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Abstract This paper introduces an enhancement to the Chen chaotic system by incorporating a constant perturbation term $$d$$ d to one of the state variables, aiming to improve the performance of pseudo-random number generators (PRNGs). The perturbation significantly enhances the system's chaotic properties, resulting in superior randomness and increased security. An FPGA-based realization of a perturbed Chen oscillator (PCO)-derived PRNG is presented, tailored for embedded cryptosystems and implemented on a Nexys 4 FPGA card featuring the XILINX Artix-7 XC7A100T-1CSG324C integrated chip. The Xilinx-based system generator (XSG) tool is utilized to generate a digital version of the new oscillator, minimizing resource utilization. Experimental results demonstrate that the PCO-generated data successfully passes the NIST and TestU01 test suites. Additionally, statistical tests with key sensitivity are performed, validating the suitability of the designed PRNG for cryptographic applications. This establishes the PCO as a straightforward and efficient tool for multimedia security.

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.

Monitoring energy quality events is crucial for maintaining the stability and reliability of power grids. This paper presents a novel system integrating Discrete Wavelet Transform (DWT) and Extreme Learning Machine (ELM) for detecting and classifying power quality disturbances. The DWT performs multi-resolution analysis to decompose signals into time-frequency components, capturing various disturbances such as sags, swells, and harmonics. The ELM classifier, trained on these decomposed signals, achieves an impressive classification accuracy of 99.69%, significantly outperforming conventional methods like STFT with SVM (97.22%) and FFT with ANN (99.30%). The system was validated on a Xilinx Zynq-7000 SoC FPGA, demonstrating real-time processing capabilities with a latency of 1.5 milliseconds and a power consumption of 1.8 W. These findings highlight the effectiveness of the proposed method for real-time, accurate, and energy-efficient power quality monitoring.

The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine-tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources.

Abstract As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua’s dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua’s solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses.

Abstract Promoting renewable energy sources, particularly in the solar industry, has the potential to address the energy shortfall in Central Africa. Nevertheless, a difficulty occurs due to the erratic characteristics of solar irradiance data, which is influenced by climatic fluctuations and challenging to regulate. The current investigation focuses on predicting solar irradiance on an inclined surface, taking into consideration the impact of climatic variables such as temperature, wind speed, humidity, and air pressure. The used methodology for this objective is Artificial Neural Network (ANN), and the inquiry is carried out in the metropolitan region of Douala. The data collection device used in this research is the meteorological station located at the IUT of Douala. This station was built as a component of the Douala sustainable city effort, in partnership with the CUD and the IRD. Data was collected at 30-min intervals for a duration of around 2 years, namely from January 17, 2019, to October 30, 2020. The aforementioned data has been saved in a database that underwent pre-processing in Excel and later employed MATLAB for the creation of the artificial neural network model. 80% of the available data was utilized for training the network, 15% was allotted for validation, and the remaining 5% was used for testing. Different combinations of input data were evaluated to ascertain their individual degrees of accuracy. The logistic Sigmoid function, with 50 hidden layer neurons, yielded a correlation coefficient of 98.883% between the observed and estimated sun irradiation. This function is suggested for evaluating the intensities of solar radiation at the place being researched and at other sites that have similar climatic conditions.

This work introduces an improved method for modeling and simulating the Boost Converter utilizing Duty Cycle Modulation (DCM) regulated by an optimum PIDF (Proportional-Integral-Derivative with Filter) regulator. We optimized the characteristic parameters of the PIDF regulator for a second-order system generated from its transfer function by using a mix of theoretical study and simulation using the Matlab/LQR tool. The conventional PID parameters in the time domain were converted into their corresponding LQR (Linear Quadratic Regulator) counterparts, allowing for the solution of the Riccati problem and the creation of an optimum state trajectory model. The results of analog virtual simulations done in a Multisim environment indicate that the system has improved dependability. It maintains a high level of accuracy in a stable condition, with no static error and a reaction time of 1.5 ms, without any overshooting. The effectiveness of the optimum PIDF control in regulating the DCM Boost Converter is highlighted by the system's strong ability to handle changes in load during transient states within a time frame of 300 ms. This study represents a substantial enhancement compared to conventional PID-based approaches, providing valuable knowledge about the possible uses in power electronics and control systems.

Electricity holds both economic significance and strategic importance in many regions globally. Persistent challenges related to electricity generation, encompassing aspects like availability, quality, and cost, have impeded the progress of nations for an extended period. While conventional electrochemical storage systems have been the benchmark, they are plagued by issues such as high expenses, technical limitations in power output, and restricted storage capacity. Power generation facilities come with their own array of problems, including noise pollution, environmental impacts, and suboptimal performance, exacerbating the existing concerns of reliability and maintenance. To navigate the path of sustainable global development amidst a sharp upswing in energy consumption, it becomes imperative to foster innovative energy solutions. Independent renewable energy systems (RES) represent a promising avenue for reshaping the prevailing reliance on conventional power grids. The Hybrid Renewable Energy System (HRES) deserves careful calibration, particularly considering the considerable load fluctuations and the need for cost efficiency. The study considers a standalone HRES in a residential area on Manoka Island, where fish farming is a primary activity. This dwelling exhibits a daily electricity consumption averaging at 9.28 kWh, with peak demand hitting 0.88 kW. To evaluate the suitability of the Hybrid Optimization Model for Electric Renewable (HOMER) program, the study investigates the optimal energy generation mix for both sustaining the fish farming industry and fulfilling the energy requirements of the chosen building. The optimization process identifies four key parameters that undergo thorough assessment in this analysis. Delving into the specifics, the study attains an impressive renewable fraction (RF) of 92.5% and achieves a Deficit of Power Supply Probability (DPSP) of 0%. Furthermore, the production of 50 kg of O2 and the emission of 1.69 kg of carbon annually prove adequate for constructing a reliable and environmentally sound energy system. In a broader sense, this research serves as a compelling case study, elucidating the concept of hybrid energy systems within the framework of eco-friendly and sustainable power generation. The study not only underscores the substantial potential of RES for the country but also constitutes a significant stride for researchers active in this field.

In rural or underdeveloped areas, microgrid failures are common occurrences, which underscores the importance of implementing an effective energy management plan. A microgrid's battery energy storage system is a critical component of such a plan. The system can regulate voltages, mitigate imbalances, and increase system reliability, making it vital to maximize the benefits of energy storage. This study proposes a method for managing energy storage and controlling battery charge and discharge operations based on load requirements in a microgrid connected to a solar system. The problem of energy management over a 24-h period is addressed, and the model is simulated using MATLAB R2022a software, employing state flow analysis and linear programming methods to minimize the total variable electricity price before and after islanding. The heuristic method results in higher operating expenses by 4.567% in clear weather and 8.5232% in cloudy weather compared to the linear programming approach when the load is satisfied for 24 hours.

The lack of accessible and reliable electrical energy in Cameroon has become a pervasive obstacle to the nation’s progress, with energy availability, quality, and cost identified as key hindrances to development over the past 15 years. Conventional solutions that rely on combustion engines and electrochemical storage systems have proven to be cost-prohibitive, limited in power output, and constrained in capacity. The dependence on traditional diesel generators has perpetuated maintenance challenges and a continuous demand for fuel supply, while the accompanying noise and pollution have restricted their use in residential areas. Recognizing the imperative of reducing dependence on fossil fuels and curbing greenhouse gas emissions, the need for clean and sustainable energy sources has emerged as a critical concern for the advancement of civilization. Against this backdrop, this research endeavors to identify the most cost-effective and efficient blend of renewable energy sources capable of meeting the power requirements of three small communities on Manoka Island, a district of Douala, Cameroon. Through a comprehensive technical, environmental, and economic analysis, this study addresses the substantial energy needs of 334 households, with an average daily power consumption of 1082.90 kWh and a peak electrical load of 183.99 kW. Leveraging the Hybrid Optimization Model for Electric Renewables (HOMER) program, this investigation assesses the feasibility of implementing Hybrid Renewable Energy Systems (HRES) to meet the region’s energy demands. The research highlights the most optimal scenario integrating solar panels, wind turbines, battery cells, fuel cell generators, biogas, and an electrolyzer within an off-grid HRES system. Notably, the study demonstrated an absence of idle load, resulting in remarkably low unit energy costs of ${\$}$ 0.1981 and a compelling net present value of ${\$}$ 2,209,741. The cost-effective arrangement featured 201 batteries, yielding a project profit of ${\$}$ 57,387, with an impressive Internal Rate of Return (IRR) of 9.09%, Return on Investment (ROI) of 6.19%, and a payback period of 8.76 years over a 25-year term. In essence, the insights gleaned from this exploration of hybrid energy systems represent a pioneering case study in sustainable electricity provision. This research significantly contributes to the knowledge base on renewable energy within the nation, underscoring its tremendous potential for sustainable development and energy security.

Having access to electricity is one of the big challenges nowadays. It is a basic requirement for any community as it can improve the living standards characterized via the improvement of healthcare, education, and the local economy at large. Henceforth, electricity plays a major role in the economic and social development in many countries. The United Nations are working to ensure an easy access to safe energy technologies as from 2030. To cope with this point of view, this paper aims to use the relation between electricity and the Human Development Index (HDI) in order to predict the value of HDI in a short term. In this relation, two parameters x and y are used to study the dependency between the HDI and electricity consumption and production conjointly. To fulfill our goal, the values of electricity production, electricity consumption and HDI for a period are used into our optimization technique namely Particle Swarm Optimization (PSO). The best values of our parameters x and y are obtained under different scenarios based on three criteria: number of iterations, population, and limit of convergence. The results obtained after simulations leads to the value of HDI of about 0.68 in 2030, with 100 iterations, 100 populations and 0.05 as limit of convergence. The predicted value obtained is closed of the theoretical value presented into the Electricity Sector Development Plan. Thus, this helps to appreciate the impact of energy on the social and economic development of Cameroon.

Electricity is becoming an important commodity in Cameroon. Within the years, its consumption and production have led to many studies. Hence, having an idea on its progression is one of research’ concerns. Thus, this paper aims to develop a model for forecasting electricity production and consumption in Cameroon based on Long Short-Term Memory (LSTM). Indeed, the LSTM approach, showing a good ability to grab the long-term dependencies between time steps of electricity production and consumption, allows a good prediction in 2030 of 7178GWh for consumption with 0.067 RMSE and 0.2965% MAPE and 8686GWh for production with 0.1631 RMSE and 0.4291%MAPE. Hence, the proposed model is more reliable, what makes possible to monitor the growth in electricity supply and demand, falling to the study of balance in Cameroon.