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Optimum operation of power system network becomes of great importance to handle the problem of power shortage. Connecting renewable energy (RE) based distributed generators (DG) into electrical networks (EN) are spreading rapidly to earn more benefits environmentally and economically. This paper aims to optimally operate DGs in order to minimize daily power loss in case of uncertainty of wind and PV system. Three different types of DGs, photovoltaic DG (PVDG), wind DG (WDG) and conventional DG (CDG) are connected to IEEE 33-bus system. Monte Carlo simulation (MCS) is used to generate probable scenarios for wind and photovoltaic power plants based on actual data for wind speed and solar radiation for one day. Fast forward scenario reduction (FFSR) is used to reduce the number of scenarios generated by MCS. Particle swarm optimization (PSO) is used to minimize daily power losses through determining the optimum operating point (operating power and power factor (PF)) for CDG to cope with PVDG and WDG uncertainties.

Traditional power system stabilizer (PSS) is an effective controller to damp out low frequency oscillation (LFO) through the excitation system. Adding proportional integral derivative (PID) controller improves the performance of PSS controller. This paper propose Archimedes optimization algorithm (AOA) to improve the performance of series PSS-PID controller through adjusting the tuning parameters to effectively damp out LFO and improve power system transient stability in western system coordinate council (WSCC) power system. AOA is used to optimize the system based on minimizing the integral time absolute error (ITAE) of rotor angular speed. A nonlinear model of WSCC system and PSS-PID controller are simulated under three phase short circuit fault applied at terminals of machines. The system has been tested without PSS-PID controller. Then, PSS-PID controller is optimized using AOA algorithm and whale optimization algorithm (WOA) to compare between their performances. The system under study is simulated in time-domain using MATLAB software.

Artificial intelligence (AI), often known as machine learning, is a powerful tool for solving engineering problems. The evaluation of the network reliability of a flow network is a NP-hard problem, with computational effort growing exponentially with the number of nodes and arcs in the network. Also, the components assignment issue is NP-hard, and the computational effort increases with the number of available components. Many candidate solutions are typically examined during optimal components or optimal capacity assignment, each requiring reliability calculation. Consequently, this paper proposes an artificial neural network (ANN) predictive model to evaluate the flow network reliability. The neural network is one of the artificial intelligence tools constructed, trained, and validated using the maximum capacity of each component input and the network reliability as the target. The proposed ANN model provides empirical proof that neural networks can accurately estimate reliability by modeling the connection between the maximum capacities of network components and the reliability value.

Hybrid renewable energy (HRE) sources have become a popular alternative in isolated and rural locations; because of the increased prices of fossil fuels and their CO2 emissions. This paper investigates the application of recent optimization methods to dedicate the optimal configuration of HRE sources. These HRE sources comprise photovoltaic (PV) panels, wind turbines (WT) with a battery storage system (BSS), and backup diesel generators (DGs). The cost function for the optimization problem has been chosen to be the cost of energy (COE) and the loss of power supply probability of the HRE system. The main function of the optimization algorithms is to minimize this cost function. However, the optimal configuration cannot be accomplished without fulfilling the system reliability and operational constraints. Salp swarm algorithms (SSA), the grey wolf optimizer (GWO), and the improved grey wolf optimizer (IGWO) were employed as optimization techniques. To assess the efficacy of these optimization techniques, they were applied using MATLAB simulation. A comparison of their outcomes was conducted in order to determine which technique was the most effective. Furthermore, a statistical study was carried out to determine the performance stability of each optimization strategy. The results revealed the optimal variables, including the number of PVs, WTs, battery banks (BBs) and the capacity of DGs. Over the course of a year, the optimized configuration was put to the test against a study of its capital and fuel expenses. IGWO showed the best performance among the three techniques, while the statistical results prove the robustness of the SSA algorithm.

A voltage gain interleaved soft switched DC–DC Boost converter without auxiliary switch is proposed in this paper. Step-up characteristics are achieved without using any transformer, coupled inductor, voltage multiplier cell, switched inductor/switched capacitor in the proposed topology. All semiconductor devices operate under soft-switching conditions. Consequently, the converter has high efficiency. The auxiliary circuit is not located in the main power path, and the control circuit remains PWM. All advantages of interleaved topologies are gathered in the proposed converter. The converter efficiency is equal to 97.6% at nominal power. All mentioned benefits candidate the converter to use in photovoltaic applications. A 200 W prototype of the converter is constructed and tested in the laboratory. The experimental results are in total agreement with the theoretical considerations.

Most silicon carbide (SiC) MOSFET models are application-specific. These are already defined by the manufacturers and their parameters are mostly partially accessible due to restrictions. The desired characteristic of any SiC model becomes highly important if an individual wants to visualize the impact of changing intrinsic parameters as well. Also, it requires a model prior knowledge to vary these parameters accordingly. This paper proposes the parameter extraction and its selection for Silicon Carbide (SiC) power N-MOSFET model in a unique way. The extracted parameters are verified through practical implementation with a small-scale high power DC-DC 5 to 2.5 output voltage buck converter using both hardware and software emphasis. The parameters extracted using the proposed method are also tested to verify the static and dynamic characteristics of SiC MOSFET. These parameters include intrinsic, junction and overlapping capacitance. The parameters thus extracted for the SiC MOSFET are analyzed by device performance. This includes input, output transfer characteristics and transient delays under different temperature conditions and loading capabilities. The simulation and experimental results show that the parameters are highly accurate. With its development, researchers will be able to simulate and test any change in intrinsic parameters along with circuit emphasis.

A very critical and integral part of the power system is the distribution networks. The final component of the power system, including transmission systems or consumers, is the distribution system (DS). This leads to the highest energy loss that happens. So improving the performance of distribution networks is required not only to provide the reliability of power supply but also to achieve the most economic cost. By optimizing the power flow and simultaneously minimizing the total emission cost and generation cost and taking into account the power losses, these objectives can be achieved. In recent years, heuristic methods are widely employed for solving such complex problems, and The main modern optimization techniques are genetic algorithm(GA). The most important issue in Evolutionary Algorithms is exploration vs. exploitation. Maybe GA is restricted for exploration features, what causes slow convergence and poor robustness Therefore, using the hybridization strategy, the main reason behind this is that such a hybrid approach is expected to create swape between the exploration and exploitation. This work presents performance improvement of a radial distribution networks using a new hybrid optimization technique of Genetic Algorithms (GA) with Equilibrium optimizer (EO) algorithm called Hybrid Genetic Algorithm Equilibrium optimizer (GAEO). It is used for optimum location and size of Renewable Energy Sources (wind energy, photovoltaic, fuel cell) on distribution systems. DG source locations and capacity have strongly influenced the improvement of the distribution network performance by reducing the entire system's power loss, enhancing the voltage profile, reducing fuel costs and emissions of contaminants.

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.

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The optimal components assignment problem in a flow network with node failure in the presence of system reliability and total lead-time constraints is studied in this paper. By using fuzzy membership functions, we formulated the problem as fuzzy linear optimization. Genetic optimization algorithm with fuzzy optimization is suggested for solving the presented problem. The suggested approach succeeded in finding an optimal solution that distinguishes with increasing reliability and minimizing total lead-time. The suggested technique is examined on various models, four nodes, six nodes, and eight nodes computer networks.

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.

This paper presents a new boost inverter topology with nine level output voltage waveform using a single dc source and two switched capacitors. The capacitor voltages are self-balancing and thus is devoid of any sensors and auxiliary circuitry. The output voltage is twice higher than the input voltage, which eliminates the need for an input dc boost converter especially when the inverter is powered from a renewable source. The merits of the proposed topology in terms of the number of devices and cost are highlighted by comparing the recent and conventional inverter topologies. In addition to this, the total voltage stress of the proposed topology is lower and have a maximum efficiency of 98.25%. The operation and dynamic performance of the proposed topology have been simulated using PLECS software and are validated using an experimental setup considering a different dynamic operation.

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.

In this paper, an enhanced version of whale optimization algorithm (EWOA) is presented to be applied in adaptive control techniques as a parameter tuner. One weakness point in this control scheme is the low efficiency of its objective function. Balloon effect (BE) is a modification introduced to increase the efficiency of the objective function of the optimization method and the ability of the controller to deal with system problems increase consequently. Controlling of the temperature of electric furnaces is considered as one of the important issues in several industrial applications. Conventional controllers such as PID controller cannot deal efficiently with the problem of parameters variations and step disturbance. This paper proposes an adaptive controller, in which the gain of the temperature controller is tuned online using EWOA supported by balloon effect. System responses obtained by the proposed adaptive control scheme using EWOA + BE have been compared with an electric furnace temperature control (EFTC) scheme response using both the PID controller-based modified flower pollination algorithm (MoFPA) and PID-accelerated PIDA-based MoFPA. From the results, it can be observed that the proposed controller tuned by the EWOA + BE method improves the time performance compared with the other techniques (PID and PIDA-based MoFPA) in case of EFTC application.

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

This paper presents theoretical analysis and experimental implementation of a classical controller with intelligent properties. The controller has constant parameters, but it performs as an intelligent controller. The controller design mimics the fuzzy logic controller in a classical form and combines the advantages of classical controllers and properties of intelligent controllers. The designed controller parameters force the controlled variable to behave such as a first order system with a desired time constant. DC motor practical system is used to demonstrate the effectiveness of the presented controller. Root locus and frequency response using Bode diagram are used to help the design of the controller parameters. Simulation and experimental results verify the high performance of the presented controller. Keywords: Classical controller, DC motor, Root locus, Frequency response, Arduino microcontroller

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.

Recently, most transportation systems have used an integrated electrical machine in their traction scheme, resulting in a hybrid electrified vehicle. As a result, an energy source is required to provide the necessary electric power to this traction portion. However, this cannot be efficient without a reliable recharging method and a practical solution. This study discusses the wireless recharge solutions and tests the system’s effectiveness under various external and internal conditions. Moreover, the Maxwell tool is used in this research to provide a complete examination of the coils’ position, size, number, and magnetic flux evolution when the coils are translated. In addition, the mutual inductance for each of these positions is computed to determine the ideal conditions for employing the wireless recharge tool for every charging application. A thorough mathematical analysis is also presented, and the findings clearly demonstrate the relationship between the magnet flux and the various external conditions employed in this investigation.

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.

Energy demand has been overgrowing in developing countries. Moreover, the fluctuation of fuel prices is a primary concern faced by many countries that highly rely on conventional power generation to meet the load demand. Hence, the need to use alternative resources, such as renewable energy, is crucial in order to mitigate fossil fuel dependency, while ensuring reductions in carbon dioxide emissions. Algeria—being the largest county in Africa—has experienced a rapid growth in energy demand over the past decade due to the significant increase in residential, commercial, and industry sectors. Currently, the hydrocarbon-rich nation is highly dependent on fossil fuels for electricity generation, with renewable energy only having a small contribution to the country’s energy mix. However, the country has massive potential for renewable energy generation, such as solar, wind, biomass, geothermal, and hydropower. Therefore, the government aims to diversify away from fossil fuels and promote renewable energy generation through policies and renewable energy-related programs. The country’s Renewable Energy and Energy Efficiency Development Plan focuses on large scale solar, wind generation as well as geothermal and biomass technologies. This paper provides an update on the current energy position and renewable energy status in Algeria. Moreover, this paper discusses renewable energy (RE) policies and programs that aim to increase the country’s renewable energy generation and its implementation status.

This study proposes a classical control algorithm for solving the transcendental set of equations for the unequal DC sources of five-level multilevel inverters (MLIs). Such sources can be generated from renewable energy sources. Two DC sources with different values are used to produce an output voltage with five levels. Then, a set of two transcendental equations is formulated with two targeted functions to control the fundamental component and cancel the stipulated single harmonic order. The proposed solution uses a simple classical proportional control with two loops to generate two switching angles. The first switching angle is assigned with an initial value, whereas the second one is calculated from the inner loop. The outer loop is used to cancel the specified harmonic by sending the error signal to the proposed proportional control that tunes the switching angles. The proposed algorithm is easy, fast, and accurate, and has a wide-range solution in terms of modulation index (MI ) and input DC source ratio (x=V1V2≤1). The proposed algorithm is tested for a wide range of MI and x to verify its feasibility. Moreover, several simulation and laboratory tests are presented to further validate the applicability of the proposed approach.

Microinverters are an essential part of the photovoltaic (PV) industry with significant exponential prevalence in new PV module architectures. However, electrolyte capacitors used to decouple double line frequency make the single-phase microinverters topologies the slightest unit in this promising industry. Three-phase microinverter topologies are the new trend in this industry because they do not have double-line frequency problems and they do not need the use of electrolyte capacitors. Moreover, these topologies can provide additional features such as four-wire operation. This paper presents a detailed discussion of the strong points of three-phase microinverters compared to single-phase counterparts. The developed topologies of three-phase microinverters are presented and evaluated based on a new classification based on the simplest topologies among dozens of existing inverters. Moreover, the paper considers the required standardized features of PV, grid, and the microinverter topology. These features have been classified as mandatory and essential. Examples of the considered features for classifications are Distributed Maximum Power Point Tracking (DMPPT), voltage boosting gain, and four-wire operation. The developed classification is used to identify the merits and demerits of the classified inverter topologies. Finally, a recommendation is given based on the classified features, chosen inverter topologies, and associated features.

Abstract Background The context and purpose of the study are as follows: Drug-borne infections may arise from non-adherence to strict microbiological quality of pharmaceuticals products. Moreover, presence of exceeding levels of microorganisms in non-sterile pharmaceuticals may lead to change of their organoleptic characteristics and loss of effectiveness. The aim of the study is to evaluate the microbiological quality of commonly used non-sterile pharmaceuticals in Alexandria, Egypt. Results Average microbiological quality of the studied products, where 17.03% and 19.23 % of samples had exceeded the maximum acceptable limit of TAMC and TYMC, respectively. No E. coli was isolated from oral products. None of S. aureus nor P. aeruginosa were isolated from topical products. Bacterial growth was recovered from 19 (10.44%) of the studied 182 samples, four Bacillus spp. had been recovered from topical products, two P. aeruginosa isolates were recovered from tablets and other two were isolated from syrups dosage forms. Other isolates were Pseudomonas stutzeri, Stenotrophomonas maltophilia, Acinetobacter Achromobacter denitrificans, Ochrobactrum anthropic and Aeromonas salmonicida. Conclusion Average microbiological quality of the tested pharmaceuticals used in Alexandria.