3. Description of the coal-grinding subsystem in thermoelectric power plant

Thermoelectric power plants are the largest producers of electricity in Serbia, contributing with more than 65% of the total electricity supply. In order to ensure their stability and operational efficiency, it is necessary to monitor their major subsystems and individual components. In this way, it is possible to detect any change in behaviour, or failure on time, which leads to the increase of energy efficiency and the reduction of the financial losses of the electric power industry.

One of the key thermoelectric power plant components is the coal-grinding subsystem. Its physical layout is shown in Figure 1. Raw coal enters the subsystem through a feeder and goes down a chute to the grinding table that rotates at a constant speed. The coal is then moved outward by centrifugal force and goes under three stationary rollers where it is ground. The outgoing coal moves forward to the mill throat where it is mixed with hot primary air. The heavier coal particles immediately move back to the grinding table for additional grinding, while lighter particles are carried by the air flow to the separator. The separator contains a large amount of particles suspended in the powerful air flow. Additionally, some of the particles drawn into the primary air-and-coal mix lose their velocity and fall onto the grinding table (as shown) for further grinding, while the particles that are fast enough enter the classifier zone. These particles are swirled by deflector plates. Lighter particles are removed as classified fuel in the form of fine powder that goes to burners, while heavier particles bounce off the classifier cone

Figure 1. Configuration of the coal-grinding subsystem.

The first step in data processing is data cleaning. This step is very important, because data (especially event data), which are entered manually always, have some mistakes. Without data cleaning, it is possible that diagnostics and prognostics will be inaccurate. The next step in data processing is data analysis. Different models, algorithms and tools for data analysis depend mostly from data type [5]. Condition-monitoring data can be classified into three categories: (1)

The last step in predictive maintenance programme is decision-making. Techniques for decisionmaking can be divided into two categories: diagnostics and prognostics. It is obvious that prognostics is superior in regard to diagnostics, because it can prevent failure to occur, and if it is possible it provides spare parts and planned human resources for problems that will occur. In this way, it is possible to reduce material losses and avoid catastrophic failures. However, prognostics cannot replace diagnostics completely, because in practice there will be always some

Here, we focus on prognostics. There are two types of prediction when we talk about failure prognostic. The first type is the prediction of how much time is left before failure will occur (one or more failures) depending on the current state of the machine and past operation profile. Time that is left before the fault is noticed is called remaining useful life (RUL). In some situations, especially when failure is catastrophic (e.g. nuclear plant), it is much a preferable second type of failure prognostic, that is, prediction of probability that the machine will work until some future time (e.g. until next interval when inspection is needed) depending on the current state of the machine and past operation profile. Actually, in any situation, it is good to know the probability that a machine will work without failure until the next inspection or condition monitoring. Most papers deal with the first type of failure prognostic, that is, with RUL estimation [13, 14]. Only few papers can be found that deal with the second type of prognostic [15]. According to Ref. [8],

1. Traditional reliability approaches—prediction based on event data (experience) [16]

3. Description of the coal-grinding subsystem in thermoelectric power

Thermoelectric power plants are the largest producers of electricity in Serbia, contributing with more than 65% of the total electricity supply. In order to ensure their stability and operational efficiency, it is necessary to monitor their major subsystems and individual components. In this way, it is possible to detect any change in behaviour, or failure on time, which leads to the increase of energy efficiency and the reduction of the financial losses of the electric power industry.

3. Integrated approaches—prediction based on event data and condition-monitoring data [19]. Every one of these approaches has some advantages and limitations. Combinations of these approaches are different according to their applicability, price, precision and complexity [20].

2. Prognostics approaches—prediction based on condition-monitoring data [17, 18]

value type, (2) waveform type and (3) multidimensional type.

30 Recent Improvements of Power Plants Management and Technology

failure prediction can be divided into three different categories:

unpredictable faults.

plant

and fall back onto the grinding table for additional grinding. Both the separator and classifier contain a significant amount of coal. These coal masses, along with the coal on the grinding table and the three recirculating loads (primary, secondary and tertiary), play a key role in the dynamic performance of the mill [21, 22].

In this research, one such system at the thermoelectric power plant 'TEKO' (Serbia) is analysed. As it is previously described, the coal inside the mill is ground by impact and friction against the grinding table that rotates around the mill centre line (CL). The only way to determine the current condition of the grinding table is to shut down the entire subsystem and open it for visual inspection. This time-based maintenance method guarantees that grinding tables will be replaced before they become dysfunctional, but at a cost of frequent shutdowns. If inspection shows that grinding table replacement is not yet necessary, then significant material losses will incur. In Figure 2, two grinding tables are shown. On the left figure is a new grinding table, immediately after replacement, and on the right figure is a worn grinding table, straight before replacement.

Figure 2. New grinding table (left) and worn grinding table (right).

In practice which is common on plant A1, at thermoelectric power plant 'TEKO', Kostolac, grinding tables are replaced every 1800 h. However, it often happens that because of the increased presence of limestone, sand and other impurities in coal, grinding tables become deteriorated already after 1400 h, or even shorter. In that case, weaker effectiveness of the mill is noticeable, it is 'chocked', and serious problem with regulation occurs in an attempt to regulate the temperature of air mixture and pressure of fresh steam in front of the turbine. This appearance has for consequence significant misbalance of temperature distribution inside the firebox, which has negative influence on increased water injection in fresh steam, knockdown of coefficient of boiler efficiency and so on. In such conditions, usually, mill must be stopped unplanned for grinding table replacement and that incurs financial losses. Because of that, system which offers predictive maintenance is of great importance.
