit Span

Objective:Digit Span has been a core Working Memory task, with extensive research conducted on the Forward and Backward components. The latest revision of the WAIS-IV introduced the Sequencing component, designed to increase the working memory and mental manipulation demands. However, relatively little research has been done to understand how Sequencing can be interpreted in clinical settings, as compared to Forward and Backward. The purpose of this study was to investigate how effectively individual components of the Digit Span task predict performance on four independent neuropsychological measures with high working memory demands.Participants and Methods:Subjects included 148 adults (Age: M= 39.22, SD= 13.61; Handedness= 130 right, 10 left and 8 mixed; Males = 88) with refractory epilepsy. Two subjects had primary generalized seizures while 146 subjects had complex partial seizures (EEG Localization: 44 right temporal; 60 left temporal; 24 independent bitemporal; 1 left extratemporal; 17 indeterminant). Dependent variables included the 2.4 second ISI trial of the Paced Auditory Serial Addition Task (PASAT); the sum of correct responses on Trial 1 and List B of the California Verbal Learning Test (CVLT); the DKEFS Tower Test raw score; and completion time on Part B of the Trail Making Test. The independent variables included the individual raw scores for the Forward, Backward and Sequencing components of the WAIS-IV. Hierarchical linear regression was conducted to determine the variance accounted for by each component of the Digit Span and if that variance was redundant or unique. The four dependent variables were analyzed separately with Digits Forward, Backward and Sequencing entered in a single block.Results:PASAT: The overall model was significant, R2= 0.36. When examining the individual components, Sequencing was the only significant predictor (ß = 0.422, p &lt; 0.001). CVLT: The overall model was significant, R2 = 0.203. When examining the individual components, Sequencing was the only significant predictor (ß = 0.410, p &lt; 0.001). Tower Test: The overall model was significant, R2 = 0.176. When examining the individual components, Sequencing was the only significant predictor (ß = 0.373, p = 0.004). Trail Making: The overall model was significant R2 = 0.315. When examining the individual components both Forward (ß = -0.287, p =0.005) and Sequencing (ß= -0.364, p &lt; 0.001) accounted for a significant amount of the variance.Conclusions:The combined model for Digit Span accounted for significant amounts of variance in performance on all dependent measures, ranging from 17.6% to 36%. Sequencing accounted for substantially more variance across all examined tasks. On the PASAT, CVLT and Tower Test, the variance accounted for by the components of Digit Span appears to be redundant. However, on Trail Making, both Forward and Sequencing accounted for significant amounts of variance that appear to be independent of one another. What specific task requirement(s) of the Trail Making Test versus the other measures analyzed are accounted for by Forward span is not clear. But this suggests that the individual components of the Digit Span test may measure different things across different tasks.

Alleviation of Power Quality Issues in MVF-DEANF-PLL Based Solar PV Systems under Polluted Grid Conditions

Solar energy is a sustainable and environmentally safe power source due to its widespread availability and cleanliness. Nowadays, the grid-integrated solar photovoltaic system (SPVS) has to work with a polluted grid, an imbalanced load, and changing solar irradiation. When the grid is polluted, it is also crucial to enhance power quality (PQ) at a common coupling point (CCP) while supplying significantly distorted and unreliable loads. For effective synchronization and the production of unit templates, it is necessary to retrieve positive sequence components (PSCs) from distorted/imbalanced grid voltages. In this study, a control algorithm for a grid-integrated SPVS is developed using a multi-variable filter dual-enhanced adaptive notch filter phase-locked loop (MVF-DEANF-PLL) which offers seamless grid synchronization and PQ issue alleviation. In a polluted grid environment, the proposed control approach aids in the reduction in current/voltage harmonics, DC offset, unity power factor (UPF) operation, and rapid estimation of sequence components. Even in unbalanced grid conditions, the proposed control approach efficiently extracts PSCs of both unbalanced load current and polluted CCP grid voltages. These PSCs are utilized to generate unit templates and reference source currents. By using a flexible step-size incremental conductance (FSSINC) maximum power point tracking (MPPT) technique, the highest available power of SPVS is gathered. MATLAB/Simulink is utilized for modelling a 7.22 kW SPV system, and results from simulations which depict that the proposed algorithm efficiently resolves PQ concerns in distribution networks with a polluted grid. Test observations of a 1 kW laboratory-developed SPVS prototype were recorded in compliance with the IEEE-519 standard. The suggested control technique complies with the aforementioned standards by providing a sinusoidal balanced source current that has a THD of 2.5%. Comparisons between the proposed control’s performance and that of a conventional SRF-PLL-based control technique were also performed.

Fault Section Recognition within Duration of Power Swing Using Teager–Kaiser Energy Operator and Sequence Components

Relaying operation is not reliable during power swing due to the presence of additional lower-frequency components of electrical signals. Relay operation is prohibited during Power swing blocking (PSB) operation to restrict the malfunctioning of the distance relay. During such periods, fault section identification (FSI) schemes need to work for safeguarding the electrical power system from disturbances. Fault detection and fault direction estimation are part of FSI. This article proposes a protection algorithm within the period of power swing where a Teager–Kaiser energy operator (TKEO) of superimposed negative sequence admittance (SNSA) is utilized for fault detection and an integrated scheme for determining their direction. In this integrated direction estimation approach, the direction of fault is recognized by calculating the phases of negative sequence admittance (NSA) and positive sequence admittance (PSA). The proposed technique is verified and tested on a WSCC 9-bus, 400 kV, 50 Hz system for different balanced and unbalanced fault cases with different fault conditions. Even, in some scenarios like CT saturation and single pole tripping (SPT), the proposed approach performs accurately. The results are simulated using EMTDC/PSCAD software.

A robust THD based communication-less protection method for electrical grids with DGs

In this paper, a protection method with two cascaded algorithms is proposed to face fault events in Electrical Distribution Systems (EDSs). The first algorithm uses the Total Harmonic Distortion (THD), the estimates of the amplitude voltages, and the zero-sequence component of the grid voltages for defining an adaptive algorithm that provides a secure detection and identification process for the faults in the grid network. The second algorithm uses the behavior of the positive and negative-sequence components of the grid voltages during the fault events to locate and isolate the faults. Its main merit is that it doesn’t need to employ communication lines for transmitting trip signals between the protective devices (PDs) of the system. A Second Order Generalized Integrator (SOGI) is used for obtaining the estimated variables and sequence components, which allows fast detection with a low computational burden. The harmonic content existing prior to the fault is taken into consideration for not affecting the detection of the fault. The performance of the protection method’s is evaluated experimentally and by using simulations in MATLAB/Simulink and employing the IEEE 9-bus standard ring system. The proposed method shows the capability for detecting, identifying, and isolating different fault types with different DG penetrations and fault resistances in different locations of the proposed network and even when the grid configuration is changed. The method is robust to the possible harmonic distortion of the network.

Phase Randomization: A data augmentation for domain adaptation in human action recognition

Human action recognition models often suffer from achieving both accurate recognition and subject independence when the amount of training data is limited. In this paper, we propose a data-efficient domain adaptation approach to learning a subject-agnostic action recognition classifier. The core component of our approach is a novel data augmentation called Phase Randomization. On the basis of the observation that individual body size is highly correlated with the amplitude component of the motion sequence, we disentangle the individuality and action features by using contrastive self-supervised learning with data augmentation that randomizes only the phase component of the motion sequence. This enables us to estimate the subject label of each motion sequence and to train a subject-agnostic action recognition classifier by performing adversarial learning with the estimated subject labels. We empirically demonstrate the superiority of our method on two different action recognition tasks (skeleton-based action recognition and sensor-based activity recognition).

Evaluation Method of the Power Grid Unbalance Factor under Cooperative Distribution of Photovoltaic and Energy Storage

With the rapid evolution of electrification, a number of asymmetric loads appear in the power system, making three-phase unbalanced pollution of the power grid increasingly serious. However, there are currently no comprehensive evaluation indexes to assess the unbalanced factor between power grids and customers, which makes it tough to locate and control unbalanced pollution sources. To solve the conundrum, the paper first summarizes the traditional assessment indexes and points out their shortcomings. Then, according to IEEE Std 1459-2010 power theory, the apparent fundamental power is represented by sequence components and propose a set of new evaluation indexes and methods of unbalanced factor. Finally, to verify the practicability and accuracy of the new indexes, simulation models of different wiring forms are constructed in MATLAB, and the simulation experiment results are analyzed. The results show that compared with the existing indexes, the new indexes can comprehensively reflect the unbalanced pollution factor of the system and the customer side under different wiring forms. They can be used to locate the unbalance sources and divide the unbalanced responsibilities.

Cryptocurrency Price Prediction Using Frequency Decomposition and Deep Learning

Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.

Fault location of untransposed double-circuit transmission lines based on an improved Karrenbauer matrix and the QPSO algorithm

Some double-circuit transmission lines are untransposed, which results in complex coupling relations between the parameters of the transmission lines. If the traditional modal transformation matrix is directly used to decouple the parameters, it can lead to large errors in the decoupled modal parameter, errors which will be amplified in the fault location equation. Consequently, it makes the fault location results of the untransposed double-circuit transmission lines less accurate. Therefore, a new modal transformation method is needed to decouple the parameter matrix of untransposed double-circuit transmission lines and realize the fault location according to the decoupled modal parameter. By improving the basis of the Karrenbauer matrix, a modal transformation matrix suitable for decoupling parameters of untransposed double-circuit transmission lines is obtained. To address the difficulties in solving the fault location equation of untransposed double-circuit transmission lines, a new fault location method based on an improved Karrenbauer matrix and the quantum-behaved particle swarm optimization (QPSO) algorithm is proposed. Firstly, the line parameter matrix is decomposed into identical and inverse sequence components using the identical-inverse sequence component transformation. The Karrenbauer matrix is then transformed to obtain the improved Karrenbauer matrix for untransposed double-circuit transmission lines and applied to identical and inverse sequence components to solve the decoupled modal parameter. Secondly, based on the principle that voltage magnitudes at both ends are equal, the fault location equation is expressed using sequence components at each end, and the QPSO algorithm is introduced to solve the equation. Finally, the feasibility and accuracy of the proposed method are verified by PSCAD simulation. The simulation results fully demonstrate that the innovative improvement on the basis of the traditional modal transformation matrix in this paper can realize the modal transformation of the complex coupling parameters of the untransposed double-circuit transmission lines. It causes almost no errors in the decoupling process. The QPSO algorithm can also solve the fault location equation more accurately. The new fault location method can realize the accurate fault location of untransposed double-circuit transmission lines.

Malacological indicators of anthropogenic and natural environmental changes of the Podhale Basin during the last 2000 years. Studies in the Rogoźnik Stream valley (the Carpathian Mountains, Southern Poland)

The lithological and malacological studies covered sediments forming the low terrace of the Rogoźnik Stream in the northwest part of the Podhale Basin. This terrace is characterised by a uniform structure within a significant part of the valley. Three layers of gravel and four layers of sandy and silty muds were found there. A rich and diversified malacofauna was discovered in fine-grained sediments. Its analysis allowed us to characterise environmental conditions during sediment deposition. The age of the individual components of the sedimentary sequence was determined by radiocarbon dating. A distinct change was found in the upper intervals of the sequence, corresponding to the warm phase of the Medieval Climate Optimum. This period is associated with the robust development of agriculture, and processes related to human activities became the main factor shaping the environment, influencing the course of geological processes, and changing the taxonomical and ecological structure of the fauna and flora assemblages found in this area.

Analysis and modelling of a phase‐locked loop based on a novel cascade structure of SOGI

AbstractIn order to attenuate the oscillations on the estimated frequency of a phase‐locked loop (PLL), the effective suppression of low‐order harmonics in grid voltage is a crucial issue in three‐phase grid‐connected inverters, which use the standard PLL based on a double second‐order generalized integrator (DSOGI‐PLL). Analysis shows that the SOGI module exerts a stronger filtering effect in generating the quadrature signal than the in‐phase signal. In this context, two novel cascaded SOGI module filtering structures have been developed, based on which the PLLs are proposed. In comparison to the previously proposed method for addressing the problem of input voltage harmonics, the proposed method has stronger filtering ability and can flexibly select the levels of SOGI module cascading based on the degree of grid voltage distortion to achieve a balance between system dynamic performance and filtering performance. The proposed PLLs' approximation small signal model is established, and the method for designing the system control parameters is also provided. In the case of frequency steps, phase angle jumps, harmonic injection, and unbalanced voltage sag, Matlab modelling and experimental results demonstrate that the proposed PLLs can efficiently extract the frequency and phase of the fundamental positive sequence component and behave better in steady‐state and dynamic performance.