In this work, a wide range of reactive power compensation is achieved for voltage unbalance mitigation in 500 km electrical power systems. An Optimal Control Technique (OCT) is proposed to encompass the unbalance load changes at a wide range of Voltage Unbalance Factor (VUF), between 3.33% and 12.4601%, and to minimize it to an acceptable value (at average less than 2%). The technique uses a combination of Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANN) in three stages. In the first stage, the PSO finds the optimal firing angles of the Thyristor Controlled Reactor (TCR) and the optimal number of bank capacitors for the Thyristor Switched Capacitor (TSC) to restore the voltage balance. In the second stage, the voltage unbalance evaluations obtained by the PSO algorithm are used to train the ANN. In the third stage, the ANN is connected to the system to control and overcome the voltage unbalance problem accurately and quickly. Results are compared with other techniques available in the literature to confirm the superiority of the OCT performance. Furthermore, a laboratory model for the electrical power system is built and the proposed OCT for real voltage unbalance mitigation is validated.

Voltage unbalance is an important power quality issue, which occurs in electrical power systems (EPSs) and causes severe problems for them. In this work, a general mathematical model for EPS including its long transmission line is developed using the generalised circuit parameters method. Then, a hybrid Particle Swarm Optimisation–Artificial Neural Network (PSO–ANN) algorithm is proposed to overcome the voltage unbalance problem by controlling the firing angles of thyristor‐controlled reactor (TCR) which varies the amount of reactive power at the load side. PSO algorithm is responsible for determining the optimal set of TCR firing angles required to retrieve the balance conditions in offline mode for different load changes, employing the developed mathematical model of the EPS. Then, a dataset is taken as training samples for the ANN to be used in online mode. Aqaba Qatranah South‐Amman (AQSA) EPS is considered as a real case study and simulated in MATLAB environment to validate the proposed algorithm. The simulation results are compared with other ANN algorithms available in the literature. Finally, a laboratory prototype is built for AQSA EPS including its long transmission line to test the proposed hybrid PSO–ANN algorithm for real unbalance conditions acquired from the laboratory prototype.

The voltage deviation is one of the most crucial power quality issues that occur in electrical power systems. Renewable energy plays a vital role in electrical distribution networks due to the high economic returns. However, the presence of photovoltaic systems changes the nature of the energy flow in the grid and causes many problems such as voltage deviation. In this work, several predictive models are examined for voltage regulation in the Jordanian Sabha distribution network equipped with photovoltaic farms. The augmented grey wolf optimizer is used to train the different predictive models. To evaluate the performance of models, a value of one for regression factor and a low value for root mean square error, mean square error, and mean absolute error are used as standards. In addition, a comparison between nineteen predictive models has been made. The results have proved the capability of linear regression and the gaussian process to restore the bus voltages in the distribution network accurately and quickly and to solve the shortening in the voltage dynamic response caused by the iterative nature of the heuristic algorithm.

In this paper, an intelligent integrated approach is proposed to control the reactive power and restore the voltage balance in a three-phase power system using particle swarm optimization (PSO), Gaussian process regression (GPR), and support vector machine (SVM). The PSO algorithm is used in offline mode to determine the optimal set of firing angles for the thyristor-controlled-reactor (TCR) compensator according to the smallest fitness value required for voltage balancing. The optimum firing angles are then used to train the GPR and SVM regression models. The GPR and SVM models are finally used as a real-time controller to retrieve the voltage balance in online mode. A simulation model and experimental setup of the electrical power system are built. The modeled system consists of a 500 km long transmission line. The line is divided into three-pi sections to guarantee a real system response. Several simulation and practical case studies have been conducted to test and validate the capability of the proposed integrated approach in solving the voltage unbalance problem. The results have revealed the supreme ability of the proposed integrated approach to restore the voltage balance quickly (within 20 ms) and for a wide range of voltage unbalance factors (VUFs) (3.90–8.42%).