Performance Assessment of Some Practical Loss of Excitation Detection Schemes Employing a Realistic Model

Hasani, Abbas; Bak, Claus Leth; Silva, Filipe Miguel Faria da

doi:10.3390/en13225928

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…Impedance-based techniques [3,4] for LOF protection are the most popular in industrial applications [2]. Such schemes detect an LOF fault when impedance falls inside the operation zones for a preset time delay.…”

Section: Category 1 (C1): Impedance-based Techniquesmentioning

confidence: 99%

A new air gap flux‐based technique to detect loss of field in synchronous generators

Haseli,

Haghjoo,

Hasani

2024

IET Electric Power Appl

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A new technique is proposed for loss of field (LOF) detection in synchronous generators (SGs). The proposed scheme employs the variations of SG angular velocity ∆ωφ and load angle ∆δφ to reach this goal, while the mentioned parameters are estimated through air gap flux estimation. The proposed scheme performance is evaluated using realistic simulation case studies based on IEEE C. 37. 102 standard compared to the conventional impedance‐based one. The obtained results show that the presented scheme excels the conventional scheme from speed and security viewpoints. Moreover, the estimation procedure of ∆ωφ and ∆δφ is experimentally validated by using a 10 kVA SG and the results demonstrate that the proposed technique can be easily set and implemented.

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Section: Category 1 (C1): Impedance-based Techniquesmentioning

confidence: 99%

A new air gap flux‐based technique to detect loss of field in synchronous generators

Haseli,

Haghjoo,

Hasani

2024

IET Electric Power Appl

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“…Due to the arrival of larger reactance machines Berdy proposed Mho relay with two negative offset zones for LOE protection as shown in figure 1.a, 1.b. The positive offset mho relay has time delay of 0.1 and 0.5s for zone1 and zone 2 [25], [26]. A time delay setting is provided for both zones in both methods to avoid mal operations of the relay however this is not wise in ride through transient conditions which may cause undesirable operation [25], [26].…”

Section: Detection Schemes Of Loementioning

confidence: 99%

“…The positive offset mho relay has time delay of 0.1 and 0.5s for zone1 and zone 2 [25], [26]. A time delay setting is provided for both zones in both methods to avoid mal operations of the relay however this is not wise in ride through transient conditions which may cause undesirable operation [25], [26].…”

Section: Detection Schemes Of Loementioning

confidence: 99%

Machine Learning Approach to Differentiate Excitation Failure in Synchronous Generators from Power Swing

Hemavathi¹,

Geethanjali²

2023

Preprint

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Loss of Excitation (LOE) is the most considerable fault in Synchronous generators since it affects both the generators and power network. The traditional protection method for LOE is based on impedance trajectory of the machine with negative offset mho relay. Meanwhile the traditional method experiences malfunctions and speed dip in LOE detection. This paper presents machine learning approach to detect LOE fault as well as classification logic to discriminate LOE fault from power swing conditions due to Line fault. This paper utilizes Hotelling’s-T2 statistical method to calculate Hotelling’s-T2 based Fault Indices (HT2 -FI) for fault detection and Support Vector Machine (SVM) for classification. The time series data of electrical quantities such as Terminal voltage and Reactive Power of the generator are extracted from simulated Single Machine Infinite Bus test system and used as input data. This data is involved in calculation of HT2 –FI and in development of classification logic. The proposed method is simulated and verified for complete, partial LOE conditions and power swing conditions. Simulation outcomes depict the remarkable signs of the proposed method in LOE identification from power swing. Comparative assessment also reports that the method is capable of saving time in detecting LOE.

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“…The second zone with diameter equal to 𝑋 𝑑 and offset of 𝑋 𝑑 ′ /2 detects LOE in light loaded condition of synchronous machine. A time delay setting is provided for both zones to avoid mal operations of the relay however this is not wise in ride through transient conditions which may cause undesirable operation [25], [26].…”

Section: Detection Schemes Of Loementioning

confidence: 99%

“…As well, zone 2 has positive offset of 𝑋 𝐵 -power grid reactance beyond the terminals of machine and diameter of 1.1𝑋 𝑑 +𝑋 𝐵 . Though the positive offset mho relay has time delay of 0.1 and 0.5s for zone1 and zone 2, still the relay has the tendency to mal-operate during system troubles [25], [26].…”

Section: Detection Schemes Of Loementioning

confidence: 99%

Discrimination of Loss of Excitation Fault in Synchronous Generators from Power Swing Using Machine Learning Approach

Hemavathi¹,

Geethanjali²

2022

Preprint

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Amidst several faults in Synchronous generators, Loss of Excitation (LOE) is the most considerable fault since it affects both the generators and power network. The traditional protection method for LOE is based on impedance trajectory of the machine with negative offset mho relay. Meanwhile the traditional method experiences malfunctions and speed dip in LOE detection. This paper presents machine learning approach to detect LOE fault as well as classification logic to discriminate LOE fault from normal operating conditions and power swing conditions due to Line fault. This paper utilizes Hotelling’s-T2 statistical method to calculate Hotelling’s-T2 based Fault Indices (HT2 -FI) for LOE detection and Support Vector Machine (SVM) for classification. The time series data of electrical quantities such as Terminal voltage and Reactive Power of the generator are extracted from simulated Single Machine Infinite Bus test system and used as input data. This data is involved in calculation of HT2 –FI and in development of classification logic. The proposed method is simulated and verified for complete, partial LOE conditions and power swing conditions. Simulation outcomes depict the notable signs of the proposed method in LOE identification from power swing. Comparative assessment also reports that the method is capable of saving time in detecting LOE.

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Performance Assessment of Some Practical Loss of Excitation Detection Schemes Employing a Realistic Model

Cited by 5 publications

References 16 publications

A new air gap flux‐based technique to detect loss of field in synchronous generators

A new air gap flux‐based technique to detect loss of field in synchronous generators

Machine Learning Approach to Differentiate Excitation Failure in Synchronous Generators from Power Swing

Discrimination of Loss of Excitation Fault in Synchronous Generators from Power Swing Using Machine Learning Approach

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