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 biomassfueled boiler with its steam cycle  ANN  
ŚlęzakŻołna, Głuch  [113]  Steam turbine  ANN  
ŚlęzakŻołna  [100]  Steam turbine  ANN  
Głuch, DrosińskaKomor  [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 highpressure part of an ultrasupercritical 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 builtin 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  Threedimensional (3D) finite element (FE)  Turbine rotor blade vibration. 
Kuo  [96]  Gas turbine  ANN and fuzzy Logic  Turbine blade faults in fan turbojet. 
Aslanidou et al.  [111]  Micro gas turbine  Machine learning  
Sławiński et al.  [112]  Gas turbine  The COMGAS 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  Nonlinear 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 machineradial basis function networks  ELMRBF 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 neurofuzzy 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 nonlinear steadystate 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 RehmusForc  [132]  Steam turbine  Material testing and mechanical integrity calculations  
Kraszewski et al.  [133]  Spherical bifurcation pipe of a live steam  A onesided numerical thermalFSI analysis  Element of a block of coalfired 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  ThermalFSI (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 ORCbased power plant  Use LabVIEW  Vibrodiagnostic system designed. 
Badur et al.  [139]  Heat exchanger  “ThermalFSI” (“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  
NiksaRynkiewicz 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 algorithmstopping 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  Coalfired 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 PoliGeneration systems 
FEM  Finite Element Method 
FSI  Fluid–Solid Interaction 
G  Electric generator 
GA  Genetic algorithms 
HE  Regenerative heat exchangers 
HP  Highpressure 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  Multiobjective 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  ThermalFlow Diagnostics 
TKEO  Teager–Kaiser energy operator 
w1  Rotor inlet relative velocity 
w2  Rotor outlet relative velocity 
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Advantages  Disadvantages  

Internal features  Strengths:
 Weaknesses:

External factors  Opportunities:
 Threats:

Advantages  Disadvantages  

Internal features  Strengths:
 Weaknesses:

External factors  Opportunities:
 Threats:

Number Element  Name of Parameter 

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.; DrosińskaKomor, 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, DrosińskaKomor 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 DrosińskaKomor, 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