Detection Of Power Disturbances Research Articles

Power Disturbance Monitoring through Techniques for Novelty Detection on Wind Power and Photovoltaic Generation.

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.

Identification and detection algorithm of electric energy disturbance in microgrid based on wavelet analysis and neural network

In the presence of power disturbance, the test accuracy of power is not good, in order to improve the performance of power testing, it is necessary to carry out the power disturbance detection design. A power disturbance detection algorithm based on wavelet analysis and neural network is proposed. The time domain and frequency domain decomposition are used to decompose the characteristic of the power disturbance signal, and the wavelet analysis method is used to improve the adaptive focusing performance of the power disturbance signal. The one-dimensional power disturbance function is mapped to the two-dimensional function of time scale and time shift by continuous wavelet transform, and the higher-order spectral characteristic quantity of the power disturbance signal is extracted, and the extracted characteristic quantity is automatically classified by neural network. The optimal identification and detection of power disturbance are realized. The simulation results show that the algorithm has higher accuracy and higher recognition ability, which improves the accurate probability of power disturbance detection and the anti-disturbance ability of power test.

The Application of S-Transform to Reduce Border Distortion Effect Based on Window Length

<p>The enhancement of powerful signal processing tools has broadened the scope research in power quality analysis.The necessity of processing tools to compute the signals accurately without border distortion effect presence has demanded nowadays. Hence, S-Transform has been selected in this paper as a time-frequency analysis tools for power disturbance detection and localization as it capable to extract features and high resolution to deal with border distortion effect. Various window length signal has been analyzed to overcome the border distortion effect in S-Transform.To ascertain validity of the proposed scheme, it is validated with IEEE 3 bus test system and simulation results show that the proposed technique can minimize the border effect while detecting transient and voltage sag during fault system. As a result, the longest window length which is four cycle, outperform the least MSE value which indicate the best performance. While, the shortest window length resulting highest MSE value which indicate the worst performance.<em></em></p>

Identification of Power Disturbances Using Generalized Morphological Open-closing and Close-opening Undecimated Wavelet

This paper proposes a new technique, generalized morphological open-closing and close-opening undecimated wavelet (GMOCUW), based on which a power disturbance identification scheme is developed. In order to extract features of power disturbances, the proposed scheme employs undecimated wavelet transform for its advantage in retaining information and reducing waveform distortion, multiscale morphological analysis for its ability in frequency analysis, and generalized morphological open-closing (GMOC) and generalized morphological close-opening (GMCO) operations for their advantages in information preserving. Power system computer aided design/electro-magnetic transient in dc system (PSCAD/EMTDC) was employed to construct a test power system to simulate eight types of power disturbances. Additionally, a laboratory platform was established to generate power quality (PQ) signals under real operating conditions. The performance of GMOCUW has been compared with that of morphological gradient wavelet (MGW), new dual neural-network-based methodology (NDNM), S-transform (ST), and Daubechies 4 wavelet (DB4W). Comparison results have proved that, in power disturbance detection, GMOCUW is more accurate and faster than these methods.

Generalized Higher Order Energy Based Instantaneous Amplitude and Frequency Estimation and Their Applications to Power Disturbance Detection

The instantaneous amplitude (IA) based on the higher order differential energy operator is proposed. And its general form for arbitrary order is also proposed. The various definitions of the IA and the instantaneous frequency (IF) estimators are considered. The IA and IF estimators based on the energy operators need less computational cost than the conventional IF and IA estimators exploiting the Hilbert transform. The IF and IA estimators are compared in terms of the frequency and amplitude tracking accuracy of the AM-FM signals. For noiseless case, the IA and IF estimators based on the Teager-Kaiser energy operator show better tracking performance than the IF and IA estimators based on the higher energy operators. However, under noisy condition, the IF and IA estimator based on the higher order energy operators with the order 3 and 4 show better tracking than the Teager-Kaiser energy based estimators. The IF and IA estimators are applied to signals in the various power anomalies to show their usefulness as the disturbance detectors.

Disturbance detection using an improved hit-or-miss transform

Power disturbances have always been an important issue in the power system protection. This paper presents a novel algorithm, improved hit-or-miss transform (IHMT), for the detection of power disturbances. IHMT is a morphological operator that uses a structuring element (SE) consisting of two sets. Besides, it employs basic morphological operators only, which ensures the algorithm to perform fast calculation. Applied to power systems, IHMT detects the location and duration of disturbances. A variety of power disturbances have been simulated to evaluate the validity of IHMT.

Detection of power disturbances using morphological gradient wavelet

This paper presents a novel algorithm, morphological gradient wavelet (MGW), for the detection of power disturbances. MGW is a nonlinear wavelet transform that involves morphological operation in the decomposition process and presents the gradient information in the output. Applied to power systems, MGW extracts the features of disturbances and detects their location and duration. A variety of power disturbances have been simulated to evaluate the validity of MGW. Moreover, the advantages of the new algorithm are also addressed in comparison with Daubechies DB4 wavelet transform (DB4).