Due to latest blackouts in the world, voltage instability and voltage collapse have become the main issues studied in electrical power systems. For this purpose, various Flexible AC Transmission Systems (FACTS) devices have been utilized for many years, increasing voltage stability while at the same time improving system performance, reliability, supply quality and providing environmental benefits. These devices location for enhancing voltage stability is a significant problem for actual power networks. When determining the best location of the controller, an optimal solution should be found to increase the loading margin and also reduce voltage deviation and power losses. In the literature, the weight coefficients of the criteria were chosen equally or approximate values were obtained by trial and error method and Multi-Criteria Decision Making (MCDM) techniques have never been used in finding the optimal location of FACTS devices. This article presents a novel technique for optimal location of Static Var Compensator (SVC) devices in power systems using MCDM. The simulation was conducted on Power System Analysis Toolbox (PSAT) in MATLAB. In the proposed approach, IEEE 14-bus, IEEE 30-bus and IEEE 118-bus test systems were used and the optimal location was found out with Analytic Hierarchy Process (AHP), an MCDM technique. The consistency of the results has been checked and the reliability has been increased, and the results of the application are promising. In addition, the optimal location of the SVC for different contingencies was found, and the effects of the overloaded lines and Phase-Shifting Transformer (PST) on the network were analyzed.

The goal of multi-criteria decision making (MCDM) is to select the most appropriate of the alternatives by evaluating many conflicting criteria together. MCDM methods are widely available in the literature and have been used in various energy problems. The key problems studied in electrical power systems in recent years have included voltage instability and voltage collapse. Different flexible alternating current transmission systems (FACTS) equipment has been used for this purpose for decades, increasing voltage stability while enhancing system efficiency, reliability and quality of supply, and offering environmental benefits. Finding the best locations for these devices in terms of voltage stability in actual electrical networks poses a serious problem. Many criteria should be considered when determining the most suitable location for the controller. The aim of this paper is to provide a comparative analysis of MCDM techniques to be used for optimal location of a static VAR compensator (SVC) device in terms of voltage stability. The ideal location can be determined by means of sorting according to priority criteria. The proposed approach was carried out using the Power System Analysis Toolbox (PSAT) in MATLAB in the IEEE 14-bus test system. Using ten different MCDM methods, the most appropriate locations were compared among themselves and a single ranking list was obtained, integrated with the Borda count method, which is a data fusion technique. The application results showed that the methods used are consistent among themselves. It was revealed that the integrated model was an appropriate method that could be used for optimal location selection, providing reliable and satisfactory results to power system planners.

It is quite important to detect stator short-circuit fault, which is the most common fault type, at incipient stage. It is possible to carry out fault detection using Motor Current Signature Analysis (MCSA) method. In this study, stator current and voltage space vectors of PMSMs were analyzed with MCSA under various load torque, speed and fault percentages conditions. The Negative & positive harmonics were obtained by applying Fast Fourier Transform (FFT) to space vectors of stator current and voltage. It is suggested that by using the obtained fault signatures, stator inter-turn fault estimation can be achieved accurately. The results of comprehensive analysis carried out under various load torque and speed conditions show that characteristics fault signatures are both present in the current and the voltage space vectors spectra.

Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a Pattern Recognition System Condition monitoring;Digital signal processing;Fault detection;Fault severity classification;Pattern recognition system;Permanent magnet synchronous motors; In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

Fault detection is an important issue in today’s distribution networks, the structure of which is becoming more complex. In this article, a data-based Cauchy distribution weighting M-estimate RVFLNs method is proposed for short-circuit fault detection in distribution networks. The proposed method detects short circuits based on current and voltage measurements. In addition, noises were added to the data to ensure the robustness of the method. The performance of the method was examined in the RTDS RTS simulator using the IEEE 33-bus-bar system model with the help of real-time simulations. The success rate of the proposed method is between 98% and 100% for low-impedance (0 ohm) short-circuit faults, depending on the fault type. The success rate of high-impedance (100 ohm) short-circuit faults, which are more difficult to detect, is between 80% and 92%, depending on the fault type.

Fault detection is an important issue for permanent magnet synchronous motors (PMSMs). In the initial stage, it is very crucial to detect stator winding inter-turn short-circuit failure, which is one of the most common types of faults. In this paper, a new approach based on electromechanical torque has been proposed to detect the stator inter-turn short circuit fault (ISCF) that occurs in surface-mounted permanent magnet synchronous motors (PMSMs). New fault signatures based on the torque signal that can be used in stator winding ISCF detection are tried to be found in the torque frequency distribution. Fast Fourier Transform (FFT) was used to extract the torque frequency components associated with the stator ISCF. It was found that the amplitudes of the 2nd and 4th harmonic components of the torque signal are distinctive features that can be used for stator winding ISCF detection in PMSM. With the proposed components of the 2nd and 4th harmonic of torque, an inter-turn fault can be easily detected at the initial stage. Both experimental results and simulation results for healthy and three different faulty states (2%, 12.5%, and 25% ISCF) at different load levels and different speeds are presented in this paper.