In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.

/pdf/an-improved-pso-algorithm-for-the-classification-of-multiple-aorghaqnzt.pdf

An Improved PSO Algorithm for the Classification of Multiple Power Quality Disturbances

Research on power quality disturbance analysis and identification based on LSTM

Electrical transmission lines are the most significant part of a power systems in terms of their spread and length with respect of other components. With their huge development due to the growing demand, network losses are an issue that needs the permanent attention of power network providers and distributors. The difficulties of predictive maintenance of power grids are related to the detection of early warning indicators of weaknesses of electrical cables. These indicators might be defined by partial discharges, corona effects, electrical arcs, all these phenomena being characterized by transient signals propagating in cables. Identifying such signals can be very helpful to assess their sources usually located in the weak parts of the grid. In this paper, we present a new approach for the detection and characterization of these types of transient phenomena in power grid using the phase diagram domain. The extracted features are classified using Support Vector Machine, Naïve Bayes and k-Nearest Neighbors. The experimental results indicate that the proposed method provides interesting results in the classification of real-life power grids signals, being a potential solution for predictive maintenance of electrical cables.

Transient power grid phenomena classification based on phase diagram features and machine learning classifiers

El monitoreo de la calidad de energia presente en una red es un requerimiento importante dada la tendencia hacia multiples generaciones que crean condiciones de operacion muy dinamicas. Este trabajo presenta nuevos algoritmos en el dominio del tiempo que detectan y cuantifican los eventos que afectan la calidad de energia en una red electrica de acuerdo con los estandares vigentes. Los algoritmos se basan en una estructura cognitiva de la senal que brinda la exposicion de manera simple y directa de la informacion embebida en ella. Se representa a la senal como una union de segmentos prefijados en lugar de muestras. Se describen los algoritmos propuestos mostrando resultados sobre formas de onda reales y simuladas.

Algoritmos de Detección y Clasificación de Eventos de Calidad de Energía en el Dominio Temporal

—Abnormal event detection is critical in the safe operation of power system. In this paper, using the data collected from phasor measurement units (PMUs), two methods based on Fisher random matrix are proposed to detect faults in power grids. Firstly, the fault detection matrix is constructed and the event detection problem is reformatted as a two-sample covariance matrices test problem. Secondly, the central limit theorem for the linear spectral statistic of the Fisher matrix is derived and a test statistic for testing faults is proposed. To save computing resources, the screening step of fault interval based on the test statistic is designed to check the existence of faults. Then two point-by-point methods are proposed to determine the time of the fault in the interval. One method detects faults by checking whether the largest sample eigenvalue falls outside the supporting set of limiting spectral distribution of the standard Fisher matrix, which can detect the faults with higher accuracy. The other method tests the faults based on the statistic proposed, which has a faster detection speed. Compared with existing works, the simulation results illustrate that two methods proposed in this paper cost less computational time and provide a higher degree of accuracy.

Fisher Matrix Based Fault Detection for PMUs Data in Power Grids

This paper, by using support vector machine (SVM) identification of power quality disturbance signals are classified. In order to obtain better classification results, we need to make a pretreatment for power quality disturbance data. Because wavelet transform has good local characteristics of the processing ability, so the disturbance signal uses wavelet transform to extract the scale of the energy difference as a feature vector At the same time the Lib — SVM is used to solve the problem of multi class SVM classification, besides, we put forward two steps grid method for SVM parameters optimization. We use MATLAB software to produce disturbance signal data samples and add SNR = 25 db gaussian white noise. Simulation result shows that the proposed classification method of the correct recognition rate is higher, so the correctness and effectiveness of the presented approach are correct and effective.

Based on SVM power quality disturbance classification algorithm

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Based on SVM power quality disturbance classification algorithm