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Even though shunt compensation of transmission lines (TLs) is a common practice to limit the overvoltages stemming from the Ferranti effect, it may cause transient and steady‐state resonant overvoltages when one phase is left open due to the single‐pole‐open condition following the temporary phase‐to‐ground (Ph‐g) fault. Furthermore, the existing shunt reactors potentially increase the duration of the secondary arc current at the fault point and hence prevent offering fast single‐pole reclosing. To exploit the mentioned issues, in recent studies, a neutral reactor is introduced, which is installed in the neutral point of the shunt reactors; nonetheless, obtaining the appropriate amount of it is remarkably challenging. In this paper, an optimal neutral reactor (ONR) is determined based on an optimization problem, which minimizes the neutral reactor cost subject to limiting both resonant overvoltages and the duration of the secondary arc current. In modelling, the Laplace domain is employed rather than the phasor domain to consider both transient and steady‐state conditions since this technique allows having better control over transient overvoltages as well as transient secondary arc. [ABSTRACT FROM AUTHOR]

Mal-operation of distance protection relays during power swing (PS) condition can endanger the power system stability. Therefore, it is crucial to distinguish between short-circuit faults and PS by a power swing blocking (PSB) function to prevent undesired tripping of distance relay. This paper develops a novel and straightforward PSB method based on the rate of impedance angle change (RIAC). Not only can the proposed approach fairly discriminate both symmetrical and asymmetrical faults from PS, but also it can appropriately preserve protection scheme security during PS under single-pole tripping (SPT) on a transmission line. Furthermore, there is no need to perform extensive dynamic stability studies for setting the PSB function parameters. Unlike the rate of impedance change approaches utilized two blinders, the developed approach only needs an inner blinder. Thus, it can be used for networks running near their operational limits. The proposed method is evaluated through a modified IEEE-PSRC benchmark system in PSCAD/EMTC environment. The obtained results verify superior performance of the proposed method to traditional impedance rate of change method under different scenarios. [ABSTRACT FROM AUTHOR]

This article presents a novel straightforward and real-time algorithm to measure power system frequency. The main idea of this approach is to improve accuracy of the estimated frequency during off-nominal frequency conditions. The proposed approach includes two stages: 1) the DFT algorithm is used to estimate voltage amplitude and phase angle by assuming that the frequency is the same as its value at the previous step, and 2) based on Taylor approximation and using the least error square algorithm, the error due to the frequency difference is calculated and thus the estimated frequency would be updated. One of the main features of the proposed approach is that it can be easily implemented on the available industrial hardware requiring low computational burden. The proposed approach is also robust against noise and harmonics and can track the deviations of the power system frequency as well. The performance of the proposed approach is evaluated through simulation studies and also verified based on implementing it on a prototype synchronizing relay. [ABSTRACT FROM AUTHOR]

Concerned with deploying zero-emission energy sources, reducing energy wasted through transmission lines, and managing power supply and demand, monitoring and controlling microgrids have found utter importance. Accordingly, this paper aims to investigate the efficacy of state estimation fusion for a synchronous generator as well as an induction motor in order to ameliorate system monitoring. A third-order nonlinear state-space model, that operates based on actual input data taken from the Smart Microgrid Laboratory, is assumed for each of the electrical machines. The model parameters are set according to the parameters of the electrical machines. A fusion structure based on the internet of things communication network, which is modified to increase uncertainty, is presented for fusing the state estimates. The data fusion topology is distributed and relies on two data fusion models. The first model is a set of state estimators, referred to as data input-feature output model. The second one fuses the estimators' outputs based on supervised machine learning methods, referred to as feature input-feature output model. The simulation results in MATLAB and Python show the efficiency of linear regression methods compared with other leveraged methods for data fusion. By comparing the results obtained from both simple and complex estimation filters, it can be deduced that combining simple filters, extended Kalman filter in this case, with simple data fusion methods, linear regression in this case, can produce much more accurate results in a short period of time. Besides, this study shows that the averaging operators are unsuitable for estimation fusion by referring to their convexity condition. • Regression methods can outperform other methods for the purpose of data fusion. • Simple filters combined with simple fusion methods boost state estimation accuracy. • The averaging operators are unsuitable for estimation fusion due to performing convex fusion. [ABSTRACT FROM AUTHOR]

• An effective protection method for i) detecting electric faults using current variations, ii) discriminating them from other disturbances based on the transient response of PV cells, and iii) locating the faulty string within PV arrays. • To maximize reliability and ensuring applicability, dynamic and static models of PV cells are both taken into account whilst formulating the method. • A minimal number of measurement sensors will be used for all the functionalities offered. Quality degradation, power loss, and fire hazards are amongst the consequences of uncleared electric faults within photovoltaic (PV) arrays. This paper proposes an effective protection method for i) detecting electric faults using current variations, ii) discriminating them from other disturbances based on the transient response of PV cells, and iii) locating the faulty string within PV arrays with a minimal number of measurement sensors. To accomplish these, both dynamic and static models of PV cells are taken into account whilst formulating the problem. To evaluate the performance of the proposed method, a prototype solar power plant is simulated under a wide variety of normal and abnormal conditions in PSCAD-EMTDC software. The simulation results verify the proposed method's high speed and desirable accuracy. Independency from meteorological data and the need for a minimal number of measurement sensors are the salient advantages of the proposed method. Therefore, the implementation of this method would be affordable for PV systems of different scales. [ABSTRACT FROM AUTHOR]

Faults in the power system should be detected as soon as possible. Otherwise, some defects in the protection system can result in imposing considerable destructive damage to the system equipment. The excitation system of a synchronous generator plays a significant role in the proper operation and control of the generator. Loss-of-excitation (LOE) may cause harmful effects on both the generator and power system. If LOE is not correctly detected, an unnecessary trip signal may be issued, resulting in the overloading of parallel generators and even a system blackout. Conventional techniques for detecting this fault are based on impedance measurement. Despite the simple structure of these techniques, they are sometimes unable to detect some faults correctly. The presence of flexible alternating current transmission system devices can significantly affect the impedance characteristic of the system and as a result, the operation of impedance relays. In this article, first, LOE and other bugs causing failures and errors in the protection of LOE are simulated in the presence and absence of a unified power flow compensator (UPFC), and parameters with different behavior are determined. Then, the most sensitive parameters are sorted using an intelligent method based on their sensitivity. Once the sort order table is obtained, the first two parameters are applied to a ground data handling neural network method, and LOE is detected. The simulation results show that the proposed algorithm is significantly reliable in detecting LOE faults. Moreover, LOE is detected very quickly in the presence or absence of the UPFC in the system by preventing the tolerance of electrical and mechanical stresses by the machine and system; thus, the maloperation of the protection system is avoided. [ABSTRACT FROM AUTHOR]

Power transformers are of the most critical and expensive equipment in the power system industry. Meanwhile, they are exposed to a variety of faulty and abnormal operating conditions. Therefore, to prevent extension of these events they are protected by different protective schemes. Among the various types of the transformer faults, detecting turn-to-turn faults is the most challenging one for the protection systems. Industrial experiences and standard documents reveal that the existing protection systems include severe shortcomings, and still more efforts are required to offer a reliable method for detecting such faults. To this end, this article puts forward the proposal of a novel turn-to-turn fault detection method to satisfy the basic protection requirements of dependability, security, and speed. This method properly detects the low-level turn-to-turn faults while is stable during system transients and external faults. Performance of the proposed method is evaluated through various experimental and simulation studies. The obtained results verify superior operation of the proposed method under different faults and conditions. [ABSTRACT FROM AUTHOR]

Here, an optimization framework is proposed for the service restoration (SR) problem in distribution networks based on mixed‐integer second‐order cone programming (MISOCP). The objective function is to minimize (i) curtailed loads, (ii) the number of switching operations, (iii) crew dispatching cost, and (iv) operational cost. A novel approach is developed for the optimal switching crew routing (SCR) to perform the manual switching operations in a short time. Self‐healing actions including (i) network reconfiguration, (ii) load shedding, (iii) adjusting the output power of the dispatchable distributed generations (DGs), and (iv) the optimal tap setting of the voltage regulation devices are adopted in the SR strategy. Both the voltage dependency and the uncertainty nature of loads are modelled. Besides, the switch failure scenario is considered in the proposed model to deal with real operating conditions. Hence, the developed optimization framework offers the most robust SR solution. The simulation studies are performed on an actual 87‐bus system in MATLAB/Yalmip environment by adopting Gurobi solver. The obtained results verify that the proposed model enhances quality of the solution in terms of SR process time compared to the other existing models. [ABSTRACT FROM AUTHOR]

• Suggesting an innovative probabilistic framework for modeling and evaluating the protection systems. • Proposing new probabilistic models for fault characteristics and protection system elements based on their realistic behavior. • Determining the most suitable setting value of zone-2 intentional time delay. • Improving the protection system operation without missing security and selectivity. The time delay of distance relay zone-2 has considerable impacts on the protection system performance. Traditional deterministic techniques for adjustment of this parameter only consider the worst cases, even with a low probability of occurrence. This leads to the increment of the fault clearing time for most of the high-probability scenarios and endangers the system's stability. To diminish this issue and achieve a suitable tradeoff between protection system selectivity and operation time, a novel probabilistic framework is proposed in this paper for proper adjustment of zone-2 time delay. This framework includes two major parts; the fault and protection system are appropriately modeled in the first part, and then in the second part, the Monte Carlo method is employed as the computational engine for analyzing the system attributes under different conditions considering local and pilot protection schemes. Finally, two indices are proposed for the quantitative assessment of operation speed and selectivity of the protection system. The simulation studies fairly reveal that the operation speed of zone-2 is significantly improved using the proposed settings. In addition, the obtained results indicate that the proposed probabilistic approach not only improves the dependability, but also does not endanger the security of the protection system. [ABSTRACT FROM AUTHOR]

• The impacts of UPFC on the conventional protection scheme is scrutinized. • Features are extracted based on simulation observance and combined with SOM method. • Classification is carried out with LVQ NN. • Proposed method compared to conventional impedance based approach. • The proposed scheme improves the LOE protection function in presence of UPFC. Loss-of-excitation (LOE) conditions may cause devastating effects on both the generator and power system. If LOE is not meticulously detected, two dangerous events may emerge: 1. unnecessary trip signal, 2. not detecting this abnormal condition or detecting it with time delay. Both mentioned flaws have considerable harmful consequences on the generator and power system. Conventional techniques for detecting LOE conditions are based on impedance measurement. The presence of flexible alternating current transmission (FACT) system devices can considerably affect the impedance characteristic of the system and as a result, the operation of impedance-based relays. This paper simulates LOE and stable power swing (SPS) conditions causing failures and errors in the LOE protection in the presence or absence of a unified power flow compensator (UPFC), and parameters with different behaviors are determined. Then, the most sensitive parameters are combined using an intelligent method. The single parameter, achieved from a combination of features in the previous process, is applied to a learning vector quantization (LVQ) neural network. The LVQ is a type of artificial neural network that is based on a prototype supervised learning classification algorithm and trained its network through a competitive learning algorithm. The simulation results show that, in a single machine infinitive bus (SMIB) test system, R-X directional scheme has 2 mal-operations and the proposed algorithm has no mal-operation without UPFC in the system. With UPFC in the system, the R-X directional scheme has four mal-operations and the proposed algorithm has no mal-operation. In the IEEE 9-bus test system, the R-X directional scheme has five mal-operations and the proposed algorithm has no mal-operation. Moreover, applying the proposed algorithm, LOE is detected very quickly in the presence or absence of the UPFC in the system and this approach prevents electrical and mechanical stresses enforced to the machine and power system. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]