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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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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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

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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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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This paper deals with a fault detection investigation of SCBs, and it is focused on the faulty phase detection and the number of faulty capacitor units. Unlike previous methods, the proposed method provides a relay decision making criterion which determine the faulty capacitors, and the number of capacitor failures in case of multiple faulty phase conditions. The proposed algorithm is applied on different wye configurations of SCBs considering different protection designs (i.e., fuseless, internally and externally fused units). Since the detection of capacitor failures in SCBs are based on the fundamental phasor component, there may occur a significant delay in decision making in the case of an external short circuit fault in the power system. The aforementioned condition, which will be mathematically proven, happens due to a capacitor discharge after fault clearance. To deal with this condition, a method is proposed by applying an algorithm, in which the fundamental component of the voltage signal is extracted in one cycle. Performance evaluations associated with the proposed method are provided for different fault conditions, fault locations, and different levels of harmonics and, they are further discussed through the implementation of the proposed method in MATLAB environment.

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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