Device reliability and manufacturability have emerged as dominant concerns in end-of-road CMOS devices. Today an increasing number of hardware failures are attributed to device reliability problems that cause partial system failure or shutdown. Also maintaining an acceptable manufacturing yield is seen as challenge because of smaller feature sizes, process variation, and reduced headroom for burn-in tests. In this project we investigate a hardware-based scheme for improving yield and reliability of a homogeneous chip multiprocessor (CMP). The proposed solution involves a hardware framework that enables us to utilize the redundancies inherent in a multi-core system to keep the system operational in face of partial failures due to hard faults (faults due to manufacturing defects or permanent faults developed during system lifetime). A micro-architectural modification allows a faulty core in a multiprocessor system to use another core as a coprocessor to service any instruction that the former cannot execute correctly by itself. This service is accessed to improve yield and reliability, but at the cost of some loss of performance. In order to quantify this loss we have used a cycle-accurate architectural simulator to simulate the performance of dual-core and quad-core systems with one or more cores sustaining partial failure. Simulation studies indicate that when a large and sparingly-used unit such as a floating point unit fails in a core, even for a floating point intensive benchmark, we can continue to run the faulty core with as little as 10% performance impact and minimal area overhead. Incorporating this recovery mechanism entails some modifications in the microprocessor micro-architecture. The modifications are also described here through a simplified model of a superscalar processor.

Wind power installations have seen a significant rise all over the world in the past decade. Further significant growth is expected in the future. The UK’s ambitions for offshore wind installations are reflected through Round 1, 2 and 3 projects. It is expected that Round 3 alone will add at least 25 GW of offshore wind generation into the system. Current research knowledge is mostly limited to smaller wind farms, the aim of this research is to improve offline and online modelling techniques for large offshore wind farms. A critical part of offline modelling is the design of the wind farm. Design of large wind farms particularly requires careful consideration as high capital costs are involved. This thesis develops a novel methodology which leads to a cost-effective and reliable design of an offshore wind farm. A new industrial-grade software tool is also developed during this research. The tool enables multiple offshore wind farm design options to be built and tested quickly with minimal effort using a Graphical User Interface (GUI). The GUI is designed to facilitate data input and presentation of the results. This thesis also develops an improved method to estimate a wind farm’s energy yield. Countries with large-scale penetration of wind farms often carry out wind energy curtailments. Prior knowledge of estimated energy curtailments from a wind farm can be advantageous to the wind farm owner. An original method to calculate potential wind energy curtailment is proposed. In order to perform wind energy curtailments a network operator needs to decide which turbines to shut down. This thesis develops a novel method to identify turbines inside a wind farm that should be prioritised for shut down and given priority when scheduling preventive maintenance of the wind farm. Once the wind farm has been built and connected to the network, it operates as part of a power system. Real-time online simulation techniques are gaining popularity among system operators. These techniques allow operators to carry out simulations using short-term forecasted wind conditions. A novel method is proposed to probabilistically estimate the power production of a wind farm in real-time, taking into account variation in wind speed and effects of turbulence inside the wind farm. Furthermore, a new probabilistic aggregation technique is proposed to establish a dynamic equivalent model of a wind farm. It determines the equivalent number and parameters of wind turbines that can be used to simulate the dynamic response of the wind farm throughout the year.