As a prominent alternative for fossil energy, solar photovoltaic (PV) resource plays a key role during the migration process of energy supply structure. However, the prediction of its electric power production (EPP) data possesses randomness and instability due to environmental changes. Therefore, a novel framework is proposed which consists of a two-stage hierarchical clustering and optimal prediction model selection. According to the stability characteristics of EPP data, initial rough clusters are attained with Fuzzy C-means clustering through the first stage. The following second stage clustering based on hierarchical clustering is applied to finally generate three clusters of stable, weakly fluctuated and strongly fluctuated EPP data. Afterwards, three properly constructed prediction models have been implemented accordingly. A CNN-LSTM quantile regression model is proposed for the prediction of stable EPP data with consistent distributions. Yet for weakly fluctuated type, D-Vine copula quantile regression is applied to explore correlations between PV characteristics, which then helps to describe the specific distributions underneath. Since strongly fluctuated type of data tends to exhibit extreme variations and randomness, probability generation model combined with regression form of D-Vine copula could precisely establish correlations of different characteristics and hence weight the predictions to yield point prediction. Our framework addresses the PV power data with different random fluctuation characteristics by applying tailored prediction models to each type. Comparative experimental results show that the proposed framework has achieved higher prediction interval coverage, lower average interval width and comprehensive evaluation index in various scenarios, verifying its effectiveness and superiority.

Escalating cybersecurity risks in communication networks pose severe challenges to the stability and reliability of microgrid control systems. This paper investigates the load frequency control problem of nonlinear networked wind-power microgrids under denial-of-service (DoS) attacks and event-triggered communication. A data-driven prediction framework is first established to construct a control-oriented dynamic linearization data model for the underlying nonlinear system. Sub-sequently, a novel event-triggered model-free adaptive predictive control (ET-MFAPC) algorithm with time-varying parameters is proposed, which integrates multi-step adaptive prediction and receding-horizon optimization. The proposed algorithm features several key innovations: 1) An output-related auxiliary variable is designed to circumvent unavailable pseudo-partial derivatives (PPDs) and their sign constraints. 2) An adaptive predictive compensation module is developed to mitigate the impacts of DoS attacks by reconstructing hijacked data packets. 3) An attack-aware online optimization mechanism is formulated to adaptively tune controller gains for improved system performance. Furthermore, a comprehensive security analysis is conducted based on a model-independent data energy function, and explicit boundary conditions are derived for tolerable attack frequency and duration. Finally, the effectiveness and robustness of the proposed method are validated through simulation studies.

The reliance on open communication networks poses significant challenges to power systems. This paper studies a prescribed performance-based scalable distributed event-triggered model-free adaptive control (DETMFAC) scheme for multi-area load frequency control (LFC) systems that are integrated with wind-energy, analyzing both scenarios without and with jamming attacks. First, a transformed-error-based dynamic linearization model (DLM) for distributed outputs is developed to handle the unknown system dynamics under performance constraints. By directly incorporating the DLM and the prescribed performance function into the criterion function, we derive a novel prescribed performance-based DETMFAC scheme. This algorithm improves the control performance while overcoming the problems caused by global topology information and model dependence as in the traditional distributed LFC design approaches by introducing a parameter adaptation mechanism. Additionally, a novel jamming attack model based on the signal-to-interference-plus-noise ratio (SINR) is introduced to illustrate the interaction between the power system and the malicious attacker in a Stackelberg game framework. Then a secure scheme with attack compensation is proposed to mitigate the negative attack impact by reconstructing the LFC commands via linear estimation. Experimental results for a four-area power system demonstrate the designed scheme. Note to Practitioners—LFC is a fundamental strategy for frequency regulation in networked power systems. However, traditional LFC systems are vulnerable to cyber-attacks, tracking performance constraints, and bandwidth limitations due to the interconnection between different subareas. This paper proposes a prescribed performance-based DETMFAC scheme and a secure control scheme with a resilience mechanism to effectively address these challenges in a coordinated manner, integrating an event-trigger, parameter identifier, attack compensator, and input update law. The control input is only updated when the triggering condition is met, based on the distributed output and identified adaptive parameters. In the case of an attack, the event-trigger ceases operations and the attack compensator generates an estimated input signal. Importantly, the proposed LFC scheme relies solely on local input and output data (i.e., model-free) and can be implemented in a fully distributed architecture (i.e., scalable). Thus, practitioners can seamlessly integrate the designed strategies with existing LFC systems to enhance performance, security, and efficiency. The results provide a valuable reference for scalable, cost-effective and secure distributed data-driven control design of multi-area LFC systems and promote related application research.

The complex optimization problems have been investigated deeply by researchers in the optimization community. The estimation of distribution algorithm (EDA) and the monarch butterfly optimization algorithm (MBO) are meta-heuristic algorithms that attracted wide attention. In this study, an improved algorithm based on Estimation of Distribution of Algorithm combined with Monarch Butterfly Optimization Algorithm named EDMBO is proposed. The weighted average of candidate solutions is embedded to estimate the mean value. A linear search strategy is introduced to enhance the exploitation of the algorithm. The CEC 2017 benchmark test suite is adopted to verify the performance of the algorithm. The experimental results show that the EDMBO is competitive.

The rapid development of Internet of Things (IoT) offers unprecedented opportunity for federated learning (FL). However, the increasing scale of IoT is accompanied by limitation of communication resource and endogenous heterogeneity of edge devices, which hinder FL’s popularization. Based on features of the edge network, this article proposes a novel one-shot screening scheme to identify edge users with homogeneous data distribution and orchestrate adaptive personalized FL tailored for any task initiator. To cope with the stipulation that raw data cannot be communicated, the learning loss calculated with a locally pretrained model is leveraged to measure the similarity between data distributions. In addition, a multiple hypothesis testing instrument, the Adaptive$p$-value Thresholding (AdaPT) is utilized to automatically control false discovery rate (FDR) of the screening below any specific level. To demonstrate the effectiveness of the proposed collaborator selection scheme, a numerical study is designed on the real-world non-i.i.d. data set FEMNIST. Evaluations confirm that stable improvement in convergence rate and learning accuracy can be gained compared to vanilla global FL, iterative personalized reweighted FL as well as iterative clustered FL, along with additional welfare of economical wireless resource occupation. Finally, the experiment is carried out under differential privacy to verify robustness further.

To achieve fault diagnosis and prognosis, obtaining adequate and reliable life-cycle data is essential. However, this poses a challenge in current high-reliable Internet of Things (IoT) systems. Fortunately, accelerated degradation testing (ADT) can be employed to overcome this hurdle. Nevertheless, a dependable testing and measuring technique is required to construct an accurate model for ADT. This testing method plays a vital role in evaluating fault diagnosis, prognosis, lifetime, and maintenance decisions for reliable products under operational stress. To ensure effective testing, it is crucial to utilize appropriate models that account for the individual heterogeneity of products. However, the commonly used single stochastic models in ADT overlook the impact of this condition in real-world applications, resulting in misspecification problem. To address this limitation, we propose a novel mixed stochastic process model that integrates multi-Wiener processes and dynamic weights. In addition, we leverage interval analysis to analyze system lifetime, considering the limited data size. The estimation of unknown parameters in our mixed model is achieved using the Metropolis–Hastings algorithm. By analyzing stress relaxation data from electrical connectors, we demonstrate the superior accuracy of our mixed model over conventional single stochastic models in ADT.

Harnessing the advantage of digital twin (DT) technology, smart grid provides tempting prospects for efficient management of energy manufacturing, conservation, demand forecasting, pricing, and scheduling. However, the development of smart grid is challenged by volatility of individual users, cyber attacks, and hesitation about data sharing. In this study, a lightweight blockchain-enhanced federated learning (FL) framework is proposed to address the above issues and promote efficient collaborative DT modeling. Through encrypted blockchain retrieval, data aggregation and outlier detection, a Proof of Distribution consensus algorithm is developed to periodically update a conformal score for each edge user over time. The conformal score measures conformity of one user with patterns of the overall data distribution at current stage and is further utilized as basis for stochastic participant selection and model averaging, which improves stability and pertinence of FL in an uncertain environment with heterogeneity and concept drift. In the meantime, based on average learning loss of users with top-ranked conformal scores and engagement of users in the learning process, a reputation score is updated in real time for each user, which offers timely detection of malicious behaviors in the cooperation. The transmission of models and data are both implemented with blockchain, which guarantees privacy preservation and makes the process auditable. Through simulation of residential load forecasting based on real-world scenarios, double robustness of our framework against data volatility and adversarial attacks is verified.

Accelerated degradation testing (ADT) data typically exhibit a time-stress-dependent structure, as well as random uncertainties due to time-varying effects and unit-to-unit variations. Existing ADT models based on Brownian motion with drift have successfully represented the fault/failure-based degradation behavior and random uncertainty by assuming that the drift parameter follows a Gaussian distribution. However, these models often lack robustness to outliers, leading to distorted analysis, affecting parameter estimation, model accuracy, decision-making, risk assessment, and potentially overlooking the influence of stress factors. A novel robust ADT model based on the Wiener process and its corresponding lifetime analysis method are proposed to address these issues. The proposed approach improves upon traditional ADT models by making the drift parameter follow a$t$-distribution rather than a Gaussian distribution, which can reduce sensitivity to outliers in real degradation processes. In addition, the proposed method allows for the simultaneous consideration of time-stress-dependent factors in the ADT model, facilitating the derivation of a closed-form robust ADT formulation. Subsequently, the lifetime is analyzed based on the ADT model using the first hitting time method in a probabilistic framework. The proposed method is applied to stress relaxation data of electrical connectors and compared to three other common methods.

This article discusses the problem of nonuniform running length in incomplete tracking control, which often occurs in industrial processes due to artificial or environmental changes, such as chemical engineering. It affects the design and application of iterative learning control (ILC) that relies on the strictly repetitive property. Therefore, a dynamic neural network (NN) predictive compensation strategy is proposed under the point-to-point ILC framework. To handle the difficulty of establishing an accurate mechanism model for real process control, the data-driven approach is also introduced. First, applying the iterative dynamic linearization (IDL) technique and radial basis function NN (RBFNN) to construct the iterative dynamic predictive data model (IDPDM) relies on input-output (I/O) signal, and the extended variable is defined by a predictive model to compensate for the incomplete operation length. Then, a learning algorithm based on multiple iteration errors is proposed using an objective function. This learning gain is constantly updated through the NN to adapt to changes in the system. In addition, the composite energy function (CEF) and compression mapping prove that the system is convergent. Finally, two numerical simulation examples are given.

In order to achieve fault diagnosis and prognosis, one needs a sufficient and valid life-cycle data. However, this requirement is difficult for current high-reliable manufacturing system. Good thing is that the technique of accelerated degradation testing can be used to address this issue. Bad thing is that it needs a reliable testing/measuring technique to build an accurate model for accelerated degradation testing. However, in practical applications, data acquisition is obtained by sensors or measurement devices, which cannot guarantee perfect working condition under the influence of external environment and stresses, resulting in unreliable signals. Furthermore, since traditional models require complex differentiation and cannot obtain analytical expressions when considering unreliable signals, traditional models rarely reflect well this situation. Motivated by these facts, an accurate model for the accelerated degradation testing is proposed in this study with considering the unreliable signals. Based on the proposed model, a closed-form expression for the useful life analysis is derived. The Metropolis–Hastings (M-H) sampling method is used to estimate the unknown parameters used in the proposed model. For illustration, the electrical connector dataset is analyzed with the proposed model and the traditional models. Comparing the obtained results, the proposed model is more accurate in the useful life analysis than the traditional accelerated degradation testing models by considering the unreliable signals.