Identification of optimal features for fast and accurate classification of power quality disturbances

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Summary Protection is the main challenge for the operation of microgrids. This paper presents a new data mining method for identification of faulted line section for the protection of microgrids. This method uses wavelet packet transform (WPT) to extract a set of features from the fault voltage and current waveforms. The features are then pre‐processed and used for identifying the faulted line section through classification. Three classifiers are examined in this paper. In order to improve the classifiers performance, two pre‐processing steps are applied on the features. First, using three different feature selection methods, the irrelevant and redundant features are removed from the feature set. Secondly, using a supervised discretisation technique, the continuous features are converted into finite interval features. The performance of the proposed method is investigated and compared with previous methods using extensive simulation study on a complex microgrid. The results indicate that with only six discretised features, the proposed method has a fast and accurate performance by using the three classifiers examined, whereas one is more effective. Moreover, the proposed method only uses the measurement at the main substation, obviating the need for communication links to exchange data as used by most previous methods.

Section: Proposed Methodsmentioning

confidence: 99%

Identification of faulted line section in microgrids using data mining method based on feature discretisation

Jamali

Ranjbar

Bahmanyar

2020

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“…Some of the recent FS methods used for PQD&C application are genetic algorithms (GAs), [166][167][168][169] extension GA (EGA), 170 genetic k-means algorithm (GKA), 171 PSO, 172,173 Gram-Schmidt orthogonal transform for FS, 174 mutual information (M) and relief (R), 175 ant colony optimization (ACO), 176 and combination of three algorithms like GA, maximum relevance minimum redundancy (mRMR), and sequential forward selection (SFS). 177…”

Section: Feature Selection Techniques For Pq Disturbance Monitoringmentioning

confidence: 99%

“…205 FDST Maximum, minimum, mean, standard deviation, kurtosis of time vs maximum amplitude (TmA) plot, THD, skewness, and standard deviation of FmA plot 242HHT Energy, entropy, skewness, minimum, and maximum of amplitude curve from the first IMF, standard deviation, skewness, and energy of phase curve 177. 177 Energy, Shannon entropy, kurtosis, minimum, skewness, standard deviation, and total harmonic distortion (THD) related to fourth level detail coefficient.…”

mentioning

confidence: 99%

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

138

Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

“…It is obvious the ACUSUM algorithm only detects abrupt voltage increase, while it is required to detect voltage depressions in this work. In order to detect the sudden decrease in voltage, it is defined by

V_{d} = V_{ref} - normalV,

where V ref is the reference voltage evaluated by the conditions described in Khodadadi et al With identifying the increase of signal V d by ACUSUM algorithm, decrease in voltage signal, V, is detected. In this paper, the ACUSUM algorithm is modified for detection of voltage depression.…”

Section: System Description and Simulation Setupmentioning

confidence: 99%

“…where V ref is the reference voltage evaluated by the conditions described in Khodadadi et al 26 With identifying the increase of signal V d by ACUSUM algorithm, decrease in voltage signal, V, is detected. In this paper, the ACUSUM algorithm is modified for detection of voltage depression.…”

Section: Detection Of Voltage Depression By Acusummentioning

confidence: 99%

Voltage‐based protection of microgrids using decision tree algorithms

Ranjbar

Farsa

Jamali

2019

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Summary This paper presents a protection method for microgrids by data mining of voltage disturbances. The occurrence of a fault on the system is associated with a sudden voltage depression, which is fast detected by the adaptive cumulative sum (ACUSUM) algorithm. There are other operational (no‐fault) events causing voltage depression such as motor starting, transformer energizing, and capacitor or heavy load switching. In order to discriminate between the fault and no‐fault events, one cycle of the voltage waveform is preprocessed by the short‐time Fourier transform (STFT) to extract and construct effective features of the disturbance. The features are then used in the decision trees (DTs) for the discrimination. The proposed protection method is tested for fault or no‐fault conditions of grid‐connected or islanded mode of the microgrid operation, as well as radial or meshed topology. The proposed method also identifies the fault type and faulted phase(s) for selective phase tripping. The immunity of the method against different noise levels is investigated. It is shown by the simulation study that by using only two features for symmetrical events and six features for asymmetrical events, any fault can be detected accurately.