An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test the robustness of the proposed technique under uniform and non-uniform irradiance conditions. The performance is compared to the Perturb & Observe, Incremental Conductance, and other hybrid MPPT techniques. Furthermore, the efficacy of the proposed technique is also assessed against a commercial PV system\u27s power output over one day. The results demonstrate that the proposed GA-FOCV technique improves the efficiency of the conventional FOCV method by almost 3%, exhibiting an average tracking efficiency of 99.96% and tracking speed of around 0.07 s with minimal steady-state oscillations. Additionally, the proposed technique can also efficiently track the global MPP under partial shading conditions and offers faster tracking speed, higher efficiency, and fewer oscillations than other hybrid MPPT techniques

Charging infrastructure for commercial electric vehicles: Challenges and future works

The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium- and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy- and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed

Robust placement and sizing of charging stations from a novel graph theoretic perspective

This paper proposes analytical approaches to extend the capacity of existing networks of electric vehicles (EVs) by placement of additional charging stations (CSs) as well as determining the sizes of existing and new CSs in order to handle future expansions of EVs. The EV flow at CSs is modeled by a graph where nodes are potential locations for CSs and edges are uncertain parameters representing the variable EV flow at CSs. The required extra CS locations are explored by transforming the CS placement problem into a controllability framework addressed by maximum matching principle (MMP). To find the sizes of each CS, the graph of CS network is partitioned featuring only one CS in each subgraph. The size of CS in each subgraph is then determined by transforming the problem into the problem of robust stability of a system with uncertain parameters where each parameter is associated with an edge of subgraph. The zero exclusion principle is then tested for the related Kharitonov rectangles and polygonal polynomials of closed loop system with selected feedback gain as CS capacity. The proposed analytical approach is tested on the existing Tesla CS Network of Sydney. The locations of extra required CSs as well as the sizes of existing and new CSs are determined to maintain the waiting times at all stations below the threshold level

Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms

Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and outdoor images of a small wind turbine with healthy and damaged blades. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception outperformed other architectures by attaining 99.92% accuracy on the test data of this dataset. Furthermore, the performance of the investigated models was compared on a dataset containing faulty and healthy images of large-scale wind turbine blades. In this case, our results indicated that the best-performing model was also the proposed Transfer Xception, which achieved 100% accuracy on the test data. These accuracies show promising results in the adoption of machine learning for wind turbine blade fault identification

Optimal sizing design and operation of electrical and thermal energy storage systems in smart buildings

Photovoltaic (PV) systems in residential buildings require energy storage to enhance their productivity; however, in present technology, battery storage systems (BSSs) are not the most cost-effective solutions. Comparatively, thermal storage systems (TSSs) can provide opportunities to enhance PV self-consumption while reducing life cycle costs. This paper proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), thermal and electrical energy storage systems. For simultaneous optimal sizing of BSS and TSS, a particle swarm optimization (PSO) algorithm is applied to minimize daily electricity and life cycle costs of the smart building. A model predictive controller is then developed to manage energy flow of storage systems to minimize electricity costs for end-users. The main objective of the controller is to optimally control HP operation and battery charge/discharge actions based on a demand response program. The controller regulates the flow of water in the storage tank to meet designated thermal energy requirements by controlling HP operation. Furthermore, the power flow of battery is controlled to supply all loads during peak-load hours to minimize electricity costs. The results of this paper demonstrate to rooftop PV system owners that investment in combined TSS and BSS can be more profitable as this system can minimize life cycle costs. The proposed methods for optimal sizing and operation of electrical and thermal storage system can reduce the annual electricity cost by more than 80% with over 42% reduction in the life cycle cost. Simulation and experimental results are presented to validate the effectiveness of the proposed framework and controller

Comparison of non-invasive to invasive oxygenation ratios for diagnosing acute respiratory distress syndrome following coronary artery bypass graft surgery: a prospective derivation-validation cohort study

Objective: To determine if non-invasive oxygenation indices, namely peripheral capillary oxygen saturation (SpO2)/ fraction of inspired oxygen (Fi O2) and partial pressure of alveolar oxygen (PAO2)/Fi O2 may be used as effective surrogates for the partial pressure of arterial oxygen (PaO2)/Fi O2. Also, to determine the SpO2/Fi O2 and PAO2/Fi O2 values that correspond to PaO2/Fi O2 thresholds for identifying acute respiratory distress syndrome (ARDS) in patients following coronary artery bypass graft (CABG) surgery. Methods: A prospective derivation-validation cohort study in the Open-Heart ICU of an academic teaching hospital. Recorded variables included patient demographics, ventilator settings, chest radiograph results, and SPO2, PaO2, PAO2, SaO2, and Fi O2. Linear regression modeling was used to quantify the relationship between indices. Receiver operating characteristic (ROC) curves were used to determine the sensitivity and specificity of the threshold values. Results: One-hundred seventy-five patients were enrolled in the derivation cohort, and 358 in the validation cohort. The SPO2/Fi O2 and PAO2/Fi O2 ratios could be predicted well from PaO2/Fi O2, described by the linear regression models SPO2/Fi O2 = 71.149 + 0.8PF and PAO2/Fi O2 = 38.098 + 2.312PF, respectively. According to the linear regression equation, a PaO2/Fi O2 ratio of 300 equaled an SPO2/Fi O2 ratio of 311 (R2 0.857, F 1035.742, < 0.0001) and a PAO2/Fi O2 ratio of 732 (R2 0.576, F 234.887, < 0.0001). The SPO2/Fi O2 threshold of 311 had 90% sensitivity, 80% specificity, LR+ 4.50, LR- 0.13, PPV 98, and NPV 42.1 for the diagnosis of mild ARDS. The PAO2/Fi O2 threshold of 732 had 86% sensitivity, 90% specificity, LR+ 8.45, LR- 0.16, PPV 98.9, and NPV 36 for the diagnosis of mild ARDS. SPO2/ Fi O2 had excellent discrimination ability for mild ARDS (AUC ± SE = 0.92 ± 0.017; 95% CI 0.889 to 0.947) as did PAO2/ Fi O2 (AUC ± SE = 0.915 ± 0.018; 95% CI 0.881 to0.942). Conclusions: PaO2 and SaO2 correlated in the diagnosis of ARDS, with a PaO2/Fi O2 of 300 correlating to an SPO2/ Fi O2 of 311 (Sensitivity 90%, Specificity 80%). The SPO2/ Fi O2 ratio may allow for early real-time rapid identification of ARDS, while decreasing the cost, phlebotomy, blood loss, pain, skin breaks, and vascular punctures associated with serial arterial blood gas measurements

Finite-Element Performance Evaluation of On-Line Transformer Internal Fault Detection Based on Instantaneous Voltage and Current Measurements

This paper investigates the performance of a recently proposed online transformer internal fault detection technique through detailed non-linear three-dimensional finite element modelling of the windings, magnetic core and transformer tank. The online technique considers correlation of transformer instantaneous input and output voltage difference and input current at the power frequency and uses the ellipse shape ΔV-I locus as the finger print of the transformer that could be measured every cycle to identify any incipient faults. The technique is simple, fast and suitable for online monitoring of in-service transformers. A detailed three-dimensional finite element model of single-phase transformer is developed and various physical winding deformations with different fault levels are simulated to assess their impacts on the online ΔV-I locus. As transformer field testing under various internal fault conditions cannot be easily conducted, the main contributions of this paper are accurate finite element based implementation, testing and performance evaluation of the online fault detection approach. Furthermore, the impact of winding short circuit fault on the ΔV-I locus has been also measured and validated

Congestion Management using Genetic Algorithm in Deregulated Power Environments

Congestion cost allocation is an important issue in congestion management. This paper presents a genetic algorithm (GA) to determine the optimal generation levels in a deregulated market. The main issue is congestion in lines, which limits transfer capability of a system with available generation capacity. Nodal pricing method is used to determine locational marginal price (LMP) of each generator at each bus. Simulation results based on the proposed GA and the Power World Simulator software is presented and compared for the IEEE 30-bus test system

A Fuzzy-Based Genetic Algorithm for Social Welfare Maximization by Placement and Sizing of Static Synchronous Series Compensator

This article presents a fuzzy-based genetic algorithm to maximize total social welfare and alleviate congestion by placement and sizing of one static synchronous series compensator device, considering its investment cost in a double-sided auction market. The generating units cost curves are considered to be quadratic with sine components to show the impacts of valve point loading. By adding the valve point effect, the model presents non-differentiable and convex regions that challenge most gradient-based optimization algorithms. In addition, the impact of distribution companies on the social welfare maximization and congestion management is presented as a quadratic function. The proposed approach makes use of the fuzzy-based genetic algorithm to optimal schedule generating companies and distribution companies and setting the static synchronous series compensator location and its size while the Newton–Raphson algorithm minimizes the mismatch of the power flow equations. Simulation results on the modified IEEE 14- and 30-bus test systems (with/without line flow constraints, before/after compensation) are used to examine the impacts of the static synchronous series compensator on the total system social welfare improvement versus its cost. Several cases are considered to test and validate the consistency of detecting best solutions. Simulation results are compared to solutions obtained by the genetic algorithm and sequential quadratic programming approach, which has been used in MATPOWER (available on-line; see [1].The aim of this article is the utilization of static synchronous series compensator for the social welfare maximization problem considering the impact of valve point loading effect on the operation point of the generating companies by inclusion of fuzzy rules in the genetic algorithm to guarantee fast convergence for locating/sizing the static synchronous series compensator. The proposed method shows the benefits of the static synchronous series compensator in a deregulated power market and demonstrates how it can be utilized by the independent system operator to improve the total social welfare and prevent congestion