Type-2 fuzzy logic system applications for power systems

PhD ThesisIn the move towards ubiquitous information & communications technology, an opportunity for further optimisation of the power system as a whole has arisen. Nonetheless, the fast growth of intermittent generation concurrently with markets deregulation is driving a need for timely algorithms that can derive value from these new data sources. Type-2 fuzzy logic systems can offer approximate solutions to these computationally hard tasks by expressing non-linear relationships in a more flexible fashion. This thesis explores how type-2 fuzzy logic systems can provide solutions to two of these challenging power system problems; short-term load forecasting and voltage control in distribution networks. On one hand, time-series forecasting is a key input for economic secure power systems as there are many tasks that require a precise determination of the future short-term load (e.g. unit commitment or security assessment among others), but also when dealing with electricity as commodity. As a consequence, short-term load forecasting becomes essential for energy stakeholders and any inaccuracy can be directly translated into their financial performance. All these is reflected in current power systems literature trends where a significant number of papers cover the subject. Extending the existing literature, this work focuses in how these should be implemented from beginning to end to bring to light their predictive performance. Following this research direction, this thesis introduces a novel framework to automatically design type-2 fuzzy logic systems. On the other hand, the low-carbon economy is pushing the grid status even closer to its operational limits. Distribution networks are becoming active systems with power flows and voltages defined not only by load, but also by generation. As consequence, even if it is not yet absolutely clear how power systems will evolve in the long-term, all plausible future scenarios claim for real-time algorithms that can provide near optimal solutions to this challenging mixed-integer non-linear problem. Aligned with research and industry efforts, this thesis introduces a scalable implementation to tackle this task in divide-and-conquer fashio

Multiple-retrieval case-based reasoning for course timetabling problems

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the retrieval process chooses those cases which are sub attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependant upon problem specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialisation method for local search methods such as Hill Climbing, Tabu Search and Simulated Annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialisation method for these approaches

Multiple-Retrieval Case-Based Reasoning for Course Timetabling Problems

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the retrieval process chooses those cases which are sub attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependant upon problem specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialisation method for local search methods such as Hill Climbing, Tabu Search and Simulated Annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialisation method for these approaches

Multiple-Retrieval Case-Based Reasoning for Course Timetabling Problems

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the retrieval process chooses those cases which are sub attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependant upon problem specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialisation method for local search methods such as Hill Climbing, Tabu Search and Simulated Annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialisation method for these approaches

The Markov network fitness model

Fitness modelling is an area of research which has recently received much interest among the evolutionary computing community. Fitness models can improve the efficiency of optimisation through direct sampling to generate new solutions, guiding of traditional genetic operators or as surrogates for a noisy or long-running fitness functions. In this chapter we discuss the application of Markov networks to fitness modelling of black-box functions within evolutionary computation, accompanied by discussion on the relationship betweenMarkov networks andWalsh analysis of fitness functions.We review alternative fitness modelling and approximation techniques and draw comparisons with the Markov network approach. We discuss the applicability of Markov networks as fitness surrogates which may be used for constructing guided operators or more general hybrid algorithms.We conclude with some observations and issues which arise from work conducted in this area so far

Regulatory network discovery using heuristics

This thesis improves the GRN discovery process by integrating heuristic information via a co-regulation function, a post-processing procedure, and a Hub Network algorithm to build the backbone of the network.Doctor of Philosoph

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods

Case-based reasoning for course timetabling problems

The research in this thesis investigates Case-Based Reasoning (CBR), a Knowledge-Based Reasoning technique that proved to be capable of providing good solutions in educational course timetabling problems. Following the basic idea behind CBR, experiences in solving previous similar timetabling problems are employed to find the solutions for new problems. A basic CBR system that is hierarchically organized with structured knowledge representations by attribute graphs is proposed in Chapter Four. The system is then further improved to solve a wider range of problems, which is described in Chapter Five. Evaluations on a large number of experiments indicate that this approach could provide a significant step forward in timetabling and scheduling research. This basic system works well on relatively small problems. To deal with this drawback a multiple-retrieval approach that partitions large timetabling problems into small solvable sub-problems is presented in Chapter Six. Good results are obtained from a wide range of experiments. In Chapter Seven, a new idea is introduced in CBR for solving timetabling problems by investigating the approach to select the most appropriate heuristic method rather than to employ it directly on the problem, in the attempt to raise the level of generality at which we can operate. All the evidence obtained from the first stage experiments indicates that there is a range of promising future directions. Finally in Chapter Eight the results of the work are evaluated and some directions for future work are present

Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management

This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears. A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL). Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions. These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96

AN APPROACH TO MACHINE DEVELOPMENT OF MUSICAL ONTOGENY

This Thesis pursues three main objectives: (i) to use computational modelling to explore how music is perceived, cognitively processed and created by human beings; (ii) to explore interactive musical systems as a method to model and achieve the transmission of musical influence in artificial worlds and between humans and machines; and (iii) to experiment with artificial and alternative developmental musical routes in order to observe the evolution of musical styles. In order to achieve these objectives, this Thesis introduces a new paradigm for the design of computer interactive musical systems called the Ontomemetical Model of Music Evolution - OMME, which includes the fields of musical ontogenesis and memetlcs. OMME-based systems are designed to artificially explore the evolution of music centred on human perceptive and cognitive faculties. The potential of the OMME is illustrated with two interactive musical systems, the Rhythmic Meme Generator (RGeme) and the Interactive Musical Environments (iMe). which have been tested in a series of laboratory experiments and live performances. The introduction to the OMME is preceded by an extensive and critical overview of the state of the art computer models that explore musical creativity and interactivity, in addition to a systematic exposition of the major issues involved in the design and implementation of these systems. This Thesis also proposes innovative solutions for (i) the representation of musical streams based on perceptive features, (ii) music segmentation, (iii) a memory-based music model, (iv) the measure of distance between musical styles, and (v) an impi*ovisation-based creative model