This paper presents a new linear state estimation model based on a current summation load flow method for three-phase distribution systems. The developed estimator may be applied to both the supervision of distribution systems under normal operating conditions and the fault location in cases of low and high impedance faults. Our studies were conducted using a real distribution feeder. For fault location analysis, the system was modeled using the ATP (alternative transient program) in order to emulate measurements of voltages and currents at the substation, and voltage magnitudes registered by other meters during the fault. We used the MATLAB^™ software to process the algorithms. The main contributions that arose after integrating the current method into system supervision in case of network failures under normal operating conditions and the fault location are as follows: (i) estimation of system losses; (ii) modeling of loads in real time to consider their contributions in the fault location process; and (iii) low influence of fault resistance in the location algorithm. The results show that the proposed method has good precision, low computational processing time, and is promising for distribution system supervision with a reduced number of meters.

This paper presents a multi-objective optimization approach for the design of a hybrid renewable energy generation system (HRES) composed of photovoltaic (PV) modules, wind turbine (WT), battery bank (BB), electromagnetic frequency regulator (EFR), and backup diesel generator. Hourly series of solar irradiance and wind speed were collected for the UFRN campus in Macau/RN/Brazil. Detailed modeling of each component (including temperature effects on PV, corrected power curve of WT, and battery SoC dynamics) and calculation of discounted cash flow for 20 years were developed. A Genetic Algorithm (GA) was customized with real representation, uniform crossover, and adaptive Gaussian mutation, including elitism. The optimal configuration, PV≈35kWp, WT=30kW, ESS≈200kWh, EFR=30kW, achieved net present value (NPV) of R$ 1.86 M in 2015 values ​​(R$ 3.11 M in 2025) and discounted payback in approximately 9 years. Sensitivity analysis to tariffs, costs, and climate scenarios confirmed technical and economic robustness. Comparison with the 2015 reference project showed gains of up to 17% in NPV. The integration of EFR and the method employed increased the capture and quality of wind energy, reducing conversion and maintenance costs.

Resumo Este artigo apresenta uma análise do comportamento dos controles droop P-f e droop Q-V quando estes são aplicados a uma Central Geradora Hidrelétrica que alimenta uma carga desequilibrada em um sistema de baixa tensão, trabalhando no modo isolado, ou seja, desconectado da rede de distribuição. Avalia-se a ação dos controles na estabilização dos níveis de tensão e frequência, verificando se os valores dessas variáveis estão dentre dos limites estabelecidos em normas vigentes. No estudo realizado, os resultados obtidos através da simulação computacional do sistema mostram que, para o nível de desequilíbrio de cargas adotado, os controles mantêm as variáveis tensão e frequência dentro dos limites aceitáveis. Através da apresentação dos dados de simulação de um sistema com carga equilibrada, compara-se os valores obtidos de tensão e frequência nos sistemas de forma a mostrar a diferença em ambas situações.

The cement industry faces the challenge of balancing high production demand with the mitigation of environmental impacts and the optimization of energy efficiency. In this context, the cement grinding stage is one of the most critical and complex processes, historically operated by reactive systems that depend on human sensitivity and experience. This article presents a case study conducted in an industrial unit located in the state of Ceará, Brazil, aiming to analyze the impacts of implementing Artificial Intelligence (AI) in the operational control of cement mills. The study was carried out in a cement industry facility in Ceará, where the grinding process is performed in vertical mills controlled by supervisory systems integrated with Programmable Logic Controllers (PLCs). The implemented AI system acted as decision-support for operational control, analyzing multiple process variables and proposing real-time adjustments in order to optimize process stability and productive performance. The results demonstrate that the application of Artificial Intelligence contributed to increased process productivity, as well as to a significant reduction in specific energy consumption per ton produced. This new operational behavior suggests that the intelligent system was able to optimize the mills’ operating conditions, reducing energy losses and improving the utilization of installed power. The research adopted a comparative approach to investigate how the transition from conventional methods to AI-based control affects process stability and performance.

This paper presents a comprehensive economic optimization of a grid-connected hybrid renewable energy system (HRES) enhanced with an electromagnetic frequency regulator (EFR) to improve frequency stability and provide clean and continuous electricity to the Macau City Campus while reducing dependence on fossil sources. The system includes photovoltaic (PV) arrays, wind turbines, battery storage, EFR, and a backup diesel generator. A genetic algorithm (GA) is employed to optimally size these components with the objective of maximizing the net present value (NPV) over the system’s lifetime. The GA implementation was validated on standard benchmark functions to ensure correctness and was finely tuned for robust convergence. Comprehensive sensitivity analyses of key parameters (discount rate, component costs, resource availability, etc.) were performed to assess solution robustness. The optimized design ($\mathrm{PV}\approx 35\mathrm{kWp}$, $\mathrm{WT}=30\mathrm{kW}$, $\mathrm{ESS}\approx 200\mathrm{kWh}$, and $\mathrm{EFR}=30\mathrm{kW}$) achieves a highly positive net present value of BRL 1.86 M in 2015 values (BRL 3.11 M in 2025) and discounted payback in approximately 9 years. A comparative assessment with the 2015 baseline project revealed up to a 10.1% enhancement in the net present value, underscoring the economic advantages of the optimized design. These results confirm the system’s strong economic viability and environmental benefits, providing a valuable guideline for future grid-connected hybrid energy systems.