Islanding detection method based on image classification technique using histogram of oriented gradient features

Feature selection is a commonly addressed problem in classification. In gene expression-based cancer classification, a large number of genes in conjunction with a small number of samples makes the gene selection problem more important but also more challenging. Support vector machine as a popular classification algorithm, has been successfully used in SVM-RFE method for gene selection. This paper proposes a variant of SVM-RFE to do gene selection for cancer classification with expression data. Multiple support vector machine classifiers from a leave-one-out procedure are used to compute the feature ranking scores. The numerical experiments also show the good and stable performance of the proposed method.

An Islanding Fault Detection Method with CFDF-SVM Based RPV Approach under Pseudo Islanding Phenomenon

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

An active islanding detection technique for current controlled inverter

Support Vector Machine (SVM) is mainly used to classify the data into two categories. To solve the multi-category problems using SVM, researchers used two approaches. The first approach based on solving multiple SVM binary classifiers, whereas another approach based on solving a single optimization problem. In this paper, we have used the first approach and proposed an Efficient Multiclass Support Vector Machine (ESVM) algorithm using a skewed binary tree. To construct the skewed binary tree, no extra efforts are required as compared to the binary tree approach. The algorithm is tested on the benchmark data sets, and the results are compared with both the multiclass approaches of SVM. The ESVM’s results are compared with five techniques of solving multiple binary SVM classifiers and four techniques of solving a single optimization problem. The comparative experiments prove the efficiency of the ESVM in terms of its accuracy as compared to other contemporary algorithms. Further, ESVM is successfully applied for classification of the email dataset into positive, negative and neutral sentiments.

The capability of islanding detection has become a requirement for grid connection of inverters utilized in distributed generation (DG) systems. The parallel RLC resonant circuit comprised, as a local load, in the standard test of islanding detection schemes offers lower impedance for higher frequency harmonics. With the presence of such a parallel RLC circuit, the full power balance between the distributed generation and local load will never be satisfied when the voltage at the point of common coupling is slightly distorted. In this paper, the performance of grid-connected inverter during islanding detection routine test is analyzed with sinusoidal and marginally distorted grid voltage. The results presented in this paper show that grid background harmonics may have significant impacts on inverter control and islanding detection algorithms.

Islanding is a major problem that is related to a grid connected distribution generator. During islanding the distribution generator gets disconnected from the grid while still being energized. It should be detected as fast as possible in order to prevent the distribution generator from getting damaged or to ensure the safety of the personnel working. The islanding detection makes use of system parameter to detect islanding at the time of occurrence. In this paper a passive islanding detection method based on ripple content of voltage at the point of common coupling is being used to differentiate between islanding and non-islanding conditions. A threshold level is set upon the ripple content of voltage at the point of common coupling and islanding is detected whenever the threshold level is exceeded for some certain period of time. This method effectively distinguishes the islanding condition from other non-islanding condition and detects the islanding condition efficiently. The working of this method is being ensured by simulating a variety of islanding and non-islanding condition. No non-detection zone was shown by this method and islanding conditions were detected within 150ms of the allowed delay time. Theoretical analysis and simulation results are presented to validate the proposed method.

Islanding is a phenomenon that may prove dangerous for the utility workers who are unaware that the connection is powered. Thus control over the network is essential and its detection may prevent further fatality and automatic reconnection of the circuit when not required. The research work aims at developing a technique in islanding detection in a microgrid environment. In this paper, the rate of change of negative sequence voltage (ROCONSV) is considered for identify islanding and non-islanding condition. The negative sequence voltage at Vpcc is calculated at the point of common coupling (PCC) and compared with a set threshold to verify the technique. The proposed technique is tested for islanding and non-islanding event in MATLAB/Simulink environment. The proposed technique is verified in various system conditions such as 0% and 50% active power mismatch, DG disconnection, different fault cases, and capacitor switching. In all cases, the technique works properly. MATLAB R2019a software is used for the proposed islanding detection method (IDM).KeywordsIslandingPassive methodAlert signalDistributed generationCircuit breaker

In this paper, we propose a framework for privacy-preserving outsourced drug discovery in the cloud, which we refer to as POD. Specifically, POD is designed to allow the cloud to securely use multiple drug formula providers’ drug formulas to train Support Vector Machine (SVM) provided by the analytical model provider. In our approach, we design secure computation protocols to allow the cloud server to perform commonly used integer and fraction computations. To securely train the SVM, we design a secure SVM parameter selection protocol to select two SVM parameters and construct a secure sequential minimal optimization protocol to privately refresh both selected SVM parameters. The trained SVM classifier can be used to determine whether a drug chemical compound is active or not in a privacy-preserving way. Lastly, we prove that the proposed POD achieves the goal of SVM training and chemical compound classification without privacy leakage to unauthorized parties, as well as demonstrating its utility and efficiency using three real-world drug datasets.

A technique for detection of islanding in a microgrid on the basis of rate of change of superimposed impedance (ROCSI)

Many techniques used and still in usage for solving the problem of islanding detection are intrinsically passive, active, or hybrid of both. Each one of them has its own benefits and drawbacks. In this paper, we propose a method, which takes the advantage of a machine learning (ML)-based algorithm, namely, support vector machine (SVM), in order to produce the results more efficiently. The results of the simulations based on the model and experimentally measured parameters of a real-life practical photovoltaic (PV) plant give much better output than the traditional reported methods. During the tests and simulations, an additional problem, namely, grid fault, emerged, posing new challenges for the proposed method. Occurrences of islanding and grid fault are grouped together with the same kernel dimension and no custom hyperplane bordering. Discrimination between islanding and grid fault events is an essential dilemma, which is handled by the proposed SVM-based algorithm to achieve more precision in islanding detection and simultaneously detect the grid faults authentically. Nondetection zones (NDZs) and detection time (DT) are tested using two dimensions, namely, the generated active energy from PV plant (0%–110% of $P_{n}$ ) and distribution network voltage levels (±10% of $U_{n}$ ). Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment, and several tests are executed for several scenarios. Finally, comparisons with previously reported techniques prove the effectiveness, authenticity, selectivity, accuracy, and precision of the proposed islanding and grid fault detection strategy with allowable impact on power quality according to UL1741 and its superiority over other methods.

The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared with Support Vector Machine (SVM) and Independent Component Discrimination Analysis (ICDA)based image classifications obtained for comparison purposes. This study aims to improve the robustness of image classification based on overall accuracy (OA) and kappa coefficient. The variation of OA with this technique, between different classes of a satellite image, is only10%, whereas that with the SVM and ICDA techniques is more than 50%.

In this paper, we proposed not only an extraction methodology of multiple feature vectors from ultrasound images for carotid arteries (CAs) and heart rate variability (HRV) of electrocardiogram signal, but also a suitable and reliable prediction model useful in the diagnosis of cardiovascular disease (CVD). For inventing the multiple feature vectors, we extract a candidate feature vector through image processing and measurement of the thickness of carotid intima-media (IMT). As a complementary way, the linear and/or nonlinear feature vectors are also extracted from HRV, a main index for cardiac disorder. The significance of the multiple feature vectors is tested with several machine learning methods, namely Neural Networks, Support Vector Machine (SVM), Classification based on Multiple Association Rule (CMAR), Decision tree induction and Bayesian classifier. As a result, multiple feature vectors extracted from both CAs and HRV (CA+HRV) showed higher accuracy than the separative feature vectors of CAs and HRV. Furthermore, the SVM and CMAR showed about 89.51% and 89.46%, respectively, in terms of diagnosing accuracy rate after evaluating the diagnosis or prediction methods using the finally chosen multiple feature vectors. Therefore, the multiple feature vectors devised in this paper can be effective diagnostic indicators of CVD. In addition, the feature vector analysis and prediction techniques are expected to be helpful tools in the decisions of cardiologists.

Study PQ control mode of the inverter-based distributed energy resources(DER), and analyses its system characteristics while DER operating connected with distribution network. When the point of common coupling (PCC) between DER and distribution network is broken and the island formed, analyzed change characteristics of frequency and voltage phase and the influence of unbalanced power in islanding system, especially remarkable phase difference changes will occur between large power bus side and DER side voltage. Based on this the islanding detection and islanding protection method based on WAN phase comparison is proposed, the islanding detection criterion is the voltage phase difference between distribution network bus side and DER side, the synchronous measurement and calculation of voltage phase on both sides are completed by the feeder automation terminals (FTU), and realize information exchange through the rapid communication system. Based on Matlab, established the islanding detection simulation model, experimental results verify the reliability and applicability of the method‥

The increasing energy demand of the consumers necessitates the incorporation of generation in distribution level. However, the increasing penetration of distributed generators (DGs) leads to complexity in designing the power system protection schemes. Islanding detection is one of such problem faced in the distributed generation system. In recent years, many techniques are proposed for islanding detection which have short coming while detecting islanding modes. In this paper, a new islanding detection technique using Hilbert Transform (HT) is proposed. The positive sequence voltages obtained at the point of common coupling (PCC) and the DGs location are utilized for the detection of islanding situation in the system. Various case studies are presented to corroborate the efficacy of the proposed technique.