This paper introduces a simplified 2-wire measurement system for accurate resistance measurement of resistive sensors, leveraging the Anderson current loop. The proposed configuration features a precision reference resistor (R_ref) and two diodes (D₁ and D₂), directly interfaced with the Analog-to-Digital Converter (ADC) and output pins of a microcontroller, eliminating the need for external stabilization resistors. Utilizing ratiometric voltage measurements and an adaptive tuning factor, the system effectively compensates for lead wire resistance and diode mismatches. A prototype implemented with an ATmega2560 microcontroller demonstrates accurate resistance measurements in the range of 60–320 Ω, corresponding to a Pt100 sensor, achieving an uncertainty error of less than 0.13% for a lead wire resistance of 4.87 Ω and 0.15% for 10.0 Ω. Experimental results confirm the system’s precision and reliability over long lead lengths, offering a cost-effective and robust solution for industrial and remote sensing applications requiring accurate temperature measurements.

This paper presents a simplified 3-wire and 4-wire measurement method for accurately determining the resistance of sensors, such as RTDs, using an Anderson current loop configuration. The proposed system employs a resistance-to-voltage conversion approach, ensuring high accuracy and simplicity while eliminating the need for additional components such as capacitors, op-amps, or complex timing circuits commonly found in resistance-to-time conversion techniques. Additionally, a tuning technique is incorporated to compensate for Analog-to-Digital Converter (ADC) errors, reference resistance deviations, and lead-wire mismatches, thereby improving measurement precision. This tuning technique requires an initial calibration to determine optimal correction factors, tailored to specific hardware and wiring conditions. A prototype was developed using a commercial ATmega2560 microcontroller with 10-bit ADCs to measure resistances ranging from 50–317 Ω, corresponding to a Pt100 thermal sensor, under varying lead-wire resistances. Experimental results, incorporating tuning factor calibration, demonstrate that the proposed technique achieves a relative uncertainty error below 0.1% for both 3-wire and 4-wire configurations, ensuring accurate and reliable measurements even in the presence of lead-wire variations.

This article presents a novel multi-objective economic load dispatch solution with the bee colony optimization method. The purposes of this research are to find the lowest total power generation cost and the lowest total power loss at the transmission line. A swarm optimization method was used to consider the non-smooth fuel cost function characteristics of the generator. The constraints of economic load dispatch include the cost function, the limitations of generator operation, power losses, and load demand. The suggested approach evaluates an IEEE 5, 26, and 118 bus system with 3, 6, and 15 generating units at 300, 1,263, and 2,630 megawatt (MW) and uses a simulation running on the MATLAB software to confirm its effectiveness. The outcomes of the simulation are compared with those of the exchange market algorithm, the cuckoo search algorithm, the bat algorithm, the hybrid bee colony optimization, the multi-bee colony optimization, the decentralized approach, the differential evolution, the social spider optimization, and the grey wolf optimization. It demonstrates that the suggested approach may provide a better-quality result faster than the traditional approach.

This study introduces an Enhanced Local Search (ELS) technique integrated into the Bee Colony Optimization (BCO) algorithm to address the Economic Dispatch (ED) problem characterized by a continuous cost function. This paper combines Lambda Iteration and Golden Section Search with Bee Colony Optimization (BCO) into a more efficient method called Enhanced Local Search for Bee Colony Optimization (ELS-BCO). The proposed methodology seeks to enhance search efficiency and solution quality. One of the main challenges with standard BCO is random initialization, which can lead to slow convergence. The ELS-BCO algorithm overcomes this issue by using Lambda Iteration for better initial estimation and Golden Section Search to refine the movement direction of the bees. These enhancements significantly improve the algorithm’s capacity to identify optimal solutions. The performance of ELS-BCO was evaluated on two benchmark systems with three and six power generators, and the results were compared with those of the original BCO, LI-BCO, GS-BCO, and traditional optimization methods such as Particle Swarm Optimization (PSO), Hybrid PSO, Lambda Iteration with Simulated Annealing, the Sine Cosine Algorithm, Mountaineering Team-Based Optimization, and Teaching–Learning-Based Optimization. The results demonstrate that ELS-BCO achieves faster convergence and higher-quality solutions than these existing methods.