Stuart Galloway

Wind Turbine Condition Assessment Through Power Curve Copula Modeling

Power curves constructed from wind speed and active power output measurements provide an established method of analyzing wind turbine performance. In this paper, it is proposed that operational data from wind turbines are used to estimate bivariate probability distribution functions representing the power curve of existing turbines so that deviations from expected behavior can be detected. Owing to the complex form of dependency between active power and wind speed, which no classical parameterized distribution can approximate, the application of empirical copulas is proposed; the statistical theory of copulas allows the distribution form of marginal distributions of wind speed and power to be expressed separately from information about the dependency between them. Copula analysis is discussed in terms of its likely usefulness in wind turbine condition monitoring, particularly in early recognition of incipient faults such as blade degradation, yaw, and pitch errors.

Optimizing the Roles of Unit and Non-unit Protection Methods Within DC Microgrids

Summary form only given. The characteristic behavior of physically compact, multiterminal dc networks under electrical fault conditions can produce demanding protection requirements. This represents a significant barrier to more widespread adoption of dc power distribution for microgrid applications. Protection schemes have been proposed within literature for such networks based around the use of non-unit protection methods. This paper shows however that there are severe limitations to the effectiveness of such schemes when employed for more complex microgrid network architectures. Even current differential schemes, which offer a more effective, though costly, protection solution, must be carefully designed to meet the design requirements resulting from the unique fault characteristics of dc microgrids. This paper presents a detailed analysis of dc microgrid behavior under fault conditions, illustrating the challenging protection requirements and demonstrating the shortcomings of non-unit approaches for these applications. Whilst the performance requirements for the effective operation of differential schemes in dc microgrids are shown to be stringent, the authors show how these may be met using COTS technologies. The culmination of this work is the proposal of a flexible protection scheme design framework for dc microgrid applications which enables the required levels of fault discrimination to be achieved whilst minimizing the associated installation costs.

High speed differential protection for smart DC distribution systems

This paper presents a high speed current differential implementation approach for smart dc distribution systems capable of sub-millisecond fault detection. The approach utilizes the natural characteristics of dc differential current measurements to significantly reduce fault detection times compared to standard applications and hence meet requirements for dc converter protection (around 2 ms). Analysis is first developed to help quantify protection implementation challenges for a given dc system. Options for implementing the proposed technique are then illustrated. Results of scaled hardware testing are presented which validate the overall protection operating times in a low voltage environment. These results show the implementation approach can consistently achieve protection system operating within the order of a few microseconds .

A Copula Model of Wind Turbine Performance

The conventional means of assessing the performance of a wind turbine is through consideration of its power curve which provides the relationship between power output and measured wind speed. In this letter, it is shown how the joint probability distribution of power and wind speed can be learned from data, rather than from examination of the implied function of the two variables. Such an approach incorporates measures of uncertainty into performance estimates, allows inter-plant performance comparison, and could be used to simulate plant operation via sampling. A preliminary model is formulated and fitted to operational data as an illustration.

Enhanced Load Profiling for Residential Network Customers

Anticipating load characteristics on low voltage circuits is an area of increased concern for Distribution Network Operators with uncertainty stemming primarily from the validity of domestic load profiles. Identifying customer behavior makeup on a LV feeder ascertains the thermal and voltage constraints imposed on the network infrastructure; modeling this highly dynamic behavior requires a means of accommodating noise incurred through variations in lifestyle and meteorological conditions. Increased penetration of distributed generation may further worsen this situation with the risk of reversed power flows on a network with no transformer automation. Smart Meter roll-out is opening up the previously obscured view of domestic electricity use by providing high resolution advance data; while in most cases this is provided historically, rather than real-time, it permits a level of detail that could not have previously been achieved. Generating a data driven profile of domestic energy use would add to the accuracy of the monitoring and configuration activities undertaken by DNOs at LV level and higher which would afford greater realism than static load profiles that are in existing use. In this paper, a linear Gaussian load profile is developed that allows stratification to a finer level of detail while preserving a deterministic representation.

Electricity Network Scenarios for Great Britain in 2050

The next fifty years are likely to see great developments in the technologies deployed in electricity systems, with consequent changes in the structure and operation of power networks. This paper, which forms a chapter in the forthcoming book Future Electricity T echnologies and Systems, develops and presents six possible future electricity industry scenarios for Great Britain, focussed on the year 2050. The paper draws upon discussions of important technologies presented by expert authors in other chapters of the book to consider the impact of different combinations of key influences on the nature of the power system in 2050. For each scenario there is a discussion of the effects of the key parameters, with a description and pictorial illustration. Summary tables identify the role of the technologies presented in other chapters of the book, and list important figures of interest, such as the capacity and energy production of renewable generation technologies.

GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems.

A PORT SYSTEM SIMULATION FACILITY WITH AN OPTIMIZATION CAPABILITY

This paper details the optimization of bulk material port handling systems through the use of an evolutionary based approach. The effective management of port systems requires effective solutions for the design, operation and maintenance of these facilities. This has to be achieved through a reduction in financial costs, and an increase in the utilization of equipment and other resources. The operation of a port system is complex and difficult to model mathematically. Consequently, through the explicit characterization of port components, a port modeling tool was developed that permits the generic construction of port simulation models. A genetic algorithm based approach was developed to provide an optimization capability to the port simulation tool. Two case studies based on real world port systems are presented and the results are discussed. A significant improvement is demonstrated in both the operational and economic performance as a result of the GA/model interaction.

Domestic Load Characterization Through Smart Meter Advance Stratification

The heterogeneity of domestic loads presents distribution network operators with operational uncertainties which may become problematic as generation capacity shrinks and network infrastructure ages. High resolution meter advances recorded by increasingly ubiquitous Smart Meters can be seen as representing base loads along with aggregations of multiple domestic appliances-in this letter, a Bayesian formulation of the finite mixture probability distribution is employed to enumerate and capture generalizations of these, from which compact representations of domestic load profiles can be formed.

Generation scheduling using genetic algorithm based hybrid techniques

The solution of generation scheduling (GS) problems involves the determination of the unit commitment (UC) and economic dispatch (ED) for each generator in a power system at each time interval in the scheduling period. The solution procedure requires the simultaneous consideration of these two decisions. Researchers have focused much attention on new solution techniques to GS. This paper proposes the application of a variety of genetic algorithm (GA) based approaches and investigates how these techniques may be improved in order to more quickly obtain the optimum or near optimum solution for the GS problem. The results obtained show that the GA-based hybrid approach offers an effective alternative for solving realistic GS problems within a realistic timeframe.