Simulation of novel hybrid method to improve dynamic responses with PSS and UPFC by fuzzy logic controller

Khaksar, Mehrdad; Rezvani, Alireza; Moradi, Mohammad Hassan

doi:10.1007/s00521-016-2487-1

Cited by 14 publications

(5 citation statements)

References 31 publications

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“…Consider the systems described by (9), the sliding-neural network controller (15), and Assumptions 1 and 2 given in [31]. If the bias term ( ), the learning rule of the weight , and the adaptation law for the unknown bound are chosen as…”

Section: Upfc Rbf Neurosliding Mode Controller Designmentioning

confidence: 99%

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A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

Kenné

Kuate

Fombu

et al. 2017

Advances in Electrical Engineering

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This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.

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Section: Upfc Rbf Neurosliding Mode Controller Designmentioning

confidence: 99%

“…However, the complexities associated with the adaption of membership functions and computation requirements for defuzzification have hindered the application of fuzzy logic [11][12][13][14][15]. Hence, recent studies have turned to artificial neural networks (ANN) to achieve the desired goals [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

Kenné

Kuate

Fombu

et al. 2017

Advances in Electrical Engineering

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“…e problem is formulated with the objective of maximizing the damping ratios of the electromechanical modes. In 2016, [24] presented a hybrid method is to damp oscillations in the power systems equipped with UPFC and PSS controllers. e hybrid method consists of an offline and an online stage.…”

Section: Introductionmentioning

confidence: 99%

Mayfly Optimization Algorithm Applied to the Design of PSS and SSSC-POD Controllers for Damping Low-Frequency Oscillations in Power Systems

Fortes

Martins

Costa

et al. 2022

International Transactions on Electrical Energy Systems

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In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems.

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“…On the other hand, the EVs can inject power into the grid during the discharging process, called vehicle to gid technology (V2G). The EVs' G2V and V2G technologies make them work as mobile energy sources that can provide the electrical networks with various ancillary services, such as primary frequency control and voltage regulation [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Optimal day‐ahead scheduling in micro‐grid with renewable based DGs and smart charging station of EVs using an enhanced manta‐ray foraging optimisation

Bastawy

Ebeed

Ali

et al. 2022

IET Renewable Power Gen

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Micro-grids (MGs) are small parts of the electrical power system that work along with the electric system or autonomously based on environmental or economic conditions. The renewable-based distributed generators (RDGs) and electric vehicle charging stations (EVCSs) are wildly incorporated in MGs. Optimal day-ahead scheduling of the MG is ahead corner of energy management for cost reduction. In addition, solving the economic load dispatch and day-ahead scheduling of the MG is a complex optimisation problem, especially considering the RDGs, EVCS, and uncertainties in the electrical system. This paper aims to optimise the day-ahead scheduling of the MG with and without a smart charging strategy for electric vehicles. An enhanced manta-ray foraging optimisation (EMRFO) algorithm is proposed to solve this optimisation problem. EMRFO depends upon boosting population diversity and the searching ability of the standard MRFO using strategies. The proposed strategies are based on quasi-oppositional-based learning and local chaotic mutation. The studied MG consists of wind turbines, fuel cells, and diesel generators. The day-ahead scheduling of the MG is solved with and without considering the uncertainties of the load demand and the wind speed. The proposed algorithm for day-ahead scheduling of the MG is compared to well-known algorithms such as anti lion optimisation, particle swarm optimisation, whale optimisation algorithm, sine cosine algorithm, and harmony search algorithm. The simulation results demonstrate that the proposed algorithm is superior to these algorithms for solving the optimisation problem. The results show that the generation cost is reduced considerably from 77,745.

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Simulation of novel hybrid method to improve dynamic responses with PSS and UPFC by fuzzy logic controller

Cited by 14 publications

References 31 publications

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

Mayfly Optimization Algorithm Applied to the Design of PSS and SSSC-POD Controllers for Damping Low-Frequency Oscillations in Power Systems

Optimal day‐ahead scheduling in micro‐grid with renewable based DGs and smart charging station of EVs using an enhanced manta‐ray foraging optimisation

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