A novel scheme is suggested for change detection and fault classification in ac microgrids (μGs) with different operating modes. The proposed method utilizes a Teager–Kaiser energy operator based approach for analyzing the current-based signal to detect changes in the μG. The current-based signal is a summation of squared three-phase currents (SSC), measured at one end of a line in the μG. Since μGs have different operating modes, a proper signal should be defined to have some valuable signatures of the changes in different conditions. The SSC signal is constant during normal conditions of the grid-connected and islanded μG, whereas in any possible change condition experiences considerable variation. After the detection, the faulty phase is determined using three similar indices, derived from the squared currents. These indices are the ratios of squared currents for each phase with half-cycle window length, before and after the change detection. The proposed protection scheme is able to handle both operation modes of the μG. The proposed technique is evaluated in a MATLAB/Simulink simulated network and laboratory small-scaled test bench. The results confirm high accuracy and quickness of the proposed approach. Furthermore, the performance of the proposed methodology is compared with some other similar methods.

When a fault occurs in transmission lines, first, it is detected, then classified, and finally, located by a distance protection. Having a fast fault detection approach helps saving the required time for the whole protection procedure. In this paper, a novel method for fault detection of transmission lines based on the summation of squared three-phase currents (SSC) and a moving average technique is proposed. The SSC is constant during a normal condition of the network, while it has considerable variation under a fault condition. Using a moving average concept, a criterion is defined, by which fault occurrence can be detected. The proposed fault detection method is evaluated in four different systems: a typical 5-bus system, a typical double-circuit transmission line, WSCC 9-bus system, and a laboratory small-scaled system. The results confirm a high degree of accuracy and speed of the proposed approach. Moreover, the performance of the method is compared with some other similar methods from different aspects.

Fault diagnosis of brush-less direct current (BLDC) motors has recently gained more attention. Inter-turn faults in BLDC motors distort three-phase currents waveform, leading to an asymmetrical condition. This paper introduces a simple and efficient method to identify inter-turn faults based on one modal current and four different simple indices. The modal current is derived by proper linear mixing of measured three-phase currents. Following the initial processing of the modal current, three main indices including moving mean, variance, and signal energy are obtained in parallel. Also, an auxiliary correlation-based index is suggested to enhance the method for discrimination of faulty conditions from non-faulty ones. The fault detection is made by passing at least two main indices (out of three indices), and also an auxiliary index from a predefined threshold. The proposed technique is evaluated under different loads, speeds, and fault severity in two different data-set: data from a simulated motor in Maxwell finite element package, and a real 4-pole motor. Moreover, the method is compared with other methods from different aspects. The results confirm a high accuracy and speed of the fault detection in the proposed approach.

Directional relays have a key role for protection of transmission and distribution networks. They are used as supervisory element for distance and overcurrent protections. Therefore, accuracy and performance of these protections are dependent on the performance of directional relays. In this paper, a high-speed current-based directional algorithm is presented. For a specified interval, the fault current samples are multiplied by corresponding prefault current samples and summation of the results is calculated as a criterion. Sign of the summation and direction of power flow at normal condition determine fault direction. In spite of most high-speed techniques, just accurate current transformers are needed to obtain the criterion and determining normal power flow can be relegated to simple voltage transformers at steady-state condition. The method is evaluated using plenty of simulations and experiments. Also, performance of the proposed algorithm is compared with some similar approaches. The results confirm reasonable accuracy, speed, and simplicity of the proposed method.

This article develops a transient monitoring function (TMF) based fault detection method for dc microgrids (DCMGs). In this method, the measured currents from one-end of the poles are first transformed into modal components. Then, the modal current is estimated using TMF concept. Afterward, the residual signal is defined as the absolute value of the difference between the modal and estimated currents. This signal has negligible fluctuations during the normal operation, while it experiences significant variations after fault occurrence. This feature is selected to reflect the fault incidence. To do so, the Teager–Kaiser energy operator is applied to the residual signal to amplify the changes, followed by a fault detection index (FDI). Comparing FDI with a threshold can reveal the fault. Furthermore, FDI can discriminate the faulty conditions in different operation modes of DCMG with a wide variation of fault conditions. To assess the developed scheme performance, a DCMG with different kinds of sources and loads is simulated in MATLAB/Simulink. The results confirm the quickness and the high accuracy of the proposed technique. Moreover, the method is validated in an experimental laboratory small-scale test bench. Finally, to demonstrate the superiority of the method, a comparative study with recent methods is presented.

Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance-current-power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods.