Discrimination of Internal Faults and Other Transients in an Interconnected System With Power Transformers and Phase Angle Regulators

Bera, Pallav Kumar; Işık, Can; Kumar, Vajendra

doi:10.1109/jsyst.2020.3009203

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…The single-phase 2-winding transformer fault model needed for faults in the exciting unit and the single-phase 3-winding transformer fault model needed for faults in the series unit (Fig. 1B) were designed in [23]. Equation (1) describes the voltage-current relationship for six-coupled coils of the 3-winding transformer.…”

Section: Modeling and Simulationmentioning

confidence: 99%

“…The problem has not been studied consistently because a complete PAR model with a provision to simulate internal faults is scarce. To adequately address this question, the ISPAR was modeled, internal faults were detected, and transients were classified for PARs and transformers in [23]; however, many problems like overexcitation, series core saturation, fault during magnetizing inrush, integration of renewables were not considered.…”

Section: Introductionmentioning

confidence: 99%

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A Data Mining Based Protection and Classification of Transients for Two-Core Symmetric Phase Angle Regulators

Bera

Işık

2021

IEEE Access

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Several conventional and non-conventional transient conditions cause differential relays associated with Phase Angle Regulators to malfunction. For Two-core Symmetric Phase Angle Regulators, this article investigates the suitability of time and time-frequency feature-based estimators to differentiate internal faults from other transient conditions such as overexcitation, external faults with current transformer (CT) saturation, and magnetizing inrush. Subsequently, the faulty core unit (series or exciting) is located, and the transients are identified. Six well-known classifiers are trained on features extracted from one-cycle of post transient 3-phase differential currents filtered by an event detector. Maximum Relevance Minimum Redundancy, Random Forest, and exhaustive search with Decision Trees are used to select the relevant wavelet energy, time-domain, and wavelet coefficient features respectively. The fault detection scheme trained on XGBoost classifier with hyperparameters obtained from Bayesian Optimization gives an accuracy of 99.8%. The reliability of the proposed scheme is verified with varying tap positions, noise levels, and transformer ratings; and under different conditions like CT saturation, fault during magnetizing inrush, series core saturation, low current faults, and integration of wind energy. As a potential application, the methodology can be deployed to supervise microprocessor-based differential relays to improve the security and dependability of the protection system.

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Section: Modeling and Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data Mining Based Protection and Classification of Transients for Two-Core Symmetric Phase Angle Regulators

Bera

Işık

2021

IEEE Access

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“…In the social and engineering fields, many practical control systems, such as power systems, 12,13 industrial processes [14][15][16] and communication networks, 17,18 are increasingly large in dimension and closely connected in structure. Such complex systems may be considered as ICSs.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy sliding mode tracking control of discrete‐time nonlinear interconnected systems via a previewable approach

Qin,

Ren

2024

Adaptive Control & Signal

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SummaryThis article investigates the fuzzy sliding mode tracking problem for discrete‐time nonlinear interconnected systems (ICSs) via a previewable information approach. First, an augmented system containing tracking errors and previewable reference and disturbance signals is established for discrete‐time nonlinear ICSs according to a previewable length. Second, novel sliding surfaces are given and some sufficient conditions of making the sliding motion asymptotically stable are derived in term of linear matrix inequalities (LMIs). Next, fuzzy sliding mode control (SMC) laws are designed so that the controlled system can reach the sliding surfaces and keep on them thereafter, and compared to not having previewable signals, these designed control laws can also enable system outputs to better track the reference signals asymptotically. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control law.

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“…Mal-operation of differential protection system occurs during transients and inrush current. Magnetizing inrush, sympathetic inrush and external faults with current transformer (CT) saturation, capacitor switching, non-linear load switching, and Ferro resonance [4] are the causes of maloperation of CT in differential protection systems.…”

Section: Introductionmentioning

confidence: 99%

Transformer Internal and Inrush Current Fault Detection Using Machine Learning

Vidhya¹,

Ranjan²,

Shanker³

2023

Intelligent Automation &Amp; Soft Computing

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Preventive maintenance in the transformer is performed through a differential relay protection system, and it protects the transformer from internal and external faults. However, the Current Transformer (CT) in the differential protection system mal-operates during inrush currents. CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays. Moreover, identification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed. For the above problem, continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the tripping in relay due to inrush or internal fault. The transformer's internal fault leads to high breathing process in the transformer breather, never for inrush currents. During inrush currents, CT temperature is increased. Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pixels detect the causes of CT saturation and differentiates maloperation. Hybrid wavelet threshold image analytics (HWT-IA) based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95% for identification of the false tripping of differential protection system of transformer.

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Discrimination of Internal Faults and Other Transients in an Interconnected System With Power Transformers and Phase Angle Regulators

Cited by 16 publications

References 38 publications

A Data Mining Based Protection and Classification of Transients for Two-Core Symmetric Phase Angle Regulators

A Data Mining Based Protection and Classification of Transients for Two-Core Symmetric Phase Angle Regulators

Fuzzy sliding mode tracking control of discrete‐time nonlinear interconnected systems via a previewable approach

Transformer Internal and Inrush Current Fault Detection Using Machine Learning

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