• Energy Research
• Open Access close
• Restricted close
• CN close
• IEEE Access close

Improving efficiency, reducing expenses, and ensuring sustainability in today’s competitive market is made possible by optimising the performance of the food supply chain (FSC). A strong decision-making framework is required to overcome obstacles including high transportation and production costs, uneven quality, and the impact on the environment. To solve these problems, this paper presents an all-inclusive multi-criteria decision making (MCDM) framework that uses enhanced relative utility and nonlinear standardisation (ERUNS) to rank alternatives, Logarithmic percentage change-driven objective weighing (LOPCOW) to determine objective weights, and subjective weight assignment by ratio analysis (SWARA) to determine subjective criterion weights. LOPCOW offers objective weights grounded in facts and indifference thresholds, whereas SWARA records decision-makers (DMs) preferences in weighing factors like sustainability and cost efficiency. The ERUNS methodology then uses utility degree assessments and matrix standardization to rank and evaluate alternatives. An improved method of assessment is to make use of intuitionistic fuzzy sets (IFS), which deal with ambiguity by adding membership, non-membership, and hesitation levels. By merging subjective and objective data, this integrated method maximizes FSC performance, provides useful insights for enhancing overall efficiency and sustainability, and offers a viable answer to contemporary difficulties. It also offers helpful insights for improving overall efficiency and sustainability, and it provides a practical response to the challenges that are now being faced.

Improving efficiency, reducing expenses, and ensuring sustainability in today’s competitive market is made possible by optimising the performance of the food supply chain (FSC). A strong decision-making framework is required to overcome obstacles including high transportation and production costs, uneven quality, and the impact on the environment. To solve these problems, this paper presents an all-inclusive multi-criteria decision making (MCDM) framework that uses enhanced relative utility and nonlinear standardisation (ERUNS) to rank alternatives, Logarithmic percentage change-driven objective weighing (LOPCOW) to determine objective weights, and subjective weight assignment by ratio analysis (SWARA) to determine subjective criterion weights. LOPCOW offers objective weights grounded in facts and indifference thresholds, whereas SWARA records decision-makers (DMs) preferences in weighing factors like sustainability and cost efficiency. The ERUNS methodology then uses utility degree assessments and matrix standardization to rank and evaluate alternatives. An improved method of assessment is to make use of intuitionistic fuzzy sets (IFS), which deal with ambiguity by adding membership, non-membership, and hesitation levels. By merging subjective and objective data, this integrated method maximizes FSC performance, provides useful insights for enhancing overall efficiency and sustainability, and offers a viable answer to contemporary difficulties. It also offers helpful insights for improving overall efficiency and sustainability, and it provides a practical response to the challenges that are now being faced.

Advanced airborne equipment liquid cooling system presents the development trend of multiple pumps in parallel, centralised heat dissipation and high efficiency circulation, but this type of system has the characteristics of many variable parameters and strong non-linearity, which makes the system operation and control become extraordinarily complex, posing a serious challenge to the system performance. Although some progress has been made in the current optimization of liquid cooling system performance, there are still gaps such as insufficient consideration of the multi-objective optimization problem and limited optimization effect. To fill this research gap, this study takes minimum specific energy consumption, minimum flow deviation and minimum impeller load as the optimization objectives, establishment of multi-objective optimization model for liquid-cooled power unit, the optimal solution set of the Pareto model is solved using the multi-objective grey wolf optimization algorithm (MOGWO), and the optimal solution set is substituted for the optimal solution, and the evaluation indexes are designed in combination with the actual working conditions to obtain the optimal schedule. By example analysis, the optimized system under variable operating conditions, the impeller load and specific energy consumption decreased by 16.88% and 3.64% respectively, while the rise in flow deviation was controlled within 0.76%. The newly established optimisation model effectively mitigates potential safety hazards during system operation. Finally, by combining all the indicators, three scheduling options are provided under variable operating conditions, focusing on reliability and economy, overall balance and high-precision flow, respectively, in order to adapt to the diversified requirements of different operating environments.

Advanced airborne equipment liquid cooling system presents the development trend of multiple pumps in parallel, centralised heat dissipation and high efficiency circulation, but this type of system has the characteristics of many variable parameters and strong non-linearity, which makes the system operation and control become extraordinarily complex, posing a serious challenge to the system performance. Although some progress has been made in the current optimization of liquid cooling system performance, there are still gaps such as insufficient consideration of the multi-objective optimization problem and limited optimization effect. To fill this research gap, this study takes minimum specific energy consumption, minimum flow deviation and minimum impeller load as the optimization objectives, establishment of multi-objective optimization model for liquid-cooled power unit, the optimal solution set of the Pareto model is solved using the multi-objective grey wolf optimization algorithm (MOGWO), and the optimal solution set is substituted for the optimal solution, and the evaluation indexes are designed in combination with the actual working conditions to obtain the optimal schedule. By example analysis, the optimized system under variable operating conditions, the impeller load and specific energy consumption decreased by 16.88% and 3.64% respectively, while the rise in flow deviation was controlled within 0.76%. The newly established optimisation model effectively mitigates potential safety hazards during system operation. Finally, by combining all the indicators, three scheduling options are provided under variable operating conditions, focusing on reliability and economy, overall balance and high-precision flow, respectively, in order to adapt to the diversified requirements of different operating environments.

To identify the DC bias state of transformers, a DC bias state identification method of transformer based on wavelet singular entropy is proposed in this paper. The vibration principle of transformers under DC bias has been analyzed. By combining continuous wavelet transform, singular value decomposition, and information entropy, the analysis method of wavelet singular entropy is proposed. The vibration signal of the transformer before and after DC bias is transformed by continuous wavelet transform, and then the wavelet time-frequency diagram is compared and analyzed. Furthermore, the DC bias state of the transformer is identified by wavelet singular entropy of vibration signal. The wavelet singular entropy under different states is in different numerical ranges, and the wavelet singular entropy of vibration signal under DC bias is significantly greater than that under no DC bias. The wavelet singular entropy effectively reflects the DC bias state of transformers. Then the proposed method is applied to a 500 kV transformer and a 220 kV transformer in the China Southern Power Grid. The results show that the proposed method can accurately and effectively identify the DC bias state of transformers.

To identify the DC bias state of transformers, a DC bias state identification method of transformer based on wavelet singular entropy is proposed in this paper. The vibration principle of transformers under DC bias has been analyzed. By combining continuous wavelet transform, singular value decomposition, and information entropy, the analysis method of wavelet singular entropy is proposed. The vibration signal of the transformer before and after DC bias is transformed by continuous wavelet transform, and then the wavelet time-frequency diagram is compared and analyzed. Furthermore, the DC bias state of the transformer is identified by wavelet singular entropy of vibration signal. The wavelet singular entropy under different states is in different numerical ranges, and the wavelet singular entropy of vibration signal under DC bias is significantly greater than that under no DC bias. The wavelet singular entropy effectively reflects the DC bias state of transformers. Then the proposed method is applied to a 500 kV transformer and a 220 kV transformer in the China Southern Power Grid. The results show that the proposed method can accurately and effectively identify the DC bias state of transformers.

H-polarization, along with E-polarization, indicates the lateral variations of the Earth conductivity, which influence the induced electric field distribution. The coast effect is a typical H-polarization phenomenon that causes local geoelectric field enhancement in coastal areas and significantly affects the geomagnetically induced currents (GIC) distributions in power grids. The influences of H-polarization on geoelectric fields and GIC form the basis for further research on power grid disasters resulting from magnetic storms. In this paper, block and thin-shell models of the coast effect are established, and the electric field distribution in the case of H-polarization is calculated using the finite element method. The results demonstrate the effects of the conductivity, frequency, and distance from the interface of a different conductivity on electric field distortion. Additionally, the relationship between H-polarization and GIC in power grids is investigated, demonstrating that the GIC can be influenced within 100 km in the H-polarization case. The methods and results provide a theoretical basis for GIC risk assessment and development of a control strategy for the power grid.

H-polarization, along with E-polarization, indicates the lateral variations of the Earth conductivity, which influence the induced electric field distribution. The coast effect is a typical H-polarization phenomenon that causes local geoelectric field enhancement in coastal areas and significantly affects the geomagnetically induced currents (GIC) distributions in power grids. The influences of H-polarization on geoelectric fields and GIC form the basis for further research on power grid disasters resulting from magnetic storms. In this paper, block and thin-shell models of the coast effect are established, and the electric field distribution in the case of H-polarization is calculated using the finite element method. The results demonstrate the effects of the conductivity, frequency, and distance from the interface of a different conductivity on electric field distortion. Additionally, the relationship between H-polarization and GIC in power grids is investigated, demonstrating that the GIC can be influenced within 100 km in the H-polarization case. The methods and results provide a theoretical basis for GIC risk assessment and development of a control strategy for the power grid.