Variable Load Research Articles

Multifractal of acoustic emission for the multi-scale fracture behavior of diatomite modified concrete

It has been attempted to determine how supplemental cementitious materials (SCMs) affects concrete's resistance to fracture. Nevertheless, acoustic emission (AE) has not been used to investigate the impact of diatomite, an SCMs, on the fracture characteristics of concrete. In this investigation, the aim was to assess the fracture behavior of concrete with variable loadings of diatomite (0 %, 1 %, 2 %, 3 %). Three-point bending is performed along with continuous AE monitoring for concrete with various contents of diatomite to obtain AE information. The fracture behavior (fracture parameters, fracture mode, multi-scale fracture and damage) is determined by analyzing AE parameters and corresponding statistical indicators such as Multifractal Detrended Fluctuation Analysis(MF-DFA) and Weibull theory. The results show that the addition of diatomite enhances the fracture property of concrete especially the optimal 2 % content. Then, the high RA value and tensile crack ratio suggest the improvement of the tensile resistance of diatomite. Moreover, a novel quantitative index called multifractal parameter is proposed to evaluate the multi-scale fracture. The finding contributes to a novel method for quantifying multi-scale cracking. The low multifractal parameter in diatomite modified concrete implies a small cracking scale in comparison with the ordinary concrete. Finally, the presence of diatomite slowed down the development of fracture damage in concrete.

Degradation prediction of proton exchange membrane fuel cell using a novel neuron-fuzzy model based on light spectrum optimizer

Proton exchange membrane fuel cell (PEMFC) is regarded as the most promising clean energy to address the fossil energy crisis and environmental pollution. However, it is susceptible to frequently variable load and the impurities of hydrogen, which can directly cause the degradation of performance over time during operations. Degradation prediction has received much attention in recent years, as it can improve the durability and reliability of the PEMFC system. This paper proposes an effective multi-step-ahead prediction for PEMFC degradation under various operational conditions by using variational mode decomposition (VMD), a double recurrent fuzzy neural network (DRFNN), and a light spectrum optimizer (LSO). The integrated method enables precise prediction of degradation trends of PEMFC using historical testing data, which brings together their advantages. To better learn degradation trends, VMD is applied to decompose the input voltage signal into a series of sub-signals with a simpler structure. Then, DRFNN with a feedback loop is developed to train each sub-signal model, which can learn and memorize past information. To further enhance the prediction precision of degradation model, LSO is adopted to automatically update the network’s weights. Finally, the prediction performance of the proposed method is experimentally verified under different load conditions. Compared with other degradation methods, the test results reveal that the proposed method can achieve significant improvements in terms of multi-step-ahead prediction accuracy and robustness.

Impact of forced aeration on vertical flow treatment wetland performances for combined sewer overflow

Combined Sewer Overflow Treatment Wetlands (CSO wetlands) are designed to remove pollutants under stochastic events with variable hydraulic loads. Upgrading them with forced aeration promises to increase the effectiveness and resilience of the treatment. We have tested two vertical CSO wetlands with forced aeration (CSOa and CSOb) to understand the effects of aeration on CSO treatment. Both filter beds have 0.95 and 0.80 m of saturated layer. CSOa uses gravel as top filtering layer, while CSOb utilizes sand and an additional transition layer. Tracer tests were conducted in both filters with and without aeration to assess the impact of aeration on hydraulic performance. CSOa operated with four different aeration conditions, with the optimal condition tested on both filters for comparison. Samples were taken for analysis of global parameters and the redox potential was monitored online. In the tracer test, CSOa allowed to observe the mixing impact of aeration, which avoids any preferential path when influent entered the filter. The addition of a sand layer at the surface (CSOb) allows for a more even distribution of water on the top, which limits preferential flows when aeration is off. In both filters, the results showed that aeration increased the residence time and mixing degree (NTIS <3). Testing different aeration strategies revealed the dependence of dissolved pollutant removal on oxygen supply. In CSOa, the median outlet concentration varied from 23 to 6.4 mg.L−1 in TSS, 153 to 32 mg.L−1 total COD (CODt), 124 to 20 mg.L−1 soluble COD (CODs) and 5 to 2.5 mg.L−1 NH4-N according to aeration strategy. The lower outlet concentrations were always under the highest aeration condition. Under the optimal condition (75 min on/15 min off), median removal of CSOa was 97% TSS, 85% CODt, 78% CODs and 75% NH4-N. Besides COD and TSS, outlet concentration and removal efficiency did not significantly differ between CSOa and CSOb. Pollutant removal demonstrated a linear correlation with organic surface load. Overall, forced aeration in CSO-TW distinctly affected filter dynamics and improved its performance.

A bridge dynamic response analysis and load recognition method using traffic imaging

As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information.

Rolling bearing fault diagnosis method based on multi-scale pooling residual convolutional neural network under noisy environment

To address the issues of unstable performance and poor generalization ability of bearing fault diagnosis model caused by strong noise and variable operating conditions in actual production，this paper proposes a rolling bearing fault diagnosis method based on MSPRCNN model. By converting vibration signals to frequency domain with FTand utilizing wide convolution kernels for feature extraction, the approach aims to enhance fault detection. A MSPFE module captures information at different scales to simplify complexity, while an UPA module establishes correlations between frequency domain positions. To reduce the impact of noise and address the vanishing gradient problem, the MSPRCNN model employs GC instead of standard convolution, and utilizes the IReLU activation function to improve model feature representation. Experimental results on two datasets show that the fault recognition accuracy is 98.71% under variable loads and 98.2% under variable speeds. The MSPRCNN model outperforms other methods in fault recognition and generalization in noisy environments.

Fault Diagnosis Method of Special Vehicle Bearing Based on Multi-Scale Feature Fusion and Transfer Adversarial Learning.

To address the issues of inadequate feature extraction for rolling bearings, inaccurate fault diagnosis, and overfitting in complex operating conditions, this paper proposes a rolling bearing diagnosis method based on multi-scale feature fusion and transfer adversarial learning. Firstly, a multi-scale convolutional fusion layer is designed to effectively extract fault features from the original vibration signals at multiple time scales. Through a feature encoding fusion module based on the multi-head attention mechanism, feature fusion extraction is performed, which can model long-distance contextual information and significantly improve diagnostic accuracy and anti-noise capability. Secondly, based on the domain adaptation (DA) cross-domain feature adversarial learning strategy of transfer learning methods, the extraction of optimal domain-invariant features is achieved by reducing the gap in data distribution between the target domain and the source domain, addressing the call for research on fault diagnosis across operating conditions, equipment, and virtual-real migrations. Finally, experiments were conducted to verify and optimize the effectiveness of the feature extraction and fusion network. A public bearing dataset was used as the source domain data, and special vehicle bearing data were selected as the target domain data for comparative experiments on the effect of network transfer learning. The experimental results demonstrate that the proposed method exhibits an exceptional performance in cross-domain and variable load environments. In multiple bearing cross-domain transfer learning tasks, the method achieves an average migration fault diagnosis accuracy rate of up to 98.65%. When compared with existing methods, the proposed method significantly enhances the ability of data feature extraction, thereby achieving a more robust diagnostic performance.

Investigating the Catalyst/Ionomer Interface in PEM Water Electrolysis Using Microelectrodes

Low temperature zero-gap PEM water electrolyzers are promising devices which are still in development. Water electrolyzers have the potential to be a critical part of the energy transition as a method to use renewable electricity to produce hydrogen which can feed into the hydrogen economy. When paired with renewable sources of electricity, electrolyzers will be subjected to variable power loads, which places a large stress on the systems. This stress causes increased degradation of many components, including the catalyst layers. It can be challenging to study the impact of degradation of the catalyst layer in an electrolysis cell, due to the many coupled degradation phenomenon occurring in the full cell system. Microelectrodes can be used to investigate the catalyst layer under electrolysis operating conditions, but in a simplified system which can more easily isolate the catalyst layer and kinetics. One of the limiting factors in water electrolyzer performance is the oxygen evolution reaction (OER) due to the sluggish kinetics. If the OER kinetics could be improved, it would greatly reduce the cost of the electrolyzer system. The ionomer forms a thin film in the catalyst layer, and controls the local conditions surrounding the catalyst. The local conditions and interactions of the ionomer with the active catalyst sites still aren't well understood, as well as the contribution of the ionomer to catalyst degradation. In this study, cavity microelectrodes will be used to investigate the impact of ionomer on IrOx catalyst OER performance and durability.

Description of step loads at high temperature creep of metallic materials

Abstract In metallic materials under high-temperature creep conditions, the damage evolution occurs. To describe it, Kachanov and Rabotnov proposed the concept of damage. In this paper, the damage parameter is defined as the relative change in material density, serving as an integral measure of damage. By incorporating the damage parameter and the mass conservation law, are we formulated interrelated kinetic equations for rate of creep deformation and the damage parameter. The case of variable loads is crucial both theoretically and practically, as most structural components typically operate under such conditions. Therefore, solutions of considered equations are derived for the case of two-stage loading. The received solutions and experimental results on the creep of titanium alloy under variable loads at 500°C were compared. There is a good agreement between the theoretical results and experimental data.

Polyethylene wear testing of a nonmechanically linked total elbow replacement

The objective of this study was to perform a polyethylene wear test on a nonmechanically linked total elbow arthroplasty implant using a clinically relevant in-vitro elbow wear test methodology that simulated 10 years of use in the light to moderate activity of daily living range. The test protocol applied an 80° arc of ulnohumeral motion beginning at 30° shy of full extension and progressing to 110° of flexion. Force was applied at 7° to recreate a valgus load on the elbow. A variable joint load profile at a frequency of 0.5Hz was employed. The implants were tested for 5 million cycles (mc) in a bovine serum lubricant. Implant component failure was characterized and polyethylene wear was determined gravimetrically. After 5 mc, the small polyethylene bushing wear rate was 0.56mg/mc. The medium size wear rate was 0.28mg/mc. Three large sizes were tested and the average wear rate was 0.39±0.07mg/mc. No implant failure was identified. The test recreated an invivo loading environment and measured polyethylene wear rates at specified cycle counts. The test demonstrated less wear than other joint replacements. Further clinical evaluation is necessary to determine if this translates into reduced complications of total elbow replacement associated with wear.

Bearing fault diagnosis method based on recurrence plot and improved EfficientNetV2-S

The non-linear and non-stationary characteristics of vibration signals in rolling bearings make it difficult to accurately extract fault features. In addition, traditional fault diagnosis methods cannot fully explore the correlation characteristics between time-series of fault signals. To address the aforementioned issues, this paper introduces a recurrence plot (RP) coding technique into the field of fault diagnosis and proposes a bearing fault diagnosis method based on RP and the improved EfficientNetV2-S. Firstly, the method uses the RP coding technique to convert one-dimensional vibration signals into two-dimensional time-frequency images as inputs to the neural network. Then, the number of layers in the EfficientNetV2-S network is optimised by a non-linear attenuation strategy to reduce network parameters and improve the recognition speed. Finally, the attention mechanism is modified and the variable load dataset is constructed for training to improve the feature extraction ability and generalisation performance of the model. To verify the effectiveness of the proposed method, experiments are conducted based on the bearing datasets provided by Case Western Reserve University (CWRU). The experimental results show that the bearing fault diagnosis method based on RP and the improved EfficientNetV2-S cannot only realise accurate identification of bearing faults but also accurately identify the degree of bearing fault with an accuracy of 99.85%.

An investigation of shaft voltage in synchronous generators under SAGE and variable load condition

Abstract Synchronous generators are widely used in power generation systems. Static air-gap eccentricity (SAGE) often occurs in synchronous generators due to the component wear over prolonged operation. This paper presents a comprehensive mathematical model specifically tailored for SAGE fault, incorporating for the influence of stator slotting. The study thoroughly examines the impacts of both eccentricity and varying loads on the shaft voltage using the developed model. Furthermore, a novel method for detecting SAGE is introduced, leveraging the mathematical model of shaft voltage. This detection method proves effective for identifying eccentricity in synchronous generators across different load conditions by reasonably combining shaft voltage and phase current. The mathematical model of shaft voltage and the proposed detection method are validated through three-dimensional finite-element calculations and experimental studies. The work is helpful to manage and predict the shaft voltage. This paper contributes to the prevention of shaft voltage damage and real-time monitoring of the SAGE fault in synchronous generators.

Flame made low Pt loading catalysts supported on different metal oxides for catalytic combustion of CO and CH4

Catalytic combustion is an effective approach to remove air pollutants from various emission sources. For this purpose, supported noble metal catalysts are preferred in commercial applications due to their outstanding catalytic activity for eliminating CO, hydrocarbon compounds and NOx. In this paper, we employ the flame spray pyrolysis method to prepare a series of Pt-based catalysts with four different supports (TiO2, ZrO2, MgO and ZnO) and variable low Pt loadings for catalytic combustion of CO and CH4. The performance of 0.5 Pt/TiO2 is the best in all samples, in which the T90 temperatures are 107 and 500 °C for 90% conversion of CO and CH4, respectively. To examine its thermal stability, a time-on-stream test at 700 °C for 420 min is carried out, resulting in a decrease of about 5% in the final conversion of CH4. The X-ray diffraction results show that TiO2 support is a mixed phase with a major amount of anatase and a small amount of rutile other than a pure phase of ZrO2, MgO and ZnO. Furthermore, X-ray photoelectron spectroscopy analysis and high-angle annular dark-field scanning transmission electron microscopy observation show that when the Pt loading is low, the Pt species exist as highly dispersed single atoms on the surface of the TiO2 support. As the Pt loading gradually increases, the state of the Pt species transitions from single atoms to Pt clusters, resulting in a decrease in dispersion. Ultimately, the Pt can successfully accumulate on the surface of the TiO2 nanoparticles, providing abundant active sites for efficient catalytic combustion reactions.

Gauging force by tapping tendons? Inaccurately in the human patellar tendon

Accurately estimating in vivo tendon load non-invasively remains a major challenge in biomechanics, which might be overcome by shear-wave tensiometry. Shear-wave tensiometry measures the speed of mechanically induced tendon shear waves by skin-mounted accelerometers. To gauge the feasibility and accuracy of this novel technique, we obtained patellar tendon shear wave speeds via shear-wave tensiometry during sustained or ramp voluntary contractions of the knee extensors in two experiments (n = 8 in both). In experiment one, participants produced a constant knee extension torque of ∼ 50 Nm at five different knee joint angles (i.e. variable tendon load), which resulted in estimated patellar tendon forces between 1005 ± 6N and 1182 ± 16 N. However, wave speed squared did not correlate with estimated tendon force within participants (rrm(31) = -0.19, p = 0.278). In experiment two, averaged correlation coefficients between normalized wave speed squared and torque of maximal and submaximal voluntary contractions across participants ranged between r = 0.43 and r = 0.94, while the time-varying correlation between these normalized signals ranged from r = -0.99 to r = 1.00. Further, the mean absolute errors (MAEs) between normalized wave speed squared and normalized torque across participants ranged between 0.03 and 0.54, which were larger than the MAEs between normalized torque and normalized summed EMG amplitude from the superficial quadriceps muscles (0.03–0.54 versus 0.06–0.26, respectively). In conclusion, there was no simple relation between shear wave speed squared and patellar tendon load, which severely limits the feasibility of shear-wave tensiometry for accurately estimating in vivo tendon load at the knee joint.

Tribological Research of Resin Composites with the Fillers of Glass Powder and Micro-Bubbles.

This study investigates the tribological properties of resin composites reinforced with the fillers of glass powder and micro-bubbles. Resin composites were prepared with varying concentrations from 1% to 5% wt of fillers. Tribological tests were conducted using a block-on-ring scheme under dry friction conditions. The measurements of friction coefficient and wear values were performed under variable rotation speeds and loading conditions. The study showed that resin composites with 2-3% glass powder fillers and resin composites with 3-4% micro-bubbles exhibited optimal tribological properties. The resin glass powder modifications reduce the wear by 63% and resin micro-bubbles reduce wear by 32%. SEM analysis of the surfaces revealed surface imperfections and structural damage mechanisms, including abrasive and fatigue wear. The study concludes that specific filler concentrations improve the friction and wear resistance of resin composites, highlighting the importance of material preparation and surface quality in tribological performance. The increased wear resistance on both composites would hopefully expand the usage of additive manufactured composite, namely industrial moving components such as polymer gear, wheel, pulley, etc.

Dynamic characteristics of a climate-adaptive radiant cooling and fresh air supply integrated system with zeotrope R290/R600a

Enhancing climate-adaptability stands as a pivotal focus in the advancement of air-conditioning technology. The nuanced control over thermodynamic properties of zeotropic mixture emerging as a critical factor in effectively accommodating variable loads. This paper proposes a radiant cooling and fresh air supply integrated system, featuring dynamic adapting to various operating conditions by composition adjustment with zeotropic mixture of R290/R600a. By synchronizing the cooling capacities of dual cooling sources with indoor thermal-humidity load, the climate-adaptive operation is accomplished by tuning the secondary fluid of the liquid separation component. A physics-based dynamic model is constructed on the liquid separation condenser in the sequence of control volume-pipeline-path based on finite volume approach. The numerical analysis indicates the spatial distribution of mass fraction and thermophysical properties of the zeotropic mixture over time. To facilitate intuitive analysis of the climate-adaptive operation, the enthalpy humidity ratio (ε) that the system can handle is developed for evaluating the applicable scenarios and operating conditions of the dual cooling source system. The results demonstrate that the liquid separation component exhibits a rapid response owing to its low thermal inertia, lagging less than 30 s to adapt to the airflow step from 0.45 to 0.4–0.5 kg/s over a total duration of 120 s. The enhancement of convective heat transfer and reduction in heat capacity both lead to a decrease in thermal inertia, thereby shortening the response time. Increasing the airflow rate shortens the response time to 60 % of its initial duration. R290/R600a at mass fraction ratio of 0.6/0.4 exhibits the highest sensitivity to airflow adjustment. The capacities of the dual cooling sources respectively fluctuate between −55 % and 62.5 %, and −84.6 % and −0.02 %, as a result of the mass flow rates of the refrigerant varying between −60 % and 120 %, and −85.2 % and −48.1 %, with the mass fraction of R290 ranging from −20 % to −10 %, and −11.8 % to −5.9 %. The system can provide fresh air of 19 ± 16 %℃ and 12 ± 23 %g/kg and chilled water supply for the radiant terminal of 15 ± 20 %℃. The system is capable of handling air-conditioned spaces with ε ranging from 1450 to 20000 kJ/kg by adjusting the airflow rate within a range of ± 12 %, and offering an adjustable dehumidification capacity ranging from 0.2 to 2.5 kg/h. The system constitutes a promising prospect of thermal-humidity decoupling technology that adapts to various climatic conditions and personalized need.

Energy consumption prediction of coal-fired unit boiler system based on different operating states

ABSTRACT The energy consumption of the coal-fired unit’s boiler system varies significantly when accommodating flexible peak shaving demands. To aid staff in comprehending the boiler’s operational status and optimize its performance, a prediction model of the energy consumption of the boiler system was established based on the different states of the unit operation. First, a dataset of boiler energy consumption under variable load was established based on theory of fuel-specific consumption, and the Mean Impact Value (MIV) algorithm was used to simplify the input features of the model. Second, the Aquila Optimizer (AO) with tent map, adaptive t-distribution, and opposites learning mechanism was introduced to determine the parameters in the prediction model. On this basis, the sliding-window method was used to classify the operating states based on the load of the unit, and the original dataset without operating state distinction, the steady state operating data, the load uplink data, and the load downlink data were used to establish Models 1–4, respectively. The result shows that Model 1 outperforms Model 2 with 24.45% and 18.22% lower aMAE and aRMSE, respectively. compared to Model 3, it shows a decrease of 24.07% and 16.98%. Compared to Model 1, Model 4 shows a reduction of 20.52% and 18.91% in aMAE and aRMSE, respectively. This indicates that distinguishing different operating states to establish boiler energy consumption prediction models can obtain better prediction accuracy.

Modelling of electromechanical coupling dynamics for high-speed EHT system used in HEV and characteristics analysis

As the development of hybrid electric vehicle (HEV) to high-speed, heavy-load, the electromechanical coupling vibration becomes the bottleneck in high-speed electromechanical hybrid transmission (EHT) system. To explore the dynamics characteristics and optimization direction for high-speed EHT system, an electromechanical coupling dynamics model under multi-source excitations is established with lumped-distributed parameter method and verified with experiment data. The dynamics model shows higher accuracy in both time and frequency domain analysis. On this basis, firstly, the comparison between lumped-shaft and distributed-shaft model is studied under time and frequency domain. Comparing with the lumped-shaft model, the distributed-shaft model shows higher accuracy, and can better reflect the coupling vibration of multi-stage planetary gears (PGs). Secondly, the inherent vibration model is derived, and the effect of electromechanical coupling and high-speed working conditions on inherent vibration characteristics are studied. Thirdly, a new method called ‘machine-electricity-magnet coupling interface’ is proposed to reveal the coupling vibration phenomenon. In addition, the signal of stator current and electromagnetic torque includes the frequency of PGs, which is a basis on the fault diagnosis and state monitor of EHT system. Last but not the least, the vibration acceleration of EHT system is analysed under variable speed and load working conditions. Rotation speed and gear meshing force is found as the main influencing factor of series EHT system, and the specific optimization direction is also given.

A comparative experimental research on the diagnosis of tooth root cracks in asymmetric spur gear pairs with a one-dimensional convolutional neural network

Gearboxes transfer rotational motion and handle precision functionalities in many fields, including aviation, wind turbines, and industrial services. Their health management is essential to minimize workforce risks, increase the level of safety, and avoid machine breakdowns. From this standpoint, the present experimental research work developed a convolutional neural network-based method for diagnosing different levels of tooth root cracks (25 %-50 %-75 %-100 %) for symmetric (20°/20°) and asymmetric (20°/30°) profiled gear pairs. A series of vibration experiments were performed on a one-stage spur gearbox to achieve this by using a tri-axial accelerometer under variable working loads. The main purpose of this experimental research study is to explore the influence of the tooth profile on spur gears’ vibration responses and whether utilizing an asymmetric tooth profile would positively impact a deep learning algorithm's classification accuracy to add to the enhancements it provides in terms of fatigue life, mesh stiffness, and impact strength. Experimental results revealed that the overall classification accuracy could be increased by 7.712 % by feeding the proposed deep learning model with vibration data measured using test samples with asymmetric teeth.

Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods

Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. DC motor speed control has an important role in industrial processes to increase efficiency, realize precise movements and optimize energy consumption. In this study, various control methods and parameter optimization techniques for speed control of DC motors, which have a wide range of applications, have been systematically analyzed. The aim of the study is to develop an effective control strategy to ensure that DC motors reach the determined target speed by monitoring them in real time at different speeds and to minimize fluctuations caused by variable loads or external factors. In our study, Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Proportional-Derivative (PD) control methods were used. The parameters of these controllers were tuned using Matlab Tuned, The Cheetah Optimizer (CO) Algorithm, a new generation heuristic optimization method, and Particle Swarm Optimization (PSO), a widely accepted optimization method. The performances of the controllers were determined using criteria such as Integral of Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE). According to the results obtained, it was found that the PID, PI and PD control parameters determined using the CO Algorithm performed better than the controllers created using Matlab Tuned and PSO methods. New optimization methods, such as the CO Algorithm, have been found to have significant potential to improve the performance of control systems. Thanks to this study, it offers a practical approach for optimizing DC motor speed control in industrial processes. As a result, it has been found that the control parameters determined by the CO Algorithm have significant potential in improving the performance of DC motor speed control and control systems compared to other optimization methods.

Modelling Steady State and Transient Stability Parameters for Kainji Hydropower Station, Nigeria

The study explores the creation of a simulator for addressing the problem of predicting response of Nigeria’s Kainji’s Hydro Power Plant (KHPP) to system load variations and to determine parameters for stable operation. A virtual KHPP Simulation Model was designed and created using MatLab/Simulink software. The components of the HSS designed and incorporated in the virtual simulator include models of all eight (8) generating units (GUs). Each GU model consists of the Hydraulic Turbine Governor (PID), synchronous generator and an excitation system. The simulator was tested with steady state stability (SSS) and transient stability (TS) disturbances to obtain response values of system operating state parameters. The tests include a variable load increase, and three-phase short circuit fault for a duration of 0.2 s. The SSS limits established are the following: active load – 810 MW, load angle – 850, terminal voltage 0.68 pu, rotor speed 1.08 pu, excitation voltage 1.55 pu, and stator current 7.5 pu. TS limits established are load angle (1700), terminal voltage (0.60 pu), rotor speed (1.05) pu, excitation voltage (1.25 pu), stator current (7.1 pu), and critical clearing time (0.2s). Clearing time should be set at 0.2s to prevent KHPP and connected system’s instability.