B. Behdani
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The increasing penetration of renewable energy sources and frequent lightning and switching events have intensified transient phenomena in modern power systems, exposing power transformers to resonance at critical frequencies. These conditions may cause internal overvoltages, and insulation failure. While many studies focus on wide-band transformer modeling and resonance identification, their primary objective is accurate frequency-domain representation rather than revealing the physical origin of resonance inside the transformer. This paper does not aim to introduce a new transformer modeling method. Instead, it presents a visualization-based approach to identify transformer components responsible for resonance. By analyzing the branch current matrix of a transformer disk model and visualizing current distribution using a color map, dominant resonance-driving elements are identified. This visualization enables protection and future design enhancement.
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The increasing penetration of renewable energy sources and frequent lightning and switching events have intensified transient phenomena in modern power systems, exposing power transformers to resonance at critical frequencies. These conditions may cause internal overvoltages, and insulation failure. While many studies focus on wide-band transformer modeling and resonance identification, their primary objective is accurate frequency-domain representation rather than revealing the physical origin of resonance inside the transformer. This paper does not aim to introduce a new transformer modeling method. Instead, it presents a visualization-based approach to identify transformer components responsible for resonance. By analyzing the branch current matrix of a transformer disk model and visualizing current distribution using a color map, dominant resonance-driving elements are identified. This visualization enables protection and future design enhancement.
High-frequency resonances in cable-transformer systems can result in excessive overvoltages, increasing the probability of insulation failure for critical components such as power transformers. These resonances occur due to the interaction between the cable and transformer and are influenced by the cable’s characteristics, including the length and wave propagation velocity. In addition, the terminating impedances of the cable’s core and sheath conductors affect the resonance characteristic of the cable as well. Applying single-point sheath grounding to the high-voltage cable connecting the switchgear to the power transformer is conventional. This paper demonstrates that the cable-transformer resonances and resulting overvoltages can vary significantly depending on the end at which the sheath conductors are grounded. An in-depth investigation of such effects is carried out through rigorous mathematical analysis, followed by experimental validation and simulations in an electromagnetic transient (EMT)-based software using models that properly represent the equipment behaviors in a wide-frequency range. The results indicate that sheath conductor grounding configuration can profoundly affect the system response, influencing the severity of transient overvoltages caused by cable-transformer resonances.
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High-frequency resonances in cable-transformer systems can result in excessive overvoltages, increasing the probability of insulation failure for critical components such as power transformers. These resonances occur due to the interaction between the cable and transformer and are influenced by the cable’s characteristics, including the length and wave propagation velocity. In addition, the terminating impedances of the cable’s core and sheath conductors affect the resonance characteristic of the cable as well. Applying single-point sheath grounding to the high-voltage cable connecting the switchgear to the power transformer is conventional. This paper demonstrates that the cable-transformer resonances and resulting overvoltages can vary significantly depending on the end at which the sheath conductors are grounded. An in-depth investigation of such effects is carried out through rigorous mathematical analysis, followed by experimental validation and simulations in an electromagnetic transient (EMT)-based software using models that properly represent the equipment behaviors in a wide-frequency range. The results indicate that sheath conductor grounding configuration can profoundly affect the system response, influencing the severity of transient overvoltages caused by cable-transformer resonances.
Power transformer energization involves a significant electromagnetic energy exchange among system components, with periodic oscillations at the system's natural frequencies. As a result, weakly damped resonance overvoltages may occur, overstressing the system and thereby leading to potential insulation failure. This phenomenon is particularly notable for topologies where a transformer is supplied via cable, as low-damping resonance frequencies are likely to be formed due to mutual interactions between the cable and the transformer. The prestriking phenomenon during circuit breaker closing plays an important role in the excitation of resonance frequencies. Specifically, repeated prestrikes can create highfrequency resonances during switching-on operations. This paper analyzes the mutual interactions between the cable and the transformer, focusing on how the resonances between the cable and the transformer are created. Then, using a suitable modeling approach, the impact of CB prestrikes on the resultant resonance excitation in cable-transformer systems is investigated. Finally, tests are conducted using an experimental test setup to validate the investigations performed. The obtained results demonstrate that resonance frequencies emerging from cable-transformer interactions lead to the excitation of oscillatory overvoltages with extreme magnitudes.
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Power transformer energization involves a significant electromagnetic energy exchange among system components, with periodic oscillations at the system's natural frequencies. As a result, weakly damped resonance overvoltages may occur, overstressing the system and thereby leading to potential insulation failure. This phenomenon is particularly notable for topologies where a transformer is supplied via cable, as low-damping resonance frequencies are likely to be formed due to mutual interactions between the cable and the transformer. The prestriking phenomenon during circuit breaker closing plays an important role in the excitation of resonance frequencies. Specifically, repeated prestrikes can create highfrequency resonances during switching-on operations. This paper analyzes the mutual interactions between the cable and the transformer, focusing on how the resonances between the cable and the transformer are created. Then, using a suitable modeling approach, the impact of CB prestrikes on the resultant resonance excitation in cable-transformer systems is investigated. Finally, tests are conducted using an experimental test setup to validate the investigations performed. The obtained results demonstrate that resonance frequencies emerging from cable-transformer interactions lead to the excitation of oscillatory overvoltages with extreme magnitudes.
Integrating renewable energy resources such as wind farms is an increasingly prominent trend for future power grids. However, the wind generator tower is consistently at risk of lightning strikes, putting the wind farms’ transformer at risk of damage by lightning transients traveling through the system. Various harmonic contents of the lightning transient can excite the transformer’s resonance frequencies, resulting in both terminal and internal overvoltages (OVs). To effectively safeguard transformers against resonance OVs, it is imperative to first identify the resonance points of the transformer. Following this, a protective method must be implemented to mitigate harmonic content magnitudes that contribute to resonance. This paper introduces a series-protection device comprising an air core reactor and suppressor resistance designed to protect the transformer. The research aims to provide solutions to safeguard wind farm transformers from both terminal and internal resonance OVs caused by lightning transients. The effectiveness of the protection device is assessed through analysis, simulation, and experiments conducted in a high-voltage laboratory setup.
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Integrating renewable energy resources such as wind farms is an increasingly prominent trend for future power grids. However, the wind generator tower is consistently at risk of lightning strikes, putting the wind farms’ transformer at risk of damage by lightning transients traveling through the system. Various harmonic contents of the lightning transient can excite the transformer’s resonance frequencies, resulting in both terminal and internal overvoltages (OVs). To effectively safeguard transformers against resonance OVs, it is imperative to first identify the resonance points of the transformer. Following this, a protective method must be implemented to mitigate harmonic content magnitudes that contribute to resonance. This paper introduces a series-protection device comprising an air core reactor and suppressor resistance designed to protect the transformer. The research aims to provide solutions to safeguard wind farm transformers from both terminal and internal resonance OVs caused by lightning transients. The effectiveness of the protection device is assessed through analysis, simulation, and experiments conducted in a high-voltage laboratory setup.
Deteriorated measurements due to the saturation of current transformers (CTs) are a major challenge in digital power system protection schemes. Failing to resolve this issue effectively can have serious consequences for the accuracy of measured components in phasor-based digital relaying algorithms. Potentially, this may compromise the secure and reliable operation of the protection system and, hence, the entire power system. Accurate fault detection or classification can be achieved by applying an algorithm that can deal with the CT saturation effects. In this paper, a method is presented that can accurately estimate the fundamental current phasor during CT saturation. Reconstruction of the deteriorated measured waveform is accomplished by applying a supervised machine learning algorithm, namely the support vector machine (SVM). The least squares (LS) method is integrated with the SVM-based algorithm to reduce the complexities of waveform reconstruction regressions. A modified discrete Fourier transform (DFT), robust to decaying DC components, is then applied to the reconstructed waveform to extract the required phasor components. The proposed approach is validated by evaluating its classification and phasor estimation performance using standard metrics over numerous simulated cases and field measurements. The results demonstrate the high accuracy of the proposed method to classify different levels of CT saturation and ensure precise estimation of the fundamental phasor component.
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Deteriorated measurements due to the saturation of current transformers (CTs) are a major challenge in digital power system protection schemes. Failing to resolve this issue effectively can have serious consequences for the accuracy of measured components in phasor-based digital relaying algorithms. Potentially, this may compromise the secure and reliable operation of the protection system and, hence, the entire power system. Accurate fault detection or classification can be achieved by applying an algorithm that can deal with the CT saturation effects. In this paper, a method is presented that can accurately estimate the fundamental current phasor during CT saturation. Reconstruction of the deteriorated measured waveform is accomplished by applying a supervised machine learning algorithm, namely the support vector machine (SVM). The least squares (LS) method is integrated with the SVM-based algorithm to reduce the complexities of waveform reconstruction regressions. A modified discrete Fourier transform (DFT), robust to decaying DC components, is then applied to the reconstructed waveform to extract the required phasor components. The proposed approach is validated by evaluating its classification and phasor estimation performance using standard metrics over numerous simulated cases and field measurements. The results demonstrate the high accuracy of the proposed method to classify different levels of CT saturation and ensure precise estimation of the fundamental phasor component.
Conference paper
(2024)
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Farzad Nasirpour, Behzad Behdani, Amir Heidary, Mohamad Ghaffarian Niasar, Forooz Ghassemi, Maarten van Riet, Mark Wilkinson, M.A.M.M. van der Meijden, Marjan Popov, More authors...
The evolution of electrical power systems demands an increasing reliance on unpredictable renewable energy resources (RES). However, integrating these resources poses challenges, as their intermittent nature introduces transient events that can significantly impact power transformers. These transient phenomena may initiate energy oscillations in the form of weakly-damped resonances between system elements, i.e., transmission lines and cables, transformers, and the grounding system. Such conditions may impose stresses beyond the tolerance of insulating materials, leading to fast lifetime degradation and, eventually, the failure of critical components in the network, such as the costly power transformers. The impedance of the grounding system can limit the dissipation of surges, hence, causing severe overvoltages upon transient phenomena. By employing detailed transient models of crucial system components, this research puts forward a comprehensive analytical study of the transient interactions. In this regard, an analytical high-frequency transformer winding model based on lumped elements, and wideband frequency-dependent models for cables and the grounding system derived by applying electromagnetic theory are presented. These models, integrated into electromagnetic transient software, enable the identification of vulnerabilities and examination of case studies involving lightning strikes and switching events. Furthermore, the details of a novel protection method applied to safeguard the transformer are discussed in this paper. The presented protection method consists of a ring toroid core and a resistive suppressor on the secondary side of the core. This protection component is connected in series with the transformer to decrease the harmonic content and magnitude of the transient signals. The design procedure of the series protection device against voltage transient signals is presented and elaborated.
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The evolution of electrical power systems demands an increasing reliance on unpredictable renewable energy resources (RES). However, integrating these resources poses challenges, as their intermittent nature introduces transient events that can significantly impact power transformers. These transient phenomena may initiate energy oscillations in the form of weakly-damped resonances between system elements, i.e., transmission lines and cables, transformers, and the grounding system. Such conditions may impose stresses beyond the tolerance of insulating materials, leading to fast lifetime degradation and, eventually, the failure of critical components in the network, such as the costly power transformers. The impedance of the grounding system can limit the dissipation of surges, hence, causing severe overvoltages upon transient phenomena. By employing detailed transient models of crucial system components, this research puts forward a comprehensive analytical study of the transient interactions. In this regard, an analytical high-frequency transformer winding model based on lumped elements, and wideband frequency-dependent models for cables and the grounding system derived by applying electromagnetic theory are presented. These models, integrated into electromagnetic transient software, enable the identification of vulnerabilities and examination of case studies involving lightning strikes and switching events. Furthermore, the details of a novel protection method applied to safeguard the transformer are discussed in this paper. The presented protection method consists of a ring toroid core and a resistive suppressor on the secondary side of the core. This protection component is connected in series with the transformer to decrease the harmonic content and magnitude of the transient signals. The design procedure of the series protection device against voltage transient signals is presented and elaborated.
Multifaceted Functionalities of Bridge-Type DC Reactor Fault Current Limiters
An Experimentally Validated Investigation
Journal article
(2024)
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Behzad Behdani, Ali Moghim, Sheyda Mousavi, Mostafa Soltanfar, Mojgan Hojabri
With the ongoing expansion and interconnection of electrical power systems, alongside the rapid proliferation of renewable distributed generations (DGs), the short-circuit extent in the power grid is experiencing a significant rise. Fault current limiters (FCLs) have been introduced in an effort to address this issue, ensuring the robustness and sustainability of expensive power system components when confronted with short-circuit faults. Among the various types of FCLs, bridge-type DC reactor fault current limiters (BDCR-FCLs) have emerged as one of the most promising options. While BDCR-FCLs have shown excellent properties in limiting harmful short-circuit currents, they are also advantageous in other respects. This paper investigates the supplementary functionalities of BDCR-FCLs as a multifaceted device towards the enhancement of the quality of supplied energy in terms of total harmonic distortion (THD) reduction, power factor (PF) correction, peak current reduction for nonlinear loads, and soft load variation effects, as well as their capability to limit fault current. To this aim, the capabilities of BDCR-FCLs have been studied through various simulated case studies in PSCAD/EMTDC software V5.0.1, in addition to experimental tests considering an AC microgrid connected to a DC system. The experimental and simulation investigations verify the superior multifaceted functionalities BDCR-FCLs introduce in addition to their excellent fault current-limiting capabilities. The results show that PF improved by 6.7% and 7%, respectively, in simulation and experimental tests. Furthermore, the current THD decreased by 20% and 18% in the simulation and experiment, respectively.
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With the ongoing expansion and interconnection of electrical power systems, alongside the rapid proliferation of renewable distributed generations (DGs), the short-circuit extent in the power grid is experiencing a significant rise. Fault current limiters (FCLs) have been introduced in an effort to address this issue, ensuring the robustness and sustainability of expensive power system components when confronted with short-circuit faults. Among the various types of FCLs, bridge-type DC reactor fault current limiters (BDCR-FCLs) have emerged as one of the most promising options. While BDCR-FCLs have shown excellent properties in limiting harmful short-circuit currents, they are also advantageous in other respects. This paper investigates the supplementary functionalities of BDCR-FCLs as a multifaceted device towards the enhancement of the quality of supplied energy in terms of total harmonic distortion (THD) reduction, power factor (PF) correction, peak current reduction for nonlinear loads, and soft load variation effects, as well as their capability to limit fault current. To this aim, the capabilities of BDCR-FCLs have been studied through various simulated case studies in PSCAD/EMTDC software V5.0.1, in addition to experimental tests considering an AC microgrid connected to a DC system. The experimental and simulation investigations verify the superior multifaceted functionalities BDCR-FCLs introduce in addition to their excellent fault current-limiting capabilities. The results show that PF improved by 6.7% and 7%, respectively, in simulation and experimental tests. Furthermore, the current THD decreased by 20% and 18% in the simulation and experiment, respectively.
The integration of renewable energy sources has significantly impacted power system protection schemes, primarily by increasing short-circuit fault currents, which, in turn, raises the possibility of current transformer (CT) saturation, and by introducing bidirectional fault current flow, which interferes with the directional selectivity of relays in detecting downstream faults. This study presents a novel fault direction identification algorithm aimed at immunization against CT saturation effects, especially in medium voltage (MV) distribution grids integrated with renewable sources. To achieve this, two distinct computational frameworks have been developed and proposed. The first framework utilizes the modified least squares (MLSs) method, while the second employs a modified Kalman filter (MKF). Both algorithms calculate the fundamental current phase angle using a sub-cycle window of current samples, ensuring resilience to heavily distorted waveforms caused by CT saturation, even under conditions of deep saturation. The effectiveness of the proposed method is validated through numerous tests conducted on fault currents recorded via simulation scenarios and field measurements, considering various fault inception times, resistances, and locations, together with different neutral grounding arrangements. Comparative assessments of the two developed frameworks across different scenarios indicate that both methods exhibit promising performances, although the least square-based method demonstrates superior efficiency compared to the Kalman filter-based method.
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The integration of renewable energy sources has significantly impacted power system protection schemes, primarily by increasing short-circuit fault currents, which, in turn, raises the possibility of current transformer (CT) saturation, and by introducing bidirectional fault current flow, which interferes with the directional selectivity of relays in detecting downstream faults. This study presents a novel fault direction identification algorithm aimed at immunization against CT saturation effects, especially in medium voltage (MV) distribution grids integrated with renewable sources. To achieve this, two distinct computational frameworks have been developed and proposed. The first framework utilizes the modified least squares (MLSs) method, while the second employs a modified Kalman filter (MKF). Both algorithms calculate the fundamental current phase angle using a sub-cycle window of current samples, ensuring resilience to heavily distorted waveforms caused by CT saturation, even under conditions of deep saturation. The effectiveness of the proposed method is validated through numerous tests conducted on fault currents recorded via simulation scenarios and field measurements, considering various fault inception times, resistances, and locations, together with different neutral grounding arrangements. Comparative assessments of the two developed frameworks across different scenarios indicate that both methods exhibit promising performances, although the least square-based method demonstrates superior efficiency compared to the Kalman filter-based method.
Power transformer energization involves a significant electromagnetic energy exchange among system components, which periodically oscillates with the natural frequencies of the system. As a result, weakly damped resonating overvoltages (OVs) may prevail, overstressing the system and thus leading to potential insulation failure. This phenomenon is particularly notable in cable-transformer systems, where the coinciding of natural frequencies is more likely to occur. The prestriking phenomenon during circuit breaker (CB) closing plays an important role in the excitation of the resonance frequencies. Specifically, repeated prestrikes can create high-frequency resonances during switching-on operations. This paper deals primarily with the mutual interactions between cable and transformer during transformer energization. Furthermore, by using a suitable model, the impact of CB prestrikes on the resultant resonance excitation is analyzed. Results demonstrate that cable-transformer interactions can lead to high-frequency oscillatory OVs with extreme magnitudes.
...
Power transformer energization involves a significant electromagnetic energy exchange among system components, which periodically oscillates with the natural frequencies of the system. As a result, weakly damped resonating overvoltages (OVs) may prevail, overstressing the system and thus leading to potential insulation failure. This phenomenon is particularly notable in cable-transformer systems, where the coinciding of natural frequencies is more likely to occur. The prestriking phenomenon during circuit breaker (CB) closing plays an important role in the excitation of the resonance frequencies. Specifically, repeated prestrikes can create high-frequency resonances during switching-on operations. This paper deals primarily with the mutual interactions between cable and transformer during transformer energization. Furthermore, by using a suitable model, the impact of CB prestrikes on the resultant resonance excitation is analyzed. Results demonstrate that cable-transformer interactions can lead to high-frequency oscillatory OVs with extreme magnitudes.
Failures of Induction Motors (IMs) can lead to unscheduled downtime and interruption in industry processes. This paper concentrates on the detection of the stator's inter-turn faults which are one of the most frequent causes of failures in IMs. The proposed detection method is based on a similarity index that uses the current waveform. To be more specific, the proposed algorithm presents a full-cycle sliding-window-based index based on cosine similarity that only uses current signals for detection of the stator's inter-turn faults. The proposed index cuts the phase difference before/after the disturbance and, as a result, it only depends on the size variations of the current waveform. The proposed method is technically unaffected by non-fault transient conditions including voltage imbalance, voltage sag, voltage swell, and heavy load changes. The performance of the proposed method is validated with numerous simulated scenarios and has good accuracy and speed of convergence.
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Failures of Induction Motors (IMs) can lead to unscheduled downtime and interruption in industry processes. This paper concentrates on the detection of the stator's inter-turn faults which are one of the most frequent causes of failures in IMs. The proposed detection method is based on a similarity index that uses the current waveform. To be more specific, the proposed algorithm presents a full-cycle sliding-window-based index based on cosine similarity that only uses current signals for detection of the stator's inter-turn faults. The proposed index cuts the phase difference before/after the disturbance and, as a result, it only depends on the size variations of the current waveform. The proposed method is technically unaffected by non-fault transient conditions including voltage imbalance, voltage sag, voltage swell, and heavy load changes. The performance of the proposed method is validated with numerous simulated scenarios and has good accuracy and speed of convergence.
Ferroresonance, as an undesirable disturbance, leads to significant overvoltage and distorted waveforms. This phenomenon can be highly damaging to voltage transformers and other parallel-connected equipment and can entail catastrophic consequences. This paper aims to design and study a solid-state ferroresonance-suppressing circuit (SSFSC) to protect voltage transformers (VTs) together with other parallel-connected equipment in wind generation systems from the adverse effects of the ferroresonance phenomenon. The proposed structure consists of low-voltage circuits, including power IGBTs. The excellent performance of the proposed SSFSC in suppressing ferroresonance overvoltage in wind generation VTs has been authenticated by analyses conducted utilizing a wind generation system model. In order to validate the performance of the proposed SSFSC, detailed analytical studies and time-domain simulations have been carried out employing a MATLAB/Simulink environment. The results verify that the proposed SSFSC can effectively suppress ferroresonance phenomena in VTs and mitigate their accompanying overvoltages with a high operational speed.
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Ferroresonance, as an undesirable disturbance, leads to significant overvoltage and distorted waveforms. This phenomenon can be highly damaging to voltage transformers and other parallel-connected equipment and can entail catastrophic consequences. This paper aims to design and study a solid-state ferroresonance-suppressing circuit (SSFSC) to protect voltage transformers (VTs) together with other parallel-connected equipment in wind generation systems from the adverse effects of the ferroresonance phenomenon. The proposed structure consists of low-voltage circuits, including power IGBTs. The excellent performance of the proposed SSFSC in suppressing ferroresonance overvoltage in wind generation VTs has been authenticated by analyses conducted utilizing a wind generation system model. In order to validate the performance of the proposed SSFSC, detailed analytical studies and time-domain simulations have been carried out employing a MATLAB/Simulink environment. The results verify that the proposed SSFSC can effectively suppress ferroresonance phenomena in VTs and mitigate their accompanying overvoltages with a high operational speed.
Conference paper
(2022)
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Mohsen Tajdinian, Behzad Behdani, Ali Goodarzi, Harold R. Chamorro, Vijay K. Sood
Fast internal detection and location in Shunt Ca-pacitor Banks (SCBs) can lead to the prevention of damages to other SCBs' elements and consequently avoid undesirable performance and effects in power system operation. This paper targets the performance of phasor-based algorithms of failure detection and fault location of SCBs. Being dependent on the fundamental phasor components which usually are calculated based on the Discrete Fourier Transform (DFT), the failure detection and fault location algorithms suffer from almost one-cycle delay. This paper provides sub-cycle phasor estimation based on the least-square technique. The proposed algorithm is evaluated for different configurations of SCBs considering different fuse protection designs. The proposed method provides a criterion for relay decision-making in the case of multiple faulty phases condition. The proposed method is designed to monitor and detect consecutive failures based on the existing data of commercial relays. Performance evaluations are conducted under different circumstances namely voltage unbalance conditions and multiple internal fault locations.
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Fast internal detection and location in Shunt Ca-pacitor Banks (SCBs) can lead to the prevention of damages to other SCBs' elements and consequently avoid undesirable performance and effects in power system operation. This paper targets the performance of phasor-based algorithms of failure detection and fault location of SCBs. Being dependent on the fundamental phasor components which usually are calculated based on the Discrete Fourier Transform (DFT), the failure detection and fault location algorithms suffer from almost one-cycle delay. This paper provides sub-cycle phasor estimation based on the least-square technique. The proposed algorithm is evaluated for different configurations of SCBs considering different fuse protection designs. The proposed method provides a criterion for relay decision-making in the case of multiple faulty phases condition. The proposed method is designed to monitor and detect consecutive failures based on the existing data of commercial relays. Performance evaluations are conducted under different circumstances namely voltage unbalance conditions and multiple internal fault locations.
Journal article
(2022)
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Behzad Behdani, Mohsen Tajdinian, Mehdi Allahbakhshi, Marjan Popov, Miadreza Shafie-khah, Joao P. S. Catalao
Geomagnetically induced currents (GICs) are referred to the quasi-DC current flows in power networks, driven by complex space weather-related phenomena. Such currents are a potential threat to the power delivery capability of electrical grids. To mitigate the detrimental impacts of GICs on critical infrastructures, the GICs should be monitored in power systems. Being inherently DC from the power frequency point of view, the components of GICs are, however, challenging and costly to monitor in AC power grids. This paper puts forward a novel methodology for the real-time estimation of GICs in power transformers. Such aim is attained by means of an extended Kalman filter (EKF)-based approach, mounted on the nonlinear state-space model of the transformer, whose parameters can be derived from standard tests. The proposed EKF-based algorithm employs the available measurements for the transformer differential protection. The proposed approach, relying on the differential current, can properly deal with the external sources of interference like harmonic excitation and loading. The EKF-based estimator presented is validated by simulation and experimental data. The results verify the ability of the proposed approach to robustly estimate the GIC level during various operating conditions.
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Geomagnetically induced currents (GICs) are referred to the quasi-DC current flows in power networks, driven by complex space weather-related phenomena. Such currents are a potential threat to the power delivery capability of electrical grids. To mitigate the detrimental impacts of GICs on critical infrastructures, the GICs should be monitored in power systems. Being inherently DC from the power frequency point of view, the components of GICs are, however, challenging and costly to monitor in AC power grids. This paper puts forward a novel methodology for the real-time estimation of GICs in power transformers. Such aim is attained by means of an extended Kalman filter (EKF)-based approach, mounted on the nonlinear state-space model of the transformer, whose parameters can be derived from standard tests. The proposed EKF-based algorithm employs the available measurements for the transformer differential protection. The proposed approach, relying on the differential current, can properly deal with the external sources of interference like harmonic excitation and loading. The EKF-based estimator presented is validated by simulation and experimental data. The results verify the ability of the proposed approach to robustly estimate the GIC level during various operating conditions.
The flow of quasi-direct currents (QDCs) in AC electrical networks, is a disturbing factor that mainly prevails upon mutual impacts between different system components or due to geophysical phenomena. These QDCs can alter the normal behavior of the system components, e.g., power transformer inrush currents. In this paper, an analysis of the inrush current phenomenon in power transformers under the influence of QDCs has been performed. The effect of QDCs on power transformer inrush currents is first mathematically analyzed, and then investigated by computer simulations in EMTP-RV software. Results show that power transformer inrush currents can severely increase in the presence of QDCs.
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The flow of quasi-direct currents (QDCs) in AC electrical networks, is a disturbing factor that mainly prevails upon mutual impacts between different system components or due to geophysical phenomena. These QDCs can alter the normal behavior of the system components, e.g., power transformer inrush currents. In this paper, an analysis of the inrush current phenomenon in power transformers under the influence of QDCs has been performed. The effect of QDCs on power transformer inrush currents is first mathematically analyzed, and then investigated by computer simulations in EMTP-RV software. Results show that power transformer inrush currents can severely increase in the presence of QDCs.
Conference paper
(2022)
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Mohsen Tajdinian, B. Behdani, Reza Shirali, Mojtaba Abbasi, Harold R. Chamorro
Proliferation of renewable energy sources (RESs) has increased the reliability and flexibility in the operation of the power networks. Nevertheless, different technologies of RESs with different sizes and grid-connection technologies may impose threats to the power grid's stability. This paper targets transient stability in distribution networks. More specifically, this paper investigates the allocation of different types of distributed generations (DGs) considering the maximization of transient stability margin. The latter maximization helps to minimize the deterioration of the inverter-based DGs. The transient stability based objective function (OF) basically relies on the critical and clearing angles. Besides transient stability, the improvement of voltage profile and minimization of power losses are taken into account. The proposed algorithm is implemented on the IEEE 33-bus test system and furthermore, several scenarios are conducted to ensure the effectiveness of the proposed algorithm under fault conditions
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Proliferation of renewable energy sources (RESs) has increased the reliability and flexibility in the operation of the power networks. Nevertheless, different technologies of RESs with different sizes and grid-connection technologies may impose threats to the power grid's stability. This paper targets transient stability in distribution networks. More specifically, this paper investigates the allocation of different types of distributed generations (DGs) considering the maximization of transient stability margin. The latter maximization helps to minimize the deterioration of the inverter-based DGs. The transient stability based objective function (OF) basically relies on the critical and clearing angles. Besides transient stability, the improvement of voltage profile and minimization of power losses are taken into account. The proposed algorithm is implemented on the IEEE 33-bus test system and furthermore, several scenarios are conducted to ensure the effectiveness of the proposed algorithm under fault conditions
Conference paper
(2021)
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Shahabodin Afrasiabi, Behzad Behdani, Mousa Afrasiabi, Mohammad Mohammadi, Alia Asheralieva, Mehdi Gheisari
One of the most challenging issues in protecting power transformers is to discriminate internal faults from inrush currents. This paper proposes a new approach for differential protection of power transformers based on the robust soft learning vector quantization (RSLVQ) method. Statistical features from the normalized differential current gradient are extracted in order to train the RSLVQ classifier. Furthermore, the performance of the proposed differential protection scheme is investigated in the presence of superconductor fault current limiter (SFCL), which can greatly affect the ability of differential protection schemes in correctly discriminating inrush from internal fault currents. The PSCAD/EMTDC software is utilized to generate sampled data in order to evaluate the performance of the proposed approach. The results obtained from the evaluation of the proposed method verified the promising performance of the RSLVQ-based differential protection scheme.
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One of the most challenging issues in protecting power transformers is to discriminate internal faults from inrush currents. This paper proposes a new approach for differential protection of power transformers based on the robust soft learning vector quantization (RSLVQ) method. Statistical features from the normalized differential current gradient are extracted in order to train the RSLVQ classifier. Furthermore, the performance of the proposed differential protection scheme is investigated in the presence of superconductor fault current limiter (SFCL), which can greatly affect the ability of differential protection schemes in correctly discriminating inrush from internal fault currents. The PSCAD/EMTDC software is utilized to generate sampled data in order to evaluate the performance of the proposed approach. The results obtained from the evaluation of the proposed method verified the promising performance of the RSLVQ-based differential protection scheme.
Conference paper
(2021)
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Behzad Behdani, Mehdi Allahbakhshi, Alia Asheralieva, Mehdi Gheisari
Arising from solar storms, the emerging disturbances imposed on Earth's magnetic field, can drive the flow of quasi-DC geomagnetically induced currents (GICs) in power transmission systems. The ground connections of power transformers provide a closed path through which GICs flow and push their cores into half-cycle saturation. In addition, series capacitor units are often utilized to compensate HV transmission systems. The half-cycle saturation of power transformers due to GICs, on the one hand, and the proximity of such saturated transformers to capacitor compensation units on the other can lead to the inception of ferroresonance in series compensated power systems. To prevent catastrophic equipment failures due to ferroresonance during GICs, it is crucial to determine ferroresonance solutions of networks. This paper's principal contribution is to develop an approach to analyze ferroresonance in series capacitor compensated networks during GICs. This is attained by employing a simplified equivalent single-phase model on which to mount the analysis. The authenticity of this method is verified through an EMTP-RV simulation of a benchmark example power system.
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Arising from solar storms, the emerging disturbances imposed on Earth's magnetic field, can drive the flow of quasi-DC geomagnetically induced currents (GICs) in power transmission systems. The ground connections of power transformers provide a closed path through which GICs flow and push their cores into half-cycle saturation. In addition, series capacitor units are often utilized to compensate HV transmission systems. The half-cycle saturation of power transformers due to GICs, on the one hand, and the proximity of such saturated transformers to capacitor compensation units on the other can lead to the inception of ferroresonance in series compensated power systems. To prevent catastrophic equipment failures due to ferroresonance during GICs, it is crucial to determine ferroresonance solutions of networks. This paper's principal contribution is to develop an approach to analyze ferroresonance in series capacitor compensated networks during GICs. This is attained by employing a simplified equivalent single-phase model on which to mount the analysis. The authenticity of this method is verified through an EMTP-RV simulation of a benchmark example power system.
Journal article
(2021)
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Behzad Behdani, Mehdi Allahbakhshi, Mohsen Tajdinian
Solar storms cause disturbances in the Earth's magnetic field, which results in quasi-DC geomagnetically induced currents (GICs) flow in the grounded sections of the system. The flow of GICs in the power system may drive the power transformers to the saturation region. Moreover, HV transmission lines are often compensated by series capacitor units for enhancing the capability of power transmission lines. The proximity of power transformers and series capacitor compensation units during GICs may enhance the chance of the ferroresonance phenomenon in power transformers. The occurrence of ferroresonance produces extensively high voltages/currents in system apparatus, including transformers which can consequently result in severe damages. This paper puts forward the analysis of the power transformer ferroresonance phenomenon due to GICs in series capacitor compensated networks. This issue has been addressed through mathematical analysis and verified by an experimental test setup, in order to demonstrate the effect of GIC on ferroresonance. Through the employment of an example test system in EMTP-RV environment, impacts of different involving parameters such as system loading, compensation level, and substations' grounding resistances are evaluated on the occurrence of ferroresonance due to GICs. The results indicate that for series capacitor compensated power grids, GICs can profoundly enhance the vulnerability of power transformers to the occurrence of ferroresonance phenomena.
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Solar storms cause disturbances in the Earth's magnetic field, which results in quasi-DC geomagnetically induced currents (GICs) flow in the grounded sections of the system. The flow of GICs in the power system may drive the power transformers to the saturation region. Moreover, HV transmission lines are often compensated by series capacitor units for enhancing the capability of power transmission lines. The proximity of power transformers and series capacitor compensation units during GICs may enhance the chance of the ferroresonance phenomenon in power transformers. The occurrence of ferroresonance produces extensively high voltages/currents in system apparatus, including transformers which can consequently result in severe damages. This paper puts forward the analysis of the power transformer ferroresonance phenomenon due to GICs in series capacitor compensated networks. This issue has been addressed through mathematical analysis and verified by an experimental test setup, in order to demonstrate the effect of GIC on ferroresonance. Through the employment of an example test system in EMTP-RV environment, impacts of different involving parameters such as system loading, compensation level, and substations' grounding resistances are evaluated on the occurrence of ferroresonance due to GICs. The results indicate that for series capacitor compensated power grids, GICs can profoundly enhance the vulnerability of power transformers to the occurrence of ferroresonance phenomena.
Conference paper
(2021)
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Shahabodin Afrasiabi, Mousa Afrasiabi, Behzad Behdani, Mohammad Mohammadi, Mohammad S. Javadi, Gerardo J. Osório, João P.S. Catalão
Photovoltaic (PV) as one of the most promising energy alternatives brings a set of serious challenges in the operation of the power systems including PV system protection. Accordingly, it has become even more vital to provide reliable protection for the PV generations. To this end, this paper proposes two-stage data-driven methods. In the first stage, a feature selection method, namely t-distributed stochastic neighbor embedding (t-SNE) is implemented to select the optimal features. Then, the output of t-SNE is directly fed into the strong data-driven classification algorithm, namely robust soft learning vector quantization (RSLVQ) to detect PV array fault and identify the fault types in the second stage. The proposed method is able to detect the two different line-to-line faults (in strings and out of strings) and open circuit fault and fault type considering partial shedding effects. The results have been discussed based on simulation results and have been demonstrated the high accuracy and reliability of the proposed two-stage method in detection and fault type identification based on confusion matrix values.
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Photovoltaic (PV) as one of the most promising energy alternatives brings a set of serious challenges in the operation of the power systems including PV system protection. Accordingly, it has become even more vital to provide reliable protection for the PV generations. To this end, this paper proposes two-stage data-driven methods. In the first stage, a feature selection method, namely t-distributed stochastic neighbor embedding (t-SNE) is implemented to select the optimal features. Then, the output of t-SNE is directly fed into the strong data-driven classification algorithm, namely robust soft learning vector quantization (RSLVQ) to detect PV array fault and identify the fault types in the second stage. The proposed method is able to detect the two different line-to-line faults (in strings and out of strings) and open circuit fault and fault type considering partial shedding effects. The results have been discussed based on simulation results and have been demonstrated the high accuracy and reliability of the proposed two-stage method in detection and fault type identification based on confusion matrix values.
Conference paper
(2021)
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Shahabodin Afrasiabi, Mousa Afrasiabi, Behzad Behdani, Mohammad Mohammadi, Mohammad S. Javadi, Gerardo J. Osório, João P.S. Catalão
The conflicting issues of growing demand for electrical energy versus the environmental concerns have left the energy industries practically with one choice: to turn into renewable energies. This duality has also highlighted the role of power transmission systems as energy delivery links in two ways, considering the increased demand of load centers, and the integration of large-scale renewable generation units connected to the transmission system such as wind power generation. Accordingly, it has become even more vital to provide reliable protection for the power transmission links. The present protection methods are associated with deficiencies e.g., acting based on a predefined threshold, low speed, and the requirement of costly devices. A two-stage data-driven-based methodology has been introduced in this paper to deal with such defects, considering wind power generation. The proposed approach utilizes a powerful feature extraction technique, namely the t-distributed stochastic neighbor embedding (t-SNE) in the first stage. In the second stage, the extracted features are fed to a robust soft learning vector quantization (RSLVQ) classifier to detect and locate transmission line faults. The WSCC 9-bus system is used to evaluate the performance of the proposed data-driven method during various system operating conditions. The obtained results verify the promising capability of the proposed approach in detecting and locating transmission line faults.
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The conflicting issues of growing demand for electrical energy versus the environmental concerns have left the energy industries practically with one choice: to turn into renewable energies. This duality has also highlighted the role of power transmission systems as energy delivery links in two ways, considering the increased demand of load centers, and the integration of large-scale renewable generation units connected to the transmission system such as wind power generation. Accordingly, it has become even more vital to provide reliable protection for the power transmission links. The present protection methods are associated with deficiencies e.g., acting based on a predefined threshold, low speed, and the requirement of costly devices. A two-stage data-driven-based methodology has been introduced in this paper to deal with such defects, considering wind power generation. The proposed approach utilizes a powerful feature extraction technique, namely the t-distributed stochastic neighbor embedding (t-SNE) in the first stage. In the second stage, the extracted features are fed to a robust soft learning vector quantization (RSLVQ) classifier to detect and locate transmission line faults. The WSCC 9-bus system is used to evaluate the performance of the proposed data-driven method during various system operating conditions. The obtained results verify the promising capability of the proposed approach in detecting and locating transmission line faults.