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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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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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.@en

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.@en

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.@en

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.@en

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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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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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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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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Power transformers being among the power system key components, are very important to protect. Various approaches have so far been proposed for this topic. Artificial intelligence (AI) has provided a concrete basis for numerous power transformer differential protection methodologies. However, the capabilities of AI-based methods have never been put into comparison. This paper provides a comparative analysis of various AI-based approaches for differential protection of power transformers. The performances of AI-based differential protection schemes are investigated in the presence of current transformer (CT) saturation condition, series capacitors compensation, and superconductor fault current limiter (SFCL), which possibly deteriorate the capability of differential protections to correctly tell inrush currents and internal faults apart. The PSCAD/EMTDC simulation tool is applied in producing the necessary test dataset for performance evaluation of the suggested novel strategy. The attained outcomes from the assessment of various methods have been analyzed to introduce the most superior AI-based differential protection scheme.

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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.

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HVDC systems under monopolar activity, give rise to flow of earth return currents (ERCs) in nearby AC systems. Medium, and large distribution transformers feeding industries, are of high sensitivity, and because of small ratings are highly prone to failure due to ERCs. Condition monitoring is the first step in the protection of these transformers from the adverse effects of ERCs. However, ERCs being quasi-DC in nature, are difficult to detect and measure in an AC power system. This paper proposes a novel method to detect and measure the ERCs flown into neutrals of medium, and large distribution transformers, needless to the installation of additional devices. The proposed method has been implemented in MATLAB software, tested through computer simulation using EMTP-rv environment, and validated by real measurements.

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The performance of measurement transformers especially in transient phenomena affects the monitoring, control and protective devices. Coupling capacitor voltage transformer (CCVT) is a measurement transformer that may experience ferroresonance phenomenon due to the presence of capacitors and non-linear reactors. This study provides a probabilistic framework for investigation of ferroresonance phenomenon in CCVT. The aim of this investigation is to find out the area of the power system in which a CCVT is highly prone to experience ferroresonance phenomenon. Also through this framework the behaviour of the different types of ferroresonance suppression circuits and possible solutions to avoid or reduce the chance of ferroresonance will be discussed. The effects of several factors such as switching moment, different fault type and the fault inception angle are investigated. The investigation will be conducted considering the effects of single-pole auto-reclosing, three-pole auto-reclosing and capacitor failure in the CCVT. As shown in the simulation results, the presented framework can provide some useful information regarding the exposure level and severity of CCVT ferroresonance in the system.

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Vacuum circuit breaker operation causes transient overvoltages that may lead to severe damages to transformer insulation. Since the parameters affecting these overvoltages have stochastic nature, a statistical analysis may provide detailed insight into the overvoltages from the point of view of insulation coordination applications. In this paper, a statistical analysis of the overvoltage variations of the step-up transformer during vacuum circuit breaker operation is conducted. Some variables, including switching angle, current chopping, high-frequency current quenching capability, and wind turbine power, are assumed as random variables. Besides, some indicators representing the switching overvoltage characteristics such as the amplitude, the number of restrikes, and the rate of rise are extracted. By performing a probabilistic analysis, the destructiveness due to switching overvoltages on typical transformer insulation can be investigated. The sensitivity of different surge protective devices and their effectivities are also analyzed and statistically evaluated. The applied methodology is very useful because of the stochastic nature of the switching overvoltages. In this work, the impact of protective equipment on each of the overvoltage indicators is analyzed. It is also found that considering only the amplitudes of transient overvoltages is not enough to draw conclusions about the safety of the transformer when exposed to frequent switching surges.@en