Currently the accepted technology for large wind turbines is the doubly-fed induction generator (DFIG). This technology is popular primarily due to the reduced cost of the partially rated power electronic converter. On the negative side is the fact that the generator requires brushes and slip rings which require regular maintainance.An alternative scheme is based on the brushless doubly-fed reluctance machine (BDFRM) which also has the cost benefit of a partially rated power converter but as its name implies does not require brushes and slip rings.The BDFRM has not been used for a wind power application. This project will experimentally examine its performance for a wind power application. There are a number of different approaches to the control of a BDFRM. The project will examine the use of Direct Power Control (DPC). This control approach will include sensorless operation and machine parameter independence. With the proliferation of wind power generation the issue of power system stabilty is of great concern. It is important to examine the fault-ride-through (FRT) capabilty of any generation system. This project will examine the FRT capability of the BDFRM and compare this to that of the DFIG. This will require that special grid fault emulation equipment is included in the laboratory test rig.

Due to climate change, extreme meteorological phenomena such as heavy precipitation, extreme temperature, strong winds and sea level rise, seem to be growing more severe and frequent, but the actual estimation of this evolution in extreme weather events remains subject to large uncertainty. For example, in December 2015 when storm Desmond hit the UK, several communities were badly affected by water level rises. Rainfall in this storm crept up to new record levels and provided us with critical lessons on how we can better prepare to withstand similar hazards. However, these lessons learned are in hindsight. When looking at the occurrence probability of such an extreme event that out-spans the range of previously recorded data, Extreme Value Theory (EVT) is the most appropriate branch of probability theory to be implemented as risk assessment and forecasting have a strong probabilistic foundation. In many operational settings, risk mitigation measures are required to balance costs with safety. For example, in insuring systems and infrastructure against extreme events, it might not be enough to sift through extreme record events that emerge from historical data, but it would also be nonsensical to channel most resources into a safety system so robust that it would spectacularly exceed the actual risk being protected against. EVT offers an appropriate statistical toolkit for forecasting extreme outcomes to a high degree of accuracy, thus providing critical evidence for assessing risk more accurately in preparing a proportionate response. There are varying layers of complexity in EVT enveloped in the recently introduced class of multivariate max-stable processes. These are promising models for the structural components that capture how extremes from multiple phenomena (hence the prefix multivariate) are likely to manifest themselves jointly across a certain region over time (hence the so-called space-time processes, also termed random fields). Real life applications abound in the multivariate infinite-dimensional max-stable processes frameworks. For example, the Fukushima nuclear disaster in 2011 was ignited by the combination of a huge earthquake followed by a tsunami. The main goal of this research proposal is to develop a general theory for multivariate infinite-dimensional extremes (extremes of two or more random fields) that will culminate in the development of statistical methodology for modelling interactions of two or more related extreme events. Recent studies have found that there exists significant long-term impact of climate change on storms that combine wind speed and precipitation, deeming it critically important that any fragility analysis be conducted in such a way as to ensure probabilistic safety levels of a nuclear power plant for extreme weather events. For example, the sting jet phenomena often unleashes very extreme local wind speeds, heavy rainfall and extreme temperatures on a nuclear plant. This is therefore the first application area of the developed statistical methodology. It is intended that this research programme will not only lead to improvement in safety standards and operational reliability of the nuclear energy fleet but also carries with it the potential of reducing costs in expensive overprotection measures that could run into millions of pounds. In addition to the nuclear energy sector other application areas will be explored. Energy supply and renewables power systems are so unwieldy that people are still trying to unravel some intriguing aspects of time dependent peak demands. The statistical methodology developed as part of this research programme will enable a better understanding to be gained of the characteristic features in smart-meter data, which will ultimately give people access to more affordable energy, providing more interaction and safety and thus more choice.

There is an urgent need to expand the use of renewable energy generation systems to meet UK government targets. The expansion of grid-connected renewable energy sources must be done in a way which does not reduce the security of the power distribution system. Integral to power distribution system security is the ability of distributed generators to reliably detect a loss of grid condition. This is important to prevent unwanted islanded sections of the system which continue to be energised after the grid connection is lost. An islanding system occurs when a part of the grid system become disconnected from the rest of the network but continues to be energised by localised distributed generation systems. Islanded systems are (i) potentially hazardous to power system workers, (ii) may operate outside voltage and frequency tolerance, (iii) may be inadequately grounded and (iv) may not re-synchronise properly leading to undesirable protection trips. The existing approaches to islanding detection either have operational regions in which they fail to work or are required to have artificial signals injected into the grid which can impact on power quality. The most difficult operational condition is when there is a power balance between a distributed generator and its local load network. During this condition many systems will not detect that the grid connection has been lost because the fundamental frequency quantities are not altered by the event. Many renewable generation systems require a grid-connected inverter to transfer power into the grid. This is necessary because the generating source itself is rarely capable of producing power at grid frequency. A grid-connected inverter must be able to detect the loss of grid event. This proposal will investigate a novel approach using pattern recognition with a high sampling rate. The pattern recognition system is required to analyse the inverter output voltages and currents to determine if a loss of grid event has taken place. The novelty in this proposal is to include the high frequency information due to PWM effects in the real-time analysis. The benefit of doing this is that information will still be available to the pattern recognition system when a power balance condition exists. There is concern that many islanded detection systems are not immune from the effects of other neighbouring equipment. This equipment may be power electronic loads or other grid-connected inverters. The basis of the proposed approach is that the pattern recognition system will be able to discriminate between the presence and absence of the grid connection despite potential interference signals from neighbouring equipment. A major advantage of the proposed scheme is that it makes use of high frequency signals generated by the PWM switching in the inverter. These signals are extracted from the output voltages and currents by using high sampling rates where a number of samples are taken during one PWM cycle. These signals will still be present when a balance load condition exits and therefore will provide valuable diagnostic information during this difficult condition. These high frequency signals will add to the signals associated with the fundamental frequency components to detect the loss of grid event. It is important that this scheme is demonstrated experimentally and therefore a test rig will be produced which contains a range of typical loads and other grid-connected generators. The rig will be used to evaluate the performance of the proposed scheme in the presence of other neighbouring power electronic equipment. If successful the research will have major impact on the integration of renewable energy generation into power distribution systems. There will be significant benefits to power distribution system security and to the manufacturers of grid-connected inverters.

Currently the accepted technology for large wind turbines is the doubly-fed induction generator (DFIG). This technology is popular primarily due to the reduced cost of the partially rated power electronic converter. On the negative side is the fact that the generator requires brushes and slip rings which require regular maintainance.An alternative scheme is based on the brushless doubly-fed reluctance machine (BDFRM) which also has the cost benefit of a partially rated power converter but as its name implies does not require brushes and slip rings.The BDFRM has not been used for a wind power application. This project will experimentally examine its performance for a wind power application. There are a number of different approaches to the control of a BDFRM. The project will examine the use of Direct Power Control (DPC). This control approach will include sensorless operation and machine parameter independence. With the proliferation of wind power generation the issue of power system stabilty is of great concern. It is important to examine the fault-ride-through (FRT) capabilty of any generation system. This project will examine the FRT capability of the BDFRM and compare this to that of the DFIG. This will require that special grid fault emulation equipment is included in the laboratory test rig.

Due to climate change, extreme meteorological phenomena such as heavy precipitation, extreme temperature, strong winds and sea level rise, seem to be growing more severe and frequent, but the actual estimation of this evolution in extreme weather events remains subject to large uncertainty. For example, in December 2015 when storm Desmond hit the UK, several communities were badly affected by water level rises. Rainfall in this storm crept up to new record levels and provided us with critical lessons on how we can better prepare to withstand similar hazards. However, these lessons learned are in hindsight. When looking at the occurrence probability of such an extreme event that out-spans the range of previously recorded data, Extreme Value Theory (EVT) is the most appropriate branch of probability theory to be implemented as risk assessment and forecasting have a strong probabilistic foundation. In many operational settings, risk mitigation measures are required to balance costs with safety. For example, in insuring systems and infrastructure against extreme events, it might not be enough to sift through extreme record events that emerge from historical data, but it would also be nonsensical to channel most resources into a safety system so robust that it would spectacularly exceed the actual risk being protected against. EVT offers an appropriate statistical toolkit for forecasting extreme outcomes to a high degree of accuracy, thus providing critical evidence for assessing risk more accurately in preparing a proportionate response. There are varying layers of complexity in EVT enveloped in the recently introduced class of multivariate max-stable processes. These are promising models for the structural components that capture how extremes from multiple phenomena (hence the prefix multivariate) are likely to manifest themselves jointly across a certain region over time (hence the so-called space-time processes, also termed random fields). Real life applications abound in the multivariate infinite-dimensional max-stable processes frameworks. For example, the Fukushima nuclear disaster in 2011 was ignited by the combination of a huge earthquake followed by a tsunami. The main goal of this research proposal is to develop a general theory for multivariate infinite-dimensional extremes (extremes of two or more random fields) that will culminate in the development of statistical methodology for modelling interactions of two or more related extreme events. Recent studies have found that there exists significant long-term impact of climate change on storms that combine wind speed and precipitation, deeming it critically important that any fragility analysis be conducted in such a way as to ensure probabilistic safety levels of a nuclear power plant for extreme weather events. For example, the sting jet phenomena often unleashes very extreme local wind speeds, heavy rainfall and extreme temperatures on a nuclear plant. This is therefore the first application area of the developed statistical methodology. It is intended that this research programme will not only lead to improvement in safety standards and operational reliability of the nuclear energy fleet but also carries with it the potential of reducing costs in expensive overprotection measures that could run into millions of pounds. In addition to the nuclear energy sector other application areas will be explored. Energy supply and renewables power systems are so unwieldy that people are still trying to unravel some intriguing aspects of time dependent peak demands. The statistical methodology developed as part of this research programme will enable a better understanding to be gained of the characteristic features in smart-meter data, which will ultimately give people access to more affordable energy, providing more interaction and safety and thus more choice.