Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence
Abstract
:1. Introduction
- Fault detection: detection or observation of a fault occurring in the object and determination of the detection time [58];
- Fault isolation: isolation, determination of type, size, and time of the fault’s occurrence [59];
- Fault identification: determination of the size and character of the fault’s variability in time [43].
1.1. Literature Gap
Author | Literature Source | Type of Analyzed Turbine: Steam and/or Gas and/or ORC | The Way of Locating the Degradation in the Turbine | Additional Remarks |
---|---|---|---|---|
Zhou et al. | [69] | Gas turbine | Convolution neural networks | |
Fast, Palme’ | [68] | Gas turbine with its heat recovery steam generator and a biomass-fueled boiler with its steam cycle | ANN | |
Ślęzak-Żołna, Głuch | [113] | Steam turbine | ANN | |
Ślęzak-Żołna | [100] | Steam turbine | ANN | |
Głuch, Drosińska-Komor | [92] | Steam turbine | ANN | |
Głuch | [95] | Steam turbine | ANN | |
Gardzilewicz et al. | [101] | Steam turbine | Statistical iterative | |
Butterweck, Głuch | [114] | Steam turbine | Neural model | Modeling CFD. |
Nowak, Rusin | [102] | Steam turbine | GA + FEM | Shape optimization of selected areas of the rotor of the high-pressure part of an ultra-supercritical steam turbine and the optimization of the turbine startup method. |
Barelli et al. | [103] | The turbocharging system of a 1 MW internal combustion engine (I.C.E.) | The fuzzy logic | Specifically for the filters and compressor modules. |
Zuming, Karimi | [104] | Combined cycle gas turbine (CCGT) power plants | Simulators such as GateCycle, Aspen HYSYS | |
Zhou et al. | [105] | Gas turbine | Support vector machine | |
Wong et al. | [106] | Gas turbine | Extreme learning machine | The comparison between extreme learning machines and support vector machines (SVM) was also made. |
Yan et al. | [99] | Steam turbine generator | Hierarchical fuzzy CMAC neural network | |
Tsoutsanis et al. | [107] | Gas turbine | Matlab’s built-in nonlinear unconstrained optimization algorithm is known as ‘‘fminsearch” in the performance adaptation process. | Adaptive diagnostics method. |
Tsoutsanis et al. | [108] | Gas turbine | The performance adaptation process. | A model that was developed in Matlab/Simulink. |
Barad et al. | [109] | Gas turbine engine | Neural network | |
Madhavan et al. | [110] | Aero gas turbine engine | Three-dimensional (3D) finite element (FE) | Turbine rotor blade vibration. |
Kuo | [96] | Gas turbine | ANN and fuzzy Logic | Turbine blade faults in fan turbo-jet. |
Aslanidou et al. | [111] | Micro gas turbine | Machine learning | |
Sławiński et al. | [112] | Gas turbine | The COM-GAS numerical code and FEM | Presents the ravages of second rotor stage failure in a gas turbine. |
Angelakis et al. | [115] | Gas turbine | ANN | Diagnose blade faults. |
Aretakis et al. | [116] | Gas turbine | Wavelet analysis | Vibration, unsteady pressure, and acoustic measurements are used to diagnose turbine faults. |
Li, Nilkitsaranont | [117] | Gas turbine | Non-linear diagnostic regression techniques, including both linear and quadratic models, | |
Breikin et al. | [118] | Aero gas turbine engine | GA | Dynamic modeling. |
Fentaye et al. | [119] | Gas turbine | Bayesian network | Used adaptive gas path analysis (AGPA). |
Dhini et al. | [120] | Steam turbine | Extreme learning machine-radial basis function networks | ELM-RBF was a comparison with back propagation neural network (BPNN). |
Yang et al. | [121] | Steam turbine | Knowledge graph and Bayesian network | |
Salahshoor et al. | [88] | Steam turbine | Fusion of a support vector machine classifier with an adaptive neuro-fuzzy inference system classifier, | |
Zeng et al. | [122] | Gas turbine | Dynamic simulation model and Cuckoo search algorithm | The model was developed in the environment of Matlab/Simulink. |
Salilew et al. | [123] | Gas turbine | Gas path non-linear steady-state model | |
Yang et al. | [124] | Gas turbine | Kalman filter | |
Asgari et al. | [125] | Gas turbine | ANN | |
Mo et al. | [126] | Gas turbine | Fuzzy inference logic | |
Zhang et al. | [127] | Steam turbine | Bayesian network | |
Chmielniak and Trela | [128] | Steam turbine | ANN and Bayesian network | |
Bzymek et al. | [129] | Steam turbine | Computational Solid Dynamic coupled Computational Fluid Dynamic | Process of improving the safety and reliability of operation of the 18K370 steam turbines Opole Power Plant. |
Banaszkiewicz | [130] | Steam turbine | FEM + Duhamel’s integral | |
Banaszkiewicz | [131] | Steam turbine | probabilistic analysis and fracture mechanics considerations | |
Banaszkiewicz and Rehmus-Forc | [132] | Steam turbine | Material testing and mechanical integrity calculations | |
Kraszewski et al. | [133] | Spherical bifurcation pipe of a live steam | A one-sided numerical thermal-FSI analysis | Element of a block of coal-fired power plant working with a 18K370 turbine. |
Badur et al. | [134] | Control stage in steam turbine | Studied analytically and numerically | Combination of CFD + CSD, often called FSI (Fluid–Solid Interaction or Fluid–Structure Interaction). |
Badur et al. | [135] | Steam turbine–rotor and casting | Thermal-FSI (Fluid–Solid Interaction) | |
Madejski et al. | [136] | Utility boilers | CFD calculations | |
Blaut, Breńkacz | [137] | Rotor unbalance | Teager–Kaiser energy operator (TKEO) | |
Andrearczyk et al. | [138] | Prototypical microturbine operating in an ORC-based power plant | Use LabVIEW | Vibrodiagnostic system designed. |
Badur et al. | [139] | Heat exchanger | “Thermal-FSI” (“Fluid–Solid Interaction”) | |
Ziółkowski et al. | [140] | Steam and gas turbine | COM GAS | |
Ziółkowski et al. | [141] | ORC | COM GAS | |
Ziółkowski et al. | [142] | Steam turbine | ECO PG, CFD | |
Lampart et al. | [143] | ORC | Hybrid algorithms | |
Niksa-Rynkiewicz et al. | [144] | Gas turbine | ANN |
1.2. Purpose and Structure of the Article
2. Methodology Obtaining a Diagnosis in Thermal and Flow System
2.1. Genetic Algorithms
- First, the whole algorithm is initiated by creating an initial population.
- Then, the chromosome adaptation in the population is assessed, and this concerns each chromosome in the population.
- The third stage concerns the stopping condition and depends on the method of applying a genetic algorithm. Two stopping conditions can occur. The first one occurs when the problem being analyzed involves an optimization task. Here, the decisive condition may be determining the optimum value (minimum or maximum). Another case may occur after the algorithm has been in operation for a specified time or when its operation does not lead to any improvement in the result obtained.
- The fourth stage introduces chromosome selection: to create a population, chromosomes with the highest value of adaptation are selected [158]. Ranking selection is used in thermal and flow diagnostics.
- The fifth stage concerns the genetic operator, which creates a population out of chromosomes obtained after the fourth stage. This is where the crossover and mutation operators can be distinguished. The crossover operator is more frequently used than the mutation operator [159].
- The penultimate stage involves creating a new population obtained after applying operators from the preceding stage. The new population can thus be subject to a specified action, i.e., checking the algorithm-stopping condition or introducing a specified chromosome.
- The last stage involves introducing the “best” chromosome; this occurs when the stopping condition is met. Such action enables the result for the entire genetic algorithm to be obtained [160].
2.2. Description of the Analyzed System
- Pressures;
- Mass flows;
- Temperatures;
- Currents and voltages supplying the motors installed on the power unit;
- Electric power.
2.3. Studied Genetic Procedures
- The first stage of the software is to prepare geometric data sets (e.g., Figure 2) for the calculation process.
- In the following phase, symptoms and signatures are determined by numerical simulation using the DIAGAR program.
- The third stage involves simulating and sampling a single degradation and then determining symptoms and signatures for the degradation.
- 4.
- The next step is to sample and perform crossover operations using geometric data subject to operational degradation. This aims to determine the component device of the cycle, for which crossover will be carried out and as a result of which the level of degradation formed is obtained.
- 5.
- After performing the above operations, a geometric data set is constructed for new data created after the crossover. At the moment, only one fault is analyzed, which can occur at any time on one of six component devices, for which six suitable searching signatures are created by performing six appropriate calculations for the relevant thermal cycle using DIAGAR software in preparation for the next phase.
- 6.
- In the sixth phase, the specialized software calculates symptoms for six datasets, accumulates them in six searching signatures, and then subjects them to the selection process.
- 7.
- In the seventh phase, the selection is carried out as mentioned earlier in the article. More precisely, the process consists of selecting the two signatures closest to the present signature. This assumption is met when the condition of a location below the minimum distance is met for at least one signature. It is one of the most difficult phases. Therefore, the authors made a big effort to accelerate and gain better accuracy of the recognition decision choosing signatures for further operation in proper order. For numerical procedures, finding appropriate single numbers defining each signature helped achieve the purpose mentioned above.
- 8.
- The moment of meeting such a condition is described as a solution. If this condition cannot be met, the fourth phase is repeated. This involves creating new data files on the two parameters closest to degradation. Files with data are subjected to the following phases mentioned above until a solution is obtained.
3. Procedure Selection and Creation of Characteristics
Symbol | Name | Degradation Description |
---|---|---|
DP1 | Clearance in the seal of the control valve nozzle box for HP parts | |
DP2 | Clearance in the outer seal of the HP part | |
DP3 | Clearances in the seals for 1 stage group of HP parts | |
DP4 | Clearances in the seals for 2 stage group of HP parts | |
DP5 | Clearance in the sealing of the IP control valve nozzle box | |
DP6 | Clearance in the external sealing of the IP part | |
DP7 | Clearances in the seals for 3 stage group of IP parts | |
DP8 | Clearances in the seals for 4 stage group of IP parts | |
DP9 | Clearances in the seals for 5 stage group of HP parts | |
DP10 | Clearances in the seals for 6 stage group of HP parts |
3.1. Crossover Operations on Chromosomes—Values of Geometric Parameters
3.2. Selection
- Preliminary selection based on selected characteristics;
- Final selection based on complete signatures.
3.3. Characteristics
4. Summary and Perspective
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Nomenclature
a1 | Stator throat diameter |
a2 | Rotor throat diameter |
AGPA | Adaptive gas path analysis |
AHP | Absorption heat pump |
AFARN | Adaptive Fault Attention Residual Network |
AI | Artificial intelligence |
ANN | Artificial neutral networks |
B | Coal-fired boiler |
b1 | Stator chord |
b2 | Rotor chord |
BECCS | BioEnergy with Carbon Capture and Storage |
BPNN | Back propagation neural network |
C1 | Stator absolute velocity |
C2 | Rotor outlet absolute velocity |
CCGT | Combined cycle gas turbine |
CFD | Computational Fluid Dynamics |
CMAC | Cerebellar Model Articulation Controller |
CON | Steam condenser |
D | Deaerator |
Delz1 | Stage external glands clearance |
Delw1 | Internal ordinary gland segment clearance |
DHX | Dedicated heat exchanger |
DP | Degradation parameter |
ECO PG | Program name from Ecological Poli-Generation systems |
FEM | Finite Element Method |
FSI | Fluid–Solid Interaction |
G | Electric generator |
GA | Genetic algorithms |
HE | Regenerative heat exchangers |
HP | High-pressure steam turbine |
I.C.E. | Internal combustion engine |
IP | Intermediate pressure steam turbine |
Lszkiel1 | Stator blade camber line length |
Lszkiel2 | Rotor blade camber line length |
LP | Low pressure steam turbine |
MOGA | Multi-objective genetic algorithm |
ORC | Organic Rankine Cycle |
P1 | Condensate pump |
RES | Renewable Energy Sources |
SWOT | Strengths–Weaknesses–Opportunities–Threats |
SVM | Support vector machines |
t1 | Stator pitch |
t2 | Rotor pitch |
TFD | Thermal-Flow Diagnostics |
TKEO | Teager–Kaiser energy operator |
w1 | Rotor inlet relative velocity |
w2 | Rotor outlet relative velocity |
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Advantages | Disadvantages | |
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Internal features | Strengths:
| Weaknesses:
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External factors | Opportunities:
| Threats:
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Advantages | Disadvantages | |
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Internal features | Strengths:
| Weaknesses:
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External factors | Opportunities:
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Number Element | Name of Parameter |
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1 | power deviation; |
2 | deviation of specific heat consumption; |
3 | steam pressure deviation at the I extraction; |
4 | steam temperature deviation at the I extraction; |
5 | steam pressure deviation at the II extraction; |
6 | steam temperature deviation at the II extraction; |
7 | steam pressure deviation at the III extraction; |
8 | steam temperature deviation at the III extraction; |
9 | steam pressure deviation at the IV extraction; |
10 | steam temperature deviation at the IV extraction; |
11 | steam pressure deviation at the V extraction; |
12 | steam temperature deviation at the V extraction; |
13 | steam pressure deviation at the VI extraction; |
14 | steam temperature deviation at the VI extraction; |
15 | steam pressure deviation at the VII extraction; |
16 | steam temperature deviation at the VII extraction. |
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Ziółkowski, P.; Drosińska-Komor, M.; Głuch, J.; Breńkacz, Ł. Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence. Energies 2023, 16, 6107. https://doi.org/10.3390/en16176107
Ziółkowski P, Drosińska-Komor M, Głuch J, Breńkacz Ł. Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence. Energies. 2023; 16(17):6107. https://doi.org/10.3390/en16176107
Chicago/Turabian StyleZiółkowski, Paweł, Marta Drosińska-Komor, Jerzy Głuch, and Łukasz Breńkacz. 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence" Energies 16, no. 17: 6107. https://doi.org/10.3390/en16176107