Optimal PI Based Secondary Control for Autonomous Micro-Grid via Particle Swarm Optimization Technique

Ebrahim, Mohamed A.; Aziz, Beshoy Abdou; Osman, Fawzy A.; Nashed, Maged N. F.

doi:10.1109/mepcon.2018.8635217

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…The triangle's center of gravity is taken as parameter values. In Figure 10, the results of the FOPI controller design are compared with controller designs, 37,38 using MG LFC in the literature for case A. In Figure 10, the parameter values of FOPI are K P = 11.13 − K I = 7.42 for τ = 1.6 and α = 0.4.…”

Section: Analysis Results Of the Mg With Fopi Controllermentioning

confidence: 99%

Enhancing stability region of time‐delayed smart power grids by non‐integer controllers

Yildirim

Khooban

2020

Int J Energy Res

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In this study, the impacts of non-integer order controller on the stable parameter space of the microgrid (MG) frequency control system with fixed communication time delay are investigated and discussed with the help of the stability boundary locus (SBL) method. This study proposes a non-integer order controller for the load frequency control (LFC) of the MG systems. To that end, the load frequency model of the MG is formulated and then the characteristic equations of this model are obtained. Then, with the help of this characteristic equation, the stable parameter space of the non-integer controller is determined with regard to different time delay (τ) and fractional integral order values (α) using the SBL method. In order to show the accuracy of the obtained stable parameter space, time domain and generalized modified Mikhailov (GMM) criterion studies are carried out for different values of (τ) and (α). According to the results obtained, the areas of stable parameter space according to different α values and τ = 1.6 are calculated as 444.8860 for α = 0.4, 342.9728 for α = 0.7, 259.3578 for α = 1, 216.2541 for α = 1.3 and 159.6826 for α = 1.6. In addition, the areas of stable parameter space according to different τ values and α = 1.4 are calculated as 784.5222 for τ = 1, 106.3219 for τ = 2, 29.6959 for τ = 3 and 11.5946 for τ = 4. Despite the extreme variability arising from nature of resources that make up the MG, the designed non integer order controller with the values selected within the stable parameter space stably carries out LFC control of the MG.

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Section: Analysis Results Of the Mg With Fopi Controllermentioning

confidence: 99%

Enhancing stability region of time‐delayed smart power grids by non‐integer controllers

Yildirim

Khooban

2020

Int J Energy Res

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“…(1) The advantages of PSO are the capability to change the position of particles in a multidimensional search space. This unique advantage paves the way for employing PSO in tackling various engineering challenges such as voltage and frequency control on interconnected power systems, maximum energy harvesting of both solar as well as wind energy conversion systems, energy management of RESs and stabilizing of inverted pendulum [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69] On the other hand, the most important disadvantage of PSO is that the regulation speed and direction of the particle are not exact. This method may not perform well in non-coordinate systems 70 .…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Mohamed,

Mahmoud,

Saied

et al. 2024

Sci Rep

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The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

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“…The artificial intelligence (AI) algorithms are employed for tuning the parameters of several controllers successfully. The authors of [19] used the grasshopper algorithm to search for the accurate gains of the voltage and frequency PI controllers in the micro‐grid, while in [20], the authors applied the particle swarm optimization (PSO) on the same application. Furthermore, the optimum controller design for reactive power control in an islanded microgrid was achieved by using PSO and bacterial foraging optimization algorithm [21].…”

Section: Optimal Design Procedures Of the Pr Controller And Its Hcsmentioning

confidence: 99%

“…The phase response of the PR controller at 𝜔 p = 𝜔 o + 2𝜔 c1 can be derived from Equation (20) as: From which the K rc can be designed according to the following equation:…”

Section: Analytical Design Procedures Of the Pr Controller And Its Hcsmentioning

confidence: 99%

Optimal design of proportional‐resonant controller and its harmonic compensators for grid‐integrated renewable energy sources based three‐phase voltage source inverters

Ebrahim

Aziz

Nashed

et al. 2020

IET Generation Trans & Dist

Self Cite

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This paper proposes new analytical and optimal design procedures of the proportional‐resonant (PR) controller and its harmonic compensators (HCs) for three‐phase grid‐connected voltage source inverters (VSIs) powered by renewable energy resources. The modeling and analysis based on stationary reference frame are performed for VSIs collaborated with an L‐type filter. The theoretical verification and simulation validation of the proposed design guidelines are done to approve its effectiveness and robustness. Particle swarm, grey wolf and Harris hawks' optimization techniques are applied and compared for a proper selection of the parameters of the proposed PR controller and its HCs. To accomplish this study, multi‐objective error functions are employed and compared to minimize the total harmonic distortion of the grid output current. The proposed PR controller and its HCs are tested, using MATLAB/Simulink, along with the allowable changes of inverter output active and reactive powers, and also under the grid voltage distortion. Moreover, their performance is evaluated according to IEEE and IEC harmonics standards, and compared with the conventional PI controller based on reference frame. Furthermore, the experimental validation for the proposed controllers is done based on the hardware‐in‐the‐loop real‐time simulator using C2000TM‐microcontroller‐LaunchPadXL‐TMS320F28377S kit.

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Optimal PI Based Secondary Control for Autonomous Micro-Grid via Particle Swarm Optimization Technique

Cited by 11 publications

References 17 publications

Enhancing stability region of time‐delayed smart power grids by non‐integer controllers

Enhancing stability region of time‐delayed smart power grids by non‐integer controllers

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Optimal design of proportional‐resonant controller and its harmonic compensators for grid‐integrated renewable energy sources based three‐phase voltage source inverters

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