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Optimization of the combustion control process in a tangentially fired pulverized coal boiler, to achieve uniform temperature distribution, is discussed in the paper. This issue is even more critical in those thermal power plants which are not equipped with modern systems for combustion enhancement, such as low NOx burners. Research has shown that the temperature distribution inside the boiler of such power plant can be controlled by adjusting firing, through coal redistribution among the mills. Furthermore, disturbed flame symmetry (i.e. non-uniform temperature distribution in the boiler) is reflected in a large difference between the output temperatures measured on the left and right sides of the boiler. Given the non-stationary conditions typical of thermal power plant boilers, an adaptive control approach is proposed, based on PI controllers which are very popular in industry and widely accepted. Self-tuning of the PI controllers is based on dynamic model parameters derived applying the weighted recursive least squares (WRLS) method to real data recorded at Nikola Tesla B thermal power plant in Serbia, whose nominal power is 650 MW. The same model was later used to test the proposed control approach.

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.

 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

A fault detection and isolation approach, specially designed for steam separators in thermal power plants, is presented in the paper. The first step of the proposed algorithm is to identify the process. Because of the presence of sporadic high-intensity measurement noise (outliers), the paper proposes a robust version of recursive identification. Starting from the parameter vector of the identified model, the second step of the proposed procedure has the form of a data-driven fault detector. This particular fault detection and isolation approach was implemented at TEKOB1 Kostolac Thermal Power Plant in Serbia, whose nominal power output is 330 MW.

Prognostic methods represent a new methodology for system maintenance which offers significant time and cost savings. The paper offers a short overview of the available prognosis techniques and proposes the implementation of one model-based and one data-driven method. As a representative of the model-based methods the autoregressive moving average (ARMA) modeling approach is chosen. The estimated model parameters are further used for implementing the early change detector which is realized as a Neyman-Pearson hypothesis test. On the other hand, hidden Markov model (HMM) based prognosis illustrates the use of data-driven techniques. Using the cross-correlation input-output functions, HMM prognosis algorithm is proposed, as a suitable way of timely detection. Both techniques were implemented to detect performance changes of the water level sensor in a steam separator system in thermal power plants.

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.

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.

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 stemseparator system in thermal power plants. The comparison to the conventional RLS approach demonstrates the effectiveness of this method in the presence of impulse noise.

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.