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This paper investigates the nature of the low saliency ratio of large permanent magnet generators with fractional-slot concentrated windings (FSCWs). A saliency ratio of at least 1.2 is typically required to enable sensorless control of large generators—a value naturally achieved in integer slot winding topologies but absent in FSCW surface-mounted permanent magnet machines reported in the literature. The low saliency ratio in FSCW designs is attributed to larger teeth, which reduce magnetic saturation and increase d-axis inductance. This work explores methods to enhance the saliency ratio of FSCW machines for offshore wind turbines, facilitating sensorless rotor position estimation. The proposed approaches are categorized into two groups: (1) those that preserve the conventional machine geometry with minimal modification to the magnetic circuit and (2) those involving magnetic circuit alterations. The results show that significant improvement in saliency ratio is only achievable through magnetic circuit modifications, such as rotor shoes, albeit with some performance trade-offs. A multi-objective genetic algorithm is employed to design two optimized 3 MW FSCW machine topologies, achieving saliency ratios of 1.15 and 1.2 with minimal performance loss. Compared to a 3 MW FSCW baseline, the optimized designs show stator power reductions of 3.40% and 6.16% for saliency ratios of 1.15 and 1.2, respectively.

This paper investigates the nature of the low saliency ratio of large permanent magnet generators with fractional-slot concentrated windings (FSCWs). A saliency ratio of at least 1.2 is typically required to enable sensorless control of large generators—a value naturally achieved in integer slot winding topologies but absent in FSCW surface-mounted permanent magnet machines reported in the literature. The low saliency ratio in FSCW designs is attributed to larger teeth, which reduce magnetic saturation and increase d-axis inductance. This work explores methods to enhance the saliency ratio of FSCW machines for offshore wind turbines, facilitating sensorless rotor position estimation. The proposed approaches are categorized into two groups: (1) those that preserve the conventional machine geometry with minimal modification to the magnetic circuit and (2) those involving magnetic circuit alterations. The results show that significant improvement in saliency ratio is only achievable through magnetic circuit modifications, such as rotor shoes, albeit with some performance trade-offs. A multi-objective genetic algorithm is employed to design two optimized 3 MW FSCW machine topologies, achieving saliency ratios of 1.15 and 1.2 with minimal performance loss. Compared to a 3 MW FSCW baseline, the optimized designs show stator power reductions of 3.40% and 6.16% for saliency ratios of 1.15 and 1.2, respectively.

Decarbonising heat is critical for achieving net zero goals. This study investigates the deployment of heat pumps for decarbonising domestic heating in the United Kingdom (UK), focusing on a local analysis in the West Midlands and North West regions. Through detailed data modelling, including weather patterns, housing characteristics, and carbon intensity data, the study quantifies the potential carbon dioxide (CO2) emissions reductions associated with air source heat pump adoption compared to conventional gas boilers. In this study, hourly temperature data for 2022 were examined for six local authorities: Birmingham, Warwick, Shropshire, Manchester, Oldham, and West Lancashire. Additionally, half-hourly carbon intensity data for 2022 were used for the two wider regions, the West Midlands and the North West of England. Results demonstrated that the North West region stands out with the highest percentage CO2 reductions due to the relatively low carbon intensity associated with the electricity grid, reaching up to 33% for an uptake of 40% of air source heat pumps. Moreover, regions with a high prevalence of detached housing, such as Shropshire and West Lancashire, show promise for emissions reductions and require continued monitoring and support for heat pump adoption. Despite limitations in modelling techniques and data sources, this study provides valuable insights for policymakers and strategic planners, guiding efforts to combat climate change and promote environmental sustainability in the UK.

Decarbonising heat is critical for achieving net zero goals. This study investigates the deployment of heat pumps for decarbonising domestic heating in the United Kingdom (UK), focusing on a local analysis in the West Midlands and North West regions. Through detailed data modelling, including weather patterns, housing characteristics, and carbon intensity data, the study quantifies the potential carbon dioxide (CO2) emissions reductions associated with air source heat pump adoption compared to conventional gas boilers. In this study, hourly temperature data for 2022 were examined for six local authorities: Birmingham, Warwick, Shropshire, Manchester, Oldham, and West Lancashire. Additionally, half-hourly carbon intensity data for 2022 were used for the two wider regions, the West Midlands and the North West of England. Results demonstrated that the North West region stands out with the highest percentage CO2 reductions due to the relatively low carbon intensity associated with the electricity grid, reaching up to 33% for an uptake of 40% of air source heat pumps. Moreover, regions with a high prevalence of detached housing, such as Shropshire and West Lancashire, show promise for emissions reductions and require continued monitoring and support for heat pump adoption. Despite limitations in modelling techniques and data sources, this study provides valuable insights for policymakers and strategic planners, guiding efforts to combat climate change and promote environmental sustainability in the UK.

Within smart homes, consumers could generate a vast amount of data that, if analyzed effectively, can improve the convenience of consumers and reduce energy consumption. In this paper, we propose to organize household appliance data into a knowledge graph by using the consumers’ usage habits, the periods of usage, and the location information for graph modeling. A framework, ‘DARK’ (Device Action Recommendation with Knowledge graphs), is proposed that includes three parts for enabling demand response. Firstly, a household device action recommendation algorithm is proposed that improves the knowledge graph attention algorithm to make accurate household appliance recommendations. Secondly, graph interpretable characteristics are developed in the DARK using trained graph embeddings. Finally, with the recommendation expectation, the consumers’ comfort level and appliances’ average power load are modeled as a multi-objective optimization problem in the DARK to participate in demand response. The results demonstrate that the proposed system can generate appliances’ action recommendations with an average of 93.4% accuracy and reduce power load by up to 20% while providing reasonable interpretations for the device action recommendation results on the customized UK-DALE dataset.

Within smart homes, consumers could generate a vast amount of data that, if analyzed effectively, can improve the convenience of consumers and reduce energy consumption. In this paper, we propose to organize household appliance data into a knowledge graph by using the consumers’ usage habits, the periods of usage, and the location information for graph modeling. A framework, ‘DARK’ (Device Action Recommendation with Knowledge graphs), is proposed that includes three parts for enabling demand response. Firstly, a household device action recommendation algorithm is proposed that improves the knowledge graph attention algorithm to make accurate household appliance recommendations. Secondly, graph interpretable characteristics are developed in the DARK using trained graph embeddings. Finally, with the recommendation expectation, the consumers’ comfort level and appliances’ average power load are modeled as a multi-objective optimization problem in the DARK to participate in demand response. The results demonstrate that the proposed system can generate appliances’ action recommendations with an average of 93.4% accuracy and reduce power load by up to 20% while providing reasonable interpretations for the device action recommendation results on the customized UK-DALE dataset.

Variable-Speed Hydropower Plants (VSHP) are becoming more promising for stabilising power grids with the increasing integration of renewable energy sources. This research focuses on improving fault ride-through capabilities and delivering efficient ancillary services for VSHPs to support the grid by developing a comprehensive control strategy. The control system proposed integrates a machine-side controller, a Frequency Support Controller (FSC), a Virtual Synchronous Machine (VSM), a Vector Current Controller (VCC) for the grid-side converter, a turbine governor for regulating turbine speed, and a DC-link controller. PID with an anti-windup scheme and a Model Predictive Controller (MPC) were employed for the turbine governor. The MPC turbine governor results demonstrate the potential of advanced control methods for enhanced performance of the VSHP. A benchmarking between the MPC and the PID governor was made. The benchmarking results have reported that the MPC can achieve reference tracking improvements up to 99.42%. Tests on a diverse set of grid scenarios were conducted, and the graphical results showed significant improvements in mitigating the frequency drops through the effective governor response. The synthetic inertia provision is swift, completing within seconds of a frequency drop. Compared to the fixed-speed approach, the VSHP improves the grid’s overall stability by minimising frequency dipping and achieving steady-state recovery remarkably faster. The fixed-speed approach only begins to recover minutes after the VSHP reaches the settling time. By effectively providing critical ancillary services such as frequency support, synthetic inertia, and smooth fault ride-through capability, the VSHP can become a transformative solution for future power grids, which are estimated to be more reliant on renewable energy sources.

Variable-Speed Hydropower Plants (VSHP) are becoming more promising for stabilising power grids with the increasing integration of renewable energy sources. This research focuses on improving fault ride-through capabilities and delivering efficient ancillary services for VSHPs to support the grid by developing a comprehensive control strategy. The control system proposed integrates a machine-side controller, a Frequency Support Controller (FSC), a Virtual Synchronous Machine (VSM), a Vector Current Controller (VCC) for the grid-side converter, a turbine governor for regulating turbine speed, and a DC-link controller. PID with an anti-windup scheme and a Model Predictive Controller (MPC) were employed for the turbine governor. The MPC turbine governor results demonstrate the potential of advanced control methods for enhanced performance of the VSHP. A benchmarking between the MPC and the PID governor was made. The benchmarking results have reported that the MPC can achieve reference tracking improvements up to 99.42%. Tests on a diverse set of grid scenarios were conducted, and the graphical results showed significant improvements in mitigating the frequency drops through the effective governor response. The synthetic inertia provision is swift, completing within seconds of a frequency drop. Compared to the fixed-speed approach, the VSHP improves the grid’s overall stability by minimising frequency dipping and achieving steady-state recovery remarkably faster. The fixed-speed approach only begins to recover minutes after the VSHP reaches the settling time. By effectively providing critical ancillary services such as frequency support, synthetic inertia, and smooth fault ride-through capability, the VSHP can become a transformative solution for future power grids, which are estimated to be more reliant on renewable energy sources.