Elektronika ir Elektrotechnika

Hydro-Turbine Coordination Power Predictive Method of Improved Multi-Layer Neural Network Considered Adaptive Anti-Normalisation Strategy

2021-03-05T03:53:19+02:00

Due to the limitation of economics and time cost, the data obtained from hydro-turbine coordination field test are insufficient to fully guide the setting of unit operating parameters. To enlarge the amount of data, realise power point tracking, and avoid the problems of high non-linearity with hydro-turbine physical model which is difficult to simulate in actual field, a mathematical prediction model is proposed based on an improved multi-layer neural network. Using the rule activation function, L2 regularisation, Adam optimiser and its gradient parameters are optimised by PSO algorithm in the prediction model. It is found that lacking true value in the process of anti-normalisation leads to difficulty for actual forecast of neural network. Therefore, an adaptive anti-normalisation strategy is proposed to improve the actual prediction accuracy, which can judge the value of the interval. According to the analysis of examples with hydro-turbine coordination and non-coordination test, the results show that the proposed prediction model and interval strategy can effectively forecast the coordination operating conditions of the turbine with high accuracy under small samples.

New Approach-based MPP Tracking Design for Standalone PV Energy Conversion Systems

2022-09-13T07:44:54+03:00

In searching for a maximum power point (MPP) using a DC boost converter for photovoltaic (PV) energy conversion systems, we realised that the fast and accurate way to find the suitable duty ratio value is the core problem to enhance the energy conversion efficiency of the PV system. Under uniform irradiation, the panels will generate the same values, so they have only one peak on the P-V curve; conventional MPP tracking methods easily obtain this MPP. However, under partial shading conditions, many peaks are created, traditional MPP tracking methods can fall into the local MPP, and this issue will cause energy loss and reduce PV energy conversion efficiency. To avoid this disadvantage, this paper proposes a hybrid method (HM) by combining the improved chicken swarm optimisation (CSO) method and the incremental conductance (InC) algorithm for a DC standalone PV energy conversion system. In this hybrid method, the improved CSO modified approach is used to search the global region, and the InC algorithm is responsible for capturing the top of this global region. MATLAB simulation and experimental results were performed to demonstrate that the proposed method has achieved the global MPP under uniform solar irradiance and partial shadow effects.

Editorial Board

2023-03-15T11:17:31+02:00

Smart Multi-Agent Framework for Automated Audio Testing

2023-01-16T20:33:57+02:00

With the widespread use of embedded software in consumer electronics, automotive industry, medical devices, and industrial environments, embedded software testing is gaining significance as an indispensable part of development and deployment of embedded products. With more than 20 years of research, development, and testing of various consumer technologies and products based on digital signal processors (DSPs) and advanced reduced instruction set computers (ARMs), we obtained insight into typical embedded development process and testing, and the pros and cons of various testing approaches and environments. In this paper, we propose the Smart Multi-Agent Framework based on IoT and Jenkins agents, customised for audio technologies in the Home Audio domain. We evaluated our solution on several complex immersive audio technologies implemented on a multicore DSP. Our uniform, customised, fully automated approach proved to be time efficient, error resilient, easy to replicate and use across all development, certification, and deployment phases of the product life cycle.

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

2022-11-30T09:53:15+02:00

In case an analytical approach to the selection of any weighting function is not possible, the selection process is usually a random and time-consuming process. In robust H∞ control theory, the selection of scalar, time, or frequency-dependent weighting functions is the main issue to shape the amplitude-frequency characteristic curve of the feedback controller. Therefore, we propose a robust H∞ control approach which utilises the multi-objective grey wolf optimisation algorithm (MOGWO) to obtain the optimal performance weighting functions in the presence of right half-plane zeros and limited bandwidth constraints. A trade-off design flowchart is proposed, providing Pareto optimal solutions to choose the optimal configuration of the robust feedback controller. The control method is structured by combining the robust H∞ optimal technique and the multi-objective algorithm. The effectiveness of the approach is compared with the non-convex single-objective heuristic solutions like the multi-verse optimisation algorithm (MVO), whale optimisation algorithm (WOA), and grey wolf optimisation algorithm (GWO). The focus of this design is to track and stabilise the output voltage of the DC-DC converter in the presence of external disturbances and parameter uncertainties. The optimised controllers are implemented using a digital signal processor (DSP) on a 200 W interleaved boost converter. The simulation results and experimental findings show that the proposed control method provides supreme disturbance rejection along with maintaining the stability of the system.

An IoT-based Smart Home Application with Barrier-Free Stairs for Disabled/Elderly People

2022-02-24T14:13:04+02:00

Home automation based on the Internet of Things (IoT) includes various components such as lighting, security, and remote control. Smart Home (SH) components should be varied and customised according to the user's specific needs. Therefore, in this study, a SH system needed by users with specials is designed and implemented on a model. The NodeMCU microcontroller with ESP8266 Wi-Fi module, Radio Frequency Identification (RFID) tags, temperature-humidity, motion detection, gas and moisture sensors and several actuators are used to build the system. Thanks to If This Then That (IFTTT), Google Assistant, and Blynk, the SH components can be managed remotely and with voice commands via a user-friendly Android-based mobile interface. The multiple control system and the ability to control the home components with different methods make this study comprehensive. In particular, the barrier-free stairs design has added innovation to the SH system for disabled people. Thus, the accessibility, security, and comfort requirements of disabled people are met, and their quality of life is improved to live independently.

Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation

2022-07-21T09:44:42+03:00

In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a significant impact on learning performance. Data augmentation or synthesis is expected to solve the issue in a small/sparse database. The problem of databasing also exists in the fingerprint-based indoor localisation system. The dense offline fingerprint database must be constructed with the accuracy requirement. However, this will affect the high cost, massive laborious work, and increase the complexity of the system. Therefore, this paper proposes to address these issues by generating synthetic data via a deep generative model. The generative adversarial network (GAN) is selected to generate the synthetic fingerprint database for indoor localisation. Our database consideration consists of power-based parameters, i.e., the received signal strength indicator (RSSI) from Wi-Fi devices obtained from the actual measurement campaign. Some of the literature mainly discusses how GAN works in a vast and complex dataset. Here, we consider applying GAN in a relatively small dataset and for a simple setup. Our results show that by only using the 20 % fraction of actual RSSI data combined with the synthetic RSSI, the accuracy validation performance is slightly higher than when using all actual data usage. Moreover, in only 60 % of actual data usage and in combination with 625 samples of synthetic data, the accuracy performance is improved to 0.73 (1.37 times higher than the use of all actual data, 0.53). Thus, this result proves that the challenges of offline fingerprint databases can be alleviated by data synthesis through GAN by using only a small dataset.

Title

2023-03-15T11:12:32+02:00

Analysis and Implementation of Switched Capacitor-based Multi-Level Inverter for Electric Vehicles Applications

2022-12-23T11:34:03+02:00

Significant interest has been shown in switched capacitor (SC)-based multi-level inverters (MLIs), which decrease the need for a DC supply and enhance power quality. The common issues with SC-MLIs include an uneven distribution of conducting paths, increased voltage drop across capacitors, the sum of all inverter DC link voltages across the highest voltage rated switches, and a higher total standing voltage (TSV). The purpose of this paper is to create a SC-MLI with less components in order to maintain a constant voltage across the capacitors, to obtain higher voltage gain with fewer parts, fewer conducting routes, lower TSV, and to create a more affordable and effective inverter. The structure of the MLI is created by a cascade interconnection between the number of SC cells. A single input multiple output (SIMO) converter boosts the DC-link voltage over the stable DC voltage of the solar panels using a modified perturb and observe (P&O) method. Additionally, fewer switches in the conduction path and 50 % of the switches operating at normal frequency guarantee a decrease in an overall loss of power in the proposed network. The benefits of the recommended MLI are made clear by comparing them with 17-level MLIs in terms of the number of elements, stress, gain, and cost factor. Detailed experimental results are shown under various transient conditions to show that the 17-level prototype is operationally viable. The total harmonic distortion (THD) is found to be identical and is less than 5 %, which meets IEEE standards.

A Non-Invasive Method of Monitoring Glucose in Blood Using a Planar Yagi-Uda Antenna and Microstrip Filter

2022-04-14T20:51:32+03:00

This work presents a non-invasive method to determine the glucose levels in blood samples using a planar Yagi-Uda antenna and a novel microstrip filter. The proposed antenna operates at 5.5 GHz and exhibiting uni-directional pattern giving a maximum gain of 6.74 dBi at the operating band. A commercially available and low-cost FR-4 substrate of dimensions 30 mm×40 mm×1.6 mm is used as a dielectric substrate. A finger phantom resembling a human finger is designed in the simulation environment, which consists of bone, skin, blood, fat as different layers. The glucose concentration is varied from 0 mg/dL to 500 mg/dL and the shifts in the frequencies are observed by keeping the phantom at various locations surrounding the antenna. A good frequency shift of 26 MHz is observed when the phantom is placed below the antenna. A good similarity is observed between the simulation and measurement results. Also, a novel microstrip filter, operating at 5.5 GHz, is developed, and the frequency shifts are studied by keeping a finger phantom at the top of the filter. The designed filter is shown to give a maximum frequency shift of 4 MHz when the glucose concentration changes from 250 mg/dL to 500 mg/dL. This study is supported by analysing transmission coefficient parameters and group delay characteristics.