Prognostic Reasoner based adaptive power management system for a more electric aircraft

This research work presents a novel approach that addresses the concept of an adaptive power management system design and development framed in the Prognostics and Health Monitoring(PHM) perspective of an Electrical power Generation and distribution system(EPGS).PHM algorithms were developed to detect the health status of EPGS components which can accurately predict the failures and also able to calculate the Remaining Useful Life(RUL), and in many cases reconfigure for the identified system and subsystem faults. By introducing these approach on Electrical power Management system controller, we are gaining a few minutes lead time to failures with an accurate prediction horizon on critical systems and subsystems components that may introduce catastrophic secondary damages including loss of aircraft. The warning time on critical components and related system reconfiguration must permits safe return to landing as the minimum criteria and would enhance safety. A distributed architecture has been developed for the dynamic power management for electrical distribution system by which all the electrically supplied loads can be effectively controlled.A hybrid mathematical model based on the Direct-Quadrature (d-q) axis transformation of the generator have been formulated for studying various structural and parametric faults. The different failure modes were generated by injecting faults into the electrical power system using a fault injection mechanism. The data captured during these studies have been recorded to form a “Failure Database” for electrical system. A hardware in loop experimental study were carried out to validate the power management algorithm with FPGA-DSP controller. In order to meet the reliability requirements a Tri-redundant electrical power management system based on DSP and FPGA has been develope

Multilevel Converters: An Enabling Technology for High-Power Applications

| Multilevel converters are considered today as the state-of-the-art power-conversion systems for high-power and power-quality demanding applications. This paper presents a tutorial on this technology, covering the operating principle and the different power circuit topologies, modulation methods, technical issues and industry applications. Special attention is given to established technology already found in industry with more in-depth and self-contained information, while recent advances and state-of-the-art contributions are addressed with useful references. This paper serves as an introduction to the subject for the not-familiarized reader, as well as an update or reference for academics and practicing engineers working in the field of industrial and power electronics.Ministerio de Ciencia y Tecnología DPI2001-3089Ministerio de Eduación y Ciencia d TEC2006-0386

Multi-agent control and operation of electric power distribution systems

This dissertation presents operation and control strategies for electric power distribution systems containing distributed generators. First, models of microturbines and fuel cells are developed. These dynamic models are incorporated in a power system analysis package. Second, operation of these generators in a distribution system is addressed and load following schemes are designed. The penetration of distributed generators (DGs) into the power distribution system stability becomes an issue and so the control of those DGs becomes necessary. A decentralized control structure based on conventional controllers is designed for distributed generators using a new developed optimization technique called Guided Particle Swarm Optimization. However, the limitations of the conventional controllers do not satisfy the stability requirement of a power distribution system that has a high DG penetration level, which imposes the necessity of developing a new control structure able to overcome the limitations imposed by the fixed structure conventional controllers and limit the penetration of DGs in the overall transient stability of the distribution system. Third, a novel multi-agent based control architecture is proposed for transient stability enhancement for distribution systems with microturbines. The proposed control architecture is hierarchical with one supervisory global control agent and a distributed number of local control agents in the lower layer. Specifically, a central control center supervises and optimizes the overall process, while each microturbine is equipped with its own local control agent.;The control of naval shipboard electric power system is another application of distributed control with multi-agent based structure. In this proposal, the focus is to introduce the concept of multi-agent based control architecture to improve the stability of the shipboard power system during faulty conditions. The effectiveness of the proposed methods is illustrated using a 37-bus IEEE benchmark system and an all-electric naval ship

Shipboard DC systems, a Critical Overview:Challenges in Primary Distribution, Power Electronics-based Protection, and Power Scalability

This article gives an overview of challenges in primary distribution, protections, and power scalability for shipboard dc systems. Given that dc technology is in development, several aspects of shipboard systems have not yet been sufficiently devised to ensure the protection and efficiency demanded. Several issues in dc systems arise from the lack of complete relevant standardization from different regulation bodies. Unipolar and bipolar bus architectures have application-specific advantages that are discussed and compared. The placement of power electronics in dc systems creates opportunities for switchboard design, and this article compares the centralized and distributed approaches. Likewise, protection architectures for shipboard dc systems have challenges. Breaker-based protection utilizes slow fuses, mechanical circuit breakers, and solid-state circuit breakers. In addition, power-electronics-based protection embeds the protective circuit in the power converters, but its development lags. This article compares the state-of-the-art technologies, reviewing their main features. Finally, the power requirement of various applications and the low production rate of vessels force the designers to utilize commercial off-the-shelf converters to scale up power. The misuse of such converters, the modular topologies, and power electronics building blocks are exposed highlighting challenges and opportunities toward the mass adoption of dc systems onboard maritime vessels.</p

Marine gas turbine monitoring and diagnostics by simulation and pattern recognition

Several techniques have been developed in the last years for energy conversion and aeronautic propulsion plants monitoring and diagnostics, to ensure non-stop availability and safety, mainly based on machine learning and pattern recognition methods, which need large databases of measures. This paper aims to describe a simulation based monitoring and diagnostic method to overcome the lack of data. An application on a gas turbine powered frigate is shown. A MATLAB-SIMULINK\uae model of the frigate propulsion system has been used to generate a database of different faulty conditions of the plant. A monitoring and diagnostic system, based on Mahalanobis distance and artificial neural networks have been developed. Experimental data measured during the sea trials have been used for model calibration and validation. Test runs of the procedure have been carried out in a number of simulated degradation cases: in all the considered cases, malfunctions have been successfully detected by the developed model

A Model-Based Holistic Power Management Framework: A Study on Shipboard Power Systems for Navy Applications

The recent development of Integrated Power Systems (IPS) for shipboard application has opened the horizon to introduce new technologies that address the increasing power demand along with the associated performance specifications. Similarly, the Shipboard Power System (SPS) features system components with multiple dynamic characteristics and require stringent regulations, leveraging a challenge for an efficient system level management. The shipboard power management needs to support the survivability, reliability, autonomy, and economy as the key features for design consideration. To address these multiple issues for an increasing system load and to embrace future technologies, an autonomic power management framework is required to maintain the system level objectives. To address the lack of the efficient management scheme, a generic model-based holistic power management framework is developed for naval SPS applications. The relationship between the system parameters are introduced in the form of models to be used by the model-based predictive controller for achieving the various power management goals. An intelligent diagnostic support system is developed to support the decision making capabilities of the main framework. Naïve Bayes’ theorem is used to classify the status of SPS to help dispatch the appropriate controls. A voltage control module is developed and implemented on a real-time test bed to verify the computation time. Variants of the limited look-ahead controls (LLC) are used throughout the dissertation to support the management framework design. Additionally, the ARIMA prediction is embedded in the approach to forecast the environmental variables in the system design. The developed generic framework binds the multiple functionalities in the form of overall system modules. Finally, the dissertation develops the distributed controller using the Interaction Balance Principle to solve the interconnected subsystem optimization problem. The LLC approach is used at the local level, and the conjugate gradient method coordinates all the lower level controllers to achieve the overall optimal solution. This novel approach provides better computing performance, more flexibility in design, and improved fault handling. The case-study demonstrates the applicability of the method and compares with the centralized approach. In addition, several measures to characterize the performance of the distributed controls approach are studied

The Fault Diagnosis and Prediction System of Marine Diesel Engines Using a Statistical Analysis Method

Recently acceleration of ship automation makes ship's crew decreased and ship's schedule fast. That results in making harbour time short and lack of ship's maintenance time. Therefore prediction maintenance using fault diagnosis system is more important to prevent accidents by shortage of maintenance. While monitoring points for ship machineries were about 600 in 1980's ship, but now over 10,000 points. So almost all systems of ship can be monitored by central control and monitoring system. With this background and circumstance, various kinds of study for fault diagnosis of machineries are carried out. Almost ship monitoring systems are event driven alarm system which warn only when the signal is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. And fault diagnosis is started by expert engineer after warning from the monitoring system. There is few study which automatically diagnose the fault from ship's monitored signal. The bigger control and monitoring system is, the more important fault diagnosis and predictive maintenance is to reduce damage brought forth by system fault. This paper proposes fault diagnosis and prediction system which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. For this all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem by analyzing ship's operation data. To extract dynamic characteristics of these subsystems, log book data of container ship of H shipping company are used. Even though almost all machineries installed on the ship including main propulsion diesel engine and various auxiliary machineries have non linear characteristics and produce different output data dependent on the operating environment, if those machineries are operating under normal condition state, correlation coefficient(CC) between monitored data of related machine each other will be high. From analyzing this data having high CC, correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part. Fault place can be ascertained by investigating specific data of fault part with decision making tree like answer tree. And fault prediction can be made by regression analysis of monitored data. To verifying capability of fault detection, diagnosis and prediction, Fault Management System(FMS) is developed by C++. Simulation experiment by FMS is carried out with population data set made by log book data of 2 months duration from a large full container ship of H shipping company. For fault detecting and diagnosing experiment from population data set, three kinds of random number are generated by computer. One is generated on the base of average and covariance of population data set, other on the base of parts of population data and the other on the base of fault detection range(FDR). In the simulation experiment FMS is ascertained to detect abnormal data from monitored data set including generated random number by abnormal detection module with abnormal detection knowledge base and diagnose the fault by abnormal diagnosis module with abnormal diagnosis knowledge base and also forecast predictive fault by fault prediction module. If the FMS is developed to include maintenance manual and ship's inventory database inside near future, the system will be able to recommend how to maintain the diagnosed fault and necessary spare parts.Abstract Ⅸ Nomenclature &#8555제 1 장 서 론 1 1.1 연구 배경 1 1.2 종래의 연구 3 1.3 연구 목적 및 내용 5 제 2 장 선박용 디젤기관의 계통분류 및 감시데이터 특성 8 2.1 선박용 디젤기관의 계통분류 8 2.2 선박용 디젤기관 계통별 감시데이터 특성분석 11 2.2.1 시운전데이터의 특성 12 2.2.2 실선운항데이터의 특성 13 2.3 시운전데이터와 실선운항데이터의 비교분석 30 제 3 장 이상감지 및 이상진단모듈 설계 36 3.1 이상감지모듈과 이상진단모듈의 구조 36 3.2 전문가에 의한 이상감지와 이상진단 38 3.3 통계적 분석기법을 이용한 이상감지모듈과 이상진단모듈39 3.3.1 통계적 분석기법 39 3.3.2 통계적 분석기법에 의한 이상감지와 이상진단 41 3.4 통계적 분석기반 이상감지지식베이스와 이상진단지식베이스 64 3.5 전문가지식기반 이상진단지식베이스 67 3.6 고장진단 알고리즘 69 제 4 장 이상감지 및 이상진단 시뮬레이션 72 4.1 통계적분석기반 이상감지 및 이상진단을 위한 실험 72 4.2 실험결과 고찰 84 4.3 실험시스템 구축 88 제 5 장 의사결정모듈의 설계 94 5.1 연소계통의 의사결정모듈 95 5.2 열교환기계통의 의사결정모듈 99 5.3 전동기 및 펌프계통의 의사결정모듈 101 제 6 장 통계적 분석기법에 의한 고장예측모듈 103 6.1 통계적 분석기반 고장예측모듈의 구조 104 6.2 실선운항데이터의 SRA 105 6.3 SRAMD를 이용한 고장예측 실험 112 제 7 장 결 론 117 참고 문헌 11