Variable Working Conditions Research Articles

A domain generalization network for imbalanced machinery fault diagnosis

Traditional models for Imbalanced Fault Diagnosis (IFD) face challenges in practical applications due to domain shifts caused by varying working conditions and machinery. Domain Generalization (DG) models provide an advantage over traditional approaches by learning class-discriminative and domain-invariant feature representations, allowing them to generalize to unseen target data. However, the scarcity of fault samples relative to healthy ones limits their application in real-world industrial scenarios. In this paper, we propose a Domain Mixed-Enhanced Domain Generalization Network (DEMDGN) that enhances IFD performance by utilizing mixup-based data augmentation and domain-based discrepancy metrics to align feature distributions across multiple heterogeneous source domains. By creating domain-invariant features, DEMDGN allows robust fault diagnosis under varying conditions. Extensive experiments on one marine machinery dataset and two bearing datasets demonstrate that the proposed method effectively addresses class imbalance and domain shift problems, achieving superior diagnostic performance.

Short-time adaptive compact kernel distribution for fault diagnosis under variable working condition bearing

Time-frequency distribution (TFD) methods are extensively employed in the diagnosis of bearings operating under variable working conditions. However, these methods often face high computational complexity, limited time-frequency resolution, and cross-terms. To address these issues, this paper presents a new approach called Short-Time Adaptive Compact Kernel Distribution (STACKD). Based on the characteristics of the vibration signals, we designed Chirp-modulated Gaussian Window and used them to segment the overall signal into small segments. Then, we used a two-step Bayesian optimization method to obtain adaptive compact kernel distributions (ACKD) for these small segments of the signal. Finally, we recombined the ACKD of these small segments into a global STACKD. Results from simulations and experiments demonstrate that STACKD offers improved robustness against interference signals, lower computational complexity, and higher time-frequency resolution without cross-term effects. This method is tailored for diagnosing rolling bearings under variable working conditions, providing enhanced visualization of diagnostic processes with superior time-frequency resolution.

A multi-source domain feature-decision dual fusion adversarial transfer network for cross-domain anti-noise mechanical fault diagnosis in sustainable city

Rotating machinery forms the critical backbone of infrastructure in a sustainable city, with bearings playing a pivotal role as key mechanical transmission components. Therefore, the health status of these bearings directly influences the safe operation of the infrastructure. Accurate and reliable diagnosis of defects in these components minimizes downtime, reduces maintenance costs, and prevents major accidents, ultimately providing insights in the construction and management of a sustainable city. Typically, in actual industrial scenarios, varying working conditions and various types of machines can result in significant discrepancies in the distribution of sample data. Moreover, the non-negligible noise may degrade the diagnostic performance. Therefore, realizing an accurate and reliable bearing diagnosis considering the cross-domain and noise environment remains a challenge. Leveraging the merits of information fusion and multi-source domain transfer learning, this article proposes a multi-source domain feature-decision dual fusion adversarial transfer network (DFATN) to break through the aforesaid limitations. Initially, an adversarial transfer framework is developed, incorporating novel feature matching evaluation and joint distribution difference losses. This framework is designed to facilitate the learning of feature invariants across domains and to enhance the sharing of domain-specific knowledge, even in noise. Relying on channel-spatial interactive feature fusion, a multi-scale feature extractor (MFE) is constructed to share the interaction and enhance the modeling of complex features in multiple dimensions. Additionally, a fault state-related decision fusion mechanism (SDF) is also implemented to integrate diagnostic information, significantly enhancing the generalization performance and robustness of the proposed network. By employing both public Paderborn University (PU) and laboratory-collected (Lab) datasets, the effectiveness and superiority of the proposed DFATN on bearing fault diagnosis are validated. For cross-working condition tasks, the proposed method realizes impressive performance, with average accuracies of 96.52% and 98.76% for Paderborn University (PU) and laboratory-collected (Lab) datasets, respectively. For cross-machine tasks, the average accuracy is 83.36%, outperforming other latest cross-domain fault diagnosis techniques.

Multi-physics simulation method for automotive motors with variable working conditions based on multi-software combination

Multi-physics simulation method for automotive motors with variable working conditions based on multi-software combination

Cross-domain transfer fault diagnosis by class-imbalanced deep subdomain adaptive network

In variable working conditions and class imbalances, fault diagnosis in gearbox systems becomes difficult due to the difficulty in learning the classification boundaries of the minority class. Moreover, the diversity in fault mode distributions under various working conditions poses significant challenges for domain adaptation. To this end, this work proposed a Class-imbalanced Deep Subdomain Adaptive Network (CIDSAN) for complex cross-domain fault diagnosis scenarios involving feature and label shifts. We design a convolutional neural network embedded with SimAM (SimAM-CNN) to extract global feature information from time-domain vibration signals. Subsequently, a novel cost-sensitive classifier is presented for class re-weighting, along with a Joint Supervised Classifier that refines intra-class and inter-class distances by re-weighting class weights in the latent space using cost-sensitive classifiers while leveraging LDAM regularization to adjust classification boundaries. Diagnostic case studies on two datasets under various working conditions validate the effectiveness and superiority of the proposed approach. The code https://github.com/Zjy0121/CIDSAN.

Study on the internal flow evolution mechanism and pressure pulsation characteristics of the transient process of variable speed regulation of centrifugal pump

Abstract In view of the fact that it is not possible to quantitatively analyze any change in the internal flow characteristics of traditional centrifugal pumps during variable speed regulation, numerical computational fluid dynamics (CFD) simulations were carried out in this study to obtain the optimal adjustment range of variable speed regulation. A single-stage IS80-65-160 centrifugal pump is modeled in 3D using Pro/E and ICEM software, followed by CFD simulations. Results are compared with experimental data, showing good agreement. Variable speed adjustments ranging from 0.5-1.0 times the original values are applied to observe changes in internal flow characteristics, such as velocity and turbulent kinetic energy. Pressure and velocity variations in different parts of the pump are studied under varied working conditions. Pressure pulsation frequency domain characteristics are measured at monitoring points in the volute. Findings indicate that as flow rate decreases due to variable speed regulation, velocity decreases linearly, while turbulent kinetic energy decreases quadratically in a uniform distribution. Pressure fluctuations within the volute remain within 0.01 Cp range, but at the tongue, fluctuations can reach up to 0.6 Cp, suggesting an optimal adjustment range within 30% of the rated value.

A novel remaining useful life prediction based on transfer hybrid deep neural network under variable working conditions

Deep learning-based methods for remaining useful life prediction (RUL) usually require the precondition that the training and test data obey the same distribution. In engineering applications, mechanical equipment is frequently under different working conditions, which can lead to significant differences in the distribution of collected data and difficulties in obtaining labels. This paper proposed a novel RUL prediction method based on transfer hybrid deep neural network to solve the above problems. Firstly, a degradation feature extraction strategy and a clustering hybrid feature screening strategy are proposed to enrich the information content of degradation features and obtain manual features with significant degradation trends. Then, a multi-stage shrinkage attention temporal convolution network is used to adaptively extract strongly expressive and information-rich deep features from the raw data. Next, a bidirectional convolutional gated recurrent unit based on bidirectional learning and convolutional operations is designed to achieve the fusion of manual and deep features and improve the quality of degradation features. Finally, the unsupervised domain adaptation strategy is used to reduce the differences in the distribution of degradation features between training and test data and to achieve feature alignment. This paper validates the effectiveness of the proposed method on six transfer tasks. The experimental results show that the RUL prediction effectiveness of the proposed method is better than other methods.

Analysis of the influence of dust particles in the peanut pickup harvester with a self-designed axial-flow dust-fall box on its dust suppression performance

The atmospheric particulate pollution poses a serious threat to human health and ecological environment. The dust particle pollution caused by the operation of agricultural machinery is becoming increasingly severe and the peanut pickup harvesters are widely used in agricultural operation. Therefore, it is necessary to reduce the concentration of dust particles in the work of peanut pickup harvesters. In this study, a self-designed dust-fall box composed of multipleaxial flow cyclone tubes in parallel was integrated with the peanut pickup harvester. Based on the CFD-DPM model, simulation analysis was conducted to explore the effects of different inlet velocities, different numbers of swirling blades, and two types of exhaust port on the dust removal performance of the cyclone. The Box-Behnken design was used to determine the optimal structural parameters of the axial flow cyclone. The influence of the pressure distribution and velocity variations of the internal flow field on the dust removal efficiency of the two schemes was simulated and analyzed. Thereafter, a reasonable parallel scheme of multiple axial flow cyclone tubes in the dust-fall box was determined. The drone was used to monitor dust in peanut harvesting operations, and dust particle concentrations at 8 detection points under varying working conditions were recorded. The distribution and diffusion rules of dust in harvesting operations were explored through cluster analysis, and the test data obtained were compared with numerical calculation results to verify the accuracy of numerical calculation. The results showed that for a single axial flow cyclone, when the inlet wind speed was 6 m/s to 8 m/s and the number of swirling blades was 4, the structural parameters of the cyclone separator had significant influence on the separation efficiency. The influencing factors ranking in descending order were cylinder diameter, cylinder length, and exhaust pipe insertion depth, respectively. To be more precisely, the separation efficiency was best when the cylinder diameter was 60 mm, the cylinder length was 150 mm, the insertion depth of the exhaust pipe was 50 mm and the cone angle was 20°. It was more reasonable to use 8 axial flow cyclone tubes in parallel, and the maximum separation efficiency can reach 84.21 %. Field operations showed that the numerical calculation results were similar to the actual harvesting process. The dust particle concentration in the peanut harvesting operation equipped with a dust-fall box was always lower than 10 mg/m3. The dust particle concentration was the highest at the rear of the whole machine and the lowest near the cab. Compared conventional harvesters, the harvester with a dust-fall box reduced its dust particle concentration by 64.37 %; the concentration of 30 μm dust particles was reduced by 69.31 %; the dust particle concentration at the rear of the whole machine also effectively reduced. This study can provide reference for controlling dust emissions during peanut harvesting operations and agricultural machinery harvesting operations.

Numerical Simulation Study on Rotary Air Preheater Considering the Influences of Steam Soot Blowing

The ash deposition is a general problem that needs to be solved effectively for the rotary air preheater of the coal-fired boiler. Taking the rotary air preheater of a 600 MW power station as the object, the mesh model of the flue gas side of the air preheater, considering the influences of steam soot blowing, is established using the Gambit 2.4.6 software. Based on the SIMPLE algorithm, the velocity field and the temperature field in the air preheater under varied working conditions are simulated using the software of Ansys Fluent 2021R1, and the influences of the boiler load, the operation parameters of the steam soot blower, and the running and outage of the soot blower on the flue gas velocity distribution in the depth direction of the corrugated plates, the soot-blowing coverage area, the inlet flue gas velocity, and the inlet flue gas temperature of the corrugated plates are analyzed. Under the base working condition, the flue gas velocity on the axis of the steam nozzle first decreases rapidly with increasing the corrugated plate depth (Z < 1.0 m), and then it decreases slowly with an almost equal slope. The longitudinal flue gas velocity has a positive correlation with the boiler load. The longitudinal flue gas velocity obviously decreases when the boiler load is decreased, and its reduction increases as the corrugated plate depth increases. It is one reason that the ash deposition is prone to occur on the cold end surface of corrugated plates under the condition of low boiler load. The longitudinal flue gas velocity increases with the soot-blowing steam velocity increasing when the corrugated plate depth is less than 1.5 m, but after that, it is almost not affected by the change in soot-blowing steam velocity. The soot-blowing coverage area has a negative correlation with the boiler load but a slight positive correlation with the steam velocity of the soot blower on the whole. The inlet flue gas velocity of the corrugated plates has a positive correlation with the boiler load and the inlet steam velocity of the soot blower. The average inlet flue gas velocity decreases by 21.7% when the boiler load is reduced by 50%. For every 5 m/s variation in the inlet steam velocity, the inlet flue gas velocity changes by about 10–14% whether the steam soot blower is put into operation or not, which has an obvious effect on the inlet gas velocity of the corrugated plates. The inlet flue gas temperature of the corrugated plates is, respectively, positively correlated with the boiler load and the inlet steam temperature of the soot blower. When the boiler load is reduced from 100% BMCR to 50% BMCR, the average inlet flue gas temperature of the corrugated plates is reduced by 44.2 K; however, when the soot-blowing steam temperature varies by 20 K, the average inlet flue gas temperature of the corrugated plates varies by only about 1.8 K. It means that it is difficult to enhance the cold end flue gas temperature of the corrugated plates only by raising the soot-blowing steam temperature at low boiler load. Adding a soot blower using high-temperature steam or hot air at the outlet of the corrugated plates may be an option to solve the ash deposition of the corrugated plates.

Optimization of water supply parameters for enhanced thermal uniformity in aquaculture ponds under varied working conditions: An experimental study

Aquaculture ponds are critical for the successful rearing of fish larvae, and achieving consistent temperature distribution within these systems is essential for optimal growth conditions. This study explores methods for precise temperature regulation on both the water and atmospheric sides of the pond ecosystem. Based on the original design, systematic changes were made to parameters such as water supply pipe layout, bend angles, perforation rates, and working conditions. Metrics including average temperature, average temperature difference, and extreme temperature difference were utilized to extensively discuss the impacts of these parameters. Through this, temperature distributions across five distinct water supply layouts were analyzed, and the impacts of four different bending angles and perforation rates were evaluated using optimized configurations. Our findings demonstrate that a water supply layout radius of 2000 mm, coupled with a 60° bending angle and a 25 % perforation rate, increased overall temperature uniformity in the pond by 5 %. Additionally, seasonal variations significantly impact temperature distribution in ponds, particularly with different extreme temperature differences observed in winter and summer. Compared to the transitional season and summer, the average temperature difference in the pond during winter decreased by 30 % and 45 %, respectively. It was further revealed that winter conditions naturally yield more uniform water-side temperatures, making seasonal fluctuations irrelevant in determining optimal perforation rates. After the implementation of a water agitator, the average extreme temperature difference decreased from 0.6 K to 0.52 K. In the shallow water layer, the water agitator reduced the extreme temperature difference in the pond by 26 %. The use of water agitators has enhanced thermal uniformity in ponds, thereby improving the growth environment for fish larvae and providing valuable insights for environmental management in aquaculture.

High-temperature fretting wear behavior and microstructure stability of a laser-cladding Ti-Al-C-N composite coating meditated by variable cycle conditions

Ti-6Al-4 V titanium alloy is used for low-pressure compressor blades of aero-engine due to its high specific strength, excellent corrosion resistance and high-temperature stability. Low-pressure compressor blades are subject to fretting wear in service, which may lead to fracture failure of the blades. The preparation of wear-resistant coatings is currently an effective solution in solving the problem of fretting wear on Ti-6Al-4 V titanium alloy. However, the fretting wear resistance and microstructure stability of the coating under variable cycle fretting conditions require more attention. In this work, the fretting wear performance and microstructure stability of the Ti-Al-C-N composite coating under variable cycle fretting conditions are investigated. The results show that the parameters of variable cycle operating conditions have a notable effect on the coefficient of friction (COF). The variable frequency and variable temperature working conditions result in the alteration of the fretting operation mechanism. Whereas the variable stroke amplitude and the variable load conditions do not cause changes in the fretting operation mechanism, and both are in the gross slip region (GSR). The lowest wear rate occurs under variable stroke amplitude conditions. The wear rate is the maximum under the variable cycle temperature condition. According to the dissipated energy calculation, the difference between the two different stroke amplitudes is the biggest among all the conditions. EBSD and TEM analyses show that the subsurface of variable stroke amplitude worn scar has a more uniform strain distribution, and reveals a unique subsurface structure with a transformation from crystalline to amorphous, which contributes to improving the wear resistance. This work provides insights into coating microstructure stability and wear mechanisms under variable cycle conditions.

Fault diagnosis method for bearings under variable working conditions based on transfer relation network

Abstract In order to achieve robust fault diagnosis under varying conditions with limited labeled data, this study combines metric-based meta-learning with feature-based domain adaptation. It introduces a new approach for variable-condition bearing fault diagnosis using transfer relation networks. To enhance the network’s ability to generalize across different domains, the paper integrates local maximum mean discrepancy (LMMD) into the relation network architecture. LMMD aligns the data distributions of various classes between the source and target domains, effectively addressing distributional differences and improving model generalization. To accurately and swiftly extract meaningful fault features, the study proposes a lightweight feature extraction module based on Shuffle Attention (SA). This module employs depth-wise separable convolutions for efficiency and integrates SA after each convolutional layer to bolster feature representation. Finally, experiments on two bearing datasets under varying conditions validate the efficacy and superiority of the proposed model over alternative methods.

Numerical study of steady-state dynamic characteristic of non-pneumatic tire with local structural damage

Non-pneumatic tires (NPTs) fundamentally avoid the risk of tire blowout of traditional pneumatic tires, but the overall and key component performance inevitably degrades due to factors such as high temperature, variable load, variable working conditions and impact in its service process. Once this leads to failure, it will significantly impact on vehicle safety. To this end, this paper studied the influence of local structural damage on the dynamic response of NPTs, which lays the foundation for realizing health monitoring of intelligent non-pneumatic tires (INPTs). Firstly, a three-dimensional nonlinear finite element model of NPTs was established, and the failure locations of NPTs were determined by fracture mechanics and maximum strain energy density; Secondly, the influence of structural damage on the static and dynamic performance of NPTs was analyzed; Finally, the sensitivity of the acceleration signal and sensor position of the tire inner liner to the local structural damage was studied. The research results show that structural damage will cause the stress of the spokes and shear layer to increase, and as the number of broken spokes increases, the shear layer will bear a larger load. Compared with the circumferential and lateral acceleration, the radial acceleration has the highest sensitivity to the damage of NPTs. The sensor closest to the damage location is the most sensitive to the damage. The research results provide a reference for the structural optimization design and health monitoring of INPTs.

Flow Mechanism Study on the Effect of Controllable Speed Casing With Different Axial Starting Points on Transonic Compressor Rotor Stability

Abstract The controllable speed casing represents an exploring approach to casing technology, designed to enhance the adaptability of casing in compressors under variable working conditions. This paper developed a numerical study into the effects of the axial starting point of the rotatable ring in the controllable speed casing on stability enhancement and performance. Additionally, the study sought to unveil the action mechanism of the rotating casing on various flow elements within the tip passage. The findings indicated that the optimal axial starting point for achieving the most pronounced stability enhancement effect in each rotating speed of the rotatable ring was located at the tip leading edge. In terms of the flow mechanism, the rotation of the rotatable ring was found to enhance the throughflow of the mainstream and the tip vortex, while exacerbating the backflow of the tip leakage flow, which occurred at middle and rear of the tip clearance and had not evolved into tip vortex.

Dual-Path Neural Network based on Deep Features and Transfer Learning for Bearing Fault Diagnosis

The safe operation of mechanical equipment is a basic requirement in the industrial manufacturing process. As an important component of mechanical equipment, rolling bearings are prone to unforeseen failures due to long-term operation under complex conditions. The occurrence of failures, no matter how big or small, will cause economic losses. Therefore, reliable diagnosis of rolling bearings is crucial. Aiming at the problem of insufficient feature recognition of convolutional neural networks under strong noise background, a deep feature extraction network integrating multiscale convolutional neural network (MSCNN) and bidirectional gated recurrent unit (BiGRU) is proposed. MSCNN and BiGRU are used to extract multiscale features and temporal features from noisy vibration signals respectively, and different weights are assigned to the fused features through the attention mechanism module to achieve important feature selection. Furthermore, in order to solve the problem of different feature distributions between the source domain and the target domain under variable working conditions, transfer learning is introduced in the proposed deep feature extraction network. The difference in feature distribution between the source domain and the target domain is measured by a multi-level distance formula, and the measurement result is added to the loss function. The back propagation of the loss is used to achieve the alignment of feature distribution between the source domain and the target domain. Finally, the model uses the SoftMax function as a classifier for rolling bearing fault diagnosis. Experimental comparison and analysis show that the proposed model has good migration ability and achieves a higher fault identification accuracy.

Study on circulating cooling water pre-mixing for thermal performance improvement of twin thermal power units sharing one dry cooling tower

For an advanced power plant of twin thermal power units sharing one dry cooling tower, power production is easily impacted by variations in cooling tower performance. Circulating cooling water pre-mixing (CCWPM) was proposed to maximize cooling potential of the tower under varying working conditions, thereby enhancing power generation for the whole power plant. An integrated simulation model was established to capture cooling behaviors of the shared tower and operating characteristics of both power units before and after implementing CCWPM. Simulation results indicated that CCWPM effectively mitigates the uneven distribution of airflow temperature inside the shared tower and reduces the high-temperature area. As power load of one specified unit drops, both units experience a decline in power cycle efficiency, and the implementation of CCWPM further decreases the efficiency of this unit while increasing the efficiency of the adjacent unit. The whole power plant benefits from CCWPM in power generation, particularly when the unit with radiators directly facing the crosswind operates at a low power load. Power generation efficiency achieves an absolute increase of 0.43 % via CCWPM for the upwind unit and its adjacent unit operating at 40 % and 100 % power loads and the shared tower working at 8 m/s crosswind speed.

Novel meta-learning for few-shot bearing fault diagnosis under varying working conditions

In practical engineering, large amount data and variable working conditions poses a challenge to most existing Deep Learning(DL) methods. To solve this problem, this paper proposes a new meta-learning approach. Under the condition of limited data, the fault diagnosis under variable working conditions is regarded as a problem with fewer lenses, and the fault diagnosis of few samples across working scenes is carried out based on the Model-Agnostic Meta-Learning(MAML). Gradient-by-gradient rules are used for parameter optimization to achieve an efficient representation of these tasks. Then, the attention mechanism is applied to improve the efficiency of the training. Finally, experiments verified the fault diagnosis accuracy under various working conditions.

Multiscale Conditional Adversarial Networks based domain-adaptive method for rotating machinery fault diagnosis under variable working conditions

Deep learning has been increasingly used in health management and maintenance decision-making for rotating machinery. However, some challenges must be addressed to make this technology more effective. For example, the collected data is assumed to follow the same feature distribution, and sufficient labeled training data are available. Unfortunately, domain shifts occur inevitably in real-world scenarios due to different working conditions, and acquiring sufficient labeled samples is time-consuming and expensive in complex environments. This study proposes a novel domain adaptive framework called deep Multiscale Conditional Adversarial Networks (MCAN) for machinery fault diagnosis to address these shortcomings. The MCAN model comprises two key components. Constructed by a novel multiscale module with an attention mechanism, the first component is a shared feature generator that captures rich features at different internal perceptual scales, and the attention mechanism determines the weights assigned to each scale, enhancing the model’s dynamic adjustment and self-adaptation capabilities. The second component consists of two domain classifiers based on Bidirectional Long Short-Term Memory (BiLSTM) leveraging spatiotemporal features at various levels to achieve domain adaptation in the output space. The deep domain classifier also captures the cross-covariance dependencies between feature representations and classifier predictions, thereby improving the predictions’ discriminability. The proposed method has been evaluated using two publicly available fault diagnosis datasets and one condition monitoring experiment. The results of cross-domain transfer tasks demonstrated that the proposed method outperformed several state-of-the-art methods in terms of transferability and stability. This result is a significant step forward in deep learning for health management and maintenance decision-making for rotating machinery, and it has the potential to revolutionize its future application.

Multi-source domain adaptation using diffusion denoising for bearing fault diagnosis under variable working conditions

Transfer learning of multi-source domain adaptation seems a promising way for fault diagnosis of roller element bearings under variable working conditions. Data imbalance affects the performance of multi-source domain adaptation greatly and is expected to be solved by GAN. However, GAN-based transfer learning diagnosis models suffer pattern collapse and training instability, leading to unsatisfying diagnosis results in practical engineering. This paper proposes a denoising diffusion multi-source domain adaptation model (DDMDA). The proposed model uses diffusion denoising, which has better performance and is simpler to train than GAN, to generate shifted source domains for solving the data imbalance problem. A new noise prediction structure in diffusion denoising named Utrans-net, is constructed to restore the data distribution in the shifted source domain. Also, a multiple-domain discriminator structure is designed to extract features from multiple source domains to solve the issue of variable working conditions. Advanced models are used in this paper to compare with the proposed model for validation. Experimental demonstrations show that the proposed model is superior to the comparison models with satisfying performance.

A multi-objective co-optimization method of controller parameters for the overall system of small pressurized water reactor

Small pressurized water reactors (SPWRs) normally have complicated and varying working conditions, and become an important direction for nuclear energy development. The overall system of SPWR, including the reactor coolant average temperature control system, the feedwater control system of the once-through steam generator (OTSG), the steam turbine speed control system and the steam dump control system, is used to guarantee the safe and stable operation. The overall system of SPWR is designed with multiple PI controllers. To accomplish intelligent self-tuning of PI controller parameters, reduce the dependence on human engineering experience, and improve the control performance of the overall system, a multi-objective co-optimization method(MMOCO) with PI controller parameters for overall system of SPWR is proposed. Firstly, based on the coordinated control theory and PI control algorithm, the overall system of SPWR is established. Then, a MMOCO for the overall system of SPWR is designed with the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, to compare the performance of the controller parameters derived from the MMOCO and the controller parameters obtained via the engineering tuning method, step load decrease transients are selected. The results show that the ability to control the overall system of SPWR is effectively improved after applying the MMOCO.