ABSTRACT
Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.
ABSTRACT
During the recording time, electrocardiogram (ECG) signals are subject to multiple artefact noises, such as muscle activity, white Gaussian noise (WGN), baseline wander, and power line interference (PLI). Therefore, pre-processing of ECG signals is essential to eliminate these artefacts and to obtain efficient ECG features. Many approaches have been proposed for removing ECG noises, including ECG signal denoising using wavelet transform (WT). However, the effectiveness and performance of the WT technique are strongly related to the configuration of its control parameters, which are typically fine-tuned through a laborious and time-consuming series of experiments. This paper introduces a technique that combines particle swarm optimisation (PSO) with WT for ECG signal denoising. The key contribution of this research is the use of PSO to determine the optimum settings for all WT parameters for ECG signal denoising (type of wavelet basis function Φ, thresholding function ß, level of decomposition L, rule for threshold selection λ, and rescaling method ρ). The efficiency of the proposed method is evaluated using the percentage root mean square difference (PRD) and the signal-to-noise ratio (SNR), employing various ECG signals available online from the MIT-BIH Arrhythmia database. Experimental results show that the proposed PSO-WT technique yields better results than state-of-the-art techniques in terms of SNR, particularly for PLI measured at 60 Hz and still acceptable at 50 Hz. For example, a denoised ECG signal resulting from the proposed technique at an SNR input of 10 dB corresponds to an SNRoutput of 27.47 dB at 60 Hz, improving the quality of the denoised ECG signal and making it more appropriate for clinical diagnosis. Furthermore, the proposed method also shows promising efficiency in the presence of WGN, making it highly relevant for IoT applications and RF transmission.
