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Stochastic nature of load demand has a great impact on the performance of electrical power system. As a result, planning of electrical power system considering load uncertainties became inevitable. This paper presents Monte Carlo simulation based different bio-inspired algorithms, grey wolf optimization (GWO), manta ray foraging optimization (MRFO), satin bower bird optimization (SBO) and whale optimization (WOA) to optimize locations of three DG units under load uncertainties considering 500 scenarios. Each scenario includes 50 iterations which means that for each run we have 25,000 iterations and 500 characteristics for different load value. Two objectives are achieved. Firstly, statistically finding the optimal probabilistic location of three DG units under load uncertainties in IEEE 33-bus and IEEE 69-bus radial distribution system based on Monte Carlo simulation integrated with different bio-inspired algorithms. Secondly, comparing between the performances of four different bio-inspired algorithms. Three objective functions are considered, minimizing active power loss, minimizing voltage deviation and maximizing voltage stability index. The active and reactive power demand are normally distributed using normal distribution function. The optimal probabilistic location is investigated considering two cases under load uncertainties, optimizing location of three DG units generally and optimizing location of one DG unit assuming two optimum locations for the other two units extracted from case I. The obtained results (after placing DG units) are compared to the base case (DG units are not connected) and compared to each other according to the optimization technique. The results show that, SBO algorithm superiors other algorithms almost in all cases. Comes next GWO which provide good results generally. However, the good performance obtained by MRFO, it consumes twice the time of other algorithms. WOA however fast convergence, it provides results worse than other algorithms. The system is applied to the well-known IEEE 33-bus and IEEE 69-bus radial distribution system.

Electrical power networks are expanded regularly to meet growing energy requirements. Reactive power dispatch (RPD) optimization is a powerful tool to enhance a system’s efficiency, reliability, and security. RPD optimization is classified as a non-linear and non-convex problem. In this paper, the RPD optimization problem is solved based on novel hybrid genetic algorithms—equilibrium optimizer (GAEO) optimization algorithms. The control variables are determined in such a way that optimizes RPD and minimizes power losses. The efficiency of the proposed optimization algorithms is compared to other techniques that have been used recently to solve the RPD problem. The proposed algorithm has been tested for optimization RPD for three test systems, IEEE14-bus, IEEE-30bus, and IEEE57-bus. The obtained results show the superiority of GAEO over other techniques for small test systems, IEEE14-bus and IEEE-30bus. GAEO shows good results for large system, IEEE 57-bus.

Optimal reactive power dispatch (ORPD) in a typical power system is a complicated multi-objective optimization problem. The proper modeling of the multi-objective optimization problem has a significant impact on system operation and control. In this paper, an Improved Heap-based optimizer (IHBO) is proposed to improve the performance of a recently published technique called Heap-based optimizer (HBO). In addition, two algorithms based on the original HBO and IHBO are developed for solving OPRD problem. Pareto front approach is utilized in the proposed OPRD algorithm with the aim of solving two or three objective functions simultaneously. The performance of HBO is improved by utilizing the chaotic sequences with the aim of improving its global search capability and avoiding getting stuck in a local optimum. Both original HBO and proposed IHBO are applied to determine the optimal settings of the generator’s voltages, shunt capacitor reactive power, and tap settings of transformers. Therefore, this study aims for minimizing three most objective functions of the real power loss, total voltage deviation (TVD) and voltage stability index (VSI), with satisfying different operational constraints. The effectiveness of the IHBO is tested on three test systems IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems. The results of the proposed IHBO are compared with recently published algorithms in the literature. The simulation results proven the superiority and robustness of IHBO in solving the ORPD problem.

This paper proposes an explicit adaptive controller to damp oscillations and to enhance the single machine infinite bus SMIB stability. Owing to the increasing requests for renewable energy and operating conditions, the identification for power systems has been increased recently. Changes in the power system parameters cause to use an explicit self-tuning control. The controller structure consists of combined on-line identifier and a feedback controller as PID and a radial basis function neural network (RBFNN) which acts as an adaptive power system stabilizer for SMIB. An adaptive linear neural network (ADALINE) depending on the input and output of open loop system is employed as on-line model identification to mimic on-line the SMIB output. The difference between SMIB and the identified model responses is used to adjust the ADALANN model weights on-line depending on a recursive least squares principle RLS and a recursive least square with adaptive directional forgetting RLSMadf. The particles swarm optimization (PSO) beside RLS and RLSMadf assess the weights of (RBFNN) and coefficients of PID controllers depending on the on-line ADALINE model weights. The proposed controller is validated with several operating conditions under various disturbances. The simulation results show the proposed controller whose parameters depend on on-line tuning techniques provides better performance than a conventional PID controller.

Hybrid micro-grid systems (HMGS) are small scale power system where the energy sources are installed to supply local customers. These systems may be considered as promising energy solution to meet the increased in energy demand and traditional sources depletion. Cost of electricity, system reliability, and environmental impacts of the system are three design criteria that must be considered in obtaining the accurate parameters of hybrid renewable energy system components. In this paper, hybrid micro-grid renewable energy system includes photovoltaic system, (PV) wind energy system, (WES) battery bank, (BB) and conventional diesel generator (DG) are proposed to meet the energy requirements in remote area, located in Red Sea called city of Bernice, Egypt, at 23° 54′ 31″ N, 35° 28′ 21″ E. Optimization of Cost of Electricity (COE), Renewable Factor (RF), and Loss of Power Supply Probability (LPSP) are main objective of the designing process of the hybrid system considered as the objective functions. Then, Multi-objective multi-verse optimization (MOMVO) algorithm is used with considering two scenarios, the first one is renewable sources and the second is renewable/diesel energy source. All the possible HMGS configurations namely: PV/battery, wind/battery, PV/wind/battery and PV/battery/diesel, wind/battery/diesel, PV/wind/battery/diesel are studied and analyzed. Moreover, one year hourly meteorological weather data for case study are recorded. Reverse osmosis desalination (ROD) is considered in conjunction with the residential load. The proposed power management strategy is used to manage the system operation when supplying the load. A linear fuzzy membership function is used for purpose of decision making. The simulation results show that MOMVO produces appropriate components size and the PV/wind/battery/diesel is the optimum configuration with values of COE = 0.2720$/KWh, LPSP = 0.1397, and RF = 92.37% at w1 = 0.5, w2 = 0.3, and w3 = 0.2. Sensitivity analysis is performed to show the effect of changing system parameters on the objective functions. It is also shown that the techno-economic feasibility of using HMGS for rural electrification systems and enhance energy access.

Background: Hepatitis C virus (HCV) lifecycle is closely connected to host cell lipid metabolism, from cell entry, through viral RNA replication to viral particle production and formation/assembly. Objective: To determine the serum levels of ox-LDL, total antioxidant capacity and superoxide dismutase, and evaluate their role in HCV hepatitis patients. In addition, the effect of direct-acting antiviral therapy on their levels was evaluated. Patients and Methods: This study included forty chronic hepatitis C (genotype 4) patients. Blood samples were taken from the patients before and after taking sofosbuvir (400 mg) and daclatsvir 60 mg; one time daily orally for 24 weeks. Forty apparently healthy personnel were used as control group. Results: Serum TAC in chronic HCV hepatitis patients were significantly low before treatment as compared to the control group. Serum levels of ox-LDL were significantly high in patients before treatment and after treatment as compared to control group. Antioxidants supplementations and direct antiviral drugs did not affect the levels of ox-LDL significantly. Serum levels of extracellular SOD were significantly higher in control group, than levels in HCV patients before treatment and after treatment. Treatment did not restore the levels of serum SOD in patients. Direct-acting antiviral agents had a sustained virological response in the chosen group of patients. Conclusions: Direct-acting antiviral agents did not normalize serum levels of ox-LDL and extracellular SOD. In addition, the currently used antioxidants did not decrease the oxidative changes in LDL.

Conventional Home Automation Systems lack many of the essential properties of home automation system, which are now easily accessible with modern technologies like IoT where every device is connected and can be controlled through internet. The proposed system presents a standard home automation system with all requirements an automation system has like – scalable, configurable, DIY and serves the most important function which is plug and play. Firstly, this paper discuss the merits and demerits of mainstream works on home automation system. Next, key enabling technologies – Cloud (Pubnub), Android Applications, WiFi Module (ESP 8266), Raspberry Pi have been discussed. User interfaces for web, pc and mobile devices are designed for this system. Also, algorithm has been constructed for connecting Wi-Fi/Bluetooth module with the system to control wirelessly. Smart home security system has been added by using PIR motion Sensor. Lastly, data have been measured from the different sensors and power consumption of the devices were calculated and showed on a graph. . Then, electricity bill consumption has been calculated and sent by sms to the user. Global and local accessing from web interface and Android Application to control devices and configurable feature (change as per require) makes the system a standard home automation framework

This paper presented an analytical and closed-form solution for the harmonic contents that were injected in the five-level inverters from asymmetrical DC sources or batteries which were suitable for renewable energy sources. In the five-level inverters, two DC sources V1 and V2 were used to synthesize the output voltage. Therefore, two transcendental equations were formulated for the fundamental and the third harmonic orders based on the unequal DC sources V1 and V2. These transcendental equations were solved analytically for low switching control technique. The proposed solution included the modulation index change which resulted in changing the fundamental component. Therefore, two switching angles are formulated and solved analytically to control the amplitude of the fundamental voltage and cancel the third order harmonic. A well-known third order equation was generated. Thus, a closed-form solution for the above two switching angles will be generated in terms of the modulation index. The proposed solution was tested for a wide range of V1/V2 ratio. Some selected simulation and experimental results were provided to validate the proposed method for wide ranges of both modulation index and V1/V2 ratio.

Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stochastic, and only the 10-unit system is studied. The proposed technique outperforms the standard EO with cost savings of 40.93 USD. The simulation results demonstrate that MPSO-EO has fairly good performance for solving the UCP with significant total operating cost savings compared to standard EO compared with other reported techniques.

In this study, a new control of grid-connected doubly-fed induction generators (DFIGs) is introduced to attain lowvoltage ride-through capability. The grid-side converter is proposed to be a multilevel converter (MLC) controlled by conventional DC-link voltage controller. The MLC is designed and integrated into the grid-connected DFIG. The rotor-side converter is considered a two-level conventional converter controlled by the proposed virtual voltage strategy. The virtual strategy is to use the designed input control signal rather than the actual measured one during system disturbance. The system disturbance in this study is the voltage dip. The proposed virtual controller is designed to use busbar voltage during normal operation, whereas it utilizes the designed virtual voltage during wide range conditions of low grid voltages. The energy conversion system currents are increased due to voltage dip disturbances. The use of designed virtual voltage values is proposed as inputs to the controller to limit these currents at the rating values. These virtual voltages are extracted using direct-quadrature machine representation and fixed machine currents at the rated values or lower. On the basis of the proposed control concerning the virtual voltage concept, the generator currents are limited to the rated values, thereby protecting the energy conversion system during low grid voltages. Simulation results prove the validity of the proposed control scheme during extreme low voltages.

The main aim of this work was the maximization of the energy saving of balanced and unbalanced distribution power systems via system reconfiguration and the optimum capacitor’s bank choice, which were estimated by using a new algorithm: modified Tabu search and Harper sphere search (MTS-HSSA). The results demonstrated that the proposed method is appropriate for energy saving and improving performance compared with other methods reported in the literature for IEEE 33-bus adopted systems, including large scale systems such as IEEE 119 and the IEEE 123 unbalanced distribution system. Moreover, it can be used for unbalanced distribution systems distributed generators (DGs). The results demonstrated that the proposed method (the optimal choice of shunt capacitor (SC) banks and the optimal reconfiguration via the proposed algorithm) is appropriate for energy saving compared with different strategies for energy saving, which included distributed generation (DG) at different cost levels.

A static VAR compensator (SVC) is a critical component for reactive power compensation in electric arc furnaces (EAFs) that is used to relieve the flicker impacts and maintain the voltage level. A weak voltage profile can not only reduce the power-quality services, but can also result in system instability in severe cases. The cybersecurity of EAFs is becoming a significant concern due to their cyber-physical structure. The reliance of SVC controllers on reactive power measurement and network communications has resulted in a cyber-vulnerability point for unauthorized access to the EAF, which can affect its normal operation. This paper addresses concerns about cyber attacks on EAFs, which can cause network communication issues in measurement data for SVCs. Three significant and different types of cyber attacks that are launched on SVC controllers—a replay attack, delay attack, and false data injection attack (FDIA)—were simulated and investigated. In order to stop the activities of cyber attacks, a secured anomaly detection model (ADM) based on a prediction interval is proposed. The proposed model is dependent on a support vector regression and a new smooth cost function for constructing the optimal and symmetrical intervals. A modified algorithm based on teaching–learning-based optimization was developed to adapt the ADM’s parameters during training. The simulation’s outcomes on a genuine dataset showed the strong capability of the proposed model against cyber attacks in EAFs.

The paper is concerned with designing an effective controller for a linear tubular homopolar (LT-H) motor type. The construction and operation of the LT-H motor are first described in detail. Then, the motor model is represented in the direct-quadrature (d-q) axes in order to facilitate the design of the control loops. The designed control system consists of two main loops: the current control loop and velocity adaptation loop. The determination of the regulator’s gains is accomplished through deriving and analyzing the transfer functions of the loops. To enhance the system’s robustness, a robust variable estimator is designed to observe the velocity and stator resistance. Different performance evaluation tests are performed using MATLAB/Simulink software to validate the controller’s robustness for variable-speed operation and load force changes as well. The obtained results reveal the appropriate dynamics of the motor thanks to the well-designed control system.

This paper introduces a generalized platform for the design selection of multilevel inverters (MLIs) topologies for any industry, especially in low- and medium-power applications. This platform is novel as it combines the component count and stress in any MLI in a normalized unique score. Each MLI topology is designed to match specific criteria to meet a specific application, which introduces diversified sets of merits and demerits in each case. Therefore, choosing a suitable MLI topology for the specific application is rather a difficult, time-consuming, and complex process. Moreover, the comparison can only be conducted between MLI topologies that fall under the same category. This paper introduces a comprehensive study to propose a new benchmark (BM) which can be used to compare MLI topologies regardless of their constructions or oriented applications. Each MLI topology gets a BM score based on comparative factors that evaluate its components quantitatively and qualitatively. Thus, comparing different types of MLI topologies is a simple process of comparing their calculated BM scores leading to better well-informed choice. First, the proposed BM-derived forms are discussed. Then, the proposed methodology is illustrated by applying to various types of MLI topologies including conventional and recently introduced ones.

In this paper, New asymmetric cascaded half H bridge multilevel inverter MLI is proposed. It is driven from the conventional asymmetric cascaded half H Bridge multilevel inverter. Thus, the proposed topology inherits its simplicity, flexibility and modularity in both construction and control. By proper choice of the input DC sources values and interconnection, proposed topology can eliminate the need for polarity generation circuit which normally associated with the cascaded half H-bridge. Therefore, reduces number of switches that require high blocking voltage. The selective harmonic elimination SHE method is applied to generate switching pattern to eliminate harmonics inherently associated with MLI. The proposed topology switching pattern naturally reduces component’s voltage stress, switching losses and finally decrees MLI size and cost. Mathematical expressions and analysis have been driven as well as comparison with other topologies. The validity of the proposed topology is verified with Matlab/Simulink model and then a prototype is built and experimental results are presented. Keywords: Single-phase inverter, Multi-level inverter, H bridge inverter, Total Harmonics Distortion (THD), SHE

The gradual depletion of fossil-fuel-based energy resources and the growing rate of greenhouse gas emission are two of the most significant global concerns in recent years. These concerns have resulted in an increasing demand for the utilization of renewable energy resources. From among all renewable resources, hydropower is the leading contributor to grid-connected electricity in the world. In this article, the hydropower potential in Malaysia, current and future prospects of large and small-scale hydropower development, and issues and challenges related to hydropower development are described. Small hydropower promotion techniques, government initiatives, and the advantages and barriers to small hydropower development are also included.

Recently, photovoltaic (PV) energy has been considered one of the most exciting new technologies in the energy sector. PV power plants receive considerable attention because of their wide applications. Consequently, it is important to study the parameters of the solar cell model to control and determine the characteristics of the PV systems. In this study, an improved bonobo optimizer (IBO) was proposed to improve the performance of the conventional bonobo optimizer (BO). Both the IBO and the BO were utilized to obtain the accurate values of the unknown parameters of different mathematical models of solar cells. The proposed IBO improved the performance of the conventional BO by enhancing the exploitation (local search) and exploration (global search) phases to find the best optimal solution, where the search space was reduced using Levy flights and the sine–cosine function. Levy flights enhance the explorative phase, whereas the sine–cosine function improves the exploitation phase. Both the proposed IBO and the conventional BO were applied on single, double, and triple diode models of solar cells. To check the effectiveness of the proposed algorithm, statistical analysis based on the results of 20 runs of the optimization program was performed. The results obtained by the proposed IBO were compared with other algorithms, and all results of the proposed algorithm showed their durability and exceeded other algorithms.