Support Vector Machine Based Fault Location Identification in Microgrids Using Interharmonic Injection

Forouzesh, Alireza; Golsorkhi, Mohammad S.; Savaghebi, Mehdi; Baharizadeh, Mehdi

doi:10.3390/en14082317

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…There has been a wide range of approaches for locating distribution network faults. Impedance-based [7], traveling wave-based [8], and intelligent approaches [9] are among the most widely deployed fault location methods. A comprehensive review of different types of fault location methods and their own sets of advantages and disadvantages are reported in [10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Mirshekali

Dashti

Keshavarz

et al. 2022

Sensors

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Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Mirshekali

Dashti

Keshavarz

et al. 2022

Sensors

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show abstract

“…For diagnosing the faulty condition of three-phase induction motor with an external rotor-bearing system, Gangsar et al has applied the MSVM algorithm while the features are obtained from the time-domain current and vibration signals 24 . By using features from interharmonic voltages, the MSVM identifies the fault positions within the defective zone 25 . Therefore, Kazemi et al developed the extended Kalman filter-based SVM model to classify the three-phase residual currents in the primary winding of a transformer, where three residual signals are defined as the discrepancies between the measured and estimated three-phase currents 26 .…”

Section: Introductionmentioning

confidence: 99%

An energy and leakage current monitoring system for abnormality detection in electrical appliances

Alam

Shahjalal

Rahman

et al. 2022

Sci Rep

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Unsafe electrical appliances can be hazardous to humans and can cause electrical fires if not monitored, analyzed, and controlled. The purpose of this study is to monitor the system’s condition, including the electrical properties of the appliances, and to diagnose fault conditions without deploying sensors on individual appliances and analyzing individual sensor data. Using historical data and an acceptable range of normal and leakage currents, we proposed a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) to determine the changes in leakage currents caused by installed devices at a certain moment. For this, we developed a sensor-based monitoring device with long-range communication to store real-time data in a cloud database. In the modeling process, RBC algorithm is used to diagnose the constructed device fault and overcurrent fault where MSVM is applied for detecting leakage current fault. To conduct an operational field test, the developed device was integrated into some houses. The results demonstrate the effectiveness of the proposed system in terms of electrical safety monitoring and detection. All the collected data were stored in a structured database that could be remotely accessed through the Internet.

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“…In Ref. [16], the authors used actual fault signals instead of normalising and extracting features. Although this gives high efficiency, a significant drawback of using the raw signals instead of transforming them into numerical features is that the ML model becomes prone to overfitting.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Protection Scheme for Low Voltage AC Microgrids

Uzair

Eskandari

et al. 2022

Energies

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The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.

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Support Vector Machine Based Fault Location Identification in Microgrids Using Interharmonic Injection

Cited by 12 publications

References 20 publications

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

An energy and leakage current monitoring system for abnormality detection in electrical appliances

Machine Learning Based Protection Scheme for Low Voltage AC Microgrids

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