Penalty Factor Research Articles

Experimental study of R600a boiling in microfin tube

This paper presents experiments to investigate flow boiling and pressure gradients of R600a. The study employed microfin and smooth copper tubes simulating conditions in a domestic refrigerator, using low mass fluxes (MFs) and saturation temperatures (Tsat). The test sections featured a tube length of (400 mm), an inner diameter of (4.3 mm), and an outer diameter of (5 mm). Test conditions varied, including MFs of (25, 35, and 45 kg m−2 s−1), vapor quality from (0.18–1), Tsat of (−15, −9, and −5 °C), and HFs of (10, 14, and 18 kW m−2). Both tubes shared identical dimensions, clearly comparing microfin impact on flow boiling characteristics. The study evaluated enhancement factor (EF), penalty factor (PF), and overall efficiency (η) by comparing heat transfer coefficients (HTC) and pressure gradients in the microfin tube versus the smooth tube. Results indicate a significantly boosted HTC in the microfin tube compared to the smooth tube, with a reasonable increase in pressure drop. Under specific conditions (HF of 10 kW m−2, Tsat of −9 °C, and MFs of 25 kg m−2 s−1), the average HTC is enhanced by 325%, while the pressure drop is increased by 150%. Experimental HTC and frictional pressure gradient comparisons aligned well with values predicted by existing literature correlations.

Closed-loop high precision human tissue temperature measurements using a joint forward and inverse method

A closed-loop forward-inverse joint multi-layer temperature prediction method is introduced to overcome the limitations of non-contact microwave-based precise temperature measurements within human tissues. This approach merges an incoherent four-layer tissue forward model with a multi-frequency high-precision inversion algorithm. Random errors were incorporated into the forward model to construct a multi-layer human tissue dataset for performance validation and optimization of the inversion algorithm. The lack of precise temperature measurements in internal human tissues was addressed using an incoherent four-layer forward model that integrates the Pennes heat transfer equation with a fluid–solid coupling boundary modeling technique. Parameter differentiation analysis was conducted in the forward modeling step using incoherent electromagnetic transport equations. The proposed high-precision multi-frequency inversion process, added to the objective function, refines the XGBoost algorithm by assigning a penalty factor and adjusting for neighboring tissue temperature distributions. The Optuna framework was then utilized to optimize XGBoost hyper-parameter sets, resulting in the Opt-XGBoost inversion algorithm. This approach achieved a root mean square error of 0.033 °C and an average absolute error of 0.0256 °C in simulations involving forward modeling-generated data.

Optimization of solar-air source heat pump heating system with phase change heat storage

The optimization of heating system has always been of much concern, among which the annual life cycle cost (ALCC) was widely used as an optimization method, while the non-guaranteed hours were often overlooked. This study proposed the elasticity-economy (E-E) objective function as an optimizing indicator, and demonstrated it taking a solar assisted heat pump heating system with phase change material (SC-ASHP-PCM) as an example. Firstly, the single variation impact was analyzed. After optimizing the thermal performance of sub-systems, the proportion of heat supplied by solar collector (SC) and phase change material (PCM) sub-systems increased by 5.4 % and 3.4 %, respectively. Subsequently, the Hooke-Jeeves algorithm was developed to optimize multi-factors, hence minimizing the E-E objective function. The optimal solution was achieved when the non-guaranteed rate was 5 % and the penalty factor was 150 CNY/h. Compared with the baseline situation, the system energy consumption (156.8 × 103 kW‧h) was reduced by 30 %, and the E-E objective function was reduced by 24 %. Furthermore, the minimum operating cost was 18.3 CNY/m2, saving up to 46 % compared to traditional heating systems. The method is generalized to obtain the optimal energy saving potential under different non-guaranteed hours, which provides guidance for the heating systems' design and operation.

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment.

An Improved Sparrow Search Algorithm for the Optimization of Variational Modal Decomposition Parameters

Variational modal decomposition (VMD) is frequently employed for both signal decomposition and extracting features; however, the decomposition outcome is influenced by the quantity of intrinsic modal functions (IMFs) and the specific parameter values of penalty factors. To tackle this issue, we propose an algorithm based on the Halton sequence and the Laplace crossover operator for the sparrow search algorithm–VMD (HLSSA-VMD) to fine-tune the parameters of VMD. First, the population initialization by the Halton sequence yields higher-quality initial solutions, which effectively addresses the issue of the algorithm’s sluggish convergence due to overlapping and the lack of diversity of the initial solutions. Second, the introduction of the Laplace crossover operator (LX) to perturb the position of the best individual in each iteration helps to prevent the algorithm from becoming ensnared in a local optimum and improves the convergence speed of the algorithm. Finally, from the simulation of 17 benchmark test functions, we found that the HLSSA exhibited superior convergence accuracy and accelerated convergence pace, as well as better robustness than the particle swarm optimization (PSO) algorithm, the whale optimization algorithm (WOA), the multiverse optimization (MVO) algorithm, and the traditional sparrow search algorithm (SSA). In addition, we verified the effectiveness of the HLSSA-VMD algorithm on two simulated signals and compared it with PSO-VMD, WOA-VMD, MVO-VMD, and SSA-VMD. The experimental findings indicate that the HLSSA-VMD obtains better parameters, confirming the superiority of the algorithm.

Distributed Voltage Reactive Power Control of Distribution Network Considering the Coordination Between DPV and SVC

With the increasing permeability rate of DPV in the distribution network, the safe and economic operation of the distribution network is faced with many challenges, among which the problem of voltage exceeding the limit must be solved urgently. A method for distributed voltage reactive power control, which involves coordinating DPV and SVC, is suggested to enhance the voltage regulation capacity in high-permeability distribution networks. Firstly, considering the limited reactive power regulation capacity of DPV, a coordinated control strategy is proposed to enable SVC when the reactive power capacity of DPV is insufficient. Secondly, a voltage-reactive power control model for distribution networks is established, with a focus on minimizing node voltage deviations and network losses. An adaptive penalty factor adjustment strategy, based on the alternating direction multiplier method, is introduced to achieve a comprehensive solution for this model. Finally, the enhanced IEEE 33-node system is utilized for verification and analysis. The outcomes demonstrate that the suggested coordination strategy involving DPV and SVC, alongside the distributed optimization control method, offers economic advantages and effectively achieves the safe and cost-effective operation of the distribution network.

Rolling bearing fault diagnosis based on fine-grained multi-scale Kolmogorov entropy and WOA-MSVM

In allusion to solve the issue of fault diagnosis for bearing and other rotatory machinery, a technique based on fined-grained multi-scale Kolmogorov entropy and whale optimized multi-class support vector machine (abbreviated as FGMKE-WOA-MSVM) is proposed. Firstly, vibration signals are decomposed by fine-grained multi-scale decomposition, and the Kolmogorov entropy of the sub-signals at different analysis scales is calculated as the multi-dimension feature vector, which quantitatively characterize the complexity of the signal at multi-scales. Aiming at the problem of sensitive parameters selection for multi-class support vector machine model (abbreviated as MSVM), the whale optimization algorithm (abbreviated as WOA) is introduced to optimize the penalty factor and kernel function parameter, and constructing optimal WOA-MSVM model. Finally, an instance analysis is carried out with Jiangnan University bearing datasets to verify the effectiveness and superiority of this technique. The results show that compared with different feature vectors and models such as K nearest neighbors (abbreviated as KNN) and Decision Tree (abbreviated as RF), the proposed technique is superior with fast computation speed and high diagnostic efficiency.

Research on elasticity behavior of Ni-Ti shape memory alloys using the reproducing kernel particle method

The unique superelastic properties of the Ni-Ti shape memory alloys (SMAs) have been found extensive practical application in many engineering fields, but the Ni-Ti SMAs exhibits elastic behavior in small load, so the elastic behavior of the Ni-Ti SMAs is studied by using meshless method of reproducing kernel particle method (RKPM) in this paper. The displacement boundary conditions are applied by utilizing the penalty function method, and the impact of penalty factor and parameter of influence domain on errors are discussed, and computational stability is analyzed. At last, the correctness of the RKPM in studying the elastic behavior of the Ni-Ti SMAs is evidenced by using three numerical examples.

No-Idle Flow Shop Scheduling with Deteriorating Jobs and Common Due Date Under Dominating Machines

The phenomenon of deterioration effects can reduce production efficiency caused by extended start time or other factors. Based on it, this paper considers the scheduling situations of the common due date assignment in a no-idle flow shop scheduling environment. Under the given four dominating relationships (i.e., increasing, decreasing, increasing-decreasing and decreasing-increasing dominating machines, correspondingly expressed as idm, ddm, idm–ddm and ddm–idm) between machines, the objective functions are, respectively, to minimize (1) the weighted sum of common due date and total earliness; and (2) the weighted sum of common due date, total earliness and total penalty factors of tardy jobs. It is verified that these problems can be solved in polynomial time under four dominating relationships.

A New Method for the Analysis of Broadband Oscillation Mode of New Energy Stations

The accurate identification of the broadband oscillation mode is the premise of solving the resonance risk of new energy stations. Reviewing the traditional Prony algorithm, the problems of the high model order and poor noise immunity in broadband oscillation mode are identified. The accuracy and running time of the Variational mode decomposition (VMD) is a symmetric trade-off problem. An improved strategy based on VMD is proposed. Firstly, the optimal value of the number of modes and penalty factors obtained by a particle swarm optimization algorithm is input into VMD to decompose the signal into multiple modes. Then, combined with the energy threshold method, the denoising and signal reconstruction of each mode component after decomposition are carried out. Finally, the Prony algorithm is used to identify the oscillation mode of the original signal and the reconstructed signal, respectively. The Signal-to-noise ratio (SNR) and model order are compared and analyzed. Through the analysis of the example and simulation data, it is shown that the proposed method effectively solves the problem of poor engineering adaptability of the traditional Prony algorithm. It also can accurately obtain the time-domain characteristics of broadband oscillation, which has a promising future in the engineering application.

Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN

Due to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults.

A failure diagnosis method of ball bearing based on optimized multiscale variational modal extraction with synchro squeezing transformation

When a compound failure occurs in a bearing, the failure information included in vibration signals is featured by being weak, complex and combined. This fact makes it difficult to identify a compound failure precisely. Variational mode extraction (VME) solves the problem of variational mode decomposition (VMD) in being difficult to determine the number of decomposition layers and has certain applications in the identification of bearing failures. However, the initial expected central frequency of VME and option of penalty factor is crucial to the extraction of expected mode. To precisely determine the central frequency and penalty factor of VME and fulfill the correct extraction of feature information of compound failures in bearing, the paper has proposed a method based on synchro squeezing transform (SST) and information entropy to adaptively determine the parameters in VME (SST-VME). Firstly, SST is used to make time-frequency analysis of original vibration signals and characteristic frequency bands with larger energy are chosen from time-frequency spectrum to adaptively determine the expected central frequency of VME. Secondly, as information entropy has representation capacity for information included in the signal, penalty factor of VME was adaptively determined according to information entropy. Thirdly, original signals were subjected to VME according to expected central frequency and penalty factor adaptively determined; the expectation mode obtained was denoised by singular value decomposition algorithm. Finally, the type of compound failures of bearing is determined according to the frequency spectrum of denoised signals. To verify the effectiveness of proposed method, a comparison with VMD algorithm is conducted. As indicated by the result, the proposed method is more precise and comprehensive than VMD algorithm to extract the feature information corresponding to compound faults of bearing, and thereby correctly determines the type of compound failures.

A novel robust intelligent fault diagnosis method for rolling bearings based on SPAVMD and WOA-LSSVM under noisy conditions

Aiming at the problem that the fault information of rolling bearings under harsh operation environment is easily submerged by strong noise interference, which causes the traditional method to be difficult to identify bearing faults effectively, this paper proposes a novel robust intelligent fault diagnosis method for rolling bearings based on sparsity-assisted parameter adjustable variational mode decomposition (VMD) and whale optimization algorithm-based optimized least-squares support vector machine (WOA-LSSVM). Firstly, a sparsity measurement named the improved Gini index is introduced as the fitness function of grid search algorithm to adaptively adjust and search for the optimal decomposed mode number K and penalty factor α of VMD. Additionally, VMD containing the optimal parameters is adopted to decompose the original bearing vibration signal into several intrinsic mode function (IMF), and the effective signal reconstruction is performed by screening the sensitive IMF components according to the effective weighted kurtosis Gini index criterion. Subsequently, the refine composite multi-scale dispersion entropy of the reconstructed signal is further calculated to establish a multi-dimensional feature vector set. Finally, the constructed feature vector set is fed into the WOA-LSSVM to achieve automatic fault identification of rolling bearings. The effectiveness of the proposed method is verified by two experimental examples. Experimental results show that the proposed method has higher fault recognition accuracy and better robustness against noise than other homologous methods in noisy conditions. This study provides a new perspective for the developing of robust diagnosis methods.

Method for Fault Diagnosis of Track Circuits Based on a Time–Frequency Intelligent Network

In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform (CWT) for signal preprocessing, along with the integration of a deep belief network (DBN) and a genetic algorithm (GA) to improve the least-squares support vector machine (LSSVM) model for intelligent time–frequency fault diagnosis. Initially, the raw induced voltage signals are transformed using continuous wavelet transformation resulting in wavelet time–frequency representations that combine temporal and spectral information. Subsequently, these time–frequency representations are fed into the deep belief networks, which perform semi-supervised dimensionality reduction and feature extraction, thereby uncovering distinct fault characteristics in the track circuit. Finally, the genetic algorithms are employed to improve the kernel function and penalty factor parameters of the least-squares support vector machine, thus establishing an optimal DBN-GA-LSSVM diagnostic model. Experimental validation demonstrates the effectiveness of the proposed time–frequency intelligent network model by leveraging the advantages of deep belief networks in hierarchical feature extraction and the superior performance of the least-squares support vector machine in addressing high-dimensional pattern recognition problems with limited samples. The achieved accuracy rate on the testing dataset reaches an impressive 99.6%. Consequently, this comprehensive approach provides a viable solution for data-driven track circuit fault diagnosis.

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Robust reliability Generation Expansion Planning (GEP) turns out to be a crucial step for an efficient energy management system in a modern power grid, especially under renewable energy employment. The integration of all such components in a GEP model makes it a large-scale, nonlinear, and mixed-variable mathematical modeling problem. In this paper, the presence of wind energy uncertainty is analyzed. Both long and short-term uncertainties are incorporated into the proposed GEP model. The first step concerns the impact of long-term wind uncertainties through the annual variations of the capacity credit of two real sites in Egypt at Zafaranh and Shark El-ouinate. The second step deals with the short-term uncertainties of each wind site. The wind speed uncertainty of each wind site is modeled by probability distribution function. Then, wind power is estimated from the wind power curve for each wind site and Monte-Carlo Simulation is performed. Fast Gas Turbine and/or Pump Hydro Storage are incorporated to cope with short-term uncertainties. Sensitivity analysis is implemented for 3, 6, and 12 stages as short and long planning horizons to minimize the total costs with wind energy penetration and emission reduction over planning horizons. Also, a novel Honey Badger Algorithm (HBA) with model modifications such as Virtual Mapping Procedure, Penalty Factor Approach, and the Modified of Intelligent Initial Population Generation is utilized for solving the proposed GEP problem. The obtained results are compared with other algorithms to ensure the superior performance of the proposed HBA. According to the results of the applicable test systems, the proposed HBA performs better than the others, with percentage reductions over CSA, AO, BES, and PSO ranging up to 4.2, 2.72, 2.7, and 3.4%, respectively.

Detection and localization of fault in DC microgrid using discrete Teager energy and generalized least square method

PurposeThis paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.Design/methodology/approachIt is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.FindingsDTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.Originality/valueVerification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.

Research on automatic detection of gradual fault of high voltage electric energy metering transformer based on fuzzy rough set and whale optimization algorithm

Based on fuzzy rough set and whale optimization algorithm, the automatic fault detection method of high-voltage electric energy metering transformer is studied to improve the fault diagnosis effect and efficiency. On the basis of constructing the mathematical model of gradual fault of high-voltage electric energy metering transformer, the fuzzy rough set theory is used to reduce the data attributes of fault samples, eliminate similar attributes, determine the minimum fault feature set, and complete the fault feature selection, which is used as the input of the fault detection model based on Whale Optimization Algorithm-based Support Vector Machine (WOA-SVM). After the kernel parameters and penalty factors of SVM are optimized by whale optimization algorithm, the type of gradual fault of high-voltage electric energy metering transformer is identified. The experimental results show that the reduced fault attributes are distributed differently in the sample data, and the fault detection accuracy can be improved by 9.5 % through fault feature selection. The fault diagnosis model with Gaussian radial basis function, kernel parameter of 0.05 and penalty factor of 10 has the best performance. This method can identify the gradual fault types of high-voltage electric energy metering transformers, and the fault diagnosis effect is outstanding.

Health Management of Bearings Using Adaptive Parametric VMD and Flying Squirrel Search Algorithms to Optimize SVM

Bearing, as one of the core parts of rotating machinery, has a running state which is related to the overall operation of the system. Due to the bearing structure and its complex operating environment, running condition monitoring and fault diagnosis is always a key problem in the field of bearing health management, which is of great significance for bearing maintenance and equipment reliability and safety. In view of the difficulty in parameter selection and poor feature extraction ability of variational mode decomposition (VMD) in existing feature extraction, this paper uses the flying squirrel search algorithm (SSA) to optimize the parametric of decomposition layer k and penalty factor α in VMD, and forms an adaptive VMD signal decomposition method. To solve the problem of high dimensionality and long extraction time of multi-domain fault feature set, kernel principal component analysis (KPCA) is used to reduce feature dimensionality. Then, the processed features are input into the support vector machine (SVM) for fault diagnosis and classification, and the parameter optimization ability of SSA is used again to build the SSA-SVM fault diagnosis model. To evaluate the running state of bearings, an alarm threshold method based on the root mean square value calculated by cosine similarity and 3σ is proposed to divide samples of different health states. Finally, the method constructed in this paper is compared with other methods by using simulation and experimental data sets, and the running condition monitoring and fault diagnosis of rolling bearings are successfully realized, which shows the superiority and effectiveness of the method proposed in this paper.

Distributed optimization of station-network collaborative operation and alliance benefit allocation for integrated energy system

With the increasingly urgent need to build a clean and efficient energy system, the interaction between energy station and networks is closer and the coupling relationship becomes more complex. To break energy barriers in independent solution of energy subsystems and solve the problem of privacy disclosure caused by centralized solution of integrated energy system, a distributed optimization of station-network collaborative operation and alliance benefit allocation for integrated energy system is provided. Firstly, a Nash bargaining model of multi-stakeholder cooperation is constructed based on cooperative game theory; Secondly, the nonconvex and nonlinear Nash bargaining model is equivalent converted into alliance economic optimization subproblem and energy transaction dynamic pricing subproblem for the convenience of solution; Thirdly, a distributed optimization architecture for subproblems is constructed based on alternating direction method of multipliers with an adaptive dynamic penalty factor updating strategy. Finally, the effectiveness of the proposed method is verified by a numerical example and the simulation results show that privacy information and independence in decision-making of each stakeholder are protected. The convergence speed and local refine search ability are improved by the adaptive dynamic penalty factor updating strategy. What’s more, more benefits are obtained than independent optimization, and higher satisfaction and richer information based on dynamic pricing are got than the widely used Shapley value, which is practical significance to improve the enthusiasm of all stakeholders to participate in energy trading and promote energy transformation.

A bearing remaining life prediction method under variable operating conditions based on cross-transformer fusioning segmented data cleaning

Bearing remaining useful life (RUL) prediction research based on deep learning mostly emphasizes model performance and effective feature vectors, overlooking different densities of outlier distributions in vibration signals at varying degradation stages. Moreover, forecasting models focus on capturing cross-time dependencies, ignoring the dependencies between different variables. To solve these problems, this paper proposes an unsupervised segmented data cleaning algorithm and a RUL prediction framework adaptable to variable operating conditions. The method consists of four steps: (1) Multi-domain feature extraction and selection establish a feature vector space reflecting degradation trends. (2) Segmented data cleaning divides degradation stages, using different penalty factors for outlier cleaning. (3) Cleaned vibration signals undergo a second round of multidomain feature engineering and degradation-stage division. (4) A two-stage Cross-Transformer model is used for RUL prediction. The method proposed has been validated on the prognostics and health management (PHM) bearing degradation dataset. In the constant condition prediction task, the root mean square error (RMSE) and mean absolute error (MAE) were improved to 1.88 and 5.78, respectively. In the variable condition prediction task, the proposed method outperformed existing methods, with an improvement of 59.10 % in RMSE, demonstrating strong generalization performance and practical application value.