Fig1

Carlos Graciós

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1. INTRODUCTION The relevant role of the hydroelectric plants in the world is achieved in their particular energy production because of the great amount of energy production between 30% – 60% accord of the total power generation around the world. The efficiency in terms of the high level actual requirements, depends of the correct balance on the generation, storage and distribution strategies reported in the literature. In the particular requirement of generation issues, the development of high efficient control architectures are preliminary explored by the primary scheme which is analyzed in recent results. Here, it is important to evaluate the performance of each part and whole power generation system to define the adequate law control behaviour. Acord to Lui et al. the transient process in hydropower stations, including the interactions among hydraulics, mechanism, and electricity, is complicated. The closure of guide vanes and spherical valve induces a change in the flow inertia, which causes changes in the turbine rotational speed and hydraulic pressure in the piping system.When the working condition dramatically changes during transients, drastic changes in the waterhammer pressure and high rotational speed may lead to serious accidents that will endanger the safety of the hydraulic structure and turbine unit [1–3] and affect the power grid stability [4]. Therefore, simulating the transient process of hydropower stations is necessary. The calculation accuracy is directly related to the design of the water diversion system, safe operation of the hydropower plant, and power quality. However hydropower generation varies greatly between years with varying inflows, as well as competing water uses, such as flood control, water supply, recreation, and in-stream flow requirements. Given hydropower’s economic value and its role in complex water systems, it is reasonable to monitor and protect the hydropower unit from harmful operation modes. A unit is often operated through rough zone which will cause the unit vibration and the stability performance will decline. Finally, in the case of Great Brittian 1/3 of the cfomplete electrical power is generated by a Hydropower plant installed in Dinorwig Wales with a special characteristics to be demostrated in this report Futhermore, section 2 is devoted to describe the Dinorwig Hydropower Plant(DHP) as structural as functional manner. The hybrid model proposed to define the unsual behaviour for the Plant is developed in section 3. Section 4 shows the Model obtained applying the MLD strategy inserted here. The results using the proposed method are discussed in Section 5. Finally, some conclusions are drawn in Section 6 followed by Acknowledgment and relevant references.
Imgpaper 2

ongaku.shalom

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INTRODUCTION In recent years great research effort has been directed toward fault diagnosis of Electrical Power Generation Systems. Many papers have been published on this subject [1, 2], and even if we restrict ourselves to the theoretical treatment of faults, there is a variety of problem formulations depending on fault models, measurement conditions, the final object of the fault diagnosis, and so forth. Key factors in the classification of diagnostic problems are the following. 1. Subsystems under diagnosis:   a. Elements contained in the subsystem: 1) “RLC or RC”, 2) “controlled sources, i. e. by definition, a two-port element which describes the energy transference or energy conversion between two subsystems.   b. Excitation of the system:    1) DC Power Supply.    2) AC Power Supply – Single-frequency or multi-frequency.   c. Number of exciting sources (independent sources or inputs):    1) Single exciting source.    2) Multiple exciting sources. 2. Fault models:   a) Short circuits or open circuits (hard faults).   b) Malfunction of an element or subsystem that occurs at intervals, usually irregular, in an element or subsystem that functions normally at other times (intermittent faults).   c) Element or parameter-value deviations outside the tolerance bounds (soft faults) 3. Measurements:   a) Voltage,   b) Current,   c) Frequency,   d) Phase. 4. Final object of fault diagnosis:   a) To determine voltages and currents,   b) To identify the faulty elements,   c) to locate and isolate the faults within a subsystem. In addition to the problem formulation directly related to electrical and mechanical elements, attempts have been made to apply techniques of system diagnosis to Electrical Power Diagnosis (EPD). In diagnosis of large- scale system, use of the computers is inevitable. One special feature of EPD strategy which is quite different from those of Energy System analysis is that the information for diagnosis or knowledge about the subpart of the EP system is very restricted. Therefore diagnosability, or whether or not the diagnostic problem formulated is solvable under the specific requirements and the expected performance indexes are achievable, must be preliminary researched in order to discriminate instability intrinsic to the problem from that due to the computational method and errors. Since there are several problem formulations, diagnosability must be described as structural as functional according to them In the following sections, the diagnosability of soft faults in a EP system which is in a “Fuzzy MLD Fault Model” is consider. An advantage of assuming soft faults only is that the EP model structural and functional description is known, and the equation derived for the Mixed Logical Dynamic (MLD) definition included simple, directed and co-directed subsystem, relationship can be inferred. It is possible to assume that the system contains as active elements, controlled-transferring conversion subsystems and several element-values relationship in terms of passive and active energy conversion in the different steps of the model. Most elements and function conversion can be represented by their equivalent equation conversion model and at last, a complete dynamic and recursive equation. Therefore, this approach not suffers loss of generality. For simplicity of discussion we further assume that the system contains direct transfer energy conversions in just one direction. Either if a cross-coupling effect is required to be expressed then an inverse transfer function will be used to describe it. Computability of the parameters in the system from measured and known values is developed first, since it is the basis of fault diagnosis. Then the result is applied to fault location by the fault verification or assume-and-check method. It is shown that the diagnosability of an EP system, like the system diagnosability, depends on the connectivity of the system under test. Finally, a brief discussion is given on diagnosis by multiple taps of evaluation mode.