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
Stability Evaluation of Power Structures with High Penetration of Renewable Energy
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
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
In today's innovative era, our smart solar system harnesses advanced technology to optimize energy efficiency and promote sustainable living worldwide. The challenges we are facing now are bad weather conditions, maintaining the system and low Sunlight utilization. The limitations of the systems we have now is that they are placed in one position to address these challenges this paper proposes the next-gen solar tracking system will make sure that sunlight is utilized and that the get 100% of clean Energy. The findings where Enhanced power Efficiency, the cost is effective now, the designs are simple, some systems are automated and give out real-time responses and that it benefits the Environment. The next-gen solar tracking system will produce enough energy than the system we have the fixed-Position system. The next-gen solar tracking system will not be so expensive but will just have new additions. The next-gen tracking system will not have negative impacts of the environment.
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 with 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.
High penetration of inverter-based renewable era (sun PV, wind) introduces new dynamics and balance challenges to trendy power systems. unlike traditional synchronous mills, inverter-interfaced sources make contributions little to machine inertia and trade fault response, voltage help, and frequency dynamics. This paper offers a comprehensive look at of balance troubles springing up from huge-scale renewable integration and proposes modelling, analytical, and manage strategies to make sure relaxed operation. We review literature, define stability classes (frequency, small-sign, transient, voltage/reactive, and rotor-perspective equivalents for low-inertia grids), endorse a hierarchical analysis and control structure, describe simulation-based totally assessment strategies (time-area EMT and phasor research), and advocate mitigation measures along with grid-forming inverters, artificial inertia, rapid frequency response, adaptive protection, and coordinated reactive power manage. consultant simulation situations display how suitable inverter manage and coordination can repair balance margins and restriction frequency nadir, voltage tours, and fee-of-exchange-of-frequency (RoCoF) beneath excessive renewable shares. We finish with deployment concerns, open studies guidelines and counseled benchmarks.
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 micro-generation.
Recent literatures investigate that voltage insecurity may be dangerous for the power system as it causes sudden voltage collapse. For making real time decision regarding the security of power system it is very much essential to decide the current operating state of the system.This paper recommends a combined artificial neural network scheme to classify the operating conditions of power system into Normal, Alarming or Insecure state. By choosing only the important input variables and eliminating unrelated ones, greater presentation is expected with lesser computational efforts. Here 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 recognise the working state of the power system with less number of inputs.
