A comparative study is performed to design an optimal PID controller for an automatic voltage regulator system using different optimization techniques. The presented approaches are referred to as particle swarm optimization (PSO) algorithm, cuckoo search optimization (CSO) algorithm, moth flame optimization (MFO) algorithm, water cycle optimization (WCO) algorithm, teaching–learning-based optimization (TLBO) algorithm, and hill climbing optimization (HCO) algorithm. Transient response parameters, which are rise time Tr, settling time Ts, and percentage overshoot Mp, are used as comparison criteria. The integral time absolute error ITAE is the used performance index. All the proposed optimization techniques improved the transient response of the AVR system in a different way and gave good preliminary results.

The detection and diagnosis of power quality (PQ) issues are critical topics in the electrical power system’s generation, transmission, and distribution. Nonlinear loads, power electronic converters, system malfunctions, and switching events are the most common causes of PQ issues. The main purpose of this work is to use an artificial intelligence (AI) technique based on automatic feature extraction to discover and identify PQ difficulties. The AI technique consists of a dedicated architecture of the Long Short-Term Memory (LSTM) network, which is a special type of Recurrent Neural Network (RNN).

The aim of this work is to study and analyze induction motor adjustable speed drive characteristics under voltage unbalance conditions. This paper tries to cover the main possible magnitude and phase unbalance. Two systems are considered: first, three phase induction motor connected directly to a three phase voltage source and the second is the three-phase adjustable speed drive, are simulated using MATLAB software. Increased torque ripple and decreased efficiency are the main effects of voltage unbalance on induction motor. Also, increased thermal stress on the power electronic switches and DC-link capacitor are the main effects of voltage unbalance on the converter.

The detection and diagnosis of power quality (PQ) issues are critical topics in the electrical power system’s generation, transmission, and distribution. Nonlinear loads, power electronic converters, system malfunctions, and switching events are the most common causes of PQ issues. The main purpose of this work is to use an artificial intelligence (AI) technique based on automatic feature extraction to discover and identify PQ difficulties. The AI technique consists of a dedicated architecture of the Long Short-Term Memory (LSTM) network, which is a special type of Recurrent Neural Network (RNN).

One achievement in smart grids is the construction of smart cities. In this kind of city houses are equipped with smart meters that can record electric energy as well as transmit and accept data regarding energy utilization and prices to consumers. Additionally, new methods, known as real-time pricing of electricity, have been introduced in which energy prices change based on an hourly timeline and depend on consumers’ energy requests. Due to the production of electricity by PV panels, a smart grid will share the surplus of the energy provided by the panels to the grid. These pricing policies will force and encourage consumers to track their power consumption and use renewable energies. In this paper, the impacts of pricing policy on the reduction in consumers’ power consumption are investigated. A small-scale smart city is presented and a policy is applied to it. Additionally, an implementation through a simulation of some houses equipped with renewable energy sources is done to study their effect on the grid performance.