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This paper proposes a new method of fault detection and classification in asymmetrical distribution systems with dispersed generation to detect islanding and perform protective action based on applying a combination of wavelet singular entropy and fuzzy logic. In this method, positive components of currents at common coupling points are decomposed to adjust detailed coefficients of wavelet transforms and singular value matrices, and expected entropy values are calculated via stochastic process. Indexes are defined based on the wavelet singular entropy in positive components and three phase currents to detect and classify the fault. This protection scheme is put forward for fault detection and is investigated in different types of faults such as single-phase to ground, double-phase to ground, three-phase to ground and line to line in distribution lines in the presence of distributed generations, and different locations of faults are verified when the distributed generation is connected to the utility. The major priority of the proposed protection scheme is its reduction in time (10 ms from the event inception) in distinguishing islanding and protection transmission lines in the presence of distributed generations. [ABSTRACT FROM AUTHOR]

To control a smart-island (which comprises three distributed generation (DG) units with their autonomous converters), based on a nonlinear backstepping control (BSC) scheme, an adaptive reference signal, and a state observer are designed to improve the steady-state performance of the system. An adaptive reference value has been designed and used instead of a constant reference signal to improve the system's steady-state performance, e.g., total harmonic distortion (THD), peak value, and effective value in the voltage control mode. The presented adaptive reference is changed based on the error between the reference signal and the output voltage signal to tend the error to zero. Compared to the classical backstepping controllers, the reference signal is pursued with a fast response and a low steady-state of loads alternations. To demonstrate the efficiency, authenticity, and compatibility of the proposed control strategy, offline digital time-domain simulation studies are carried out on a master-slave organized inverter-based microgrids in various load variations like linear and nonlinear loads in MATLAB/Simulink software environment. The obtained results are compared with previously reported techniques. Moreover, the obtained simulation results are verified by performing laboratory-based experimental tests based on digital signal processing (DSP), which validate the proposed control strategy's accuracy, authenticity, and effectiveness under different conditions. [Display omitted] • A Novel adaptive reference signal has been presented. • The system steady-state performance has been enhanced by applying a state observer in a classic backstepping controller. • The reference signal is pursued with a fast response and a low steady-state ahead of loads alternations. • a master-slave organized inverter-based microgrids has been proposed. [ABSTRACT FROM AUTHOR]

• Study various applications of Blockchain technology in AC smart Microgrid. • Securing the datum transfer in various control layer on the basis of Blockchain technology. • Cyber-attack mitigation in smart microgrids. • Practical and applicable method for the power and energy society. Industrial Internet of Things (IIoT) has been defined as an architecture that uses the Internet of Things (IoT) and cloud computing to facilitate distributed control of modern industrial systems like AC smart microgrids (MGs). This paper proposes a novel secure energy policy and load sharing approach for renewable MGs for independent utilization of off-grid MGs with power electronic jointing (PEJ) on the basis of master-slave (M-S) which is formed in the IIoT environment. Assume that computations for system dispatch are performed by an upper layer however a lower layer calculates proper control proceedings for the PEJ. A decentralized multi-agent system (MAS) realizes the upper layer of intelligent control on the basis of communication. The layer has 2 control mechanisms: economic dispatch and MAS power balance control. Numerous operating, controlling, and planning to be in the energy industry pay special attention to Blockchain technology. In addition to allowing a common and distributed database, Blockchain technology (B.CT) enables safe, automated, transparent, and economic operations in power distribution systems. If a hacker manipulates and alters the data exchanged between agents, it will result in disrupting system performance in terms of economy and stability MG voltage profile, load distribution, optimized parameters including cost, environmental pollution, and unit output. Therefore, it is necessary to maintain the cyber security of AC smart MG and increase the security of data measured in the sensors and the transaction data between agents. In this paper, B.CT is presented to secure the exchanged data against malicious cyber-attacks in an AC smart MG whose control layers are M-S organized. The simulated system consists of the MG with several distributed generation units that examine cyber-attack points and then compare the results in normal mode and cyber-attack mode and B.CT is presented to increase the cyber security of AC smart MG. [ABSTRACT FROM AUTHOR]

• Proposes a novel secured management method for renewable microgrids. • Considering the policies required for diagnosing cyber-attacks happening in the communication networks. • A new framework for cyber-attack detection in DC smart MG. • Ability of detecting cyber-attacks with great precision and distinguishing cyber-attack from load changes. This study proposes a novel secured management method for renewable microgrids considering the policies required for diagnosing cyber-attacks happening in the communication networks, usually applied in the secondary control layer of microgrids (MGs). Due to the so long stochastic and bad information entering the systems in order to make malicious attacks, their location and time data links have the ability of straying of those acting in normal conditions that attempt to have an effect on the precise voltage regulation and current dividing via influencing sensors of current and voltage. The ability to extract high-level features due to the usage of fast fourier transform (FFT) and deep learning (DL) for attack detection in cyberspace has made them to be considered as a strong technique in the face of small mutations or new attacks. These self-educated and compaction abilities of DL architectures have been considered as basic techniques for hidden scheme detection from the training datum for this reason attacks have been distinguished from benign traffic. A novel method, deep learning and FFT, for cyber-security has been used in the following paper with the aim of enabling the attacks detection in DC smart MG. The deep model and traditional machine learning way are evaluated in terms of performance, and distributed attack detection has been compared to the centralized diagnosing procedure. The tests proved that the distributed attack detection system studied can be more advanced in comparison with centralized detection systems applying FFT in the role of the input index of the DL model. This suggested distributed method enables for scalable monitoring of a MG and has the ability of detecting the existence of cyber-attacks in the communications between distributed generation agents (DGAs) controlled via a control on the basis of consensus and isolating the communication link over that the attack has been injected. Any local attack detection needs restricted information about its neighbor's dynamics. The most important factor of the proposed detection plan can be that has the ability of detecting cyber-attacks with great precision and distinguishing cyber-attack from load changes.in addition, this has been shown that the suggested model can be further useful in the detection of the attack. [ABSTRACT FROM AUTHOR]

Smart Island (SI) refers to the condition where distributed generation (DG) units e.g. wind turbines (WT), fuel cell (FC), and photovoltaic cells (PV) have to control current, voltage, and frequency of the grid by themselves without any support from the main grid. Therefore, design and implementation of robust controller to overcome disturbances and load variations is very crucial. This paper presents a new approach using General Type-II Fuzzy controller to control smart island in combination with a novel modified optimization algorithm to increase the load sharing throughout the DGs operating in an islanding mode. This case study assumes that there are two DGs in the smart island where each DG has its own responsibilities. In particular, one of the DGs regulates the frequency and voltage based on a reference feedback, while the other DG operates in load current control mode to share load between each DG properly. The simulation results indicate that the suggested controller has a quick response to load alternations with a low steady state error and low THD. Moreover, the proposed controller is independent from system states and other units. The experimental analysis indicate that the suggested control system is effectively capable to coordinate the operations of the DG units in smart island to validate the stable operation of the overall smart island. Image 1 • The main contributions of this paper are as follows: • Introducing a novel and intelligent control model for controlling the smart island, • Establishing a fuzzy based control over load sharing between DG units, voltage, frequency, and current, • Applying fixed frequency PWM modulation which leads to the switching frequency to be constant, • Developing a real-time measuring and amount of desired (reference) signal as an exact proportion of load current. [ABSTRACT FROM AUTHOR]

• Optimal allocation of various automation equipment in a real distribution network. • Switches optimal configuration plan is guided to the actual project in various cases. • Branch lines influence is modeled on the automatic equipment configuration. Sectionalizing switches (SSs) have been installed in Distribution networks with the aim of providing maneuver and ring points, so raising service reliability. It is usually assumed that these switches are totally reliable although, in practice, they have not ideal efficiency at all times. In fact, the switches might occasionally face failure, which decreases their capability with the aim of enhancing system reliability. A model with the aim of considering the failure probability of the switches in their optimal placement issue is proposed in this paper. The model aids with the aim of minimizing the whole costs of SSs and the interruption costs incurred through interrupted users. This study uses the discrete Markov chain model with the aim of obtaining the malfunction possibility under various states. Afterwards, the placement problem is solved with the aim of obtaining the global optimal solution with genetic algorithm. The advantage of the suggested model has been investigated according to sensitivity analysis and various scenarios. The research illustrates how the outcomes of the placement issue have been influenced through the uncertainty arising from the possible failure of the switches and how ignoring the uncertainty is able to cause deviation of the anticipated profit from the real one. The efficiency of the suggested procedure has been evaluated and shown through investigating on the Ahwaz city distribution network. The outcomes of simulation prove the ability and precision of the suggested procedure. [ABSTRACT FROM AUTHOR]