Aleksandra Marjanovic

Sensor fault diagnosis in water-steam power plant: A combined observer-based/pattern-recognition approach

This work deals with the problem of model-based sensor FDI in water-steam power plants where, due to extreme pressure and temperature conditions, measurement sensors are prone to failures. Faults in the measurement devices of output variables (water flow and level) and of input variable (steam flow) are considered. When both the output and input measurements are subject to faults it is hard to detect and estimate them. To overcome this limitation and achieve FDI, we propose to use a sliding mode observer (SMO) and to make an appropriate signature analysis on the resulting output injection terms in order to identify a “distinguishing” signature for each fault. The performance of the proposed scheme has been evaluated off-line using real-data taken from the TEKO B1 Thermal Power Plant of Kostolac (Serbia) whose nominal power is 330 MW.

Combustion distribution control using the extremum seeking algorithm

Quality regulation of the combustion process inside the furnace is the basis of high demands for increasing robustness, safety and efficiency of thermal power plants. The paper considers the possibility of spatial temperature distribution control inside the boiler, based on the correction of distribution of coal over the mills. Such control system ensures the maintenance of the flame focus away from the walls of the boiler, and thus preserves the equipment and reduces the possibility of ash slugging. At the same time, uniform heat dissipation over mills enhances the energy efficiency of the boiler, while reducing the pollution of the system. A constrained multivariable extremum seeking algorithm is proposed as a tool for combustion process optimization with the main objective of centralizing the flame in the furnace. Simulations are conducted on a model corresponding to the 350MW boiler of the Nikola Tesla Power Plant, in Obrenovac, Serbia.

The robustness of the differential quantizer in the case of the variable signal to noise ratio

In this paper a short theoretical overview of differential quantizer and its implementations is given. Afterward, the effect of the order of prediction in differential quantizer and the effect of the difference in order of predictor in the input and output of differential quantizer is analyzed. Then it was proceeded with the examination of the robustness of the differential quantizer in the case in which a noise signal is brought to the input of the differential quantizer, instead of the clean speech signal. The analysis was conducted with a uniform distribution, as well as the noise with the gaussian distribution, and the obtained results were adequately commented on. Also, experimentally a limit was set which refers to the intensity of the noise and still enable results which are better that a regular uniform quantizer. The whole analysis is done by using the fixed number of bits in quantization, i.e. 12-bit quantizer is used in all the implementations of differential quantizer. In the conclusion of this paper there is a discussion about the possibility of implementing a differential quantizer which will be able to recognize which noise attacks the system, and in addition to that, in what form it adapts its coefficients so that it at any moment acquires the optimal signal to noise ratio.

Control of Thermal Power Plant Combustion Distribution Using Extremum Seeking

High demands for increasing robustness, safety, and efficiency in thermal power plants are the main motivation behind ongoing attempts to optimize combustion. This paper presents a study of modeling and control of the combustion process in a tangentially fired pulverized-coal boiler. It proposes an approach to flame geometry and position control by means of reallocation of firing. Such control ensures flame focus maintenance away from the walls of the boiler, and thus prevents many unwanted by-products of combustion. In addition, uniform heat dissipation over mills enhances the energy efficiency and reliability of the boiler. First, experimental data obtained from the 350-MW boiler of the Nikola Tesla power plant, Serbia, are analyzed in detail. This results in a model identification procedure using an adaptive parameter estimation method. Second, constrained multivariate extremum seeking (ES) is proposed in this paper, to optimally tune boiler operation in order to maintain the desired flame configuration in the furnace. Finally, the effectiveness of the ES adaptive controller in the presence of disturbances is demonstrated through simulations performed on the experimentally identified model of the boiler.

TOWARD ACOUSTIC NOISE TYPE DETECTION BASED ON QQ PLOT STATISTICS

Fault detection and state estimation using acoustic signals is a procedure highly affected by ambient noise. This is particularly pronounced in an industrial environment where noise pollution is especially strong. In this paper a noise detection algorithm is proposed and implemented. This algorithm can identify the times in which the recorded acoustic signal is influenced by different types of noise in the form of unwanted impulse disturbance or speech contamination. The algorithm compares statistical parameters of the recordings by generating a series of QQ plots and then using an appropriate stochastic signal analysis tools like hypothesis testing. The main purpose of this algorithm is to eliminate noisy signals and to collect a set of noise free recordings which can then be used for state estimation. The application of these techniques in a real industrial environment is extremely complex because sound contamination usually tends to be intense and nonstationary. The solution described in this paper has been tested on a specific problem of acoustic signal isolation and noise detection of a coal grinding fan mill in thermal power plant in the presence of intense contaminating sound disturbances, mainly impulse disturbance and speech contamination.

Multiple fault diagnosis by signature recognition of time-varying residuals

A Fault Detection and Diagnosis scheme able to deal with concurrent, incipient, sensor and actuator faults is presented. The architecture allows the diagnosis whenever the systems outputs are less than the number of faults. Residual generation is achieved by taking advantage from observer-based design, whereas the classification is performed by extending the concept of directional residual towards a time-varying setting. The scheme is designed to leverage the power from both model-based and data-driven approaches while mitigating their inherent drawbacks. The performances of the proposed strategy are evaluated by employing real data coming from the TEKOB1 Thermal Plant of Kostolac, Serbia.

Fault diagnosis in nonlinear stochastic systems via particle filtering

We consider the problem of detecting malfunctions in the actuators or sensors of systems which can be described by nonlinear/non-Gaussian stochastic state-space models. The basic idea is to estimate the state vector of such models using a sequential Monte Carlo technique known as particle filtering. We present several approaches to detecting faults and pinpointing their location within the system, using either one or a bank of particle filters.

The use of Bayesian Networks in Detecting the States of Ventilation Mills in Power Plants

 Abstract—The main objective of this paper is to present a new method of predictive maintenance which can detect the states of coal grinding mills in thermal power plants using Bayesian networks. Several possible structures of Bayesian networks are proposed for solving this problem and one of them is implemented and tested on an actual system. This method uses acoustic signals and statistical signal pre-processing tools to compute the inputs of the Bayesian network. After that the network is trained and tested using signals measured in the vicinity of the mill in the period of 2 months. The goal of this algorithm is to increase the efficiency of the coal grinding process and reduce the maintenance cost by eliminating the unnecessary maintenance checks of the system

To robust identification of water steam-separator process in thermal power plants

One of the primary requirements for a high performance process control is a good quality and reliability of obtained measurements. In order to overcome the problem of sporadic high-intensity irregular measurements (outliers) presence, a robust process identification procedure must be used. The paper presents an application of an adaptive approach to robust parameter estimation of a linear dynamic discrete-time system, based on QQ-plot method together with robustified winsorization technique. The proposed procedure is implemented in a stem separator system in thermal power plants. The comparison to the conventional RLS approach demonstrates the effectiveness of this method in the presence of impulse noise.

Comparison of identification procedures in the frame of fault detection and isolation

Paper presents a model-based fault detection and isolation technique which relies on system identification procedure. Several open-loop identification methods are discussed since they are usually less complex and thus easier for implementation in large-scale systems. However, since the system analysis is usually conducted in a closed-loop structure, prediction error based direct closed-loop method was also taken into consideration. The efficiency of these four identification methods is demonstrated keeping in mind the final goal of the algorithm, which is the fault detection and isolation. Therefore, the performance of these methods in the light of FDI is emphasized, while the accuracy of parameter estimates is considered less important. The comparative analysis of suggested methods was done using data obtained from the steam separator in TEKO B1 unit of the Kostolac thermal power plant, Republic of Serbia.