Cyber attacks can disrupt the digital protection relays and change their output command to the circuit breaker. The aim of this research is to provide new condition monitoring for overcurrent relays during cyber treatments. This research is modeled three common types of attacks with randomize concept and its detection method via MATLAB/SIMULINK software into IEEE 3‐machine 9‐bus model that is applicable in real time situation. Finally a new index has been defined for detection that employs wavelet analysis of the substation currents and its energy which has been called cyber attack detection index (CADI). Proposed index is robust to variation of sampling signal, sampling rate, cyber attack type and current transformer saturation. The results show that the index has an acceptable performance with accuracy of 100% without any false trigger. This method is not dependent on operating point, relay protection scheme and attack type and also could distinguish the abnormal actions of breakers due to unexpected cyber contingencies caused by natural disasters or software malfunctions rather than cyber attacks in a certain operating points. The level of CADI curve in cyber attacks is always higher than symmetrical fault and current signal detects them more quickly and clearly than other signals.

Power transformers are vital and costly components in power systems, essential for ensuring a reliable and uninterrupted supply of electrical energy. Their protection is crucial for improving reliability, maintaining network stability, and minimizing operational costs. Previous studies have introduced differential protection schemes with harmonic restraint to detect internal transformer faults. However, these schemes often struggle with computational inaccuracies in fault detection due to neglecting current transformer (CT) saturation and associated uncertainties. CT saturation during internal faults can produce even harmonics, disrupting relay operations. Additionally, CT saturation during transformer energization can introduce a DC component, leading to incorrect relay activation. This paper introduces a novel feature extracted through advanced wavelet transform analysis of differential current. This feature, combined with differential current amplitude and bias current, is used to train a deep learning system based on long short-term memory (LSTM) networks. By accounting for existing uncertainties, this system accurately identifies internal transformer faults under various CT saturation and measurement uncertainty conditions. Test and validation results demonstrate the proposed method’s effectiveness and superiority in detecting internal faults in power transformers, even in the presence of CT saturation, outperforming other recent modern techniques.

Storage systems play an important role in performance of micro-grids. Storage systems may decrease fluctuations caused by periodic and unpredictable nature of distributed generation resource. Some micro-grids are connected to the network via a grid-interface converter. The phase-locked loop (PLL) is a commonly technique for the grid synchronization of network-connected converters. Various parameters affect the stability of PLL (including the network-side and microgrid-side parameters). The effect of the micro-grid-side parameters on the stability of the PLL has not been studied so far. In this paper, the stability of PLL influenced by microgrid-side parameters has been evaluated after a detailed analytical modeling of micro-grid components (including the production power fluctuations, energy storage system, microgrid-side loads, controller parameters etc.). This paper proposes two new stability analysis criteria for PLL affected by micro-grid and hybrid storage system parameters. Using proposed criteria for stability of PLL, optimized rate of micro-grid and hybrid storage system parameters are obtained using statistical methods (Taguchi approach). Finally, behavior of PLL affected by hybrid storage system is investigated. The simulation results and eigenvalues analysis confirm the theoretical analysis and proposed criteria.

Power transformers are one of the most critical elements of the electrical Power System because of the aforementioned function in the voltage regulation and power supply. It is very important in the field of power engineering to be able to differentiate the inrush currents caused due to the energization of the transformer from the internal fault currents created in the transformer. This paper represents an efficient approach to solving this issue by employing DWT feature extraction and ANN classification. This approach is based on the determination of waveforms by distinguishing the D4 and D5 coefficients of instantaneous differential currents using DWT. These coefficients present much useful information related to the waveform type, making it possible to differentiate between the inrush and the internal fault currents. This is a key factor when making classification in that these criteria are related to the energy content involved within these coefficients. This energetic approach forms the basis for the ANN controller to determine particular decisions about the quality of the current. This proposed approach is supported with simulation to represent empirical data in supporting the use of this approach. The results always confirm the efficiency of such an approach to the differentiation between inrush and internal fault currents with a high percentage of accuracy. The effectiveness of this method goes beyond accuracy as it is reliable, responds quickly to abnormal conditions, and can be applied to a variety of power transformer types. Applying this concept in real grid power systems can lead to increased reliability and less downtime thereby strengthening the electrical system as a whole. The reliability and safety of power transformers remain a critical concern in power engineering. The present paper proposes a new method to improve power transformer protection for differentiating internal faults from other abnormal situations. The method described herein utilizes advanced signal processing and machine learning with the help of DWT and ANN to reach higher standards of accuracy and reliability.