PV-Supercapacitor Cascaded Topology for Primary Frequency Responses and Dynamic Inertia Emulation

Owing to rapid increase in PV penetration without inherent inertia, there has been an unremitting deterioration of the effective inertia of the existing power systems. This may pose a serious threat to the stability of power systems during disturbances if not taken care of. Hence, the problem of how to emulate Synthetic Inertia (SI) in PV Systems (PVS) to retain their frequency stability demands attention. Super Capacitor (SC)-based storage become an attractive option over the other energy storage types because of its high-power density, burst power handling capability, faster response and longer life cycle. Considering this, the authors here propose a novel PV-SC Cascaded Topology (PSCT) as a cost-effective approach to emulate SI by integrating a low voltage SC to a high voltage grid-connected PVS. The proposed PSCT helps in operating the SC as a voltage source rather than a current source. Thus, it eliminates the high gain requirements of the SC interfacing converters. The aim is to target two main frequency response services, i.e., Primary Frequency Response (PFR) and Synthetic Inertial Response (SIR), using a novel common control scheme, but without affecting any other energy intensive services. The authors introduced a Droop-Inspired (DI) method with an adjustable inertia constant to emulate dynamic inertia so that a wider range of Rate of Change of Frequency (RoCoF) values can be serviced with a limited storage. A very streamlined analysis was also carried out for sizing of the SC stage based on a simple Three-Point Linearization (TPL) technique and DI technique with a limited knowledge of the disturbance parameters. The whole system was initially validated in a MATLAB Simulink environment and later confirmed with the OPAL-RT Real-Time Simulator. The investigated response was subject to variation in terms of control parameters, changes in solar irradiance, grid frequency variation, etc.

Enhancing the Durability of Connecting Rod of a Heavy-Duty Diesel Engine

This paper investigates the mechanical loads resulting from the combustion pressure and dynamic inertia and their effects on the connecting rod of a direct injection turbocharged diesel engine. The main purpose is to enhance the durability of the connecting rod in order to withstand more engine power increase. The distribution of the axial (compressive/tensile) stress, deformation, and safety factors are calculated in order to predict any possible mechanical failure. The finite element routine is used by ANSYS Workbench to analyse the loading on the connecting rod model. The current study is applied to the connecting rod of a 300 hp diesel engine in order to increase the engine power by 17%. The connecting rod operates safely and withstands the applied loads until the power increase reaches 72%. The most stressed points are at the connecting rod shank, while less stressed are experienced at the big end. Calculations show that introducing some changes to the connecting rod geometry may result in decreasing the excessive stresses. These changes include increasing the thickness of the shank cross-section, increasing the fillets radii and slightly reducing the dimensions of the big end in order to maintain the same mass. The new geometry could significantly reduce the maximum stress by 25.5% with an insignificant reduction in the total mass of the connecting rod.

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

Memetic Strategy of Particle Swarm Optimization for One-Dimensional Magnetotelluric Inversions

The heuristic algorithm represented by particle swarm optimization (PSO) is an effective tool for addressing serious nonlinearity in one-dimensional magnetotelluric (MT) inversions. PSO has the shortcomings of insufficient population diversity and a lack of coordination between individual cognition and social cognition in the process of optimization. Based on PSO, we propose a new memetic strategy, which firstly selectively enhances the diversity of the population in evolutionary iterations through reverse learning and gene mutation mechanisms. Then, dynamic inertia weights and cognitive attraction coefficients are designed through sine-cosine mapping to balance individual cognition and social cognition in the optimization process and to integrate previous experience into the evolutionary process. This improves convergence and the ability to escape from local extremes in the optimization process. The memetic strategy passes the noise resistance test and an actual MT data test. The results show that the memetic strategy increases the convergence speed in the PSO optimization process, and the inversion accuracy is also greatly improved.

Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient.