Application of EOS-ELM With Binary Jaya-Based Feature Selection to Real-Time Transient Stability Assessment Using PMU Data

Li, Yahui; Yang, Zhen

doi:10.1109/access.2017.2765626

Cited by 127 publications

(93 citation statements)

References 44 publications

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“…Recent research has demonstrated that the interconnection of large-scale power systems produces many well-known economic, social and environmental benefits, but on the other hand, it also significantly exaggerates the potential impact of a large disturbance [4,5]. In this sense, the uncertainties in power system operation significantly increase under the growing penetration of new components such as distributed generations [6,7,8] and plug-in electric vehicles [9,10], which inevitably leads to more serious consequences resulted from the loss of stability [11]. In addition, as a typical cyber-physical system, the increasing cyber-attacks, natural disasters and the reliance on control and communication are resulting in new sources and propagation paths of cascading failures in power systems [12,13].…”

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confidence: 99%

Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering

2019

IEEE Access

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As the last resort of emergency control, controlled islanding is an effective means of preventing fault-propagation and a system-wide blackout. However, conventional AC transmission lines are unavailable to be employed for power exchange between these islands. To make full use of the DC power modulation capability of VSC-HVDC links, a new controlled islanding model is put forward for an AC/VSC-HVDC hybrid grid to minimize the composite power-flow disruption, in which the DC-terminals belonging to a VSC-HVDC link are placed in different islands. To solve this model, a semi-supervised spectral clustering-based approach is proposed by transforming the problem into a weighted undirected graph segmentation problem. The novelty of our work is to find an optimal islanding solution in real time such that the power exchanges between islands are implemented via a VSC-HVDC link to reduce the generation-load imbalance. The simulation results on the IEEE 39-bus system and a real-world system verify the effectiveness and superiority of the proposed approach.

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confidence: 99%

Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering

2019

IEEE Access

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“…A simple OPF model is employed in this paper, and traditional constraints are used. A more practical OPF model will be explored in the future to consider real-world demands, such as dynamic security [41,59], and the reactive power and voltage magnitude [60]. Aimed at security problems in the power system, two safety-related functions are contained in the OPF model.…”

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confidence: 99%

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

2018

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To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Paretooptimal solutions. The primary contribution of the proposal is the innovative application of manyobjective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.Keywords: optimal power flow; optimal operation; power systems; multi-objective optimization; knee point-driven evolutionary algorithm; decision analysis; best compromise solutions Recent research suggests that multi-objective evolutionary algorithms (MOEAs) are promising tools for addressing various challenging optimization tasks in engineering fields [29][30][31][32]. In order to optimal distributed generation planning, a MOEA is employed in [29]. Reference [30] reviews the most representative MOEAs that have been reported, and MOEA has developed as an effective method to solve such an optimization problem. In [31], MOEA is employed for planning overtime of software engineers. The layout of wind farms is optimized via MOEA in [32]. In particular, MOEAs can be also applied to solve the OPF issue [23][24][25][26][27][28]. Unfortunately, the MOPF can only cope with the optimization issue with two to three objectives, which, to a certain extent, limits the practicality of this type of methods. In addition, many-objective optimization problems (MaOPs), considering four or more objective functions in the OPF problem [33][34][35][36], are commonly existed phenomenon in the practice of real-world power system operation [33]. In [34], a specially tailored MOEA is presented for tackling the current large-scale MaOPs. Another MOEA based on adaptive search strategy is presented for coping with MaOPs in [35]. In [36], six different evolutionary algorithms (EAs) are tested, and the results prove that MOEAs exhibit their own capabilities in dealing with different MaOPs. For this reason, MaOPs have recently gained a great deal of attention as most existing MOEAs are inadequate for solving OPF problems with four or more objectives, and it has become a hotspot to enhance the ability of MOEAs for addressing MaOPs issues [37][38][39]. However, manyobjective OPF (MaOPF) is quite challenging for solving sinc...

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“…The key idea of the Jaya algorithm is that a solution must move away from the worst solution and move to the best solution [35]. If Xj,k,i is the value of variable j for candidate k at iteration i, then Xj,k,i is calculated as ' , , ,…”

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confidence: 99%

Incorporating Demand Response of Electric Vehicles in Scheduling of Isolated Microgrids With Renewables Using a Bi-Level Programming Approach

2019

IEEE Access

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In this work, a novel optimal scheduling approach is proposed for isolated microgrids (MGs) with renewable generations by incorporating demand response of electric vehicles (EVs). First, a bi-level programming-based MG scheduling model is proposed under real-time pricing environments, where the upper-and lower-levels seek to minimize the MG net operating cost and the EV charging cost. Second, a hybrid solution algorithm called JAYA-interior point method is put forward to solve the model. And finally, the simulation results demonstrate that incorporating demand response of electric vehicles is able to guide EV users to actively participate in MG scheduling and achieve the peak load shaving, which offers a fundamental way to balance the interests between MG and EV users.INDEX TERMS Microgrid, optimal scheduling, demand response, electric vehicles, renewable generations, bi-level programming

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Application of EOS-ELM With Binary Jaya-Based Feature Selection to Real-Time Transient Stability Assessment Using PMU Data

Cited by 127 publications

References 44 publications

Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering

Controlled Islanding for a Hybrid AC/DC Grid With VSC-HVDC Using Semi-Supervised Spectral Clustering

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

Incorporating Demand Response of Electric Vehicles in Scheduling of Isolated Microgrids With Renewables Using a Bi-Level Programming Approach

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