Fault diagnosis has been studied actively across the electrical industry to help maintain the stability of electrical equipment. Among this equipment, shielded cables, which are widely used in various industrial sectors, require careful and periodic diagnosis, owing to their poor installation environments and potential for creating huge economic losses. Reflectometry is a representative solution to locate the cable faults. However, conventional reflectometry techniques require prior knowledge about the cable under test, such as the reference wave velocity, total length of the cable, etc. Moreover, the degree of failure cannot be determined using conventional methods. In this paper, a novel reflectometry technique is proposed to locate and evaluate the faults in a cable, without requiring any prior knowledge. A general regression neural network based on the kernel density estimation is utilized with special feature extraction procedures. The proposed method is tested in an actual test bed with two types of emulated faults, and is found to estimate both the fault location and reflection coefficient successfully. It is expected that the proposed method can improve the stability of industrial equipment.

Diagnosis of Shielded Cable Faults via Regression-Based Reflectometry

This paper develops and applies an inductive directional coupling technology based on spread spectrum time domain reflectometry for non-intrusive power cable fault diagnosis. Different from existing capacitive coupling approaches with large signal attenuation, an inductive coupling approach with a capacitive trapper is proposed to reduce signal attenuation, restrict transmitting the detection signal to power source, and eliminate the effect of the power source impedance mismatch. Moreover, a novel structure with two parallel couplers is developed to enhance the effect of capacitive trapper with small capacitance. The development, design, analysis, and implementation of the proposed approach are discussed in details. A series of simulations and experiments on cables with different fault modes at different locations are conducted, along with comparison with existing capacitive coupling approaches, to verify and demonstrate the effectiveness of the proposed method.

Non-Intrusive Cable Fault Diagnosis Based on Inductive Directional Coupling

In most spatial data management applications, objects are represented in terms of their coordinates in a 2-dimensional space and search queries in this space are processed using spatial index structures. On the other hand, bitmap-based indexing, especially thanks to the compression opportunities bitmaps provide, has been shown to be highly effective for query processing workloads including selection and aggregation operations. In this paper, we show that bitmap-based indexing can also be highly effective for managing spatial data sets. More specifically, we propose a novel compressed spatial hierarchical bitmap (cSHB) index structure to support spatial range queries. We consider query workloads involving multiple range queries over spatial data and introduce and consider the problem of bitmap selection for identifying the appropriate subset of the bitmap files for processing the given spatial range query workload. We develop cost models for compressed domain range query processing and present query planning algorithms that not only select index nodes for query processing, but also associate appropriate bitwise logical operations to identify the data objects satisfying the range queries in the given workload. Experiment results confirm the efficiency and effectiveness of the proposed compressed spatial hierarchical bitmap (cSHB) index structure and the range query planning algorithms in supporting spatial range query workloads.

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Compressed spatial hierarchical bitmap (cSHB) indexes for efficiently processing spatial range query workloads

In this paper, new time–frequency-based anomaly detection methodology is proposed for the condition monitoring of high-temperature superconducting (HTS) cable systems. The time–frequency-based anomaly detection methodology includes two indices, which are obtained via cross Wigner–Ville distribution and scattering parameter. For the validation of the proposed methodology, the methodology is applied to an ac 22.9-kV/50-MVA HTS cable system connected to the real power grid network. Furthermore, the changes in dielectric properties of the HTS cable system during the cooling process and the current imbalance failure are monitored. The proposed anomaly detection indices are shown to be able to detect the heat and the current imbalance caused by the malfunction of the joint box. For the application of the two validated indices, new anomaly detection procedure for HTS cable systems is also presented.

Time–Frequency-Based Condition Monitoring of 22.9-kV HTS Cable Systems: Cooling Process and Current Imbalance

This paper describes the paraphrase generation based on n-gram approach. N-grams are relevant words of text document that can be applied for a range of Natural Language Processing (NLP) applications. The candidate paraphrases are generated based on trigrams approach. The reference paraphrases (keyphrases) are the set of relevant paraphrases, which acts like training data set for generating candidate paraphrases. The task of paraphrase generation is similar to machine translation; hence we used machine translation evaluation metrics. R-precision evaluation metric is used to find the number of common words between candidate and reference paraphrases.

N-gram based paraphrase generator from large text document

Although there are many reasons why medium voltage (MV) overhead lines with covered conductors (CCs) are used instead of aluminum-conductor steel-reinforced cables, the operation of CCs takes into account a difficult detection of faults, e.g., the contact of a tree branch with a CC. In this paper, we continue with the previous work on an electric voltage-based method enabling the detection of the CC faults; we introduce a new indicator of the faults based on the frequency of peaks in the signal of partial discharges. We mainly demonstrate the fault detection in a real MV distribution system, including a number of fault detectors; a fault is detected in 99.2% of occurrences within a long-term testing, although the cost of the detector is minimized in comparison with other similar fault detectors.

A Novel Method for Detection of Covered Conductor Faults in Medium Voltage Overhead Line Systems

N-grams are applied in some applications searching in text documents, especially in cases when one must work with phrases, e.g. in plagiarism detection. N-gram is a sequence of n terms (or generally tokens) from a document. We get a set of n-grams by moving a floating window from the begin to the end of the document. During the extraction we must remove duplicate n-grams and we must store additional values to each n-gram type, e.g. n-gram type frequency for each document and so on, it depends on a query model used. Previous works utilize a sorting algorithm to compute the n-gram frequency. These approaches must handle a high number of the same n-grams resulting in high time and space overhead. Moreover, these techniques are often main-memory only, it means they must be executed for small or middle size collections. In this paper, we show an index-based method to the n-gram extraction for large collections. This method utilizes common data structures like B+-tree and Hash table. We show the scalability of our method by presenting experiments with the gigabytes collection.

Index-based n-gram extraction from large document collections

Multidimensional data structures have become very popular in recent years. Their importance lies in efficient indexing of data, which have naturally multidimensional characteristics like navigation data, drawing specifications etc. The R-tree is a well-known structure based on the bounding of spatial near points by rectangles. Although efficient query processing of multidimensional data is requested, the R-tree has been shown to be inefficient in many cases. From the disk access cost point of view, the main issue of range query processing is the expensive cost of random accesses during the tree traversal. In the case of queries with low selectivity, the sequential scan of all tuples may be more efficient than the range query processing. We focus on efficiency of the disk access cost and we present an optimization of the disk access cost during range query processing. Our method focuses on a leaf node retrieval and it can be simply adopted by any tree. We put forward our tests using the R-tree since it is the most common multidimensional data structure.

Optimization of disk accesses for multidimensional range queries

Multidimensional data are commonly utilized in many application areas like electronic shopping, cartography and many others. These data structures support various types of queries, e.g. point or range query. The range query retrieves all tuples of a multidimensional space matched by a query rectangle. Processing range queries in a multidimensional data structure has some performance issues, especially in the case of a higher space dimension or a lower query selectivity. As result, these data are often stored in an array or one-dimensional index like B-tree and range queries are processed with a sequence scan. Many real world queries can be transformed to a multiple range query: the query including more than one query rectangle. In this article, we aim our effort to processing of this type of the range query. First, we show an algorithm processing a sequence of range queries. Second, we introduce a special type of the multiple range query, the Cartesian range query. We show optimality of these algorithms from the IO and CPU costs point of view and we compare their performance with current methods. Although we introduce these algorithms for the R-tree, we show that these algorithms are appropriate for all multidimensional data structures with nested regions.

On the efficiency of multiple range query processing in multidimensional data structures

Current main stream CPUs provide SIMD (Single Instruction Multiple Data) computational capabilities Although producers of current hardware provide other computational capabilities like multi-cores CPU, GPU or APU, an important feature of SIMD is that it provides parallel operations for one CPU’s core In previous works, authors introduced an utilization of the SIMD instructions in some indexing data structures like B-tree Since multidimensional data structures manage n-dimensional tuples or rectangles, the utilization of these instructions seems to be straightforward in operations manipulating these n-dimensional objects In this article, we show the utilization of SIMD in the R-tree data structure Since the range query is one of the most important operation of multidimensional data structures, we suppose the utilization of SIMD in range query processing Moreover, we show properties and scalability of this solution We show that the SIMD range query algorithm is up-to 2× faster then the conventional algorithm

Peter Chovanec

Papers

A Novel Method for Detection of Covered Conductor Faults in Medium Voltage Overhead Line Systems

Index-based n-gram extraction from large document collections

Optimization of disk accesses for multidimensional range queries

On the efficiency of multiple range query processing in multidimensional data structures

Processing of Multidimensional Range Query Using SIMD Instructions