Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty

Yin, Sihua; Yang, Haidong; Xu, Kangkang; Zhang, Shaqing; Liu, Guosheng

doi:10.1016/j.apenergy.2021.118314

Cited by 22 publications

(3 citation statements)

References 42 publications

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“…The significance of this study lies in its potential to improve energy monitoring [53] systems with a real-time algorithm to mitigate risks promptly. Real-time experiments validate that the proposed method is effective and has good application prospects [46]. For this reason, article [54] presented a simplified algorithm implementation in the programmable logic controller, which resulted in sufficient calculation precision compared to the measurement.…”

Section: Introductionmentioning

confidence: 85%

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Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Plienis,

Deveikis,

Jonaitis

et al. 2023

Energies

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The decline in power quality within electrical networks is adversely impacting the energy efficiency and safety of transmission elements. The growing prevalence of power electronics has elevated harmonic levels in the grid to an extent where their significance cannot be overlooked. Additionally, the increasing integration of renewable energy sources introduces heightened fluctuations, rendering the prediction and simulation of working modes more challenging. This paper presents an improved algorithm for calculating power transformer losses attributed to harmonics, with a comprehensive validation against simulation results obtained from the Power Factory application and real-world measurements. The advantages of the algorithm are that all evaluations are performed in real-time based on single-point measurements, and the algorithm was easy to implement in a Programmable Logic Controller (PLC). This allows us to receive the exchange of information to energy monitoring systems (EMSs) or with Power factor Correction Units (PFCUs) and control it. To facilitate a more intuitive understanding and visualization of potential hazardous scenarios related to resonance, an extra Dijkstra algorithm was implemented. This augmentation enables the identification of conditions, wherein certain branches exhibit lower resistance than the grid connection point, indicating a heightened risk of resonance and the presence of highly distorted currents. Recognizing that monitoring alone does not inherently contribute to increased energy efficiency, the algorithm was further expanded to assess transformer losses across a spectrum of Power Factory Correction Units power levels. Additionally, a command from a PLC to a PFCU can now be initiated to change the capacitance level and near-resonance working mode. These advancements collectively contribute to a more robust and versatile methodology for evaluating power transformer losses, offering enhanced accuracy and the ability to visualize potentially critical resonance scenarios.

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Section: Introductionmentioning

confidence: 85%

“…Monitoring alone is insufficient, and it is necessary to detect abnormal energy consumption data in real-time [46]. In article [47], real-time monitoring was presented ac-cording to IEEE Std C57.110-2018 [48], which evaluates the increase in the temperature of the transformer due to harmonic distortion.…”

Section: Introductionmentioning

confidence: 99%

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Plienis,

Deveikis,

Jonaitis

et al. 2023

Energies

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show abstract

“…According to the production operation data, equipment maintenance plan, historical energy consumption and other data of the same period of traditional Chinese medicine production, the energy supply and demand plan of the month and quarter was formulated. According to the production plan, the tracking information is implemented to guide the enterprise to rationally carry out energy production and distribution [3] .…”

Section: Energy Planmentioning

confidence: 99%

Design and Research of Intelligent Energy Monitoring System for Traditional Chinese Medicine Production Enterprises

Cui

2023

Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2023, Sept

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Energy consumption is high in traditional Chinese medicine production enterprises. The production of traditional Chinese medicine includes the extraction of traditional Chinese medicine components and preparations. The extraction process can be divided into several unit operations, such as filtration, evaporation, crystallization, drying, etc. The preparation process can be divided into crushing, sieving, granulation, tabletting, etc. These processes require energy consumption. Energy consumption is high in traditional Chinese medicine production enterprises. The production of traditional Chinese medicine includes the extraction of traditional Chinese medicine components and preparations. The extraction process can be divided into several unit operations, such as filtration, evaporation, crystallization, drying, etc. The preparation process can be divided into crushing, sieving, granulation, tabletting, etc. These processes require energy consumption.

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A Monitoring Model for Abnormal Electricity Consumption Based on K-Means++ Clustering and Improved K-Nearest Neighbor Algorithm

Yang,

Que,

Liu

et al. 2024

Smart Grids and Energy

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Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty

Cited by 22 publications

References 42 publications

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Design and Research of Intelligent Energy Monitoring System for Traditional Chinese Medicine Production Enterprises

A Monitoring Model for Abnormal Electricity Consumption Based on K-Means++ Clustering and Improved K-Nearest Neighbor Algorithm

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