This paper presents a novel event-triggered approach for regulating energy prices of heterogeneous agent-based demand response including prosumer groups and energy companies. First, a price-based demand response management (PDRM) model is developed using networked evolutionary game theory. Then, a necessary and sufficient criterion is proposed to verify the stabilizability of the controlled PDRM system, significantly reducing iterative complexity compared to existing methods. By utilizing truth matrices, an algorithm is proposed to calculate the event-triggered feedback gain matrices and determine the triggering times. The proposed matrix-based event-triggered control guarantees that the strategies of all participants converge to the target game equilibrium set while reducing the update frequencies of the controller. Finally, the simulation results demonstrate the effectiveness of the proposed method.

Thanks to its excellent nonlinear representation ability, deep neural network (DNN) has been designed to aid canonical correlation analysis (CCA) for stable kernel representation. These DNN-aided CCA methods make use of correlation as the optimization objective and exploit its change to distinguish system states. However, the maximum correlation-based training strategy lacks robustness to effectively tackle system false alarms caused by changes in operating conditions. To this end, this work proposes a self-attention sliding window enhanced CCA (SaECCA) for incipient fault detection in dynamic industrial processes. The main novelties of this work include the following: first, with the aid of DNNs, an adaptive weighting mechanism with self-attention is developed to amplify the incipient fault information; second, residual estimation-based, a novel and robust optimization objective for nonlinear CCA is formulated; third, an SaECCA-based fault detection algorithm is designed, whose convergence and detectability are illustrated via theoretical analysis. Studies on a three-tank system simulation and a multiphase flow industrial process are presented to verify the effectiveness of the proposed SaECCA method.

This article investigates energy trading management involving users, suppliers, and the utility company, focusing on a periodic energy trading mechanism that incorporates time-varying delays in the information transmission process. Compared with previous studies, the time-varying delays considered in this paper are different among participants. The time-varying delays for each participant are distributed according to an independent probability. A novel model for energy trading with time-varying delays is proposed using networked evolutionary game theory. Based on the algebraic state space representation, a criterion is provided for determining the convergence of the networked evolutionary game-based energy trading model. In order to converge all strategies of the networked evolutionary game-based energy trading model to the target game equilibrium set, a networked evolutionary game-based energy trading model with strategy feedback control is proposed. Then, an algorithm is presented for designing the strategy feedback control gain, which enables the strategies of all users and suppliers to converge to the target game equilibrium set, thereby regulating energy trading prices to the desired level. Finally, the effectiveness of the proposed approach is verified through an illustrative example.

This work concerns the issue of quality-related fault detection and diagnosis (QrFDD) for nonlinear process monitoring. A kernel principal component analysis (KPCA)-based canonical correlation analysis (CCA) model is proposed in this article. First, KPCA is utilized to extract the kernel principal components (KPCs) of original variables data to eliminate nonlinear coupling among the variables. Then, the KPCs and output are used for CCA modeling, which not only avoids the complex decomposition of kernel CCA but also maintains high interpretability. Afterwards, under the premise of Gaussian kernel, a proportional relationship between process variables sample and kernel sample is introduced, on the basis of which, the linear regression model between process and quality variables is established. Based on the coefficient matrix of the regression model, a nonlinear QrFDD method is finally implemented which has both the data processing capability of nonlinear methods and the form of linear methods. Therefore, it significantly outperforms existing kernel-based CCA methods in terms of algorithmic complexity and interpretability, which is demonstrated by the simulation results of the Tennessee Eastman chemical process.

In this paper, an adaptive canonical correlation analysis method is proposed for fault detection in time-varying processes. Firstly, a designed forgetting factor is used to update the canonical correlation analysis (CCA) model that builded with initial normal process data. Then, Mahalanobis distance is introduced as a classifier to distinguish whether data changes are caused by system modal changes or system abnormalities. In this way, the new model can not only be updated according to the system modality in real time, but also can accurately response to the occurrence of faults. Compared with traditional CCAbased methods, the proposed new method has the following two advantages: 1) it has a wider range of application scenarios since it can adapt to slow changes in the system or changes in operating points; and 2) it has a smaller amount of calculation because it only performs a simple data classification rather than require complex iterative operations on the threshold. The effectiveness of the new method is verified in a simulated superheated steam spray water temperature reduction process.

This article is devoted to solving the problem of quality-related root cause diagnosis for nonlinear process. First, an orthogonal kernel principal component regression model is constructed to achieve orthogonal decomposition of feature space, such that quality-related and quality-unrelated faults can be separately detected in the subspaces of opposite correlations to the output, without any effect on each other. Then, in view of the high complexity of traditional nonlinear fault diagnosis methods, an efficient method of kernel sample equivalence replacement is established to replace the partial differential operations of the kernel gradient algorithm, which can convert nonlinear fault detection indicators into the standard quadratic forms of the original variable sample, thereby making it possible to solve the nonlinear fault diagnosis problem by linear manners. Furthermore, a transfer entropy algorithm is utilized to the new model to analyze the causality between the diagnosed candidate faulty variables to find out the accurate root cause of the fault. Finally, comparative studies between the latest result and the proposed one are carried out in the Tennessee Eastman process to verify the effectiveness and superiority of the new method.

Contribution plots and reconstruction-based contribution (RBC) are efficient linear diagnosis tools in multivariate statistical process monitoring. Unfortunately, they cannot be directly applied to nonlinear fault diagnosis with kernel-based methods due to kernel function covers up the information of the original process variables. Although existing kernel gradient-based approaches have solved this problem to a certain extent, they are still far from suitable for practical applications because they require extremely huge amounts of computation. Their calculations cannot be obtained in a tolerable time unless expensive hardware costs are involved. This paper will thoroughly address this issue by revealing a hidden but important equivalent relationship between the variance-covariance matrix of a centralized process variables matrix and the centralized kernel matrix. Based on this relationship, the nonlinear detection index can be transformed into an explicit quadratic form of variables sample, such that contribution plots and RBC can be directly applied to kernel-based fault diagnosis with a very limited amount of computation, just as their usages in the linear cases. Simulation results obtained from two industrial examples demonstrate the effectiveness of the new method.

This article considers the issue of quality-related process monitoring. A novel kernel direct decomposition (KDD) algorithm is proposed and a KDD-based nonlinear quality-related fault detection approach is designed. The proposed KDD algorithm first maps original process variables into feature space to deal with the nonlinearities among these variables. Feature matrix is then directly decomposed into two orthogonal parts according to its full correlation with output matrix without building any regression model. Compared with conventional nonlinear methods, the KDD-based approach has the following advantages: 1) it is simpler in design as it omits the steps of constructing a regression model like kernel partial least squares (KPLS); 2) its performance is more stable because it extracts the full correlation information of feature matrix unlike KPLS-based methods which only use the partial correlation information of several selected latent variables; and 3) it has a simpler diagnosis logic since it only uses two statistics to determine the type of fault while most existing methods need four. Simulations on a literature example and a simulated industrial process are used to demonstrate the advantages of the new method.

Partial least squares (PLS) is an efficient tool widely used in multivariate statistical process monitoring. Since standard PLS performs oblique projection to input space X, it has limitations in distinguishing quality-related and quality-unrelated faults. Several postprocessing modifications of PLS, such as total projection to latent structures (T-PLS), have been proposed to solve this issue. Further studies have found that these modifications fail to reduce false alarm rates (FARs) of quality-unrelated faults when fault amplitude increases. To cope with this problem, this paper proposes an enhanced quality-related fault detection approach based on orthogonal signal correction (OSC) and modified-PLS (M-PLS). The proposed approach removes variation orthogonal to output space Y from input space X before PLS modeling, and further decomposes X into two orthogonal subspaces in which quality-related and quality-unrelated statistical indicators are designed separately. Compared with T-PLS, the proposed approach has a more robust performance and a lower computational load. Two case studies, including a numerical example and the Tennessee Eastman (TE) process, show the effeteness of the proposed approach.

This paper presents a data-driven method for the task of fault detection in nonlinear systems. In the proposed approach, locally weighted projection regression (LWPR) is employed to serve as a powerful tool for modeling the nonlinear process with locally linear models. In each local model, partial least squares (PLS) regression is performed and PLS-based fault detection scheme is applied to monitor the regional model. The diagnosis for the global process is based on the normalized weighted mean of all the local models. Both conventional and quality-related statistical indicators are designed to compute the test statistics. Two nonlinear systems, a numerical one and a benchmark, are used to demonstrate the effectiveness of the proposed method.

This paper introduces a robust data-driven fault detection method and its application on a wind turbine benchmark. The benchmark is provided by a Simulink Model, which contains nonlinear wind turbine model and complex wind disturbances. The model-based fault detection technique is hardly to be applied to solve this problem because modeling this wind turbine is quite difficult. Besides, the unknown wind disturbances and the large measurement noises are two enormous challenges for most of the fault detection techniques. To overcome these difficulties, this paper applies a robust data-driven fault detection scheme, which is based on a standard residual generation and decision logic structure. In the residual generation step, a robust residual generator with an optimal parity vector is constructed directly from the measurement data. Moreover, a filter algorithm is used in the residual evaluation step to reduce false alarms rate. Simulation results show that the performance and effectiveness of the proposed scheme are satisfied.