New pilot protection method based on the current fault component waveform similarity

When a single-phase grounding fault occurs in a resonant grounding system, the determination of the fault location remains a significant challenge due to the small fault current and the instability of the grounding arc. In order to solve the problem of low protection sensitivity when a high-resistance grounding fault occurs in a resonant grounding system, this paper proposes a fault location method based on the combination of dynamic time warping (DTW) distance and fuzzy C-means (FCM) clustering. By analyzing the characteristics of the zero-sequence current upstream and downstream of the fault point when a single-phase grounding fault occurs in the resonant grounding system, it is concluded that the waveform similarity on both sides of the fault point is low. DTW distance can be used to measure the similarity of two time series, and has the characteristics of good fault tolerance and synchronization error tolerance. According to the rule that the DTW value of faulty section is much larger than that of nonfaulty sections, FCM clustering is used to classify the DTW value of each section. The membership degree matrix and cluster centers are obtained. In the membership degree matrix, the section corresponding to the data in a class of their own is the faulty section, and all other data correspond to the nonfaulty section; otherwise, it is a fault occurring at the end of the line. The simulation results of MATLAB/Simulink and the field data test show that the method can accurately locate the faulty section.

The similarity between a pair of time series, i.e., sequences of indexed values in time order, is often estimated by the dynamic time warping (DTW) distance, instead of any in the well-studied family of measures including the longest common subsequence (LCS) length and the edit distance. Although it may seem as if the DTW and the LCS(-like) measures are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted. For a given pair of time series of n integers between zero and c, we propose an integer sequence that represents any substring-substring DTW distance as its band-substring LIS length. The length of the produced integer sequence is O(c⁴ n²) or O(c² n²) depending on the variant of the DTW distance used, both of which can be translated to O(n²) for constant cost functions. To demonstrate that techniques developed under the LCS(-like) measures are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.

Dynamic time warping(DTW) distance is the most effective similarity measurement methods in time-series data mining area. Because of the high computational complexity of DTW, it is not suitable for huge amounts of data. Thus, some DTW distance lower bound methods are proposed, which can improve the efficiency of DTW distance calculation. In this paper, we summarized the existing lower bound of DTW method and then proposed a closely related field with the application and exacted Lower Bounding distance measure with Segmentation(eLBS). Experiment results show that this method meet the need of non-omission.

The similarity between a pair of time series, i.e., sequences of indexed values in time order, is often estimated by the dynamic time warping (DTW) distance, instead of any in the well-studied family of measures including the longest common subsequence (LCS) length and the edit distance. Although it may seem as if the DTW and the LCS(-like) measures are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted. For a given pair of time series of length n such that the dissimilarity between any elements is an integer between zero and c, we propose an integer sequence that represents any substring-substring DTW distance as its band-substring LIS length. The length of the produced integer sequence is O(c n^2), which can be translated to O(n^2) for constant dissimilarity functions. To demonstrate that techniques developed under the LCS(-like) measures are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.

Synchronized spontaneous low frequency fluctuations of the so called BOLD signal, as measured by functional Magnetic Resonance Imaging (fMRI), are known to represent the functional connections of different brain areas. Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient and the usage of the DTW algorithm has further advantages: beside the DTW distance, the algorithm generates the warping path, i.e. the time-delay function between the compared two time-series. In this paper, we propose to use the relative length of the warping path as classification feature and demonstrate that the warping path itself carries important information when classifying patients according to cannabis addiction. We discuss biomedical relevance of our findings as well.

Rice is a food commodity that is a basic need for Indonesian people. Since the end of 2022, average rice prices in Indonesia have been increasing, breaking the record for the highest price from August to October 2023. The price of rice in each province in Indonesia is different. This can happen because rice center provinces will distribute their rice production to other regions to meet rice needs. The grouping of provinces in Indonesia based on rice prices over time is an interesting thing to research. The analysis method used to group similar objects into groups for time series data is called clustering time series. The distance that can be used to measure the closeness of two-time series is the Dynamic Time Warping (DTW) distance. The clustering analysis used is the single, complete, average, Ward, and median linkage method. The results of the analysis show that time series clustering in provinces in Indonesia based on rice prices is best using median linkage hierarchical clustering. The median linkage method has a cophenetic correlation coefficient value of 0.899064, meaning that clustering using the DTW distance with the median difference is very good. The resulting clusters contained 3 clusters which had different characteristics between the clusters. There are 2 clusters that can be of concern to the government, because there are clusters that have rice prices that have always been high in the last period and there are provincial clusters that have rice prices that are very diverse or can be said to be unstable.

In this paper, we propose an efficient approach that computes the dynamic time warping (DTW) distance in time-series similarity search. The DTW distance is known to offer the high accuracy in similarity search, but it has difficulty in supporting the large database due to its high computational complexity. Recently, FastDTW and FTW have been proposed for efficient computation of DTW distances, but they have still performance limitations. In this paper, we propose a hybrid approach, called HybridFTW, which combines the advantages of both FastDTW and FTW. First, HybridFTW takes the advantage of FastDTW that provides fast computation through the limitation of allowable ranges. We call these allowable ranges dynamic (warping) bands, which reduce the computation spaces on the fly, and we reanalyze previous FastDTW and FTW in the viewpoint of static and dynamic bands. Second, HybridFTW also takes the advantage of FTW that exploits the early abandon effect by using the segment-based tight lower bound. To maximize the synergy of combining two methods, we obtain the dynamic band of FastDTW during the process of computing the lower bound in FTW. Using HybridFTW, we next propose range search and k-NN search algorithms and prove their correctness through formal theorems. Experimental results on real and synthetic data sets show that HybridFTW improves the search performance by up to 38 times over FastDTW and by up to 12 times over FTW.

One of the most famous algorithms for time series data clustering is k -means clustering with Euclidean distance as a similarity measure. However, many recent works have shown that Dynamic Time Warping (DTW) distance measure is more suitable for most time series data mining tasks due to its much improved alignment based on shape. Unfortunately, k -means clustering with DTW distance is still not practical since the current averaging functions fail to preserve characteristics of time series data within the cluster. Recently, Shape-based Template Matching Framework (STMF) has been proposed to discover a cluster representative of time series data. However, STMF is very computationally expensive. In this paper, we propose a Shape-based Clustering for Time Series (SCTS) using a novel averaging method called Ranking Shape-based Template Matching Framework (RSTMF), which can average a group of time series effectively but take as much as 400 times less computational time than that of STMF. In addition, our method outperforms other well-known clustering techniques in terms of accuracy and criterion based on known ground truth.

One of the advantages of the kernel methods is that they can deal with various kinds of objects, not necessarily vectorial data with a fixed number of attributes. In this paper, we develop kernels for time series data using dynamic time warping (DTW) distances. Since DTW distances are pseudo distances that do not satisfy the triangle inequality, a kernel matrix based on them is not positive semidefinite, in general. We use semidefinite programming (SDP) to guarantee the positive definiteness of a kernel matrix. We present neighborhood preserving embedding (NPE), an SDP formulation to obtain a kernel matrix that best preserves the local geometry of time series data. We also present an out-of-sample extension (OSE) for NPE. We use two applications, time series classification and time series embedding for similarity search, to validate our approach.

Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization. The current seismic waveform clustering algorithms are predominantly based on a fixed time window, which is applicable for layers of stable thickness. When a layer exhibits variable thickness in the seismic response, a fixed time window cannot provide comprehensive geologic information for the target interval. Therefore, we propose a novel approach for a waveform clustering workflow based on a variable time window to enable broader applications. The dynamic time warping (DTW) distance is first introduced to effectively measure the similarities between seismic waveforms with various lengths. We develop a DTW distance-based clustering algorithm to extract centroids, and we then determine the class of all seismic traces according to the DTW distances from centroids. To greatly reduce the computational complexity in seismic data application, we propose a superpixel-based seismic data thinning approach. We further propose an integrated workflow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms. We evaluated the performance by applying the proposed workflow to synthetic seismograms and seismic survey data. Compared with the the traditional waveform clustering method, the synthetic seismogram results demonstrate the enhanced capability of the proposed workflow to detect boundaries of different lithologies or lithologic associations with variable thickness. Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workflow correlates well with the geological characteristics of wells in terms of reservoir thickness.

A fault location method for flexible DC power grid based on active control of fault current is proposed. Based on the high controllability of hybrid MMC, the fault current is divided into two stages: S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . Stage S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> is mainly used to realize fast suppression of fault current and provides conditions for smooth transition of stage S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . In stage S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , the fault can be accurately located by injecting reverse sinusoidal current with specific frequency. This paper firstly takes the asymmetric "hand in hand" flexible DC grid as a typical topology to analyze the rising rate of fault current in the initial stage of DC fault, and then a fault current reference signal is designed based on the characteristics of initial fault current. Next, it introduces the fault location principle and corresponding fault location process of stage S2. Finally, it builds a simulation model of ± 10kV /21 level two terminal DC power grid in PSCAD/EMTDC to verify the accuracy of fault location. Compared with other location methods, the simulation results show that this method has a better ability of anti-noise interference and fault resistance. Morever, not only is this method economical, but also the location error of it is stable within 1.5%.

The fast dc line protection is one of the key techniques for the multi-terminal flexible dc grid whose main technical difficulty lies in quick and reliable fault identification. The traditional protection principles for the ac system are not suitable for the flexible dc grid, and those for the conventional dc transmission system can also not be applied directly. Therefore, the frequency-domain traveling-wave boundary characteristics after faults are analyzed first in this paper to find the critical condition for distinguishing the internal and external faults as well as the forward and backward faults. And then, a novel single-ended transient-voltage-based protection strategy for flexible dc grid was proposed, which mainly consists of the fault identification criterion and the direction criterion. The protection scheme used wavelet transform to extract transient voltage energy. In order to eliminate the influences of the wavelet transform boundary effect and converter blocking, the principle to select the proper data window was proposed. Compared with the existing traveling-wave-based dc protection, the proposed method has a higher reliability and stronger ability to endure high transition resistance, which was verified by lots of simulation cases based on the PSCAD/EMTDC.

Ice coating caused by ice and snow disasters is extremely harmful to DC power grids. Therefore, the research and application of ice melting technology is of great significance for DC grids to combat ice disasters. According to the advantages and limitations of a variety of DC transmission line melting methods, the paper decided to select the two types of operation modes, namely, power flow control and converter station bipolar parallel connection, to analyze the ice melting of overhead lines. Firstly, the paper calculates the minimum ice-melting current of the overhead line and the corresponding ice-melting time of different ice-melting currents. Then the ice-melting analysis is performed on two typical overhead lines using the power flow control method, and one of the overhead lines is melted. For another line, the ice-melting is implemented using the bipolar parallel operation method. On this basis, the paper proposes a switching operation scheme for switching to bipolar parallel operation. Finally, it is concluded that the power flow control method and the bipolar parallel operation of the converter station can improve the current of the transmission line, thus achieving the melting of the overhead line of the four-terminal flexible DC grid.

Flexible DC grids are an important technological means for optimizing power supply structures and promoting energy transition. However, as a system with low inertia and weak damping, the flexible DC grid inherently faces challenges, such as rapid rising of fault currents, vulnerability to significant damage, difficulty in fault interruption, and with regard to the poor overcurrent-withstanding capabilities of power electronic devices. To address these issues, this paper proposes a method for calculating the single-pole ground fault current in a symmetrical monopolar DC grid, and further introduces a matrix exponential calculation method. This method enables quantitative analysis of the influence of various component parameters on the fault current, taking into account the dynamic characteristics of both the faulted and healthy poles in the DC system. The results demonstrate the high accuracy of this calculation method. The analysis reveals that the inductance of the faulted branch has the greatest impact on the fault current, while the inductances of the adjacent outgoing lines also have a certain influence. In contrast, the inductances of lines not adjacent to the faulted branch have minimal impacts on the fault current. Furthermore, the grounding electrode parameters of the converter station connected to the faulted branch exert the most significant influence on the fault current, with the grounding electrode parameters of neighboring converter stations also showing a notable effect. This indicates that the fault current is impacted by the topology of the nearby DC grid, but is not affected by the fault currents at remote converter stations.

To ensure the safe operation of complex equipment and specify maintenance strategies, the prediction of the remaining useful life (RUL) and the identification of failure modes are of great significance. Generally, it is difficult to directly differentiate the fault modes from the raw data. Thus, a novel fault clustering and RUL prediction framework under multiple failure modes is proposed in this paper. First, dynamic time warping (DTW) distance is combined with k-medoids to identify multiple failure modes. Following that, a convolutional neural network connecting with a long short-term memory (CNN-LSTM) is constructed to extract hidden features and predict RUL. In the end, the experiment is conducted on an engine dataset provided by NASA. The results show that the proposed framework has a better performance compared with the competitive prediction methods.