SVC damping controller design based on novel modified fruit fly optimisation algorithm

Zhang, Kaoshe; Shi, Zhaodi; Huang, Yuehui; Qiu, Chengjian; Yang, Shuo

doi:10.1049/iet-rpg.2017.0401

Cited by 17 publications

(7 citation statements)

References 19 publications

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“…In MATLAB ® , a function eig calculates the eigenvalues. Figure 22 shows stability criteria for eigenvalues are represented in the s-plane MATLAB ® /SIMULINK ® for linear controller design, for example the power system toolbox (PST) developed by Rogers [133], PSAT [134], MatDyn [135], Matpower, and MatSim [47]. Researchers use these toolboxes in damping controller design to analyze power system oscillation.…”

Section: Eigenvaluesmentioning

confidence: 99%

Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review

et al. 2022

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Massive growth in global electrical energy demand has necessitated a genuine exploration and integration of solar and wind energy into the electrical power mix. This incorporation goes a long way in improving the cumulative generated power capacity of the power system. However, wind and solar photovoltaic (PV) are intermittent in nature, making the provisioning of a good maximum power tracking (MPPT) scheme necessary. Furthermore, the integration is characterized by synchronization challenges and introduces various modes of power system oscillations as it is converter-driven. This greatly affects the overall stability of the integrated power mix. Consequently, various technological models have been designed to address these challenges ranging from MPPT schemes, phase-lock loop (PLL), virtual synchronous generator (VSG), power system stabilizers (PSS), flexible AC transmission system (FACTS), coordinated control and artificial intelligence (AI). In this work, a multi-machine power system model is reviewed for integration stability studies. Various technical solutions associated with the integration are also reviewed. MPPT, PLL, VSG, PSS, FACTS, coordinated control, and various optimization technique schemes used for damping controller design are discussed.

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Section: Eigenvaluesmentioning

confidence: 99%

Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review

et al. 2022

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“…The method used in this research is the MFOA, which is a metaheuristic algorithm based on the olfactory search process inherent in fruit flies' foraging behavior. Fruit flies have much stronger senses of smell and vision than other insects, and this helps them find food sources [33], [34].…”

Section: Controller Designmentioning

confidence: 99%

A Hybrid Coordinated Design Method for Power System Stabilizer and FACTS Device Based on Synchrosqueezed Wavelet Transform and Stochastic Subspace Identification

Faraji

Naghshbandy

Baayeh

2021

Journal of Modern Power Systems and Clean Energy

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The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system stability and security. In this paper, a new method is introduced based on a combination of synchrosqueezed wavelet transform and the stochastic subspace identification (SSI) algorithm to investigate the low-frequency electromechanical oscillations of large-scale power systems. Then, the estimated modes of the power system are used for the design of the power system stabilizer and the flexible alternating current transmission system (FACTS) device. In this optimization problem, the control parameters are set using a hybrid approach composed of the Prony and residual methods and the modified fruit fly optimization algorithm. The proposed mode estimation method and the controller design are simulated in MATLAB using two test case systems, namely IEEE 2-area 4-generator and New England-New York 68-bus 16-generator systems. The simulation results demonstrate the high performance of the proposed method in estimation of local and interarea modes, and indicate the improvements in oscillation damping and power system stability.

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“…It has similar accuracy as the MIMO subspace state space model but a lower model order. From a device perspective, the static VAR compensators are adopted under various system conditions and types of generations [23][24][25]. Although these methods have merits from a different perspective, some of them neglect communication delays and some assume correct network topology and system parameters.…”

Section: Introductionmentioning

confidence: 99%

Twin-Delayed Deep Deterministic Policy Gradient for Low-Frequency Oscillation Damping Control

2021

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Due to the large scale of power systems, latency uncertainty in communications can cause severe problems in wide-area measurement systems. To resolve this issue, a significant amount of past work focuses on using emerging technology, including machine learning methods such as Q-learning, for addressing latency issues in modern controls. Although the method can deal with the stochastic characteristics of communication latency, the Q-values can be overestimated in Q-learning methods, leading to high bias. To address the overestimation bias issue, we redesign the learning structure of the deep deterministic policy gradient (DDPG). Then we develop a damping control twin-delayed deep deterministic policy gradient method to handle the damping control issue under unknown latency in the power network. The purpose is to address the damping control issue under unknown latency in the power network. This paper will create a novel reward algorithm, taking into account the machine speed deviation, the episode termination prevention, and the feedback from action space. In this way, the system optimally damps down frequency oscillations while maintaining the system’s stability and reliable operation within defined limits. The simulation results verify the proposed algorithm in various perspectives, including the latency sensitivity analysis under high renewable energy penetration and the comparison with conventional and machine learning control algorithms. The proposed method shows a fast learning curve and good control performance under varying communication latency.

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SVC damping controller design based on novel modified fruit fly optimisation algorithm

Cited by 17 publications

References 19 publications

Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review

Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review

A Hybrid Coordinated Design Method for Power System Stabilizer and FACTS Device Based on Synchrosqueezed Wavelet Transform and Stochastic Subspace Identification

Twin-Delayed Deep Deterministic Policy Gradient for Low-Frequency Oscillation Damping Control

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