This study assesses the reliability of a proposed 50 MW wind farm in Zuwara, Libya, under variable wind conditions. A 10-year wind dataset comprising 3,652 daily observations was obtained from the NASA POWER database to characterize the site wind resource. Several probability distributions were evaluated, and the Johnson SB distribution was selected based on goodness-of-fit using the Kolmogorov–Smirnov (KS) test. The resulting wind-speed model was integrated with the power characteristics of a 50-turbine wind farm, each rated at 1 MW. Monte Carlo simulation was applied to represent wind-generation variability and evaluate its effect on generation adequacy under different demand levels. Using the Resistance–Load (R–L) approach, loss-of-load conditions were identified when available wind generation was insufficient to meet the considered demand. The results show that the estimated failure probability was approximately 88.39% for a 32-MW demand and 92.99% for a 38-MW demand. Increasing the number of simulation trials reduced the statistical error and improved the stability of the estimates. The findings demonstrate the strong influence of wind variability and demand level on the reliability contribution of the proposed wind farm and highlight the need for complementary resources to improve supply adequacy.

Peak electricity demand presents a continuing challenge to power-system operation in Libya, particularly during periods of high ambient temperature. This study examines a proposed 60-MW above-ground Compressed Air Energy Storage (CAES) system intended to provide short-duration peak-power support for the GT12 gas turbine at the West Tripoli Gas Power Plant. The proposed discharge duration is 15 minutes. The assessment draws on plant operating data collected on June 2–3, 2024, and on a validated thermodynamic model used to evaluate compressor and turbine performance. The analysis considers mass-flow rate, heat input, net power output, and the sizing of the compressed-air storage system. The results demonstrate that the proposed CAES system increases the available peak power from approximately 100 MW to 135 MW by completely offsetting the compressor work. Despite an increase in fuel consumption required to compensate for the lower temperature of the injected stored air, the system achieves a significant enhancement in net electrical power output compared to the baseline. Furthermore, the proposed modular above-ground storage configuration, utilizing an array of thirty 40-bar industrialtanks, demonstrates practical feasibility for regions lacking suitable underground geological formations. The study confirms that integrating CAES with existing gas turbine plants represents a practical solution for improving grid reliability and operational flexibility .

Accurate wind speed modeling is essential for reliable wind resource assessment and wind farm planning. Although the Weibull distribution is widely used in wind energy studies, its accuracy may vary depending on regional climatic characteristics. This study evaluates the suitability of the Weibull distribution for modeling wind speed data in Libya and compares its performance with several alternative probability distributions. The analysis is based on 7,305 daily wind speed observations obtained from the NASA POWER database for the period 2004–2023 at a reference height of 50 m above Sea level for five locations in Libya: Tripoli, Ajdabiyah, Alkufra, Awbari, and Darnah. The mean wind speeds range from 6.419 m/s in Tripoli to 7.270 m/s in Darnah. Statistical analysis was performed using EasyFit and MATLAB, while the Kolmogorov–Smirnov (K–S) test was applied to evaluate the goodness of fit between observed and theoretical distributions. The results show that the Weibull distribution does not provide the best statistical representation for any of the studied locations. Instead, Johnson SB, Dagum (4P), and Burr distributions demonstrated superior performance. The estimated wind power density ranged from 52.57 W/m² in Tripoli to 78.97 W/m² in Darnah, highlighting the importance of flexible multi-parameter distributions for improving wind resource assessment and supporting more reliable wind farm planning in Libya.

This review examines 56 peer-reviewed studies on gas-turbine emission modeling, covering data-driven, semiempirical,

physics-based, and hybrid approaches. It compares model fidelity, computational cost, and interpretability

across these methods and finds a clear hierarchy in which physics-based models provide the mechanistic

foundation, while hybrid and data-driven systems extend that fidelity for scalable, real-time prediction.

Together they establish a coherent basis for modeling NOₓ and CO under both conventional and fuel-flexible

conditions.

A structured search of Scopus, Web of Science, and supplementary databases (records = 260; screened = 230;

full-text = 110; included = 56) followed PRISMA methodology to ensure transparency and reproducibility.

Eligible studies offered quantitative validation of combustion or emission models and were classified into four

methodological groups. Each was examined for governing variables, validation approach, and performance

metrics, allowing comparison of predictive reliability across operating regimes.

Recent research shows that hybrid architectures, which couple mechanistic transparency with data-driven

adaptability, are now dominant. Physics-based simulations generate the high-fidelity datasets used for

training, whereas machine-learning surrogates interpolate rapidly within validated domains. Remaining challenges

involve transient validation, fuel-flexible kinetics, limited domain coverage, and the absence of standardized

uncertainty quantification. Adaptive, physics-guided learning frameworks that preserve interpretability

and quantify reliability across fuels, loads, and stages of engine aging appear most capable of addressing these

gaps.

A new method for the thermoeconomic optimization of triple-pressure heat recovery steam generators (HRSGs) for combined cycle power plants (CCGT) is proposed in this paper. The new optimization method is based on the ant colony optimization algorithm (ACO) and by applying the global optimization software (MIDCO). The optimal values of the most influencing operating parameters are obtained by maximizing the objective function while satisfying a group of constraints. The proposed method is based on optimization of the following operating parameters: the temperature difference between the gas and steam (pinch point) and the drum pressures in the HRSG. The purpose of the thermoeconomic optimization was increasing of the annual cash flow. The optimized combined cycle was compared with the initial case. The results show that there is a potential for considerable improvement of the plant economy when compared to the initial case, where the overall thermal efficiency increased by 0.54 % and the annual cash flow was increased by1.53M$. In addition, the new method could be suitable for research work of the power plants optimization.

Components under fatigue loading have significant amounts of scatter and inconsistency considered in fatigue failure data. Due to uncertainties in predicting component fatigue life, probabilistic approaches were to quantify the influence of uncertainties on the fatigue life. However, computation time associated with the commonly used Monte Carlo simulation can be prohibitive during design stage assessments. Therefore, the purpose of the current work was to demonstrate the use of efficient probabilistic methods, as alternatives to Monte Carlo method, to accurately predict fatigue lives for Aluminum 6061-T6 sheets. Based on experimentally determined of stress-life distributions, predicted distributions of fatigue life agreed closely with replicate experimental test results. The study showed that with significantly less computational time, there is a very good agreement for advanced mean value (AMV) and the first order reliability method (FORM) with Monte Carlo. However, the mean-value (MV) method was less accurate in tail regions that indicates poor life prediction. The sensitivity analysis showed that S-N slope parameters (a and b) had insignificant contributions to the scatter in fatigue life compared to the dynamic loading and component geometries.

Monitoring smart grids and detecting faults in such huge networks has recently become an active area of research. The huge amount of data transferred from the measurement units to the control center makes it difficult to detect faults in a reasonable amount of time. Some existing methods have been investigated and tested on many IEEE models such as wavelet transform, principal component analysis (PCA) to extract the abnormal behavior of signals under monitoring [1]. However, such techniques pose difficulties in detecting different faults properly. In this paper, the multidimensional scaling (MDS) is investigated as an alternative technique for reducing the dimensionality of the data to lower dimensions, while maintaining the necessary information needed for fault detection. MDS is then used to investigate the behavior of some IEEE models under different types of faults in order to detect and locate the faulty bus bars.

Due to the increased rate of failure of wind generators, condition-monitoring system plays a significant role in overcoming failures resulting from the harsh operation conditions. The mathematical, thermal, and electrical analyses may be utilized to detect the faults of wind generators by monitoring the changes in their characteristics under different operation conditions. The behavior of the rotating permanent magnet of the generator can indicate the wind generator’s condition. For instance, the torque of the permanent magnet of the generator is affected by the oscillation of the magnet temperature. Therefore, monitoring the torque of the permanent magnet with respect to the rate of change in the permanent magnet temperature defines the generator health. Furthermore, the rate of change in the generator temperature is considered an additional indicator to define the health of the wind generators with respect to the induced electrical torque. That is because of the negative effect of the elevated generator temperature on the induced electrical torque. In this study, a different methodology has been adopted to implement a proper condition monitoring system on the wind generators by evaluating the rate of change in the generator temperature and permanent magnet temperature with respect to the induced electrical torque and the

.driving torque of the rotating permanent magnet under different operation conditions.

A case study, which is based upon collected data from actual measurements, is presented in this work in order to demonstrate the adequacy of the proposed model.

This paper presents an application of the hazard model reliability analysis on wind generators, based on a condition monitoring system. The hazard model techniques are most widely used in the statistical analysis of the electric machine’s lifetime data. The model can be utilized to perform appropriate maintenance decision-making based on the evaluation of the mean time to failures that occur on the wind generators due to high temperatures. The knowledge of the condition monitoring system is used to estimate the hazard failure, and survival rates, which allows the preventive maintenance approach to be performed accurately. A case study is presented to demonstrate the adequacy of the proposed method based on the condition monitoring data for two wind turbines. Such data are representative in the generator temperatures with respect to the expended operating hours of the selected wind turbines. In this context, the influence of the generator temperatures on the lifetime of the generators can be determined. The results of the study can be used to develop the predetermined maintenance program, which significantly reduces the maintenance and operation costs. 

Due to the increase in the number of failures in the wind turbine generators, the condition monitoring system plays a significant role in overcoming the negative effects resulting from the difficult operation conditions. Mechanical and electrical properties can be combined to detect the faults coming from wind turbine generators by analyzing their behavior under different (normal and abnormal) operation conditions. Studying the trend and effect of the electrical torque pulsations on wind turbine generators under different conditions allows for a proper condition monitoring. In this paper, different methodology has been adopted to develop a proper condition monitoring system on the wind generators by evaluating the generator electrical torque based on mechanical torque and taking into account the acceleration torque, which has not been considered in previous work. Using the electric torque with respect to the rotor angular speed of the generator, when it is running under different operation conditions, indicates the generator health, which is the main methodology of the proposed work. A case study, which is based upon collected data from actual measurements, is presented in this work in order to demonstrate the adequacy of the proposed model.

Due to the increase in the failures of the wind generators, Condition Monitoring System (CMS) plays a significant role in overcoming these failures resulting from the harsh operation conditions. The mechanical, thermal, and electrical analyses can be utilized to detect the faults, which are coming from the wind generators by monitoring the changes in their characteristics under different (normal and abnormal) operation conditions. Observing the trend of the electrical torque pulsations of the wind generators under different conditions is beneficial to perform proper condition monitoring. In this paper, different methodology has been adopted to implement a proper condition monitoring system on the wind generators by evaluating the generator electrical torque based on the mechanical and the acceleration torque. Then, in order to specify the generator faults, the trend of the electrical torque with respect to the rotor angular speed of the wind generator under different operation conditions is analyzed. Further, the rate of change in the generator temperature is considered as well as an indicator to define the health of the wind generators with respect to the induced electrical torque, because of the negative effect of the elevated generator temperature on the induced electrical torque. Case study, which is based upon collected data from actual measurements, is presented in this work in order to demonstrate the adequacy of the proposed model.

This paper presents an application of a condition-monitoring system (CMS) based on a polynomial regression model (PRM) to study the influence of heat loss on a wind generator’s temperatures. Monitoring the wind generator temperatures is a significant for efficient operation, and plays a key role in an effective CMS. Many techniques, including prediction models can be utilized to reliably forecast a wind generator’s temperature during operation and avoid the occurrence of a failure. PRMs are widely used in situations when the relationship between the response and the independent variables are curve-linear. These techniques can be used to construct a normal behavior model of an electrical generator’s temperatures based on recorded data. Many independent variables affect a generator’s temperature; however, the degree of influence of each independent variable on the response is dissimilar. In many situations, adding a new independent variable to the model may cause unsatisfactory results ;therefore, the selection of the variables should be very accurate. A generator’s heat loss can be considered a significant independent variable that greatly influences the wind generator with respect to the other variables. A generator’s heat loss can be estimated in intervals by analyzing the exchange in the heat between the hot and cold fluid through the heat exchangers of wind generators. A case study built on data collected from actual measurements demonstrates the adequacy of the proposed model. 

Effective cooling is required in power generation, processing, and distribution to avoid failure that could occur during operations. As heat loads continue to increase, manufacturers of wind turbines are turning to improve the cooling system to remove high intensity heat loads from many active parts particularly in the generator part. Wind generators need an effective cooling system due to the large amount of heat that is released during power production. Many large wind turbines (more than 5MW rated power) particularly offshore wind turbines where the water is available, heat exchangers with water-air cooling system is used, in which the water is utilized to cool the hot air. This type of heat exchangers are desired, since they are more efficient and reliable than the air-air heat exchangers, which are used in small wind turbines. Because of the growing number of failures that occurred in wind turbine generators due to high generator’s temperatures owing to power losses of generator, applying condition monitoring system on wind generators depending on heat transfer analysis through the heat exchangers of wind generators plays an effective role. This helps avoid failures and maintain wind turbines to be protected. In this paper new methodology has been applied by considering the heat transfer and fluid mechanics analysis through a heat exchanger of wind generator, which uses water to air cooling system. Case study based on data collected from actual measurements demonstrates the adequacy of the proposed model. 

The development and implementation of condition monitoring system become very important for wind industry with the increasing number of failures in wind turbine generators due to over temperature especially in offshore wind turbines where higher maintenance costs than onshore wind farms have to be paid due to their farthest locations. Monitoring the wind generators temperatures is significant and plays a remarkable role in an effective condition monitoring system. Moreover, they can be easily measured and recorded automatically by the Supervisory Control and Data Acquisition (SCADA) which gives more clarification about their behavior trend. An unexpected increase in component temperature may indicate overload, poor lubrication, or possibly ineffective passive or active cooling. Many techniques are used to reliably predict generator's temperatures to avoid occurrence of failures in wind turbine generators. Multiple Linear Regression Model (MLRM) is a model that can be used to construct the normal operating model for the wind turbine generator temperature and then at each time step the model is used to predict the generator temperature by measuring the correlation between the observed values and the predicted values of criterion variables. Then standard errors of the estimate can be found. The standard error of the estimate indicates how close the actual observations fall to the predicted values on the regression line. In this paper, a new condition-monitoring method based on applying Multiple Linear Regression Model for a wind turbine generator is proposed. The technique is used to construct the normal behavior model of an electrical generator temperatures based on the historical generator temperatures data. Case study built on a data collected from actual measurements demonstrates the adequacy of the proposed model.