Design of Optimal Model Predictive Controller for LFC of Nonlinear Multi-area Power System with Energy Storage Devices

Elsisi, Mahmoud; Aboelela, Magdy A.S.; Soliman, M.; Mansour, W.

doi:10.1080/15325008.2018.1469056

Cited by 50 publications

(38 citation statements)

References 21 publications

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“…For example, Bat inspired algorithm (BIA) was used to determine the optimal constraints of the MPC for the LFC of (thermal-hydro) interconnected power grid where the system performance-based an optimal MPC has been compared the system performance-based PI controller [39]. As well, the gravitational search algorithm was applied to select the optimal parameters of MPC to reduce the frequency fluctuation for the LFC study considering three different area power systems [40]. Also, in a wind energy conversion system, the crow search algorithm has been applied to select the optimal parameters of the AMPC for blad batch control [41].…”

Section: A Literature Reviewmentioning

confidence: 99%

Optimal Model Predictive and Linear Quadratic Gaussian Control for Frequency Stability of Power Systems Considering Wind Energy

et al. 2021

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This work presents a new robust control technique which combines a model predictive control (MPC) and linear quadratic gaussian (LQG) approach to support the frequency stability of modern power systems. Moreover, the constraints of the proposed robust controller (MPC-LQG) are fine-tuned based on a new technique titled Chimp optimization algorithm (ChOA). The effectiveness of the proposed robust controller is tested and verified through a multi-area power system (i.e., single-area and two-area power systems). Each area contains a thermal power plant as a conventional generation source considering physical constraints (i.e. generation rate constraint, and governor dead band) in addition to a wind power plant as a renewable resource. The superiority of the proposed robust controller is confirmed by contrasting its performance to that of other controllers which were used in load frequency control studies (e.g., conventional integral and MPC). Also, the ChOA's ingenuity is verified over several other powerful optimization techniques; particle swarm optimization, gray wolf optimization, and ant lion optimizer). The simulation outcomes reveal the effectiveness as well as the robustness of the proposed MPC-LQG controller based on the ChOA under different operating conditions considering different load disturbances and several penetration levels of the wind power.

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Section: A Literature Reviewmentioning

confidence: 99%

Optimal Model Predictive and Linear Quadratic Gaussian Control for Frequency Stability of Power Systems Considering Wind Energy

et al. 2021

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“…MPC is involved with only a SMES system in [15] without considering RESs, in which the SMES is used to reduce the eddy current losses and control the power and voltage. Meanwhile, in [23], a SMES system based on MPC using a bat-inspired algorithm, and a gravitational search algorithm is proposed for LFC in a multi-area power system. A PI controller is proposed in [24] to minimize the fluctuation in power and voltage, but this method fails to achieve power leveling during PV power fluctuation.…”

Section: Introductionmentioning

confidence: 99%

Integration of PV system with SMES based on model predictive control for utility grid reliability improvement

Bakeer

Salama

Vokony

2021

Prot Control Mod Power Syst

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This paper describes the integration of a photovoltaic (PV) renewable energy source with a superconducting magnetic energy storage (SMES) system. The integrated system can improve the voltage stability of the utility grid and achieve power leveling. The control schemes employ model predictive control (MPC), which has gained significant attention in recent years because of its advantages such as fast response and simple implementation. The PV system provides maximum power at various irradiation levels using the incremental conductance technique (INC). The interfaced grid side converter of the SMES can control the grid voltage by regulating its injected reactive power to the grid, while the charge and discharge operation of the SMES coil can be managed by the system operator to inject/absorb active power to/from the grid to achieve the power leveling strategy. Simulation results based on MATLAB/Simulink® software prove the fast response of the system control objectives in tracking the setpoints at different loading scenarios and PV irradiance levels, while the SMES injects/absorbs active and reactive power to/from the grid during various events to improve the voltage response and achieve power leveling strategy.

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“…Optimization tool applied by researchers to non-linear model predictive controller [3], new variable structure control using meta-heuristics algorithms for frequency regulation [4], Energy Management of Controllable Loads New Supervisor Fuzzy Nonlinear Sliding Mode Control [5] and Predictive Controller for LFC of Nonlinear Multi-area Power System with Energy Storage Devices [6].…”

Section: Introductionmentioning

confidence: 99%

Optimal PID and fuzzy logic based position controller design of an overhead crane using the Bees Algorithm

2021

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Different industrial applications frequently use overhead cranes for moving and lifting huge loads. It applies to civil construction, metallurgical production, rivers, and seaports. The primary purpose of this paper is to control the motion/position of the overhead crane using a PID controller using Genetic Algorithms (GA) and Bee Algorithms (BA) as optimization tools. Moreover, Fuzzy Logic modified PID Controller is applied to obtain better controller parameters. The mathematical model uses an analytical method, and the PID model employs Simulink in MATLAB. The paper presents the PID parameters determination with a different approach. The development of membership functions, fuzzy rules employ the Fuzzy Logic toolbox. Both inputs and outputs use triangular membership functions. The result shows that the optimized value of the PID controller with the Ziegler-Nichols approach is time-consuming and will provide only the initial parameters. However, PID parameters obtained with the optimization method using GA and BA reached the target values. The results obtained with the fuzzy logic controller (0.227% overshoot) show improvement in overshoot than the conventional PID controller (0.271% overshoot).

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Design of Optimal Model Predictive Controller for LFC of Nonlinear Multi-area Power System with Energy Storage Devices

Cited by 50 publications

References 21 publications

Optimal Model Predictive and Linear Quadratic Gaussian Control for Frequency Stability of Power Systems Considering Wind Energy

Optimal Model Predictive and Linear Quadratic Gaussian Control for Frequency Stability of Power Systems Considering Wind Energy

Integration of PV system with SMES based on model predictive control for utility grid reliability improvement

Optimal PID and fuzzy logic based position controller design of an overhead crane using the Bees Algorithm

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