Event-Triggered <i>H<sub>∞</sub>
                  </i> Load Frequency Control for Multi-Area Nonlinear Power Systems Based on Non-Fragile Proportional Integral Control Strategy

Zhong, Qishui; Yang, Jiankai; Shi, Kaibo; Zhong, Shouming; Li, Zhixiong; Sotelo, Miguel Ángel

doi:10.1109/tits.2021.3110759

Cited by 74 publications

(28 citation statements)

References 36 publications

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“…To determine the optimal location and capacity, one SVC and TCSC are installed, which are considered as a sequence with three cells for the optimization problem, where the first two cells represent the location (SVC bus number and line number for the TCSC) and the third cell represents capacity. According to the generator bus model for the SVC, its optimal reactive capacity is calculated using the power flow [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The capacitance range of the SCV is between −50 to 50 MVAr.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

et al. 2021

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Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

et al. 2021

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“…Regarding possible future applications, introduce the control design technology synthesized in this technical article into some practical problems in engineering, including but not limited to high‐speed trains, semi‐Markovian multiagent systems, 63 wind energy conversion systems, permanent magnet synchronous motor, 5‐DOF active semi‐vehicle suspension systems, 64 electric power steering system, unmanned surface vehicles, multi‐area power systems 65 and ummanned marine vehicles are challenging yet meaningful further research subjects, and the corresponding results will be appear in the near future.…”

Section: Discussionmentioning

confidence: 99%

On event‐triggered guaranteed cost control for discrete‐time semi‐Markovian neural networks having communication delays and dual‐terminal probabilistic faults

She

Shi

et al. 2022

Intl J Robust & Nonlinear

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This article aims to address the guaranteed cost control synthesis problem for discrete‐time semi‐Markovian neural networks (D‐SMNNs) considering communication delays along with probabilistic faulty sensors and actuators (dual‐terminal probabilistic faults) by adopting a modified event‐triggered mechanism. Our primary target of this research is to implement a suitable feedback controller such that the resulting D‐SMNNs achieve stochastically stable. To be more concrete, the structural and coefficient switchings of the NNs are characterized by a semi‐Markovian process (SMP) along with the lower and upper bounds of the transition probabilities (TPs), which can reflect the reality more accurately and has a wider application scope. For optimize the resource utilization of the band‐limited communication networks, a modified event‐triggered transmission strategy is put forward to lessen the unnecessary data transmission over network. Additionally, according to different failure rates and the measurement distributions of intelligent modules, a more practical probabilistic sensors and actuators faulty model for networked D‐SMNNs is synthesized, which including additivity and multiplicity. The anamorphic probability of sensors and actuators are governed by a battery of mutually independent stochastic variables that obeys certain probabilistic distribution over the preknown interval false[0,𝕋false]false(𝕋≥1false). Therein, the distortion degree and failure rate for each sensor and actuator can be separately quantized and characterized with the aid of mathematical variance and expectation. Briefly, in comparison to the existing faulty models, the novelty of this extended fault model lies in it can characterize more features in practical industrial applications. Afterwards, by resorting to the Lyapunov functional method and combining with both the stochastic analysis technique and matrix inequality decoupling manipulation, several sufficient conditions on ensuring stabilization for solving both the guaranteed cost controller gains and event‐triggered coefficients are formulated in the shape of a group of linear matrix inequalities (LMIs), which can be verified by exploiting standard compute software. After that, the controller design is reformulated as an optimization problem with LMI constraints immediately. Eventually, three simulation examples are carried out to substantiate the potency and accuracy of the proposed control synthesis methodology.

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“…Our criteria improved the published results in the literature, but added computational complexity, which have been shown by two illustrative examples. Future study will be paid to T‐S fuzzy systems, 50 complex networks, 51,52 discrete‐time systems, 53,54 and event‐triggered control 55 …”

Section: Discussionmentioning

confidence: 99%

“…Future study will be paid to T-S fuzzy systems, 50 complex networks, 51,52 discrete-time systems, 53,54 and event-triggered control. 55…”

Section: Discussionmentioning

confidence: 99%

Nonfragile consensus of nonlinear multiagent systems with intrinsic delays via aperiodic memory sampled‐data control

Han

Zhong

Wang

et al. 2022

Intl J Robust & Nonlinear

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This paper focuses on the consensus issue of nonlinear multiagent systems (MASs) with intrinsic time-varying delays via the nonfragile and aperiodic memory sampled-data control scheme. An improved continuous Lyapunov-Krasovskii functional (LKF) with some two-sided looped-functional terms is introduced, in which several free-weighting-matrices are not necessarily positive definite. Furthermore, based on the property of Laplacian matrix, the consensus issue of original MASs can be investigated directly without transforming into error systems. Then, several sufficient criteria for the consensus of MASs are presented by means of the recently developed generalized integral inequality and the constructed LKF. Next, the required nonfragile controller gain is obtained by resorting to linear matrix inequalities and the maximal length of sampling period is derived as well. Finally, two illustrative examples are employed to exhibit the effectiveness and feasibility of the proposed consensus protocol.

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Event-Triggered H_∞ Load Frequency Control for Multi-Area Nonlinear Power Systems Based on Non-Fragile Proportional Integral Control Strategy

Cited by 74 publications

References 36 publications

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

On event‐triggered guaranteed cost control for discrete‐time semi‐Markovian neural networks having communication delays and dual‐terminal probabilistic faults

Nonfragile consensus of nonlinear multiagent systems with intrinsic delays via aperiodic memory sampled‐data control

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Event-Triggered H∞ Load Frequency Control for Multi-Area Nonlinear Power Systems Based on Non-Fragile Proportional Integral Control Strategy

Cited by 74 publications

References 36 publications

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

On event‐triggered guaranteed cost control for discrete‐time semi‐Markovian neural networks having communication delays and dual‐terminal probabilistic faults

Nonfragile consensus of nonlinear multiagent systems with intrinsic delays via aperiodic memory sampled‐data control

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Event-Triggered H_∞ Load Frequency Control for Multi-Area Nonlinear Power Systems Based on Non-Fragile Proportional Integral Control Strategy