The Rise of Smart Solar: How Intelligent Tracking Systems are Changing the Industry
A Hybrid Sensitivity–Tracing Framework for Real Power Loss Allocation to Generators and Loads in AC Networks
A Modular Low-Cost TEG-PV Hybrid Waste-Combustion Energy Harvester for Off-Grid AC Power Supply with Integrated Passive Particulate Emission Mitigation and Decentralized Sustainable Waste-to-Energy Conversion
K-Means Algorithm Based Input Reduction for Voltage Security State Classification of Power Distribution System
Stability Evaluation of Power Structures with High Penetration of Renewable Energy
Multi Area Load Frequency Control of a Hybrid Power System with Advanced Machine Learning Controller: Case Study of Andhra Pradesh
A New Hybrid Cuckoo Search-Artificial Bee Colony Approach for Optimal Placing of UPFC Considering Contingencies
Efficiency and Investment Comparison of Monocrystalline, Polycrystalline, and Thin Film Solar Panel Types at Karabuk Conditions
Design of a Grid Connected PV System and Effect of Various Parameters on Energy Generation
Comparative Analysis of Harmonics by Shunt Active Filter using Resonant Current Control in Distribution System
Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI
Development of Power Flow Controller for Grid Connected Renewable Energy Sources Using Lyapunov function
Detection and Location of Faults in Three Phase 11kv Underground Power Cables By Discrete Wavelet Transform (DWT)
Design of PV-Wind Hybrid Micro-Grid System for Domestic Loading
Applications of Artificial Neural Networks in various areas of Power System; A Review
Despite the growing global reliance on renewable energy, traditional static photovoltaic (PV) installations suffer from efficiency losses caused by the angular misalignment between solar panels and the sun's trajectory throughout the day. To overcome these geometric constraints and improve photon capture, this study develops an IoT-enabled Smart Solar Tracking System based on the Arduino Uno microcontroller. The system employs a closed-loop control mechanism utilizing two Light Dependent Resistors (LDRs) configured as differential optical sensors, which continuously monitor variations in light intensity to estimate the sun's azimuthal position. When a significant imbalance in light distribution is detected, the microcontroller computes the error signal and actuates a servomotor to reorient the PV panel, thereby minimizing the angle of incidence. Experimental evaluations comparing the proposed tracker with a fixed-tilt PV panel demonstrate substantial performance improvements, with the tracking mechanism yielding an average 48% increase in power output and enhanced voltage stability during peak irradiance periods. By integrating sensor-based actuation with microcontroller logic, the prototype presents a scalable and technically feasible approach to improving the energy yield of small-scale solar-harvesting systems without manual intervention. The use of low-cost and easily deployable components further reinforces its suitability for rural, domestic, and off-grid renewable energy applications. This work addresses a key research gap, the absence of affordable, experimentally validated, sensor-driven solar tracking systems, by presenting a differential LDR-based, IoT-supported design that offers quantifiable improvements over traditional fixed-tilt PV configurations. The findings demonstrate that a low-cost, sensor-assisted tracker with IoT connectivity provides a practical and scalable solution for enhancing PV energy capture. Limitations such as servo power consumption and reduced LDR sensitivity under cloudy conditions are identified and discussed to guide future development.
This paper sets out a hybrid sensitivity-tracing framework for allocating real-power losses to generators and loads in AC transmission networks. Conventional marginal-loss and tracing-based methods fail to meet important requirements such as fairness, stability, slack independence, and physical interpretability. The proposed approach integrates AC marginal-loss sensitivities with flow-tracing participation factors by using a convex weighting mechanism that incorporates incremental loss effects and physical line usage. The hybrid formulation provides revenue neutrality, non- negativity, and robustness under varying network conditions without steep gradients and slack dependence in marginal- based allocation. Moreover, in numerical studies for IEEE 14-bus, 39-bus, and 118-bus networks, the hybrid method provides smoother allocation profiles and fairness scores and is also relatively well immune to slack-bus relocation and has very low computational costs. These results assure that the developed framework is technically consistent, capable of scaling, and operationally feasible for real-life AC grid power loss allocation.
The growing volume of municipal solid waste (MSW) and the persistent demand for reliable decentralized electricity underscore the need for low-cost, fuel-free, and environmentally conscious waste-to-energy (WTE) solutions. This work presents the design and experimental validation of a modular thermoelectric-photovoltaic (TEG-PV) hybrid energy harvester that converts controlled waste combustion into usable off-grid 230 V AC power. Thermal gradients from combustion drive TEC1-12706 TEG modules, while flame irradiance and ambient daylight are simultaneously captured using miniature monocrystalline PV cells, forming a dual-domain heat-light energy recovery architecture. The combined DC output is isolated through Schottky diodes and routed to a protected battery-charging system with PWM/MPPT control, powering a 12 V battery bank that feeds a 150 W single-phase inverter. The prototype successfully operated three 9-12 W LED loads for over two hours, demonstrating practical domestic applicability. A key innovation is the integration of passive particulate emission mitigation using a multi-layer cotton-tissue exhaust filter, which captures visible soot without imposing energy penalties. Although overall efficiency is lower than that of large centralized plants, the system offers significant value through waste-volume reduction, modularity, affordability, silent operation, and improved emission awareness, establishing a viable pathway for sustainable decentralized WTE microgeneration.
Recent literature investigates that voltage insecurity may be dangerous for the power system as it causes sudden voltage collapse. For making real-time decisions regarding the security of the power system, it is very much essential to determine the current operating state of the system. This paper recommends a combined artificial neural network scheme to classify the operating conditions of a power system into normal, alarming, or insecure states. By choosing only the important input variables and eliminating unrelated ones, greater presentation is expected with lesser computational efforts. Here the K-means clustering method reduces the number of inputs for the proposed neural network structure to calculate the voltage security state with sufficient accuracy and speed. The success of the suggested technique is verified by two standard IEEE systems and one practical 85-bus system. Results show that the proposed K-means algorithm provides a compact artificial neural network model that can effectively and correctly recognize the working state of the power system with fewer numbers of inputs
The increasing penetration of inverter-based renewable energy sources has fundamentally altered the dynamic behavior of modern power systems by displacing conventional synchronous generation. Reduced system inertia, higher rates of change of frequency, modified fault responses, and complex voltage regulation have emerged as critical stability challenges in low-inertia grids. This paper presents a comprehensive study of power system stability under high renewable energy penetration through a structured literature review, a unified methodological framework, and a realistic transmission-level case study. The proposed framework integrates grid characterization, component and control modeling, multi-domain stability assessment, contingency analysis, and coordinated mitigation strategies using both phasor-domain and electromagnetic transient simulations. A three-zone network with varying levels of renewable integration is analyzed to quantify impacts on frequency, voltage, and small-signal stability following severe contingencies. Results demonstrate that stability degradation increases nonlinearly with renewable share in baseline grid-following configurations. The deployment of grid-forming inverter controls, synthetic inertia, and fast frequency response from battery energy storage significantly improves system performance by reducing RoCoF, restoring frequency nadir, and enhancing voltage recovery. Eigenvalue analysis further confirms improved damping of critical interaction modes. The findings highlight the necessity of hybrid control architectures and coordinated mitigation strategies to ensure reliable operation of future power systems with high renewable penetration.
