Home energy management requires accurate information about the appliances’ consumption pattern. This information can help consumers save energy, control their usage by shifting their usage to off-peak hours and reduce their electricity costs. Non-intrusive load monitoring (NILM) in which the power consumption profile of appliances are extracted from the aggregated signal of a household, provides this information. For the NILM problem, machine learning approaches as the training-based solutions require large training datasets for an accurate disaggregation and the optimization-based approaches employs prior information about the characteristics of appliances. This paper proposes a novel event-based optimization algorithm. In its first stage, the prior information about appliances is extracted from the events of the consumption profiles of appliances by means of clustering. Then, a new event-based down-sampling method and transition filtering are designed for decreasing the computation time of optimization. At the last stage of the proposed algorithm, post-processing considering ON duration of appliances and varying states are proposed to increase the accuracy of the power profile reconstruction. The proposed approach was successfully tested for the low-frequency dataset of a house from the REDD. Numerical results show the advantages of the proposed algorithm, marked improvement over classification-based NILM considering small training dataset and its applicability in disaggregating the power consumption measured by the smart meter.

In this paper, we introduce a distributed secondary voltage and frequency control scheme for an islanded ac microgrid under event-triggered communication. An integral type event-triggered mechanism is proposed by which each distributed generator (DG) periodically checks its triggering condition and determines whether to update its control inputs and broadcast its states to neighboring DGs. In contrast to existing event-triggered strategies on secondary control of microgrids, the proposed event-triggered mechanism is able to handle the consensus problem in case of asynchronous communication. Under the proposed sampled-data based event-triggered mechanism, DGs do not need to be synchronized to a common clock and each individual DG checks its triggering condition periodically, relying on its own clock. Furthermore, the proposed method efficiently reduces communication rate. We provide sufficient conditions under which microgrid's frequency and a critical bus voltage asymptotically converge to the nominal frequency and voltage, respectively. Finally, effectiveness of our proposed method is verified by testing different scenarios on an islanded ac microgrid benchmark in the MATLAB/Simulink environment as well as a hardware-in-the-loop (HIL) platform, where the physical system is modeled in the Opal-RT and the cyber system is realized in Raspberry Pis.

Synchronization between two coupled complex networks with fractional-order dynamics, hereafter referred to as outer synchronization, is investigated in this work. In particular, we consider two systems consisting of interconnected nodes. The state variables of each node evolve with time according to a set of (possibly nonlinear and chaotic) fractional-order differential equations. One of the networks plays the role of a master system and drives the second network by way of an open-plus-closed-loop (OPCL) scheme. Starting from a simple analysis of the synchronization error and a basic lemma on the eigenvalues of matrices resulting from Kronecker products, we establish various sets of conditions for outer synchronization, i.e., for ensuring that the errors between the state variables of the master and response systems can asymptotically vanish with time. Then, we address the problem of robust outer synchronization, i.e., how to guarantee that the states of the nodes converge to common values when the parameters of the master and response networks are not identical, but present some perturbations. Assuming that these perturbations are bounded, we also find conditions for outer synchronization, this time given in terms of sets of linear matrix inequalities (LMIs). Most of the analytical results in this paper are valid both for fractional-order and integer-order dynamics. The assumptions on the inner (coupling) structure of the networks are mild, involving, at most, symmetry and diffusivity. The analytical results are complemented with numerical examples. In particular, we show examples of generalized and robust outer synchronization for networks whose nodes are governed by fractional-order Lorenz dynamics.

In the majority of current supervised nonintrusive load monitoring (NILM) techniques the characteristics of the training and test datasets are assumed to be constant. However, in practice, a new appliance may be added to the set of electrical appliances, an appliance may be replaced by a new one, the efficiency of an appliance may change during the time, or a fault may occur. All these issues of appliances cause abnormal consumption in total signal and not detecting them reduces the NILM accuracy. In this sense, submetering is widely used to address the above challenge. However, it leads to additional costs for consumers as it requires additional meters, disturbs consumers’ comfort and privacy, and is not scalable. In this paper, the non-intrusive load monitoring approach is utilized to detect the above anomalies based on two main factors: 1) the power distribution and 2) the participation index of appliances. Since the proposed method is nonintrusive it is cost-efficient and accommodates the consumer’s privacy and comfort. Furthermore, detecting all the above appliance anomalies not only increases the NILM accuracy but also reduces energy waste and avoids appliance breakdown. The experimental results prove that the proposed method yields outstanding performance in detecting the aforementioned anomalies.

Deep learning-based approaches for diagnosing bearing faults have attracted considerable attention in the last years. However, in real-world applications, these methods face challenges. For proper training of these models, a considerable amount of labeled data are necessary, and due to limitations in industry, obtaining this amount of data may not be possible. Because of load variations, the distribution of training and test data may vary, which reduces the accuracy of the trained model for various working conditions. Furthermore, noise has a significant impact on bearing fault diagnosis performance in real-world industrial applications. This study introduced the deep subdomain adaptation convolutional neural network (DSACNN) method to overcome these challenges in real scenarios. The local maximum mean discrepancy (LMMD) method reduces the difference between each class distribution in the source and target domains. We validated our proposed method by CWRU bearing dataset under various loads and noise with different SNRs. The results show that DSACNN outperforms other comparative methods in anti-noise performance and reduction of domain distribution discrepancies.