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The low system inertia and the high sensitivity to load and generation fluctuations represent the main challenges for future ambitious plans of modern power systems accompanied by high penetrations levels of the renewable energy sources (RESs). Therefore, this paper presents a new approach for solving the load frequency control (LFC) in addition to the virtual inertia control (VIC) in interconnected RESs penetrated power systems using cooperative tilt-based controllers and a hybrid modified particle swarm optimization with genetic algorithm (MPSOGA). The VIC system is adopted using superconducting magnetic energy storage (SMES) to provide sufficient inertial energy for system stability. Two tilt-based controllers are employed in each area using the tilt-integral-derivative (TID) controller for the SMES and TID with filter (TIDF) for the LFC function. The cooperative optimum design of the TID/TIDF controllers leads to the enhancement of frequency stability in studied two-area power systems. The formulated optimization process aims to minimize the frequency nadir settling time during abrupt changes of RESs and/or load changes, considering the cooperative control of LFC and VIC. The proposed approach has been applied to a case study consisting of two-area power systems, connected via hybrid high voltage DC/AC (hybrid HVAC/HVDC) tie-line, integrated with distributed conventional generations, photovoltaic (PV), and wind generation systems. Performance analysis has been conducted to demonstrate the effectiveness of the proposed method is compared to the genetic algorithm (GA) and particle-swarm optimization (PSO) using high fluctuations of renewable generations under extreme changes in loading conditions and physical parameters variation. The obtained results show the superiority of MPSOGA approach on the other competitive optimization techniques.
Ahmed Elmelegi; Emad A. Mohamed; Mokhtar Aly; Emad M. Ahmed; Al-Attar Ali Mohamed; Osama Elbaksawi. Optimized Tilt Fractional Order Cooperative Controllers for Preserving Frequency Stability in Renewable Energy-Based Power Systems. IEEE Access 2021, 9, 8261 -8277.
AMA StyleAhmed Elmelegi, Emad A. Mohamed, Mokhtar Aly, Emad M. Ahmed, Al-Attar Ali Mohamed, Osama Elbaksawi. Optimized Tilt Fractional Order Cooperative Controllers for Preserving Frequency Stability in Renewable Energy-Based Power Systems. IEEE Access. 2021; 9 (99):8261-8277.
Chicago/Turabian StyleAhmed Elmelegi; Emad A. Mohamed; Mokhtar Aly; Emad M. Ahmed; Al-Attar Ali Mohamed; Osama Elbaksawi. 2021. "Optimized Tilt Fractional Order Cooperative Controllers for Preserving Frequency Stability in Renewable Energy-Based Power Systems." IEEE Access 9, no. 99: 8261-8277.
Multi-area power systems inhere complicated nonlinear response, which results in degraded performance due to the insufficient damping. The main causes of the damping problems are the stochastic behavior of the renewable energy sources, loading conditions, and the variations of system parameters. The load frequency control (LFC) represents an essential element for controlling multi-area power systems. Therefore, the proper design of the controllers is mandatory for preserving reliable, stable and high-quality electrical power. The controller has to suppress the deviations of the area frequency in addition to the tie-line power. Therefore, this paper proposes a new frequency regulation method based on employing the hybrid fractional order controller for the LFC side in coordination with the fractional order proportional integral derivative (FOPID) controller for the superconducting energy storage system (SMES) side. The hybrid controller is designed based on combining the FOPID and the tilt integral derivative (TID) controllers. In addition, the controller parameters are optimized through a new application of the manta ray foraging optimization algorithm (MRFO) for determining the optimum parameters of the LFC system and the SMES controllers. The optimally-designed controllers have operated cooperatively and hence the deviations of the area frequency and tie-line power are efficiently suppressed. The robustness of the proposed controllers is investigated against the variation of the power system parameters in addition to the location and/or magnitude of random/step load disturbances.
Emad A. Mohamed; Emad M. Ahmed; Ahmed Elmelegi; Mokhtar Aly; Osama Elbaksawi; Al-Attar Ali Mohamed. An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems. IEEE Access 2020, 8, 213899 -213915.
AMA StyleEmad A. Mohamed, Emad M. Ahmed, Ahmed Elmelegi, Mokhtar Aly, Osama Elbaksawi, Al-Attar Ali Mohamed. An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems. IEEE Access. 2020; 8 (99):213899-213915.
Chicago/Turabian StyleEmad A. Mohamed; Emad M. Ahmed; Ahmed Elmelegi; Mokhtar Aly; Osama Elbaksawi; Al-Attar Ali Mohamed. 2020. "An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems." IEEE Access 8, no. 99: 213899-213915.
Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.
Mahmoud G. Hemeida; Salem Alkhalaf; Al-Attar A. Mohamed; Abdalla Ahmed Ibrahim; Tomonobu Senjyu. Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO). Energies 2020, 13, 3847 .
AMA StyleMahmoud G. Hemeida, Salem Alkhalaf, Al-Attar A. Mohamed, Abdalla Ahmed Ibrahim, Tomonobu Senjyu. Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO). Energies. 2020; 13 (15):3847.
Chicago/Turabian StyleMahmoud G. Hemeida; Salem Alkhalaf; Al-Attar A. Mohamed; Abdalla Ahmed Ibrahim; Tomonobu Senjyu. 2020. "Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)." Energies 13, no. 15: 3847.
This paper proposes a new method for diagnosis Broken Rotor Bar (BRB) faults in three phase squirrel-cage induction motors. The proposed method is based on the stator current signature analysis using Discrete Wavelet Transform (DWT) and Adaptive Neural Fuzzy Inference System (ANFIS) artificial intelligence approach. The DWT technique plays an important role for signal feature extraction. The abnormal transient signals can be applied to recognize the BRB faults by DWT. The DWT is considered to identify fault features accurately. The dataset is established by feature vectors are applied as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to classify and identify the BRB fault. The fault diagnosis is verified experimentally on 1.5 Hp three phase induction motor under different fault conditions and different load conditions. The experiment results demonstrate that this technique is valid and effective for the BRB faults diagnosis.
Menshawy A. Mohamed; Al-Attar Ali Mohamed; Mohamed Abdel-Nasser; Essam E. M. Mohamed; M. A. Moustafa Hassan. Induction motor broken rotor bar faults diagnosis using ANFIS-based DWT. International Journal of Modelling and Simulation 2019, 1 -14.
AMA StyleMenshawy A. Mohamed, Al-Attar Ali Mohamed, Mohamed Abdel-Nasser, Essam E. M. Mohamed, M. A. Moustafa Hassan. Induction motor broken rotor bar faults diagnosis using ANFIS-based DWT. International Journal of Modelling and Simulation. 2019; ():1-14.
Chicago/Turabian StyleMenshawy A. Mohamed; Al-Attar Ali Mohamed; Mohamed Abdel-Nasser; Essam E. M. Mohamed; M. A. Moustafa Hassan. 2019. "Induction motor broken rotor bar faults diagnosis using ANFIS-based DWT." International Journal of Modelling and Simulation , no. : 1-14.
This article offers a multi-objective framework for an optimal mix of different types of distributed energy resources (DERs) under different load models. Many renewable and non-renewable energy resources like photovoltaic system (PV), micro-turbine (MT), fuel cell (FC), and wind turbine system (WT) are incorporated in a grid-connected hybrid power system to supply energy demand. The main aim of this article is to maximize environmental, technical, and economic benefits by minimizing various objective functions such as the annual cost, power loss and greenhouse gas emission subject to different power system constraints and uncertainty of renewable energy sources. For each load model, optimum DER size and its corresponding location are calculated. To test the feasibility and validation of the multi-objective water cycle algorithm (MOWCA) is conducted on the IEEE-33 bus and IEEE-69 bus network. The concept of Pareto-optimality is applied to generate trilateral surface of non-dominant Pareto-optimal set followed by a fuzzy decision-making mechanism to obtain the final compromise solution. Multi-objective non-dominated sorting genetic (NSGA-III) algorithm is also implemented and the simulation results between two algorithms are compared with each other. The achieved simulation results evidence the better performance of MOWCA comparing with the NSGA-III algorithm and at different load models, the determined DER locations and size are always righteous for enhancement of the distribution power system performance parameters.
Al-Attar Ali Mohamed; Shimaa Ali; Salem Alkhalaf; Tomonobu Senjyu; Ashraf M. Hemeida. Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm. Sustainability 2019, 11, 6550 .
AMA StyleAl-Attar Ali Mohamed, Shimaa Ali, Salem Alkhalaf, Tomonobu Senjyu, Ashraf M. Hemeida. Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm. Sustainability. 2019; 11 (23):6550.
Chicago/Turabian StyleAl-Attar Ali Mohamed; Shimaa Ali; Salem Alkhalaf; Tomonobu Senjyu; Ashraf M. Hemeida. 2019. "Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm." Sustainability 11, no. 23: 6550.
Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. “Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy.” This work presents a new meta-heuristic optimization approach, called Parasitism-Predation Algorithm (PPA), which mimics the interaction between the predator (cats), the parasite (cuckoos) and the host (crows) in the crow–cuckoo–cat system model to overcome the problems of low convergence and the curse of dimensionality of large data. The proposed hybrid framework combines the relative advantages of cat swarm optimization (CSO), cuckoo search (CS) and crow search algorithm (CSA) to attain a combinatorial set of features to boost up the classification accuracy. Nesting, parasitism, and predation phases are supposed to help exploration ability and balance in the context of solving classification problems. In addition, Levy flight distribution is applied to help better diversity of conventional CSA and improve ability of exploration. Meanwhile, an effective fitness function is utilized to enable the proposed PPA-based feature selector using K-Nearest Neighbors algorithm (KNN) to attain a combinatorial set of features. The proposed PPA and four standard heuristic search algorithms are looked at to gauge how efficient the proposed option is. Additionally, eighteen classification datasets are deployed to gauges its efficacy. The results highlight that the algorithm proposed is both effective and competitive in terms of performance of classification and dimensionality reduction as opposed to other heuristic options.
Al-Attar A. Mohamed; S.A. Hassan; A.M. Hemeida; Salem Alkhalaf; M.M.M. Mahmoud; Ayman M. Baha Eldin. Parasitism – Predation algorithm (PPA): A novel approach for feature selection. Ain Shams Engineering Journal 2019, 11, 293 -308.
AMA StyleAl-Attar A. Mohamed, S.A. Hassan, A.M. Hemeida, Salem Alkhalaf, M.M.M. Mahmoud, Ayman M. Baha Eldin. Parasitism – Predation algorithm (PPA): A novel approach for feature selection. Ain Shams Engineering Journal. 2019; 11 (2):293-308.
Chicago/Turabian StyleAl-Attar A. Mohamed; S.A. Hassan; A.M. Hemeida; Salem Alkhalaf; M.M.M. Mahmoud; Ayman M. Baha Eldin. 2019. "Parasitism – Predation algorithm (PPA): A novel approach for feature selection." Ain Shams Engineering Journal 11, no. 2: 293-308.
This work outlines a novel technique for optimization, which stems from the composition of two random distributions: Maxwell and Gaussian, so-called Maxwell Gaussian Algorithm (MGA). The proposed algorithm tends to find the optimum elements of traditional PI controllers for the PMSG-based WECS, in a manner whereby the optimal dynamic performance of PMSG through another grid fault and operation could be achieved easily. In order to realize an optimum search, Maxwell-Gaussian distribution is employed to control the standard deviation of Gaussian normal in addition to a new selection of the mating solutions with adaptive manner control. Furthermore, four different updating equations were created for the purpose of generating the given solution to increase the exploration over research space. MGA-based coordinate control strategy is implemented in the machine side converter (MSC) and grid side converter (GSC). The MGA is compared with the different optimization techniques such as the Ant Lion Optimizer (ALO) and Satin bowerbird optimizer (SBO). In order to ensure the robustness of the proposed algorithm, four case studies namely; step change of wind speed, variables of wind speed, Random wind speed variation and three phase symmetrical faults. The simulation results indicate the superiority of the proposed algorithm over other used optimization techniques.
Al-Attar Mohamed; A.L. Haridy; T. Senjyu; Hany M. Hasanien; Salem Alkhalaf; A.M. Hemeida. WITHDRAWN: PMSG driven by wind energy controller based Maxwell-Gaussian optimization technique. Ain Shams Engineering Journal 2019, 1 .
AMA StyleAl-Attar Mohamed, A.L. Haridy, T. Senjyu, Hany M. Hasanien, Salem Alkhalaf, A.M. Hemeida. WITHDRAWN: PMSG driven by wind energy controller based Maxwell-Gaussian optimization technique. Ain Shams Engineering Journal. 2019; ():1.
Chicago/Turabian StyleAl-Attar Mohamed; A.L. Haridy; T. Senjyu; Hany M. Hasanien; Salem Alkhalaf; A.M. Hemeida. 2019. "WITHDRAWN: PMSG driven by wind energy controller based Maxwell-Gaussian optimization technique." Ain Shams Engineering Journal , no. : 1.
In this paper, the performance of different optimization techniques namely, multi-objective dragonfly algorithm (MODA) and multi-objective differential evolution (MODE) are presented and compared. The uncertainty effect of a wind turbine (WT) on the performance of the distribution system is taken into account. The point estimate method (PEM) is used to model the uncertainty in wind power. Optimization methods are applied to determine the multi-objective optimal allocation of distributed generation (DG) in radial distribution systems at a different load level (light, normal, heavy load level). The multi-objective function is expressed to minimize the total power loss, total operating cost, and improve the voltage stability index of the radial distribution system (RDS). Multi-objective proposed algorithms are used to generate the Pareto optimal solutions; and a fuzzy decision-making function is used to produce a hybrid function for obtaining the best compromise solution. The proposed algorithms are carried out on 33-bus and IEEE-69-bus power systems. The simulation results show the effectiveness of installing the proper size of DG at the suitable location based on different techniques.
Salem Alkhalaf; Tomonobu Senjyu; Ayat Ali Saleh; Ashraf M. Hemeida; Al-Attar Ali Mohamed. A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels. Sustainability 2019, 11, 5323 .
AMA StyleSalem Alkhalaf, Tomonobu Senjyu, Ayat Ali Saleh, Ashraf M. Hemeida, Al-Attar Ali Mohamed. A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels. Sustainability. 2019; 11 (19):5323.
Chicago/Turabian StyleSalem Alkhalaf; Tomonobu Senjyu; Ayat Ali Saleh; Ashraf M. Hemeida; Al-Attar Ali Mohamed. 2019. "A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels." Sustainability 11, no. 19: 5323.
This work presents a novel Moth Swarm Algorithm (MSA), inspired by the orientation of moths towards moonlight to solve constrained Optimal Power Flow (OPF) problem. The associative learning mechanism with immediate memory and population diversity crossover for Lévy-mutation have been proposed to improve exploitation and exploration ability, respectively, in addition to adaptive Gaussian walks and spiral motion. The MSA and four heuristic search algorithms are carried out on the IEEE 30-bus, 57-bus and IEEE 118-bus power systems. These approaches are applied to optimize the control variables such as real power generations, load tap changer ratios, bus voltages and shunt capacitance values under several power system constraints. Fourteen different cases are executed on different curves of fuel cost (e.g., quadratic, valve-loading effects, multi-fuels options), environmental pollution emission, active power loss, voltage profile and voltage stability for contingency and normal conditions, in single and multi objective optimization space. Furthermore, the impacts of the updating mechanism of optimizers on those objective functions are investigated. The effectiveness and superiority of the MSA have been demonstrated in comparison with many recently published OPF solution
Al-Attar Ali Mohamed; Yahia S. Mohamed; Ahmed A.M. El-Gaafary; Ashraf M. Hemeida. Optimal power flow using moth swarm algorithm. Electric Power Systems Research 2017, 142, 190 -206.
AMA StyleAl-Attar Ali Mohamed, Yahia S. Mohamed, Ahmed A.M. El-Gaafary, Ashraf M. Hemeida. Optimal power flow using moth swarm algorithm. Electric Power Systems Research. 2017; 142 ():190-206.
Chicago/Turabian StyleAl-Attar Ali Mohamed; Yahia S. Mohamed; Ahmed A.M. El-Gaafary; Ashraf M. Hemeida. 2017. "Optimal power flow using moth swarm algorithm." Electric Power Systems Research 142, no. : 190-206.
This paper presents a novel controller design of a thyristor controlled series capacitor based on an optimized adaptive neuro-fuzzy inference system. The modified states of matter search algorithm are implemented to fit the premise and consequent parameters of the neuro fuzzy system. The design objectives are to reduce the power system oscillations and find a minimum number of strongest fuzzy rules, trends toward building a low-size controller model. Therefore, fuzzy decision-making mechanism is employed to rank the Pareto-optimal set to extract the best compromise solution. The proposed controller design depends upon the expected wide range of operating conditions. The effectiveness of smart control strategy based controller is tested and compared on single machine infinite bus and multimachine power systems under small scale disturbance as well as large scale disturbances.
Al-Attar Ali Mohamed; Ahmed A.M. El-Gaafary; Yahia S. Mohamed; Ashraf Mohamed Hemeida. Multi-objective states of matter search algorithm for TCSC-based smart controller design. Electric Power Systems Research 2016, 140, 874 -885.
AMA StyleAl-Attar Ali Mohamed, Ahmed A.M. El-Gaafary, Yahia S. Mohamed, Ashraf Mohamed Hemeida. Multi-objective states of matter search algorithm for TCSC-based smart controller design. Electric Power Systems Research. 2016; 140 ():874-885.
Chicago/Turabian StyleAl-Attar Ali Mohamed; Ahmed A.M. El-Gaafary; Yahia S. Mohamed; Ashraf Mohamed Hemeida. 2016. "Multi-objective states of matter search algorithm for TCSC-based smart controller design." Electric Power Systems Research 140, no. : 874-885.