Unsupervised Clustering for Fault Diagnosis in Nuclear Power Plant Components

International audienceThe development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are 'unlabeled', i.e., the indication of the type of fault is usually not available. Then, the objective of the present work is to develop a methodology for the identification of transients of similar characteristics, under the conjecture that faults of the same type lead to similar behavior in the measured signals. The proposed methodology is based on the combined use of Haar wavelet transform, fuzzy similarity, spectral clustering and the Fuzzy C-Means algorithm. A procedure for interpreting the fault cause originating the similar transients is proposed, based on the identification of prototypical behaviors. Its performance is tested with respect to an artificial case study and then applied on transients originated by different faults in the pressurizer of a nuclear power reactor

Multi-Criteria Performance Evaluation and Control in Power and Energy Systems

The role of intuition and human preferences are often overlooked in autonomous control of power and energy systems. However, the growing operational diversity of many systems such as microgrids, electric/hybrid-electric vehicles and maritime vessels has created a need for more flexible control and optimization methods. In order to develop such flexible control methods, the role of human decision makers and their desired performance metrics must be studied in power and energy systems. This dissertation investigates the concept of multi-criteria decision making as a gateway to integrate human decision makers and their opinions into complex mathematical control laws. There are two major steps this research takes to algorithmically integrate human preferences into control environments: MetaMetric (MM) performance benchmark: considering the interrelations of mathematical and psychological convergence, and the potential conflict of opinion between the control designer and end-user, a novel holistic performance benchmark, denoted as MM, is developed to evaluate control performance in real-time. MM uses sensor measurements and implicit human opinions to construct a unique criterion that benchmarks the system\u27s performance characteristics. MM decision support system (DSS): the concept of MM is incorporated into multi-objective evolutionary optimization algorithms as their DSS. The DSS\u27s role is to guide and sort the optimization decisions such that they reflect the best outcome desired by the human decision-maker and mathematical considerations. A diverse set of case studies including a ship power system, a terrestrial power system, and a vehicular traction system are used to validate the approaches proposed in this work. Additionally, the MM DSS is designed in a modular way such that it is not specific to any underlying evolutionary optimization algorithm

Planning and operating energy storage for maximum technical and financial benefits in electricity distribution networks

PhD ThesisThe transmission and distribution networks are facing changes in the way they will be planned, operated and maintained as a result of the rise in the deployment of Low Carbon Technologies (LCTs) on the power grid. These LCTs provide the benefits of a decarbonised grid and reduce reliance on fossil fuels and large centralised generation. As LCTs are close to the demand centres, a significant amount will be deployed in distribution networks. The distribution networks face challenges in enabling a wide deployment of LCTs because they were traditionally built for centralised generation and most are operated passively as demand patterns are well understood and power flows are unidirectional to load centres. The opposite will be the case for distribution networks with LCTs. Utilities that own and operate distribution networks such as the DNOs in the UK will face a host of problems, such as voltage and thermal excursions and power quality issues on their networks. Traditional reinforcement methods will be expensive for DNOs, so they are considering innovative solutions that provide multiple benefits; this is where Energy Storage Systems (ESS) could play a role to provide multiple technical and economic benefits across the grid from voltage and power flow management to upgrade deferral of network assets. This is due to the multifunctional nature of ESS allowing it to act as generation, transmission, demand or demand response based on requirements at any specific time based on the requirements of the stakeholder involved with the asset. ESS is technically capable of providing benefits to DNOs and other stakeholders on the electricity grid but the business case is not proven. Unless multiple benefits are aggregated, investment in ESS is challenging as they have a substantial capital cost and some components will require more frequent replacement than traditional network assets which typically last between 20 – 40+ years. As a result there is a reluctance to include them in future distribution network planning arrangements. IV Furthermore, the electricity regulatory and market design, which was set up in the time of traditional centralised generation and networks, limits investment in ESS by regulated bodies such as DNOs. The regulations and market structures also affects revenue streams and the resulting business case for ESS. This thesis investigates the feasibility of ESS in distribution networks by first studying the effect of current electricity regulatory and market practices on ESS deployment, investigating how ESS can be used under the present rules, and establishing whether there are limitations that can be reduced or removed. Secondly, short and medium term planning is carried out on model Medium Voltage distribution networks (6.6 kV) provided by the IEEE and Electricity North West Limited to establish the technical and financial viability of investing in ESS over conventional reinforcement methods by: Assessing the impact of the proliferation of LCTs in distribution networks using both deterministic and stochastic methods under different scenarios based on current developments and government policies in the UK. This stochastic evaluation considers both spatial and temporal aspects of LCTs in distribution networks with datasets obtained from real distribution network customers; Developing and applying ESS voltage and power flow management, and market control algorithms to resolve distribution network issues resulting from growing LCTs and allowing ESS to participate in the electricity spot market over a planning period up to the year 2030; Providing a framework for assessing the business case of ESS under a DNO or third-party ownership structure where technical and commercial benefits from network asset upgrade deferral, energy arbitrage, balancing market and ancillary services (frequency response and short term operating reserves), distribution and transmission system use of system benefits are evaluated; V Optimising the operation of ESS considering multiple technical and commercial objectives to establish the technical benefits and revenues that can be obtained from an ESS deployment and the trade-off of benefits that applies for differing ownership types. The simulation results show that, under the scenarios investigated, ESS can be used as a technical solution for DNOs. They show that the ESS capital costs can be offset by aggregating benefits from both technical and commercial applications in distribution networks if regulatory and market changes are made. The conclusions offer a perspective to DNOs and third parties’ considering investing in ESS on the electricity grid as it evolves towards a more active, decarbonised system.Electricity North West Limited and Scottish Power Energy Networks sponsored my stud

Mathematical formulations and optimization algorithms for solving rich vehicle routing problems.

Objectives and methods of study: The main objective of this work is to analyze and solve three different rich selective Vehicle Routing Problems (VRPs). The first problem is a bi-objective variant of the well-known Traveling Purchaser Problem (TPP) in which the purchased products are delivered to customers. This variant aims to find a route for which the total cost (transportation plus purchasing costs) and the sum of the customers’s waiting time are simultaneously minimized. A mixed integer bi-objective programming formulation of the problem is presented and tested with CPLEX 12.6 within an ǫ-constraint framework which fails to find non-dominated solutions for instances containing more than 10 nodes. Therefore, a heuristic based on relinked local search and Variable Neighborhood Search (VNS) is proposed to approximate the Pareto front for large instances. The proposed heuristic was tested over a large set of artificial instances of the problem. Computational results over small-sized instances show that the heuristic is competitive with the ǫ-constraint method. Also, computational tests over large-sized instances were carried out in order to study how the characteristics of the instances impact the algorithm performance. The second problem consists of planning a selective delivery schedule of multiple products. The problem is modeled as a multi-product split delivery capacitated team orienteering problem with incomplete services, and soft time windows. The problem is modeled through a mixed integer linear programming formulation and approximated by means of a multi-start Adaptive Large Neighborhood Search (ALNS) metaheuristic. Computational results show that the multi-start metaheuristic reaches better results than its classical implementation in which a single solution is build and then improved. Finally, an Orienteering Problem (OP) with mandatory visits and conflicts, is formulated through five mixed integer linear programming models. The main difference among them lies in the way they handle the subtour elimination constraints. The models were tested over a large set of instances of the problem. Computational experiments reveal that the model which subtour elimination constraints are based on a single-commodity flow formulation allows CPLEX 12.6 to obtain the optimal solution for more instances than the other formulations within a given computation time limit. Contributions: The main contributions of this thesis are: • The introduction of the bi-objective TPP with deliveries since few bi-objective versions of the TPP have been studied in the literature. Furthermore, to the best of our knowledge, there is only one more work that takes into account deliveries in a TPP. • The design and implementation of a hybrid heuristic based on relinked local search and VNS to solve the bi-objective TPP with deliveries. Additionally, we provide guidelines for the application of the heuristic when different characteristics of the instances are observed. • The design and implementation of a multi-start adaptive large neighborhood search to solve a selective delivery schedule problem. • The experimental comparison among different formulations for an OP with mandatory nodes and conflicts

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Efficient targeted optimisation for the design of pressure swing adsorption systems for CO2 capture in power plants

Pressure swing adsorption (PSA) is a cyclic adsorption process for gas separation and purification, and can be used in a variety of industrial applications, for example, hydrogen purification and dehydration. PSA is, due to its low operational cost and its ability to efficiently separate CO2 from flue gas, a promising candidate for post-combustion carbon capture in power plants, which is an important link in the Carbon Capture and Storage technology chain. PSA offers many design possibilities, but to optimise the performance of a PSA system over a wide range of design choices, by experimental means, is typically too costly, in terms of time and resources required. To address this challenge, computer experiments are used to emulate the real system and to predict the performance. The system of PDAEs that describes the PSA process behaviour is however typically computationally expensive to simulate, especially as the cyclic steady state condition has to be met. Over the past decade, significant progress has been made in computational strategies for PSA design, but more efficient optimisation procedures are needed. One popular class of optimisation methods are the Evolutionary algorithms (EAs). EAs are however less efficient for computationally expensive models. The use of surrogate models in optimisation is an exciting research direction that allows the strengths of EAs to be used for expensive models. A surrogate based optimisation (SBO) procedure is here developed for the design of PSA systems. The procedure is applicable for constrained and multi-objective optimisation. This SBO procedure relies on Kriging, a popular surrogate model, and is used with EAs. The main application of this work is the design of PSA systems for CO2 capture. A 2- bed/6-step PSA system for CO2 separation is used as an example. The cycle configuration used is sufficiently complex to provide a challenging, multi-criteria example

Numerical modelling of bidirectional dry gas face seals

The optimization of the geometrical parameters of the aerodynamic lift features and the analysis of the fluid flow in the seal interface are inter-twined. Any small changes in the geometrical parameters of the aerodynamic lift features significantly affect the performance of a non-contacting gas face seal. For a gas face seal to function with optimum performance requires that the optimum geometrical parameters be identified. This can be achieved through a lengthy trial and error process, often heavily dependent on the designer’s depth of insight, itself dependent on experience, or can be achieved through automated numerical methods. The purpose of this research was to develop a reliable numerical model that can serve as a design tool for simulating the performance of both unidirectional and bidirectional dry gas face seals. This was achieved in three steps. The first approach consisted in developing a 2D numerical model that employed the Reynolds equation for seals operating at very low rotating speeds and low pressure differentials. In the second step a 3D-CFD model was assembled and the practicability of using CFD, in a seal design loop, for seals operating in wide range of operating conditions, was investigated. This model employed a commercial CFD package (ANSYS CFX version 11). For last approach both models were incorporated into an automatic optimization tool that can generate optimal seal geometries with a minimum of human intervention. An extensive set of results from the analysis of dry gas face seals spanning across different operating conditions and geometrical seal face profiles, with the inclusion of convergent radial taper, are presented and discussed in this thesis. The results obtained from the Reynolds equation and 3D CFD models are compared and critically analysed. Results obtained with both models are validated against test data obtained from AESSEAL plc, the sponsor of this research. The 3D CFD model predictions showed a better agreement with the test data on the seal leakage than the Reynolds equation model. The leakage rates and fluid film thickness predictions illustrate how the 3D CFD model can be used for seal design while overcoming some of the shortcomings of the Reynolds equation based models. The major limitation of the 3D CFD model is that it is computationally expensive. An automatic optimization tool which can be used for the design of dry gas face seals has been presented. The improvements achieved from the optimization of a spiral groove face seal utilising the automatic optimization tool are: 4.8% increase of opening force, 13.2% reduction of seal leakage, 20.7% increase of design efficiency parameter, 28.3% increase of axial film stiffness and 15.9% reduction of power consumption. A proposed new design of dry gas face seal capable of bidirectional operation has been presented. This type of seal outperformed the spiral groove face seal, in reverse rotation of the sealing shaft, in terms of opening force and positive axial film stiffness