Recently, solar energy has been intensively employed in power systems, especially using the photovoltaic (PV) generation units. In this regard, this paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV. In the proposed method, a variable voltage step size is estimated according to the degree of ascent or descent of the power-voltage relation. For this purpose, a novel unique treatment is proposed based on introducing five effective regions around the point of maximum PV power. To vary the step size of the duty cycle, a fuzzy logic system is developed according to the locations of the fuzzy inputs regarding the five regions. The developed fuzzy inputs are inspired from the slope of the power-voltage relation, namely the current-voltage ratio and its derivatives whereas appropriate membership functions and fuzzy rules are designed. The benefit of the proposed method is that the MPPT efficiency is improved for varying the step size of the incremental conductance method, thanks to the effective coordination between the proposed fuzzy logic based algorithm and the INC method. The output DC power of the PV array and the tracking speed are presented as indices for illustrating the improvement achieved in MPPT. The proposed method is verified and tested through the simulation of a grid-connected PV system model. The simulation results reveal a valuable improvement in static and dynamic responses over that of the traditional INC method with the variation of the environmental conditions. Further, it enhances the output dc power and reduce the convergence time to reach the steady state condition with intermittent environmental conditions.

This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

Power transformer represents an important equipment in electric power systems. Transformers are not only a source of power outages for electric utilities, but they also affect customers because they interrupt power supplies. Transformers require frequent maintenance and service, which can be time-consuming and expensive. Power systems and their maintenance and servicing can be planned in advance when transformer faults are detected early with high accuracy rates. In order to avoid unplanned shutdowns, it is crucial to monitor and diagnose faults of power transformers. Based on how electrical and thermal stresses impact the insulating oil, it is possible to detect or classify transformer faults by checking for dissolved gases. In this paper, a new transformer monitoring and fault diagnosis technique are developed by utilizing an industrial Internet of Things (IoT) platform combined with an ensemble machine learning (EML) strategy. In order to prevent overlapping between diverse transformer defects, an innovative feature engineering strategy is developed based on introducing new features for gases’ concentrations and their ratios. Through this, EML efficiency is enhanced due to the separation of overlapping in the datasets between diverse transformer faults. The datasets from Egyptian chemical laboratories and literature are used to train and test the proposed EML. As shown in the experiments, the proposed fault diagnosis model provides the best diagnosis accuracy, which is significantly better than the state-of-the-art machine learning approaches. Finally, the robustness of the developed EML model is further demonstrated by including white Gaussian noise that is based on the Fast Gradient Sign Method (FGSM) as well as the zeroth order optimization (Zoo) attack to validate the model against adversarial attack scenarios.

Demand side management (DSM) has become one of the major concerns of the smart grids to cope with the penetration of renewable energy. The availability of new communication technologies can enhance the resilient operation of smart grids for DSM. However, the false injection data and adversarial attacks that are against the operation of signal analysis models represent the biggest challenge against the resiliency of energy management operations by deploying these technologies in the smart grids. In this regard, this paper proposes a new reliable industrial Internet of Things (IoT) architecture and deep convolution neural network (CNN) with an image processing strategy based on continuous wavelet transform (CWT) for DSM and resilience operation of energy management. The main contribution involved in this paper includes establishing a real-time signal processing model and developing an industrial IoT platform with CWT-based CNN; verifying the IoT architecture with different levels of adversarial attacks; providing a cybersecurity analysis for the smart buildings with DSM considering the device-level attacks, and developing defense strategies from the aspects of detection, mitigation, and prevention. In addition, the proposed deep CNN is designed with proper hyperparameters to counter adversarial attacks. The proposed CWT-based CNN performed the highest accuracy compared with different machine learning and deep learning models. Various testing scenarios are presented and executed to ensure and demonstrate the performance and robustness of the proposed method under different levels of adversarial attacks.

Resilient design of robust multi-objectives PID controllers via the D-decomposition method is presented in this paper for automatic voltage regulators (AVRs). The stabilizing interval of derivative gain ( k _d ) is analytically calculated by the Routh-Hurwitz criterion. The k _p -k _i domain, for a fixed value of k _d , is decomposed in root invariant regions by mapping the stability boundary from the complex plane. Two regions, described by fixed damping isoclines, are assigned for pole-clustering in the open-left half plane (LHP). Other than regional pole clustering, gain and phase margins, as frequency domain specifications, are considered. Both robust stability and robust performance are considered by stabilizing a set of principle segment plants simultaneously. Optimal pole-placer PID controllers are computed analytically. If a robust control basin does exist for a specific compromise of control objective, the criterion of the maximum inscribed circle is considered to compute the maximum radius of controller resiliency. The merit of the proposed design is the simultaneous consideration of three control concerns, namely performance optimality, stability robustness and controller resiliency. Computation, validation, and simulation results are presented to show the simplicity and efficacy of the suggested method in tracing control basins (CBs) of all admissible PID controllers.

The effectiveness of positioning techniques that utilize the receiver signal strength (RSS) is highly dependent on the instability of the received signal strength indicator (RSSI). Up to now, there is no strategy that effectively lowers the influence of such instability on the accuracy of positioning. Moreover, recent studies showed that indoor positioning techniques are vulnerable to noise in RSSI data and cyber-attacks, which make them more expensive. In this study, a new internet of things (IoT) paradigm is proposed for the indoor positioning of automated guided vehicles (AGVs) using a deep convolution neural network (CNN). The proposed method handles signal processing by converting the RSSI signal into an image. In which, the 1-dimensional RSSI signal is converted into 2-dimensional image data in order to generate the new features based on continuous wavelet transform (CWT), and then the proposed deep CNN is implemented for the indoor positioning system. The test results show that the proposed model can outperform other state-of-the-art positioning techniques with small position errors. Furthermore, the robustness of the proposed model is validated against various adversarial attacks. In addition, the proposed method can have a lower impact on RSSI change compared with other methods