AS a principal element of a large-scale power plant, it is essential

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1 546 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 2, JUNE 2010 An Intelligent Power Plant Fault Diagnostics for Varying Degree of Severity and Loading Conditions Liangyu Ma, Yongguang Ma, and Kwang Y. Lee, Life Fellow, IEEE Abstract Practical fault diagnosis of a thermal system is very important in ensuring safe and reliable operation of a power plant. However, it is a difficult task due to the structural complexity of a thermal system, varying degree of severity of a fault, and the wide range of operation of the power generating unit. An artificial neural network combined with optimal zoom search is proposed in this paper for recognizing varying degrees of faults in a power plant thermal system operating at different load level. The zoom search technology is based on the similarity rules of the feature variables to a same fault with different severity when the system topological structure does not change with fault or with different loading conditions. Two different types of symptoms, a trend symptom and a semantic symptom, are calculated and jointly used for on-line fault recognition, which results in a faster and more stable fault diagnosis. A feedforward neural network structure is adopted and an improved training method is introduced. A high-pressure feedwater heater system is taken as a target system for investigation. Several simulation tests for diagnosing a multidegree fault under different operating conditions are carried out on a 300-MW power plant simulator to demonstrate the validity of the method. Index Terms Fault diagnosis, feedwater heaters, neural networks, optimal zoom search, power plants, varying-degree faults. I. INTRODUCTION AS a principal element of a large-scale power plant, it is essential for a thermal system to have a real-time fault detection and diagnosis system. This is because faults in a thermal system will directly influence the safety and economy of the whole plant, and even bring possible hazards to personnel. However, it is not easy to fulfill this difficult task. One reason is the structural complexity of the thermal system. Each subsystem in the thermal system is made up of many components, such as pumps, heaters, valves, etc., connected to each other by pipes. If a fault occurs in a single component of the system, not only its own variables, but also the variables of other components connected to it may become abnormal. In addition, the severity of a fault may be different for different times. The problem lies in how to select a typical sample for each fault in forming the Manuscript received May 06, 2009; revised July 16, 2009; accepted November 17, Date of publication January 29, 2010; date of current version May 21, Paper no. TEC L. Ma and Y. Ma are with the Department of Automation, School of Control Science and Engineering, North China Electric Power University, Baoding, Hebei , China ( maliangyu@ncepu.edu.cn; mr_ma@163.com). K. Y. Lee is with the Department of Electrical and Computer Engineering, Baylor University, Waco, TX USA ( kwang_y_lee@baylor. edu). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TEC fault knowledge base. Is one sample enough or should three or more samples be included? Another reason is that the thermal system often works under a wide range of different load levels to meet the load requirements of the power system. The fault diagnosis system must be designed not only for the rated full-load operation, but also for other secondary operation. Should we include fault samples with different severity for each fault under different load levels? In practice, too many fault samples will make the knowledge base too complex and usually, it is not easy to collect so many fault samples in an actual plant. As we understand, artificial neural networks (ANNs) have been successfully applied in fault diagnosis [1] [7]. However, most of their applications consider only a single operating point (usually the rated full load). Very few worked on fault diagnosis of varying-degree faults at different operating conditions [6 ][7]. Thus, by combining neural network with an optimized zoom search, a new approach is proposed for recognizing varying-degree faults in a thermal system, under different operating points. In this paper, the zoom search technology is put forward by summarizing the changing similarity rules of different faults in a power plant thermal system. Two different types of symptoms, a trend symptom and a semantic symptom, are calculated and jointly used for on-line fault recognition, which results in a faster and more stable fault diagnosis. A feedforward neural network is employed for thermal system fault diagnosis and an improved training scheme is introduced to improve its convergence. A high-pressure feedwater heater system of a 300-MW coal-fired power unit is taken as a target system for investigation. Several faults with varying degree of severity at different load levels are simulated in a power plant simulator and diagnosed with trained neural network. The testing results demonstrate the validity of the method. II. APPROACH TO FAULT DIAGNOSIS A. Introduction Usually, a complete fault diagnosis process can be divided into two stages. In the first stage, the fault knowledge base, which includes all typical faults of a given system, is summarized and an ANN is trained offline with these samples. In the second stage, the trained ANN is used for online condition monitoring and real-time fault diagnosis. During the first stage, the feature variables used for fault recognition are selected and their nominal values under different loads are predetermined. The typical fault samples used for ANN off-line training are collected either from an actual power plant or by using its simulator. A proper calculating method must be built to preprocess the feature variables into normalized fault symptoms, which can be accepted by ANN, /$ IEEE

2 MA et al.: AN INTELLIGENT POWER PLANT FAULT DIAGNOSTICS FOR VARYING DEGREE OF SEVERITY AND LOADING CONDITIONS 547 Fig. 1. New fault diagnosis scheme for power plant thermal system. usually taking values within [0, 1] or [ 1, 1]. The ANN structure also needs to be determined and then trained with the normalized fault samples. In order to realize the fault diagnosis for faults of variable degrees under different loading conditions, a new fault diagnosis scheme with ANN is put forward, as shown in Fig. 1. It can be seen from Fig. 1 that the difference lies in the addition of the optimized fault symptom zoom search into the new proposed fault diagnosis scheme compared with other traditional approaches. The ANN outputs are evaluated by a novel concept, fault separation degree (FSD), which is defined as the difference between the ANN's maximum output and its secondmaximum output. The new real-time fault diagnosis process include following steps. 1) To monitor the feature variables of a given system, once the symptoms of several feature variables are over threshold values (typically 0.1), the ANN will start to work to give a result. 2) The program calculates the FSD with the ANN's outputs. If the FSD is higher than the predetermined value (such as 0.85), the ANN's diagnostic result is considered as the final result. 3) If the FSD is less than the predetermined value, an optimal search program will start automatically to search for the optimal zoom factor for all feature variables continuously, until the ANN gives a result with a FSD higher than the predetermined value. B. Optimal Symptom Zoom Search As we know, the severity of a given fault may be quite different, either slight, medium, or severe. Since ANN-based fault diagnosis is actually a process of matching the abnormal plant condition with the typical fault samples, it brings a dilemma in choosing a typical fault sample used for ANN training. If a severe fault data are selected, the recognition ability for a slight fault will be poor. Conversely, if a slight fault data are selected, the same problem exists when the actual fault is severe. The ordinary disposing method is classifying a fault into several levels (sometimes treated as different faults) and includes more samples. Its negative effect is apparent for a complex thermal system, in which many types of possible faults exist. The fault knowledge library will become too large, and the structure of ANN will also become too complex and will not be easy to train. Sometimes, it is also difficult to find enough samples for so many different faults. The problem will become more complicated if we consider the wide range of different operating points of a power unit, which often need be adjusted to meet the varying load demand. Therefore, an effective fault diagnosis approach to deal with multidegree faults under different loading condition is very essential. After a long-term investigations of the changing rules for different faults in power plant thermal systems, several constructive conclusions are drawn [8], which are as follows. Rule 1: Under a fixed steady-load condition, if the topological structure of the thermal system does not change after a fault occurs, its related feature variables will change monotonically, either increase or decrease, along with the increase of its severity degree. Rule 2: If a power unit keeps the same operating mode under different loads and the fault does not change the system's topological structure, the rules of its related feature variables under different loads will be similar for the same fault with similar severity. The two aforementioned similarity rules are universal for thermodynamic faults in power plant thermal systems, especially for the faults that are relevant to energy/mass transfer and balance. Based on the two aforementioned rules, an optimal symptom zoom search technique can be employed to make the ANN structure simple and to allow fewer fault samples to be used for ANN training. In fact, only one typical sample with obvious change for those fault-related feature variables at 100% rated load condition is needed for each fault. An optimal match can be found by optimal symptom zoom search for an actual fault, either slighter or severer than this typical sample, or when the same fault occurs under different loading condition with similar operating mode. It should be noticed that the optimal symptom zoom search is bound by the two limitations of Rule 1 and Rule 2: 1) the system topological structure should remain the same after a fault occurs and 2) the unit operating modes should be similar under different loading condition. Fortunately, within a wide operating range, at least from 100% to 60% of rated load, the load change of a large-scale power unit is usually fulfilled automatically by its coordinated control system (CCS), and the overall operating mode of the unit usually remain unchanged. Under most cases, even if there is a little change for the whole unit's operating mode, the optimal symptom zoom search can still be used for a subsystem when the subsystem's topological structure remains unchanged. III. FAULT SYMPTOM CALCULATION A. Nominal Values for Feature Variables The feature variables for a thermal system fault have different values under different loads. If a fault diagnosis system is to be designed to be competent for different operating points, the

3 548 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 2, JUNE 2010 nominal reference values for these feature variables must be predetermined or calculated dynamically for real-time symptom calculation. Within the automatic load-changing scope (at least from 100% to 60% load change), which can be reached automatically with CCS without external intervention, the nominal value of a feature variable under different loads can be expressed with a unified function where is the actual load of the power unit and are some other variables closely related to, such as steam temperature, feedwater pressure, etc. In engineering practice, the nominal values for these feature variables under different loads can be determined with mathematical fitting, such as interpolation, or predicated with dynamic neural networks (such as recurrent networks [12]). Sometimes an auxiliary calculation based on internal mass and energy relationships of different variables may also be necessary. After the reference values of all feature variables have been predetermined or predicated dynamically, their corresponding fault symptoms can be calculated with a proper prescaling for use in online ANN fault diagnostics. The symptom calculation is also needed in normalizing the typical fault samples for offline ANN training. B. Symptom Calculation If a fault occurs inside a thermal system, the fault-correlated variables will increase or decrease notably, while other irrelevant variables will experience small changes. If a feature variable decreases from its nominal value after a fault occurs, its corresponding symptom may take a negative value [ 1, 0). On the contrary, if a variable increases from its nominal value, its symptom may take a positive value (0, 1]. If it does not change, its fault symptom will take the value 0. With this prescaling method, a fault symptom valued within [ 1, 1] can reflect a possible double-sided change of a feature variable under different faults [8] [10]. Considering both the dynamic process and the final steady state after a fault occurs, two different types of symptoms are brought forward and applied. One symptom is called a semantic symptom, which is obtained by comparing the difference between a variable's current value and its nominal reference value. A semantic symptom only becomes notable for a period of time after a fault occurs, but it keeps in existence when the system enters another balanced state over time. Another symptom is called a trend symptom, which is expressed with the changing rate of a feature variable. A trend symptom is very sensitive at the initial stage of a sudden fault and preferred for detecting a fault quickly. But after a fault occurs and when the system gradually steps into another balanced state, its value will become 0 and disappear. Since a semantic symptom and a trend symptom each has its merits and demerits, using them jointly will be in favor of getting (1) a faster and more reliable result in the thermal system real-time fault diagnosis. 1) Semantic Symptom Calculation: It is supposed that the change of a feature variable is bidirectional under different faults and its given maximum change is within ( ) after a fault occurs in the system under a certain load. If a linear prescaling method is used for fault symptom calculation and a semantic symptom takes values 0 corresponding to its nominal reference value, then the semantic fault symptom ( ) can be calculated as where is the reference value of variable under different load and is a given maximum width of change for the variable under different faults. 2) Trend Symptom Calculation: A trend symptom provides another kind of important information reflecting the changing direction and rate of a feature variable during the dynamic transition process after a fault occurs. It is supposed that the change of a feature variable is bidirectional under different faults and the maximum of its rate of change is within (, ). If a linear function is also adopted for trend symptom calculation and a trend symptom takes value 0, 1, or 1, respectively, corresponding to a steady condition ( equals 0), the maximum positive rate, or the maximum negative rate B, then the trend symptom ( ) can be written as Typically, can be reasonably predetermined as a certain percentage (such as 1%) of in (2). 3) Integration of Two Symptoms: Both a semantic symptom and a trend symptom have their merits and demerits. It is beneficial to use them jointly to form an integrated symptom ( )to obtain a faster and more stable fault diagnostic result. There are two possible cases for the integration of these two symptoms. Case A: The semantic symptom ( ) and the trend symptom ( ) take the same sign, i.e., both are greater than 0 or less than 0, as shown in Fig. 2 (a). Under this case, the integrated symptom ( ) is given as If and then If and then Case B: The semantic symptom ( ) and the trend symptom ( ) take different sign, as sections and of the plots shown in Fig. 2(b). Under this case, the semantic symptom reflects the transient relationship better than the trend symptom between the feature variable of the actual fault and its typical fault sample. (2) (3)

4 MA et al.: AN INTELLIGENT POWER PLANT FAULT DIAGNOSTICS FOR VARYING DEGREE OF SEVERITY AND LOADING CONDITIONS 549 Fig. 2. Relation of the two types of fault symptoms. (a) The two symptoms have the same signs. (b) The two symptoms have different signs. Fig. 3. Structure of a three-layer feedforward neural network. Thus, the integrated symptom ( ) takes the value of the semantic symptom, i.e., 1) Network initialization. The connecting weights { } and { } between the input, hidden, and output layers and the thresholds of all neurons are given random initial values. 2) Network training. Each typical fault sample is chosen in turn to train the network iteratively with proper learning rate. The connecting weights and the threshold values of the network are adjusted gradually through alternate pattern forward propagation and error BP process to guide the global error of the network between the actual output value and the expected value to decrease gradually. The training lasts until the global error meets the minimum error requirement. A standard steepest descent algorithm with a constant learning rate may be used to train the network. According to the algorithm, the BP learning updates the network weights and biases in the direction in which the performance function decreases most rapidly, i.e., in the direction of negative gradient. One iteration of this algorithm can be written as where is a vector of current weights and biases, is the current gradient vector, and is the learning rate. In practice, it is not easy to give a suitable value to the learning rate before training. If the learning rate is too high, the algorithm may oscillate and become unstable. If it is too small, the algorithm will take very long time to converge. Therefore, the training with a momentum term is often used and combined with adaptive learning rate to improve the convergence while maintaining the learning stable [12]. For this improved scheme, we select MSE, the average squared error between the network outputs, and the target outputs for the training set, as the network performance function. If the training set includes samples and the network has outputs, the MSE can be written as (4) (5) If and or if and then IV. ANN STRUCTURE AND ITS TRAINING SCHEME Because a feedforward neural network with hidden layers has an ability for nonlinear pattern recognition, a three-layer feedforward neural network shown in Fig. 3 is employed for diagnosing faults in varying degrees under different operating conditions. The transfer functions of neurons in both the middle layer and the output layer of the neural network take the form of sigmoid function:. A multilayer feedforward neural network must be trained with typical fault samples, before it can be applied for thermal system real-time fault diagnosis. The training of a feedforward network often adopts error back-propagation (BP) algorithm [11]. The steps are as follows. where and are data for the network output and the target output, respectively. In this paper, a simple approach for improving the convergence is employed. If the new MSE exceeds the old error by more than a predefined ratio (typically 1.04), the new weights and biases are discarded. In addition, the learning rate is decreased by multiplying a learning rate decreasing factor. Otherwise, the new weights are kept. If the new error is less than the old error, the learning rate is increased by multiplying a learning rate increasing factor. To further speed up the network training process, an adaptive learning rate is used to keep the MSE decreasing, at least at a fixed percentage in each epoch. If the MSE drop in one epoch is less than this given percentage, the learning rate will automatically increase [4]. A similar training algorithm called TRAINGDX is available in MATLAB neural network toolbox [13]. However, we have developed a real-time neural-network fault-diagnosis program

5 550 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 2, JUNE 2010 Fig. 4. High-pressure heater system of a 300-MW unit. Fig. 5. Structure of a three-stage high-pressure feedwater heater. with this improved algorithm to realize real-time fault diagnosis for power plant thermal systems. V. FAULT DIAGNOSIS SIMULATION TESTS A. Thermal System Under Investigation The feedwater heater system of a 300-MW coal-fired power generation unit is shown in Fig. 4. The three heaters are named no. 1, no. 2, and no. 3 high-pressure heaters, according to the high-to-low sequence of their extraction steam pressure. As shown in Fig. 5, each heater includes three heat transfer stages: overheated steam cooling stage, saturated steam condensing stage, and drain water cooling stage. The measured points related to this system in the data acquisition system (DAS) include: speed of each feedwater pump; feedwater pressure and temperature at pump exit pipe; each heater's inlet and exit feedwater temperature, and its drain water temperature; each heater's inlet steam pressure and temperature; each heater's water level and the openings of its normal and emergency drain valves; feedwater pressure and temperature before economizer; water level of the deaerator and the opening of its water level control valve, etc. B. Selection of Fault Samples For the feedwater heater system in Fig. 4, 16 typical faults are generalized as follows [14][15]: 1) feedwater leakage from pipe to shell side in each heater (F1, F2, and F3); 2) leakage from water-intake chamber to outlet chamber in each heater (F4, F5, and F6); 3) pipe block inside each heater (F7, F8, and F9); 4) shell-side air accumulation in each heater (F10, F11, and F12); 5) steam extraction valve choke for each heater (F13, F14, and F15); 6) the inlet three-way valve's bypass side not fully closed or inner leak (F16) To diagnose aforementioned faults, 16 feature variables are selected. These variables are either directly taken from the distributed control system (DCS) or obtained through simple calculation with several DCS variables. The selected variables are listed as follows: 1) terminal temperature difference (TTD) of each heater (S1, S2, and S3); 2) drain subcooling approach (DCA) of each heater (S4, S5, and S6); 3) feedwater temperature rise of each heater (S7, S8, and S9); 4) entrance steam pressure of each heater (S10, S11, and S12); 5) total opening of the two drain valves of each heater (S13, S14, and S15); 6) average speed of the running feedwater pumps (S16). Detailed fault simulation tests have been made for the aforementioned 16 typical faults on a full-scope simulator of a 300-MW coal-fired power unit. With the fault symptom prescaling method proposed in Section III, 16 typical fault samples under 300-MW rated loading condition are selected and normalized to form the fault knowledge base. Every fault's severity degree is reasonably set to ensure that its interrelated symptoms change distinctively and the topological structure of the system maintains unchanged. Only these 16 samples, i.e., only one sample per fault under full-load operating point are selected and used for training of the neural network. The trained ANN will be used for diagnosing faults of varying degrees under different operating conditions. The operating points investigated are in between 100% and 60% of rated load, which can be reached automatically with CCS. Usually no equipment needs to be stopped, and the topological structure of the system remains unchanged when the load is changing within this load region. C. Network Training For the aforementioned high-pressure heater system, its fault samples include 16 faults and 16 fault symptoms. Therefore, the input layer and the output layer of the neural network both include 16 neurons. The hidden layer of the network takes 22 neurons. The initial value of the learning rate takes 0.6. The learning rate decreasing factor takes 0.7 and the learning rate increasing factor takes 1.1. The minimum MSE correction percentage in each step takes 0.55%. Then with the improved training method explained in Section IV, the MSE of the network will be less than after about 850 training epochs. Had this improved method not been adopted, the network needs

6 MA et al.: AN INTELLIGENT POWER PLANT FAULT DIAGNOSTICS FOR VARYING DEGREE OF SEVERITY AND LOADING CONDITIONS 551 Fig. 6. MSE curves with different ANN training methods. over epochs to reach the same accuracy with the same initial learning rate. The MSE curves with two different methods are compared in Fig. 6. D. Test Results for Different Faults Under Different Loads In the following tests, faults of varying degrees are simulated on a full-scope power plant simulator under two different loading conditions, 300 and 210 MW. The power unit is running in turbine-base boiler-following coordination mode for the 300-MW loading condition. The automatic control systems, including drum water level control, high-pressure heaters water level control, superheater steam and reheater steam temperature control, etc., are all in normal operation. The 210-MW-load steady-state condition is obtained by dropping load with slidepressure mode from the full-load condition. The nominal reference values of the feature variables under the two operating points are taken from the simulation when the unit is operating stably with no fault. The constants and in (1) and (2) are set rationally through fault simulation under 300-MW load and will not change under 210-MW loading condition. First, a fault with different severity degree under 300-MW rated load-operating condition is recognized with the trained neural network. Then, the simulator's initial condition is changed to 210 MW point and the same fault of different severity degree will be simulated again and diagnosed in real time with the ANN program. Whenever the FSD of the ANN diagnostic result is less than 0.85, whereas more than three symptom values are over the given threshold value 0.1, something must be abnormal in the system, and the optimal symptom zoom search program will start automatically to search for the optimal symptom zoom value in order for the neural network to give a good result with a higher FSD value. Simulation results are shown in Figs For comparison purpose, all faults are applied at the fourth second. In each subfigure (a), (b), or (c), the left-hand side figure shows the fault number indicated by ANN, and the right-hand side figure shows the symptom zoom factor marked with L and FSD with R. It is obvious that the higher the FSD, the better is the fault diagnosis result when the ANN indicates a correct fault number. The fault diagnosis result given by ANN can be regarded as effective when the FSD is higher than 0.5. But, it is certainly not the best result with an optimal symptom zoom factor. A higher Fig. 7. Diagnostic results of the F5 fault under 300-MW load. (a) No. 2 heater leaking 5% from intake chamber to exhaust chamber. (b) No. 2 heater leaking 50% from intake chamber to exhaust chamber. Fig. 8. Diagnostic results of the F5 fault under 210-MW operating point. (a) No. 2 heater leaking 5% from intake chamber to exhaust chamber. (b) No. 2 heater leaking 50% from intake chamber to exhaust chamber. FSD threshold value of 0.85 is selected in our tests in order to find a nearly optimal symptom zoom factor. But due to the similarity deviation between an actual fault and its typical fault sample, if the given threshold value for FSD is too high (i.e., close to 1.0), it may become difficult or even impossible for the optimal search program to find a proper symptom zoom factor, with which the ANN gives a result with a higher FSD value. Usually, the threshold value for FSD may be selected between 0.7 and 0.9.

7 552 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 2, JUNE 2010 Fig. 9. Diagnostic results of the F1 fault under 300-MW operating point. (a) No.1 heater feedwater leaking 2% to shell side. (b) No.1 heater feedwater leaking 8% to shell side (emergency drain valve partly opened). (c) No.1 heater feedwater leaking 10% to shell side (system isolated). Fig. 10. Diagnostic results of fault F1 at 210-MW operating point. (a) No.1 heater feedwater leaking 2% to shell side. (b) No.1 heater feedwater leaking 14% to shell side. (c) No.1 heater feedwater leaking 16% to shell side (system isolated). 1) Case 1 (No. 2 Heater's Feedwater Leaking From Intake Chamber to Exhaust Chamber, F5): The severity degree of the typical sample for the F5 fault used for ANN training is 20% leakage of the total feedwater flow under 300-MW loading condition. The F5 fault is simulated for different severities, 5% and 50% of total flow, under 300- and 210-MW loading conditions. The ANN fault diagnostic results with optimal symptom zoom search are given in Figs. 7 and 8. It can be seen from Fig. 7(a), the fault with 5% feedwater leakage under 300-MW load, that the ANN begins to indicate correct fault number F5 after 50 s. The steady-state FSD is The optimal symptom zoom factor is 3.764, much higher than 1.0, thus showing that the actual fault degree (5%) is much less than that of the typical fault sample (20%). For the fault with 50% leakage of feedwater flow, the ANN gives the correct fault number F5 after 31 s. The last steady-state FSD is and the optimal symptom zoom value is 0.775, thus showing that the actual fault is severer than the typical fault sample [see Fig. 7(b)]. Similarly, it can be observed from Fig. 8(a) and (b) that the ANN recognizes the F5 fault for both 5% and 50% leakage under 210-MW load correctly. For the fault with 5% leakage, ANN gives correct fault number after 58 s. The stable FSD value is 0.9 and the optimal symptom zoom value is For the fault with 50% leakage, ANN gives correct fault number F5 after 35 s. The stable FSD is The optimal symptom zoom factor is ) Case 2 (No. 1 Heater Feedwater Leaking From Pipe to Shell Side, F1): The severity degree of the typical sample for the F1 fault used for ANN training is 7% of the total feedwater flow under 300-MW load. Under this leakage, the normal drain valve for no. 1 heater is with a very large opening, but the emergency drain valve is still kept closed. The water level can be maintained at normal level and the topological structure of the system is not changed. The F1 fault is simulated for different severities under 300- and 210-MW loading conditions. The ANN fault diagnostic results with optimal symptom zoom search are given in Figs. 9 and 10.

8 MA et al.: AN INTELLIGENT POWER PLANT FAULT DIAGNOSTICS FOR VARYING DEGREE OF SEVERITY AND LOADING CONDITIONS 553 Under 300-MW load, it is observed from Fig. 9(a) and (b) that ANN gives correct results for the fault with both 2% and 8% leakage severities. Actually, with 8% feedwater leakage, the emergency drain valve of no. 1 heater has been partly opened to maintain its level normal and there exists some change in the system's topological structure, but the ANN can still give correct result. When the leakage reaches 10% of total feedwater flow, the water level of no. 1 heater cannot be maintained any longer, even if the emergency valve is fully opened. So the water level keeps rising, until the whole heater system is isolated. In this case, the structure of the system has been greatly changed and ANN cannot give a correct diagnostic result after 31 s, as shown in Fig. 9(c). Under 210-MW load, the F1 fault is simulated and diagnosed with different severities, 2%, 14%, and 16% leakage of the total feedwater flow. It is observed from Fig. 10(a) and (b) that the ANN gives correct results for the fault with 2% and 14% leakage. For the same severity (% leaking), its corresponding absolute leakage flow under 210-MW loading condition is far less than that under 300-MW loading condition. Therefore, with 14% feedwater leakage, the water level of no.1 heater can still be maintained at normal level with very little changes in system structure, and thus, the ANN can still recognize the fault in time. When the leakage reaches 16% of the total feedwater flow under 210-MW load, the water level cannot be maintained any longer and the whole system will be isolated as a result. Due to the great change in the system structure, the ANN cannot indicate a correct fault number after 57 s, as shown in Fig. 10(c). These simulation tests have demonstrated the scope of application for ANN fault diagnosis with optimal symptom zoom search, i.e., the topological structure of the system remains unchanged after a fault occurs under different operating conditions. VI. CONCLUSION A neural network method combined with optimal symptom zoom search technique is proposed for diagnosing faults with varying degrees of severity under different loading conditions in a power plant thermal system. The optimal symptom zoom search is based on the similarity rules of the feature variables for a fault with different degrees of severity if the system topological structure remains unchanged after a fault occurs or under different loading conditions. With the optimal symptom zoom search technique, the ANN trained only with relatively fewer fault samples under rated load can be used for recognizing faults with varying degrees of severity under a wide range of loading conditions. Two types of fault symptoms, a semantic symptom that reflects the magnitude of variable changes and a trend symptom that reflects the variable's changing rate and direction, are jointly used to diagnose a fault timely and with confidence. The feedwater heater system of a 300-MW coal-fired power generating unit is taken as an example for testing the proposed diagnostic technique, and several fault diagnosis examples are given to verify the validity of the method. Although the aforementioned results are very encouraging in providing a new approach to deal with fault diagnosis problem for faults with varying degrees of severity under different operating conditions, more investigation should be conducted in future work. The scope of loading conditions in fault diagnosis should be further expanded. The research needs to step into the case of gradual load-changing dynamics and multiple faults under different stable operating conditions. 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9 554 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 2, JUNE 2010 Liangyu Ma received the B.S. degree in thermal power engineering, the M.S. and Ph.D. degrees from North China Electric Power University (NCEPU), Baoding, China, in 1993, 1996, and 2004, respectively. From March 2008 to February 2009, he was a Visiting Scholar at Baylor University, Waco, TX. He is currently an Associate Professor with the Department of Automation, School of Control Science and Engineering, NCEPU. His research interests include power plant modeling and simulation, condition monitoring and intelligent fault diagnosis for power station thermal facilities and systems, and intelligent control and application to power plants. Yongguang Ma received the B.S. and M.S. degrees in measurement and automation, and the Ph.D. degree in thermal power engineering from North China Electric Power University (NCEPU), Baoding, China, in 1984, 1990, and 1997, respectively. He is currently a Professor and the Chairman of the Department of Automation, School of Control Science and Engineering, NCEPU. His research interests include industry process simulation and control, and application of intelligent technologies in power plant control and diagnosis. Kwang Y. Lee (LF'08) received the B.S. degree in electrical engineering from Seoul National University, Seoul, Korea, in 1964, the M.S. degree in electrical engineering from North Dakota State University, Fargo, ND, in 1968, and the Ph.D. degree in systems science from Michigan State University, East Lansing, in He has been engaged in the area of power plants and power systems control for more than 30 years at Michigan State University, East Lansing, Oregon State University, Corvallis, the University of Houston, Houston, the Pennsylvania State University, University Park, and the Baylor University, Waco, TX, where he is currently a Professor and the Chairman of the Department of Electrical and Computer Engineering. His research interests include control, operation, and planning of energy systems, computational intelligence, intelligent control and their applications to energy and environmental systems, and modeling, simulation, and control of renewable and distributed energy sources.