The dynamic voltage restorer’s (DVR) transient, steady-state, and dynamic responses are essential requirements for protecting sensitive loads against upstream voltage disturbances via the DVR’s ride-through capabilities. DVRs also look after transient oscillation at the instant of entering, and /or exiting by the DVR. This paper presents an enhanced, optimized, and less complex DVR control system structure, which is capable of improving the transient, steady-state, and dynamic responses as well as eliminating inherent transient oscillations. The control system comprises a closed-loop feedback control signal and feedforward upstream disturbance detection signal. Incorporating the feedforward term helps, dramatically, in improving the system response and eliminating the transient oscillations in the load voltage. The error signals are adapted using a PI controller to make the load voltage faithfully track its predefined reference waveform. The controller is implemented in the dq synchronous rotating reference frame. The parameters of the PI controller are selected using modern population-based optimization called the Harris Hawks Optimization (HHO) technique. The results obtained using the HHO technique are compared with two other optimization algorithms, namely Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The results show that the HHO gives the best system response. The system is simulated using MATLAB/Simulink and the validation via Typhoon HIL402 real-time emulator. Both HIL402 validation and simulation results show that the proposed control scheme recovers normal operation against voltage disturbance within approximately 1.2 milliseconds without overshoot with steady-state error near zero and significantly dampens the inherent voltage oscillation that occurs at the instant of DVR entrance and/or exit.

Comprehensive analysis of the starting period of inverter-fed large induction motors reveals that these motors are subjected to additional components of pulsating torsional torque. These torque pulsations may coincide with the natural torsional frequency of the large motor system and produce hazardous shaft torque oscillations. To alleviate the torsional toque problem and limit the motor starting current, a constant air-gap flux using slip frequency control scheme is proposed to operate the motor inverter. Simulation results show that the proposed scheme is capable of drastically reducing the torsional torque oscillations and limiting the motor line current to approximately 22% of its direct online starting value without prolonging its starting period.

SCADA systems contain many important components that communicate with each other through communication protocols designed for SCADA systems. This paper concerns distributed network protocol 3 (DNP3), which is considered a sufficient, trustworthy, and standard protocol for improving communications between multiple vendors. The vulnerabilities of this protocol form a disaster threat over the whole system, so this paper mentions these weakness points of this protocol. Also, the paper mentions the different types of attacks that exploit these vulnerabilities. So, it is necessary for researchers to continuously study mitigating these attacks without affecting the efficiency of the system. This goal is introduced in deep learning model algorithms dependent on neural networks. This paper introduces an ensemble deep learning algorithm (autoencoders) with decision tree (DT) multiple classification and support vector machine (SVM) multiple classification. After that, applying these two classifications models to a dataset to study the efficiency of each model and compares the results between each of them using performance metrics of deep learning algorithms and confusion matrixes which show the accuracy of each classifier.

This paper proposed a modified algorithm for controlling a single-phase split-source inverter. The proposed algorithm is a modified model predictive control based on Harris Hawks optimization, where the AC output voltage, the DC-link voltage, and the DC input current are controlled within one cost function. Hence, the discrete time models of both AC-side and DC-side are obtained. For proper operation of the modified MPC, each error term within the cost function has a weighting factor. Harris Hawks optimization technique is used to determine the weighting factors at each term of the cost function. The proposed algorithm is validated using MATLAB/Simulink. Simulation results show that the system has succeeded in controlling AC load voltage, input current, and achieving constant DC-link voltage over a wide operating range.