Research and Technology in Electrical Industry
Volume:1 Issue: 2, Spring 2022

Fault detection and classification of brushless DC motors (BLDCM) is considered in this paper. A novel solution is introduced to diagnose multiple electromechanical faults that includes the stator inter-turn, the rotor dynamic imbalance, the rotor static imbalance, and different combinations of them. The current signal of the BLDCM is used together with the motor torque and the motor speed to achieve the classification of a wide range of defects. The fault features of the measured signals are extracted using packet wavelet transform (PWT). These features which include the energy, in the two modes of BLDCM operation: without load and with load, are used as input data for the radial basis function (RBF) neural network. Therefore, the designed algorithm maintains its efficiency in all operating conditions of the BLDCM. Besides, by the combination of the mentioned algorithms, the relationship between the fault types and different affected parameters of the measured signals are obtained more precisely. The neural network weights are updated by the particle swarm optimization (PSO) and the genetic algorithm (GA) that improve the convergence speed and provide better flexibility for local problems. Finally, the effectiveness of the proposed methods is validated by comparing the results obtained for different combinations of the neural networks and optimization methods.

In this paper, a 3.1kW DC motor is used to simulate the static and dynamic behavior of a horizontal axis wind turbine. The effect of the difference between the wind turbine's inertia and DC motor on the dynamic behavior of the system, torque oscillation due to the effect of the tower shadow and wind shear, and the effect of rotary losses in the mechanical torque of the DC motor are considered. The torque of the DC motor is controlled by the closed-loop PID controller. This closed loop has two feedbacks for the speed and current of the DC motor. The turbine calculations are performed in Matlab/Simulink and the reference signal of the turbine's torque is sent to the chopper using an interface card. Two methods are proposed to estimate the acceleration of the DC motor to improve the compensation of the turbine inertia effect: the Kalman Filter, and the second-order low-pass filter. Adjustable parameters of proposed methods are optimized using the particle swarm optimization algorithm (PSO). A 2.2 kW synchronous generator is coupled with the wind turbine emulator and feeds a constant load. To show the effectiveness of the proposed methods the results are reported. The results indicate that the Kalman filter has a better performance in the compensation of the turbine inertia effect.

In this paper prediction error measurement (PEM) method is used for online synchronous generator parameters estimation. Unlike the usual off-line standard methods, in the proposed method instead of removing the automatic voltage regulator (AVR) from the circuit and manually disturbing the excitation voltage, a change in the reference signal of the AVR is applied and the parameters are estimated for the AVR-generator interconnected system. To adapt this modeling to real conditions, the input and output signals are mixed with noise. Since the direct application of the PEM method on noisy signals will cause a significant estimation error, the wavelet transform is used as a signal-denoising tool. Using Matlab/Simulink, synchronous generator parameters are estimated under colored noise and white noise conditions. To validate the estimated parameters, the results compare with the standard standstill frequency response (SSFR) test that was applied to the Shahid Rajaee power plant. Moreover, a three-phase short circuit to earth for a period of 2.5 cycles at the generator terminal is simulated and analyzed. The results indicate the proper accuracy of the proposed parameter estimation.

Sectionalizing switches (SS) and lines play an essential role in improving the performance of the distribution automation system as well as reducing the outage duration of power grid customers. The operation of the sectionalizing switches and whether the lines are energized or de-energized are also dependent on each other. Considering the structural complexities related to the mathematical modelling of such problems, optimizing the planning and performance of switches and lines usually requires the use of heuristic and meta-heuristic approaches or oversimplification of network complex topology. To deal with such issues, this research presents an efficient computational model for reliability-based simultaneous switching in distribution networks with complex topologies using binary teaching learning-based optimization (BTLBO) algorithm. The performance of proposed model is demonstrated on the IEEE 123-bus system. The results show that the proposed model is effective for placement of sectionalizing switches to enhance power system reliability.

Power transformers used in the subtransmission substations of the Iranian power grid have YNd connection. In addition, a grounding transformer with YNd connection is used to create an artificial neutral on the medium voltage side. The phase to ground capacitor may have a high value in the medium voltage network. In some conditions, such as interruption of the phase-to-ground fault current in one of the output feeders, a kind of resonance is created in the network that not only damages the grounding transformer but also causes protection problems related to the misalignment of ground fault relays. In this paper, the resonance between the grounding transformer and the phase-to-ground capacitor of the medium voltage output feeders of a sample subtransmission substation is investigated, considering that the phase-to-ground fault is interrupted. In this regard, based on available currents and voltages, several indicators are defined to adjust the surge arrester and other elements of the subtransmission network. In addition, appropriate solutions are presented in order to damp the aforementioned resonance and avoid unwanted challenges.

Considering the sensitivity of high voltage direct current transmission system protection and the difficulty in identifying external DC faults, this paper presents two methods for fault detection and classification in VSC-HVDC transmission lines. These studied methods are evaluated in terms of efficiency and accuracy. This research is focused on DC faults at different distances along the lines. DC line current and voltage are selected as input to wavelet transform, and in the next step, unique and valuable features of each signal are extracted with modern signal processing methods, and then these features are used as input data for algorithms to detection and classification faults. Deep Neural Network (DNN)  and Support Vector Machine (SVM) have been investigated to detect and classify faults. In the next step, the efficiency of these algorithms was investigated and analyzed in noise conditions. The innovation of this research is replacing a new method of extracting features from the fractal dimension, which has been used to study more prominent features, and improve performance with a small number of study data and considering different conditions, and using the new feature extraction method and improving the performance of the algorithms, 96% accuracy has been achieved.

In recent decades, the welfare of human being is seriously threatened by climate change. As a result, numerous energy regulations have been put in place to encourage the expansion of investments in renewable energy. In this context, open questions remain regarding the impacts that these policies may have on generation expansion planning (GEP). This paper addresses this issue by applying three of the most widely adopted energy strategies, namely quota obligation, feed-in tariffs, and emission trade system, to the GEP problem, resulting in an integrated renewable-conventional generation expansion planning (IRCGEP) model with a properly modified cost function and extra constraints. To achieve this aim, first, the IRCGEP model is solved using general algebraic modeling system from a generation company (GENCO) perspective. Afterward, according to the obtained optimized expansion strategies, the policies impact on the social welfare terms including consumer surplus, GENCO profit, and environmental damages cost are investigated, while they are included on the Bergson-Samuelson social welfare function. Moreover, to assess the financing mechanism effect of the policies on consumer surplus, a suitable attribute known as the "virtual price" is put forth. Numerical studies shed light on the reactions of investment decisions and the social welfare to the energy policies.

Coordination between protection devices is one of the important issues in any protection scheme of an electrical power system. The tendency to increasingly use distributed generation has jeopardized the selectivity of overcurrent relays in active distribution networks. To increase the penetration level of distributed generation in these networks, an increase in the Protection Coordination Index (PCI) of the network is a must. For this purpose, in this paper, voltage-current-time characteristics and Dual Setting Directional Overcurrent Relays are used. In this case, coordination is not a trivial task and imposes further complexity. Since Dual Setting Directional Overcurrent Relays are capable to break current in both directions with separate tuning, they have the flexibility to improve the protection coordination index. Moreover, voltage-current-time characteristics employ voltage in the operation of relays. This capability can also be used in reducing the optimization of the protection scheme and provides more flexibility in coordination procedures. Afterward, the protection scheme is developed by user-adjustable voltage-current-time characteristics to improve the PCI as much as possible. The proposed scheme is modeled as a nonlinear programming approach which is solved by a Genetic Algorithm (GA).

Power transformers are one of the main components of the power grids. Several main and back up protection relays are provided to protect them against different fault types. Restricted Earth Fault (REF) protection relay is used as main protection to protect transformer against in-zone phase-to-ground faults. This protection has high accuracy to detect earth faults inside the transformer protection zone. Operational experiences show that in some cases and during external high current out-zone faults which leads to current transformers (CTs) core saturation, REF relay operates incorrectly and cause unwanted trip. Relay setting base and operation curve methods to solve the problem have been devised, but the problem has not yet been completely solved. In this article, different fault types such as symmetric/asymmetric, single/multiple phases, low/high current are simulated. Then, causes of REF protection maloperation and commercial relays settings and curves are studied. The optimal setting for the relay is extracted and its effectiveness in different fault types validated by simulating real data with PSCAD and MATLAB software.

In transmission networks, the recloser is used to detect and interrupt transient faults, isolate faults, manage the network structure, and increase reliability. This instrument interrupts momentary faults by opening and closing its faulty downstream power system. An auto-reclose method can be used to trip the faulted line and re-energize it after an intentional time delay. This time delay is normally required for the de-energization of the fault arc otherwise the arc will restrike. One of the most important factors that should be considered in reclosing in long lines is the ability to turn off the secondary arc by means of Neutral grounding resistors (NGR) in the reactor neutral location. Due to many factors such as line length change, NGR design, etc., it is very important to determine the proper dead time. This paper presents an auto-reclosing study of 400kV transmission lines. The success of auto-reclosing depends on the extinction of the secondary arc. In the proposed method, it is explained with some real examples of Yazd transmission network lines that NGR cannot be expected to completely suppress the secondary arc, so the maximum value of the secondary arc current and dead time will be calculated with the reverse calculation method.

The protection of transmission and sub-transmission lines is conducted by overcurrent, line differential and distance protections among which the use of distance protection is very common.Mal-operation of protection equipment, including distance protection, can occur due to improper choose of the setting. This paper presents the proper method for calculation of the phase-to-phase loop resistance reach of distance protection with Quad characteristic and the impedance starter at the sub-transmission level to prevent mal-operation of the distance relay when processing its algorithm. In this work, based on the practical experiences on several industrial distance relays, it is shown that there is a possibility of improper detection of the fault loop by distance protection and then improper relay operation for out of zone faults. After review of the subject, and expressing the mathematical relations governing the problem, the appropriate phase-to-phase loop resistance reach determination method is put forward. Finally, using the information obtained from a practical incident, the efficiency of the proposed method is verified.

The idea of this paper is to provide a framework for simultaneous energy cost optimization and congestion management by using shared energy storage. As a case study, this paper addresses this aim by proposing the integration of a community energy storage (CES) within a distribution system, connected to four microgrids (MGs). The shared storage system enables the MGs to reduce their energy costs by optimizing the operation of the battery using a Heuristic optimization algorithm, specifically the Teaching-Learning-Based Optimization (TLBO) algorithm. Simultaneously, the distribution system operator (DSO) leverages the shared storage to alleviate congestion by purchasing charged power from the storage manager. Interestingly, the DSO is willing to pay a premium price for the charged power from the shared storage, surpassing the prevailing electricity price during congested hours. Moreover, to account for uncertainties arising from load variations and intermittent renewable energy resources (RES), Monte Carlo simulation is employed in this study. Through comprehensive simulations and analyses, the proposed approach demonstrates the potential of CES as an effective tool for congestion relief and operational cost optimization in distribution systems, and providing economic benefits to both the MGs and the DSO.