Reactive Power Dispatch (RPD) is a complex, non-continuous and it is famous and essential problem in the power system. The calculation of this problem is really part of optimal load flow calculations. In this study, two types of Particle Swarm Optimization (PSO) algorithm are utilize as an optimization tools to solve RPD problem in order to minimize real Power Loss (P_Loss) in the power system and keep voltage at all buses within acceptable limit. First type of PSO algorithm is Conventional PSO and the second type is utilize to improve the searching quality, also to decrease the time calculation and to enhance the convergence characteristic in the first type, it is called Modified PSO (MPSO). These types of PSO algorithm are tested on IEEE Node- 14, Node-30, Node-57 and Node-118 power systems to test their efficiency and ability in solving RPD problem in small and large power systems. The simulation results in four power systems show that the MPSO algorithm has a better performance in decreasing losses, decreasing time calculation and enhancement of voltage profile when compared to the Conventional PSO and other algorithms that reported in the literature.

In recent years, the use of renewable energy sources by many power grid companies worldwide has increased significantly. The trend towards the use of renewable energy sources is mainly due to environmental issues and rising fuel prices associated with conventional electricity generation. Distributed generation units are power generation plants that are very important for the grid architecture of today’s power system. The benefit of adding these Distributed Generation (DG) units is to increase the power supply to the grid. However, the installation of DG units can cause a negative impact if not properly allocated and/or sized. Therefore, there is a need for their optimal sizing and allocation to avoid situations such as voltage instability and high investment cost. In this paper, four heuristic based algorithms, namely Particle Swarm Optimization (PSO) algorithm, Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO), and Slime Mould Algorithm (SMA) are applied to solve the optimal placement and sizing of DG units in distribution network planning. Three cases were used to address the network problems, which are represented by adding photovoltaic cells and wind turbines individually. In the last case, both were used, and the feasibility of algorithms was confirmed for two systems, IEEE 33-bus and 69-bus test systems. The comparison results showed that the SMA algorithm produces good solutions. In general, the SMA algorithm was able to reduce the two system losses. The reduction of real power losses in SMA, taking into account the technical and economic constraints in the IEEE 33 system, is reduced to a minimum of 66.31%, 67.3%, and 81.1%, While in 69 bus, reduced to a minimum of 90.7%,91%,and 97.30% for three cases respectively, as well as improving the voltage profile, thus obtaining a more efficient system.

Distribution System Reconfiguration (DSR) and Optimal Capacitor Placement (OCP) are the most alternative techniques for increasing the power system generation and covering the growth of power demands. These techniques reduce the Radial Distribution System (RDS) losses and enhance the voltage profile. Combining both techniques gives better performance than using the individual technique. In this paper, two operation modes were implemented. First, the individual mode of OCP is applied. Second, the dual mode of DSR after OCP process is applied. Multiobjective functions with considering the weighting factors are used for minimizing real losses, improving voltage profile, and increasing saving cost. The optimal selections of open switches, location, and size of capacitors in the individual and dual design for RDS are achieved using four different optimization algorithms. These algorithms are Modified Biogeography-Based Optimization (MBBO) algorithm, Cuckoo Search (CS) algorithm, Modified Imperialist Competitive (MIC) algorithm, and Modified Bacterial Foraging-Based Optimization (MBFBO) algorithm. These algorithms are applied for two standard networks (IEEE 33- and IEEE 69-bus). Comparisons among the proposed algorithms are done, and the results demonstrated that the MBBO algorithm is the most strong and fast algorithm to attain the optimum solution. In addition, comparisons with literature works are done to validate the effectiveness of proposed algorithms.

The devastating impact of Covid-19 pandemic accelerated the shift to e-learning in the higher education. Particularly in the electrical machines courses, that often include laboratory experiments. However, no detailed models of DC motors, developed in MATLAB/Simulink, were reported in literature. Hence, in this paper, a virtual laboratory consist of models of DC motors was built for the first time. The proposed models are easy to use and modify, and allow all machines’ parameters to be altered for students to replicate easily to support and enhance the learning process of electrical machines courses. Consequently, the developed models are effective tools for educational and research purposes. Dynamic models of DC motors were developed using MATLAB/Simulink, namely separately excited, permanent magnet, shunt-connected and series-connected DC motors. Two different approaches for modelling were proposed, the block diagram representation and Simscape based models. The two modelling methods were validated against the built-in DC machine model. The proposed models are easy to use and modify, and allow all machines’ parameters to be altered for monitoring and comparison purposes. Consequently, the developed models are effective tools for research and educational purposes.

Radial Distribution System (RDS) suffer from high real power losses and lower bus voltages. Distribution System Reconfiguration (DSR) and Optimal Capacitor Placement (OCP) techniques are ones of the most economic and efficient approaches for loss reduction and voltage profile improvement while satisfy RDS constraints. The advantages of these two approaches can be concentrated using of both techniques together. In this study two techniques are used in different ways. First, the DSR technique is applied individually. Second, the dual technique has been adopted of DSR followed by OCP in order to identify the technique that provides the most effective performance. Three optimization algorithms have been used to obtain the optimal design in individual and dual technique. Two IEEE case studies (33bus, and 69 bus) used to check the effectiveness of proposed approaches. A Direct Backward Forward Sweep Method (DBFSM) has been used in order to calculate the total losses and voltage of each bus. Results show the capability of the proposed dual technique using Modified Biogeography Based Optimization (MBBO) algorithm to find the optimal solution for significant loss reduction and voltage profile enhancement. In addition, comparisons with literature works done to show the superiority of proposed algorithms in both techniques.