Power measurements based on the Conservative Power Theory with a reduced sampling rate for lowering implementation cost

This paper proposes a digital implementation of the Conservative Power Theory (CPT) using reduced sampling rates below 300 Hz, while maintaining acceptable accuracy and precision. Computer simulations at an 8 kHz sampling rate showed power calculation errors below 5% compared to an ideal continuous-time reference. Sampling rates under 300 Hz were found to yield similar performance. The study included active, reactive, and non-conventional power quantities under unbalanced load and distorted waveform conditions, tested on unbalanced RL and nonlinear three-phase circuits. Hypothesis testing confirmed that 84.6% of cases had mean percentage errors below 10%, and 61.5% below 2%. This approach significantly reduces computational costs, enabling low-cost, low-power microcontroller implementations without compromising accuracy or precision

Hybrid Quantum-Classical Optimization Algorithms for Energy-Efficient Smart Grids

The efficient management of energy resources in modern smart grids is becoming increasingly critical due to growing energy demands and the need for sustainability. To address these challenges, this study introduces a novel hybrid optimization approach that combines quantum computing techniques with classical algorithms. By leveraging the strengths of Variational Quantum Algorithms (VQAs) alongside traditional optimization methods for preprocessing and postprocessing, the proposed framework offers an effective solution to complex combinatorial problems inherent in smart grid operations. Experimental evaluations on simulated grid models demonstrate significant improvements in energy efficiency—up to 25%—compared to conventional optimization techniques. This work highlights the transformative potential of quantum computing in advancing the operational efficiency of energy systems and ensuring scalability for future smart grid applications

Hybrid MPPT Method for Controlling an Open-Channel Hydrokinetic Microgenerator in Grid-Connected Mode

Hydrokinetic energy conversion has been gaining prominence among renewable energy sources due to its reasonable predictability and low environmental impact. In this context, this paper proposes the development of a maximum power point tracking (MPPT) algorithm for controlling a 10-kW hydrokinetic microgenerator in grid-connected mode. To maximize energy capture and ensure operational reliability, the strategy employs a hybrid process that uses the fixed-step “perturb and observe” (P&O) method to fine-tune the search within the high-efficiency region, established based on the unit’s V–P (voltage–power) curve scan. The power stage consists of an in-stream turbine, a permanent magnet synchronous generator (PMSG), an uncontrolled rectifier, and a voltage source inverter. The control stage integrates the MPPT subsystem, along with a DC-link voltage regulator, output current compensators, phase-locked loops (PLLs), an islanding detector, and pulse width modulation (PWM) devices. The technique was validated using a simulation-based approach on the MATLAB/Simulink® platform, achieving an average tracking factor (TF) of 96.21% under varying flow conditions. Comparative results suggest that the algorithm makes the controller robust to small changes in plant parameters, such as those from mechanical wear, as the tolerance of the characteristic curve effectively mitigates their impact on the conversion rate

Automatic Analysis System for Monitoring and Diagnosing the Condition of Transformer Insulating Oils

Mineral insulating oil degrades over time due to oxidation, accelerated by metallic compounds, oxygen, water, and heat. This degradation leads to color changes, acidic compound formation, and, in advanced stages, sludge precipitation. Monitoring these changes requires periodic chemical, physical-chemical, and chromatographic analyses, which can be time-consuming and costly. This work presents a pilot unit that simulates transformer conditions and enables real-time, automated measurement of oil color, dissolved oxygen, conductivity, and pH, allowing for reliable degradation diagnostics. The results indicated an upward trend in analyzed parameters, showing that degradation begins quickly. The obtained values provided a more precise diagnosis of oil condition. The statistical correlations further justify the findings, demonstrating the effectiveness of automated monitoring. Implementing this automation can significantly reduce equipment downtime and unnecessary laboratory analyses, with chemical tests serving as confirmation

Novel Flip-OFDM Modulation Scheme based on Discrete Hartley Transform

In the domain of visible light communication (VLC), rapid data transmission is based on intensity modulation/direct detection (IM/DD) using flip-orthogonal frequency division multiplexing (flip-OFDM), which is based on the discrete Fourier transform (DFT). Because DFT operates on complex-valued signals, it requires Hermitian symmetry (HS) to produce a real-valued output, thereby doubling computational complexity. This study presents a novel approach that doubles spectral efficiency while halving computational complexity by using the discrete Hartley transform (DHT) instead of the DFT. The analytical results show that the system reduces computational complexity by 45.5\% compared to the conventional scheme. Furthermore, the simulation results show that the new system achieves the same bit-error rate (BER) while reducing the peak-to-average power ratio (PAPR) by 0.3 dB.  The comparison seeks to substantiate the advantages and efficacy of the new system relative to the current technique

Design of a Resonant Point-Multipoint Wireless Power Transmission System

With the exponential growth of IoT (Internet of Things), Industry 4.0, and electrical vehicles, there is a growing need for flexible energy sources. Wireless energy transfer is becoming increasingly important in this context, as it enables physical devices to be more flexible and allows for the simultaneous powering of multiple loads. This study presents a wireless energy transfer system that uses solenoid coils in inductive resonant mode. The system is configured in a point multipoint setup, with a circular transmitter coil and two identical circular receiver coils placed inside the transmitter. We use mathematical modeling to develop circuit theory models and identify the most efficient topology for the system. In addition, we propose a simple and cost-effective self-oscillating electronic converter design with two switches for system supply. Our numerical and experimental results demonstrate that the proposed system is viable and functional, achieving a power output of 1.7 W and efficiency of 27%

Optimization of Solar PV System Efficiency in Bangladesh

This paper presents a comprehensive review and analysis of the Sarishabari Engreen Solar Plant Ltd., a 3.3 MW grid-connected solar photovoltaic (PV) system located in Sarishabari, Jamalpur, Bangladesh. The study evaluates the plant's economic and operational performance, revealing a competitive payback period of 10.1 years and a levelized cost of energy (LCOE) of 0.11 USD/kWh. These metrics highlight the plant's financial viability, largely due to the low cost of public land used for construction. However, profitability may be challenged if similar projects require significant investments in private land acquisition. Key areas for improvement identified include optimizing the tilt angle and integrating smart automation systems. Additionally, the potential for hybrid renewable energy systems combining solar and wind power is discussed. The paper also provides actionable recommendations for future renewable projects, emphasizing the importance of  advanced technologies, and supportive policies. These insights aim to inform the optimization of existing solar PV systems and guide the development of future renewable energy projects in Bangladesh, contributing to the country's sustainable energy goals

LLC Converter Design for EV Battery-Pack Charger using Household Electricity in Indonesia

Electric Vehicles (EVs) as one alternative of clean transportation now has a growing market. To enhance the ecosystem of the EVs we must reduce dependencies on limited number of public batteries swapping stations or public battery charging station. This research aims to create a switched-mode power supply that specific to charge EVs battery using households electricity power especially in Indonesia, which is commonly limited to 900VA. Since electric motorcycle commonly using 80V/20Ah battery this charger design must follow the maximum rated voltage and current of the LiPo batteries. To be more specific our charger equipped with active power factor correction rectifier, with efficiency >97% and power factor >99%, and LLC dc-dc converter, with efficiency >90%, to convert input voltage 220VAC (rms) into output voltage 80VDC. The charger also designed to be deliver 800W power into the batteries, so it will not surpassed the limited household powe

Comparative Analysis of Distribution Network Cost Allocation Alternatives

This paper presents a comparative study among methods of allocation of fixed costs in Distribution Networks (DNs). The application of these methods allows obtaining distribution use-of-system charges. Three pricing mechanisms composed of two part tariffs are evaluated. In the three mechanisms, the first part is obtained by the Long Run Incremental Cost (LRIC) method. The second one is determined by the following methods: Postage Stamp (PS), MW-Mile (MWM) or Ramsey-Boiteux (RB). The characterization and comparison of each method are performed from the point of view of regulatory principles that define guidelines on network pricing. The results allow identifying the adherence of the methods in compliance with the regulatory principles. Conflicting character between different principles is observed. The methods are tested and validated in the IEEE13-node distribution network. A study along these lines points to the proposition of new alternatives in pricing the use of DNs in the face of the growth of Distributed Energy Resources

Prediction of Probabilistic Transient Stability Using Support Vector Machine

Transient stability assessment is an integral part of dynamic security assessment of power systems. Traditional methods of transient stability assessment, such as time domain simulation approach and direct methods, are appropriate for offline studies and thus, cannot be applied for online transient stability prediction, which is a major requirement in modern power systems. This motivated the requirement to apply an artificial intelligence-based approach. In this regard, supervised machine learning is beneficial for predicting transient stability status, in the presence of uncertainties. Therefore, this paper examines the application of a binary support vector machine-based supervised machine learning, for predicting the transient stability status of a power system, considering uncertainties of various factors, such as load, faulted line, fault type, fault location and fault clearing time. The support vector machine is trained using a Gaussian Radial Basis function kernel and its hyperparameters are optimized using Bayesian optimization. Results obtained for the IEEE 14-bus test system demonstrated that the proposed method offers a fast technique for probabilistic transient stability status prediction, with an excellent accuracy. DIgSILENT PowerFactory and MATLAB was utilized for transient stability time-domain simulations (for obtaining training data for support vector machine) and for applying support vector machine, respectively