An Evaluation System for HVDC Protection Systems by a Novel Indicator Framework and a Self-Learning Combination Method

Ge, Leijiao; Li, Yuanliang; Zhu, Xiaoyan; Zhou, Yue; Wang, Ting; Yan, Jun

doi:10.1109/access.2020.3017502

Cited by 8 publications

(5 citation statements)

References 40 publications

(50 reference statements)

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“…In recent years, the SDA model has become a mainstream economic analysis tool in the field of input-output technology and has been widely used in economic analysis of economic growth [7], trade [8], labour [9], prices [10], energy [11], and environmental protection [9]. Much classical work has been done on the theoretical foundations of the SDA model [12]. However, there are some problems with the SDA model, mainly the uniqueness of the measurement results, the comparability of factor weights, and the decomposition method of interaction effects.…”

Section: Introductionmentioning

confidence: 99%

Economic Development Analysis of China’s Coastal Industries Based on Deep Learning and SDA Model Weighting

2022

Wireless Communications and Mobile Computing

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China’s coast is the main spatial carrier and important geographical unit for future industrial development in China. We propose a weighted average decomposition method based on three basic problems in the theory of SDA model decomposition method, investigate the relevant properties of this method, and prove that two decomposition methods widely used in the literature—bipolar decomposition method and midpoint power decomposition method—are the approximate solutions of this decomposition method. Based on the idea of the Wasserstein distance algorithm for machine learning, the Wasserstein distance algorithm and its solution are improved by using the matrix expansion Sinkhorn algorithm and the entropy regularization constraint method, and the industry copolymerization index is constructed through hypothesis testing and Monte Carlo simulation to measure the level of industry copolymerization in coastal China. The results show that the level of industry copolymerisation within the same quantile industry is greater than that between industries across quantile industries in China’s coasts.

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Section: Introductionmentioning

confidence: 99%

Economic Development Analysis of China’s Coastal Industries Based on Deep Learning and SDA Model Weighting

2022

Wireless Communications and Mobile Computing

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“…Self-learning weight model based on the relative rate of change of weights Different types and values of indicators within an industrial user safety risk assessment indicator system exist, and the size of the weights assigned to different types of indicators reflects the amount of information that different types of indicators can reflect, which requires high dynamic adaptability of the weighting methods. In this regard, a self-learning integrated weighting method that uses the relative rate of change of weights of different weighting methods as a self-learning criterion and integrates the characteristics of different assignment methods is proposed here [19]. This method overcomes the shortcomings of the linear weighted combination weighting method, which is not conducive to differentiating between good and bad information in the subjective and objective weights, and the product combination weighting method, which is prone to the "multiplication effect" when the weights of indicators are not evenly distributed, making the final combination weights more reasonable by adaptively integrating the subjec-tive and objective ideas.…”

Section: 21mentioning

confidence: 99%

“…Meanwhile, the problem of information redundancy may exist between indicators, and the independence weighting method is used to eliminate the lack of information redundancy between indicators. The optimally combined weight vector W new is obtained by the self-learning weight model based on the relative rate of change of the weights by the above three weighting methods, as shown in Equation (19).…”

Section: 23mentioning

confidence: 99%

Multi‐round load control method considering the safe electricity consumption for industrial users based on situational awareness

Chen,

Xu,

Pan

et al. 2023

IET Generation Trans & Dist

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This paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here.

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“…The self-learning interval analytic hierarchical process in [28] is adopted to calculate the weights. N experienced engineers are invited to score the importance intervals of different loads to obtain the interval weight of each load by the interval analytic hierarchical process.…”

Section: B Evaluating Indicatorsmentioning

confidence: 99%

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

Yan

et al. 2021

Journal of Modern Power Systems and Clean Energy

Self Cite

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To improve energy efficiency and protect the environment, the integrated energy system (IES) becomes a significant direction of energy structure adjustment. This paper innovatively proposes a wavelet neural network (WNN) model optimized by the improved particle swarm optimization (IPSO) and chaos optimization algorithm (COA) for short-term load prediction of IES. The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models. First, the Pearson correlation coefficient is employed to select the key influencing factors of load prediction. Then, the traditional particle swarm optimization (PSO) is improved by the dynamic particle inertia weight. To jump out of the local optimum, the COA is employed to search for individual optimal particles in IPSO. In the iteration, the parameters of WNN are continually optimized by IPSO-COA. Meanwhile, the feedback link is added to the proposed model, where the output error is adopted to modify the prediction results. Finally, the proposed model is employed for load prediction. The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network (ANN), WNN, and PSO-WNN.

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An Evaluation System for HVDC Protection Systems by a Novel Indicator Framework and a Self-Learning Combination Method

Cited by 8 publications

References 40 publications

Economic Development Analysis of China’s Coastal Industries Based on Deep Learning and SDA Model Weighting

Economic Development Analysis of China’s Coastal Industries Based on Deep Learning and SDA Model Weighting

Multi‐round load control method considering the safe electricity consumption for industrial users based on situational awareness

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

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