Syncretic Application of IBAS-BP Algorithm for Monitoring Equipment Online in Power System

Liu, Zhengguang; Tan, Qinyue; Zhou, Yayun; Xu, Hengshan

doi:10.1109/access.2021.3055247

Cited by 21 publications

(9 citation statements)

References 33 publications

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“…The BP neural network is a neural network form that is currently widely used. It is used in a variety of scenarios because of its strong non-linear mapping and self-adaptive capabilities [24,25]. The basic structure is shown in Figure 2.…”

Section: Bp Neural Network Algorithmmentioning

confidence: 99%

An improved evaluation model for supplier selection based on particle swarm optimisation‐back propagation neural network

Wang

et al. 2022

IET Collab Intel Manufact

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With the trend of supply chain globalisation, competition among enterprises is becoming more intense. Enterprises urgently need to improve their core competitiveness, and the enhancement of the competencies can depend on technologies services and the quality of suppliers. Since external factors are less controllable, this study starts with the quality of suppliers and proposes a supplier evaluation method that combines particle swarm optimisation with neural network algorithm to maximise the interests of enterprises. The particle swarm algorithm to lock the approximate location of the global optimum is first employed. Based on this, we establish an evaluation model of suppliers to train for the minimum errors between the desired and predicted values by constructing a back propagation (BP) neural network. Finally, the output results of the proposed method is compared with the BP neural network without the particle swarms optimisation. The proposed model is less empirically sensitive to the initialisation and can quickly converge to the local optimums, which overcomes the shortage of traditional neural networks and is more applicable to supplier evaluation.

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Section: Bp Neural Network Algorithmmentioning

confidence: 99%

An improved evaluation model for supplier selection based on particle swarm optimisation‐back propagation neural network

Wang

et al. 2022

IET Collab Intel Manufact

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“…However, due to the mismatch of output and load of PV power generation and the intermittency of PV output, the distribution network has limited capacity to absorb PV energy, and ESS that can smooth power fluctuation have become the key to building a new type of power system [4], [5]. Therefore, how to optimize the configuration of energy storage systems in distribution networks to better meet the needs of the power grid, smooth the power fluctuation of PV in the grid, remains an important research topic [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Configuration of ESS in Distribution Network Considering Generation of PV-Load Time Series Scenarios

Xiao,

Shi,

et al. 2024

IEEE Access

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With the accelerating application demand for new energy-based power systems, a energy storage system (ESS) optimization configuration method is proposed to fully consider the load demand and the uncertainty of photovoltaic (PV) output and improve the adaptability of the ESS configuration scale. Firstly, the Gaussian kernel function of the kernel method is used to map the temporal variation characteristics of the PV output scenario to establish a comprehensive evaluation index scenario for the uncertainty and temporal correlation of PV-Load. The iterative self-organizing data analysis algorithm (ISODATA) is used to optimize clustering of the comprehensive evaluation index scenario to generate PV-Load typical scenarios. A distribution network ESS configuration model is constructed with the objectives of minimizing voltage fluctuation indicator, line loss rate, and minimizing ESS investment cost. To solve the high-dimensional multi-objective functions in the model, an non-dominated sorting genetic algorithm-Ⅲ (NSGA-Ⅲ) was employed, and the optimal solution is selected by using the entropy weight method (EWM). Finally, the proposed method and model are analyzed and verified by case simulation, and the results show that the proposed model and method can comprehensively consider the actual operating characteristics of PV and load, and can effectively formulate the ESS configuration scale and operation strategy in the distribution network.

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“…In addition, real-time detection, identification, and diagnosis of faults in safetycritical systems that need to ensure human safety, environmental health, and economic security are particularly important [12]. This includes medical and surgical equipment, aviation and air traffic control, hazardous and toxic chemical processes, large-scale power systems [13][14][15][16][17][18][19] and transmission lines [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Yan

Jing

et al. 2023

Processes

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In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

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Syncretic Application of IBAS-BP Algorithm for Monitoring Equipment Online in Power System

Cited by 21 publications

References 33 publications

An improved evaluation model for supplier selection based on particle swarm optimisation‐back propagation neural network

An improved evaluation model for supplier selection based on particle swarm optimisation‐back propagation neural network

Optimal Configuration of ESS in Distribution Network Considering Generation of PV-Load Time Series Scenarios

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

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