Iranian Journal of Electrical and Electronic Engineering
Volume:16 Issue: 3, Sep 2020

Watermarking has increased dramatically in recent years in the Internet and digital media. Watermarking is one of the powerful tools to protect copyright. Local image features have been widely used in watermarking techniques based on feature points. In various papers, the invariance feature has been used to obtain the robustness against attacks. The purpose of this research was based on local feature-based stability as the second-generation of watermarking due to invariance feature to achieve robustness against attacks. In the proposed algorithm, initially, the points were identified by the proposed function in the extraction and Harris and Surf algorithms. Then, an optimal selection process, formulated in the form of a Knapsack problem. That the Knapsack problem algorithm selects non-overlapping areas as they are more robust to embed watermark bits. The results are compared with each of the mentioned feature extraction algorithms and finally, we use the OPAP algorithm to increase the amount of PSNR. The evaluation of the results is based on most of the StirMark criterion.</span></span></span></div>

This paper suggests a new control method to modify the virtual impedance performance in unbalanced conditions. The proposed method compensates the voltage drop that occurred due to the virtual impedance and adjusts the voltage of the point of common coupling at a desirable level. To compensate the voltage drop, the reference voltage in the droop control varies according to the proposed algorithm. Moreover, a modified decoupled double synchronous reference frame is introduced to achieve appropriate active and reactive power sharing and voltage balancing, simultaneously. Finally, the simulation results in MATLAB/Simulink are provided to validate the accuracy and effectiveness of the proposed approach.</span></span></span></div>

Kalman filtering has been widely considered for dynamic state estimation in smart grids. Despite its unique merits, the Kalman Filter (KF)-based dynamic state estimation can be undesirably influenced by cyber adversarial attacks that can potentially be launched against the communication links in the Cyber-Physical System (CPS). To enhance the security of KF-based state estimation, in this paper, the basic KF-based method is enhanced by incorporating the dynamics of the attack vector into the system state-space model using an observer-based preprocessing stage. The proposed technique not only immunizes the state estimation against cyber-attacks but also effectively handles the issues relevant to the modeling uncertainties and measurement noises/errors. The effectiveness of the proposed approach is demonstrated by detailed mathematical analysis and testing it on two well-known IEEE cyber-physical test systems.</span></span></span></div>

State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and pseudo measurements data. This study presents a new approach to model errors for the distribution system state estimation purpose. In this paper, pseudo measurements are generated using a couple of real measurements data by means of the artificial neural network method. In the proposed method, the radial basis function network with the Gaussian kernel is also implemented to decompose pseudo measurements into several components. The robustness of the proposed error modeling method is assessed on IEEE 123-bus distribution test system where the problem is optimized by the imperialist competitive algorithm. The results evidence that the proposed method causes to increase in detachment accuracy of error components which results in presenting higher quality output in the distribution state estimation.</span></span></span></div>

To realize the self-healing concept of smart grids, an accurate and reliable fault locator is a prerequisite. This paper presents a new fault location method for active power distribution networks which is based on measured voltage sag and use of whale optimization algorithm (WOA). The fault induced voltage sag depends on the fault location and resistance. Therefore, the fault location can be found by investigation of voltage sags recorded throughout the distribution network. However, this approach requires a considerable effort to check all possible fault location and resistance values to find the correct solution. In this paper, an improved version of the WOA is proposed to find the fault location as an optimization problem. This optimization technique employs a number of agents (whales) to search for a bunch of fish in the optimal position, i.e. the fault location and its resistance. The method is applicable to different distribution network configurations. The accuracy of the method is verified by simulation tests on a distribution feeder and comparative analysis with two other deterministic methods reported in the literature. The simulation results indicate that the proposed optimized method gives more accurate and reliable results.</span></span></span></div>

Improving transient voltage stability is one of the most important issues that must be provided by doubly fed induction generator (DFIG)-based wind farms (WFs) according to the grid code requirement. This paper proposes adjusted DC-link chopper based passive voltage compensator and modified transient voltage controller (MTVC) based active voltage compensator for improving transient voltage stability. MTVC is a controller-based approach, in which by following a voltage dip (VD) condition, the voltage stability for the WF can be improved. In this approach, a voltage dip index (VDI) is proposed to activate/deactivate the control strategy, in which, two threshold values are used. In the active mode, the active and reactive power are changed to decrease the rotor current and boost the PCC voltage, respectively. Based on the control strategy, in a faulty grid, DFIG not only will be able to smooth DC-link voltage fluctuations and reduces rotor overcurrents but also it will increase the voltage of point of common coupling (PCC). Therefore, it improves transient voltage stability. The simulation results show the effectiveness of the proposed strategy for improving voltage stability in the DFIG.</span></span></span></div>

In this paper, a novel method is proposed to monitor the power system voltage stability using Support Vector Machine (SVM) by implementing real-time data received from the Wide Area Measurement System (WAMS). In this study, the effects of the protection schemes on the voltage magnitude of the buses are considered while they have not been investigated in previous researches. Considering overcurrent protection for transmission lines not only resolves some drawbacks of the previous studies but also brings the case study system closer to the realities of actual systems. Online monitoring of system stability is performed by prediction of the Voltage Stability Index (VSI) and carried out by using Support Vector Regression (SVR). Due to the direct effect of appropriate SVR parameters on the prediction quality, the optimum value is chosen for learning machine hyperparameters using Differential Evolution (DE) algorithm. The obtained simulation results demonstrate high accuracy, effectiveness, and optimal performance of the proposed technique in comparison with Back-Propagation Neural Network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) approaches. The presented method is carried out on the 39 bus New England system.</span></span></span></div>

Increasing the short circuit current due to the penetration of distributed generations (DGs) in various voltage levels and meshed topology is a basic problem in power systems. Using fault current limiter (FCL) is an efficient approach to mitigate the exceeded short circuit levels. In this paper, a new approach is presented for multiple FCLs locating to decrease short circuit levels in meshed networks with several subsystems and multi-level voltages. Modified hybrid genetic algorithm (GA) and sensitivity analysis (SA) are used to determine the type, number, location, and voltage level of FCLs. Also, an effective sensitivity index is proposed, which can reduce the search space for optimal allocation. This method suggests the optimal allocation with the least investment cost in multi-level voltages networks according to the FCL costs. The proposed method is evaluated in the IEEE 30-bus, 57-bus, and 300-bus test systems. Numerical results indicate the accuracy and efficiency of the proposed method.</div>

The progression towards smart grids, integrating renewable energy resources, has increased the integration of distributed generators (DGs) into power distribution networks. However, several economic and technical challenges can result from the unsuitable incorporation of DGs in existing distribution networks. Therefore, optimal placement and sizing of DGs are of paramount importance to improve the performance of distribution systems in terms of power loss reduction, voltage profile, and voltage stability enhancement. This paper proposes a methodology based on Dragonfly Optimization Algorithm (DA) for optimal allocation and sizing of DG units in distribution networks to minimize power losses considering variations of load demand profile. Load variations are represented as lower and upper bounds around base levels. Efficiency of the proposed method is demonstrated on IEEE 33-bus and IEEE 69-bus radial distribution test networks. The results show the performance of this method over other existing methods in the literature.</span></span></span></div>

Optimization of specific power of axial flux permanent magnet brushless DC (PMBLDC) motor based on genetic algorithm optimization technique for an electric vehicle application is presented. Double rotor sandwiched stator topology of axial flux permanent magnet brushless DC motor is selected considering its best suitability in electric vehicle applications. Rating of electric motor is determined based on vehicular dynamics and application needs. Double rotor sandwiched stator axial flux PMBLDC motor is designed considering various assumed design variables. Initially designed axial flux PMBLDC motor is considered as a reference motor for further analysis. Optimization of the specific power of electric motor for electric vehicle applications is a very important design issue. The Genetic Algorithm (GA) based optimization technique is proposed for optimization of specific power of axial flux permanent magnet brushless DC motor. Optimization with an objective of maximum specific power with the same torque rating is performed. Three-dimensional finite element analysis is performed to validate the proposed GA based specific power optimization. Close agreement between results obtained from finite element analysis and analytical design establishes the correctness of the proposed optimization technique. The performance of the improved motor is compared with the initially designed reference motor. It is analyzed that the specific power of axial flux PMBLDC motor is enhanced effectively with the application of GA based design optimization technique.</span></span></span></div>

Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used.  The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.</span></span></span></div>

Regarding the advances in technology and anxieties around high and growing prices of fossil fuels, government incentives increase to produce cleaner and sustainable energy through distributed generations. This makes trends in the using microgrids which consist of electric demands and different distributed generations and energy storage systems. The optimum operation of microgrids with considering demand-side management increases efficiency and reliability and maximize the advantages of using distributed generations. In this paper, the optimal operation scheduling and unit commitment of generation units installed in a microgrid are investigated. The microgrid consists of technologies based on natural gas that are microturbine and phosphoric acid fuel cell and technologies based on renewable energy, including wind turbine and photovoltaic unit along with battery energy storage system and plug-in electric vehicle commercial parking lot. The goal of the paper is to solve a multi-objective problem of maximizing revenues of microgrid operator and minimizing emissions. This paper uses an augmented epsilon constraint method for solving the multi-objective problem in a stochastic framework and also implements a fuzzy-based decision-maker for choosing the suitable optimal solution amid Pareto front solutions. This new model implements the three type of the price-based and incentive-based demand response program. It also considers the generation reserve in order to enhance the flexibility of operations. The presented model is tested on a microgrid and the results demonstrate the efficacy of the proposed model economically and environmentally compared to other methods.</span></span></span></div>

Distribution load flow (DLF) calculation is one of the most important tools in distribution networks. DLF tools must be able to perform fast calculations in real-time studies at the presence of distributed generators (DGs) in a smart grid environment even in conditions of change in the network topology. In this paper, a new method for DLF in radial active distribution networks is proposed. The method performs a very fast DLF using zooming algorithm associated with a fast-decoupled reactive power compensation (ZAFDRC) technique, not in all of the buses of the grid, causes to reduce the solution time, which is the most important issue in the real-time studies. The proposed method is based on the zooming algorithm and does not require to calculate the bus-injection to branch-current (BIBC) matrix which reduces the computational burden and helps to decrease the solution time. The method is tested on the IEEE 69-bus systems as a balanced network and the IEEE 123-bus as a very unbalanced system. The results confirm the high accuracy and high speed of the proposed method.</span></span></span></div>