Active Power Research Articles

Reduced Switch Multilevel Inverter Based Shunt Active Power Filter With ANN Controller for Power Quality Improvement

In order to improve power quality, this research investigates the use of SAPF with Seventeen Level Reduced Switch Multilevel Inverter. The electricity grid's stability is significantly influenced by various non-linear loads. To address this, SAPF is proposed and designed to monitor, manage the current movement between the power source and the load. The variance between the intended and real currents drawn from utility grid is assessed using p-q theory based hysteresis current controller. The calculated error current guides the controllers in forecasting the best-suited switching signals and modulation index for the seventeen level RSMLI-SAPF system. In this paper the traditional PI controller, Artificial Neural Network based controller are compared and the results are provided to validate the performance of SAPF. The proposed system is modelled and analysed using MATLAB/Simulink. The simulation results are provided for various modulation index using Hybrid Pulse Width Modulation in which Triangle saturated Phase disposition PWM using Unipolar Third harmonic injection as Reference is used and are subjected to non-linear loads.

Research on Data-Driven Fault Scenario Simulation Model of Distributed Network

Distribution lines are critical components of today's power system, and can directly impact on power supply security and stability. An effective power system protection system should detect any faults as soon as they occur. There are two steps in fault diagnosis. One is defect classification, which has already achieved excellent accuracy rates. As a result, this study concentrates on the opposite objective, which is fault location. This research introduces a War Strategy Water Wave Optimization (WSWWO) based Deep Q Network for radial distribution networks using synchronous generators for Distributed Generation (DG). The algorithm estimates the fault site by analyzing voltage and current samples at the main feeder head, and scheduled active and reactive power injections by network synchronous generators. A full-order synchronous machine model was utilized to analyze the dynamic behavior of DG plants during fault transients. Here, the Deep Q Network is trained by WSWWO, and hybridized by two optimization algorithms, War Strategy Optimization (WSO) and Water Wave Optimization (WWO). Furthermore, the experimental results revealed that the WSWWO-Deep Q Network outperformed state-of-the-art models in terms of Accuracy and Error, with values of 0.997 and 0.141, respectively.

Experimental Validation of a Passive-Adaptive Slat Concept and Characterization under Sinusoidal Fluctuations in the Angle of Attack

This article presents an experimental investigation of a passive-adaptive slat concept, an aerodynamic control mechanism aimed at avoiding separation in the inwards region of a horizontal axis wind turbine blade. The passive-adaptive slat is designed to autonomously adjust its position due to the aerodynamic forces acting on it, without the need of any active control system or external power source. The slat opens when the angle of attack increases beyond a certain threshold so that stall is delayed and closes for smaller angles of attack to increase the lift-to-drag ratio of the airfoil. A thorough aerodynamic characterisation of the passive-adaptive slat is performed in the wind tunnel followed by testing it under different sinusoidal inflows generated by a 2D active grid. It is observed that the slat system is able to leverage the advantages of both a clean airfoil and an airfoil with a fixed slat. It has the capability of delaying stalls for higher angles of attack, as well as having higher lift-to-drag ratio for lower angles of attack. It is also observed that, for fluctuating inflow, the passive-adaptive slat is able to achieve similar mean lift values as an airfoil with fixed slat while showing significant reduction in the lift fluctuations.

Design of Automatic Power Factor Correction for Optimization of Electric Energy Consumption

Electrical energy is a major human need which, when viewed from its main source, namely fossil energy, will increasingly run out. There needs to be an effort to utilize electrical energy more optimally. The problem discussed in this research is the decline in power factor. The State Electricity Company (PLN) has a minimum power factor standard of above 0.85 which is found in the SPLN 70-1 regulation and PERMEN ESDM No. 09 of 2011. If the power factor is below the minimum limit, it will result in losses, because consumers have to pay for excess kilo Volt Ampere Reactive hours (kVARh). Power factor drops are caused by the use of inductive loads. With automatic power factor correction, the poor power factor from the use of inductive loads will be resolved and optimization of the use of electrical energy can be achieved. From the test results conducted using 3 cumulative loads, the worst power factor value was obtained before PF repair was carried out by the system, namely 0.38 and after PF repair by the best power factor system reached 0.99 and obtained the value of active power and apparent power after PF repair is almost the same (comparable) and it can be said that the optimization of electrical energy occurs in this study.

Social Media Data Mining and Mental Health Status Assessment Based on Sentiment Analysis

The usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.

Monitoring System for Electrical Energy Use and Charging Electricity Tokens Based on Website and Whatsapp Application

The large number of equipment or electrical loads used causes large amounts of electrical energy usage. To determine the amount of electrical energy used, you can install an electrical energy meter whose results can be monitored via an LCD display. In this research, a website-based application was designed that can monitor electrical energy usage, by providing information in the form of voltage, current, power, and active power usage over time. Apart from that, this website can also top up electricity tokens as a feature to limit the use of electrical energy that will be used by users and is equipped with a notification message for remaining electricity tokens to the Whatsapp application. This system was developed using an ESP32 microcontroller which will provide data, process data, and present data from instrument measurements, then send the measurement results data every 15 seconds to the monitoring website. From the test results, the tool can measure electrical quantities such as current and voltage. The tool can also send measurement results to the website and store measurement data in a database. Test results for sending notification messages, the message was successfully sent to WhatsApp media if the electricity token met the specified conditions.

Disentangling periodic and aperiodic resting EEG correlates of personality

Previous studies of resting electroencephalography (EEG) correlates of personality traits have conflated periodic and aperiodic sources of EEG signals. Because these are associated with different underlying neural dynamics, disentangling them can avoid measurement confounds and clarify findings. In a large sample (n = 300), we investigated how disentangling these activities impacts findings related to two research programs within personality neuroscience. In Study 1 we examined associations between Extraversion and two putative markers of reward sensitivity—Left Frontal Alpha asymmetry (LFA) and Frontal-Posterior Theta (FPT). In Study 2 we used machine learning to predict personality trait scores from resting EEG. In both studies, power within each EEG frequency bin was quantified as both total power and separate contributions of periodic and aperiodic activity. In Study 1, total power LFA and FPT correlated negatively with Extraversion (r ∼ -0.14), but there was no relation when LFA and FPT were derived only from periodic activity. In Study 2, all Big Five traits could be decoded from periodic power (r ∼ 0.20), and Agreeableness could also be decoded from total power and from aperiodic indices. Taken together, these results show how separation of periodic and aperiodic activity in resting EEG may clarify findings in personality neuroscience. Disentangling these signals allows for more reliable findings relating to periodic EEG markers of personality, and highlights novel aperiodic markers to be explored in future research.

Solar shunt active power filter based on optimized direct power control strategy with disturbance rejection principle

Introduction. This paper focuses on a renewable energy system coupled to a dual purpose power grid via a parallel active power filter for injecting photovoltaic energy into the grid and improving the power quality in the presence of the non-linear load. The novelty of the work consists in the combination of two advanced techniques – Fuzzy Logic Controller (FLC) and the optimized Anti-Windup Fractional Order Proportional-Integral Differentiator (AW-FOPID) regulator based on Particle Swarm Optimization with the Spreading Factor (PSO-SF) algorithm, applied to the improved Direct Power Control (DPC) strategy under different conditions related to climate changes and healthy or infected electrical network. Purpose. Its main role is to improve the power quality and reject the perturbations deforming the electrical network under distorted, unbalanced and balanced grid voltage conditions. Besides, the FLC is employed the Maximum Power Point Tracking (MPPT) under any weather conditions. In addition, the optimized AW-FOPID controller leads to keep the DC bus voltage at its reference value with small undershoots and overshoots in the voltage with a short response time in steady or dynamic states. Methods. The rejection of disturbances affecting the grid is offered by the improved DPC. On the other hand, an intelligent method based on fuzzy logic was used MPPT under any weather conditions. Furthermore, an AW-FOPID regulator based on PSO-SF algorithm is used to keep the DC bus voltage at its reference value with small undershoots and overshoots in the voltage, while keeping a fast response time. Results. The proposed system control is evaluated in various states of power source: distorted, unbalanced, and balanced by simulation using MATLAB/Simulink. The simulation results illustrate the effectiveness and performance of the studied control strategies. References 26, tables 8, figures 16.

Grid Quality Services from Smart Boilers: Experimental Verification on Realistic Scenarios for Micro-Grids with Demand-Side Management Oriented to Self-Consumption

The deeper penetration of renewables in the energy mix is an intense requirement in order to reduce global carbon dioxide emissions. In addition, new technologies are being developed, such as electric mobility and Distributed Generation (DG) in urban areas. However, the unpredictable fluctuations in energy generation from roof-installed PVs and the switching operation of their inverters greatly aggravate the already-present grid quality problems. In this paper, the Smart Boiler (SB) concept for grid quality improvement is presented. Furthermore, its experimental verification is implemented on a flexible testbed that accurately emulates several realistic scenarios for the low voltage distribution grid, under complex operating conditions. The proposed low-cost electronic kit, which contains a converter of fairly simple topology and requires connection to the internet, is used to upgrade conventional domestic boilers to smart devices. The SB automatically regulate the local reactive power flow, helping to stabilize the voltage level and suppress the grid current harmonic content, with both services provided in a matter of seconds. The higher the active power consumed and the denser the SB cluster, the wider the beneficial impact on the affected network area. While this service is provided, excess energy generated by PVs is temporarily stored as heat in the boiler tanks, given the users’ hot water consumption habits. The whole application, as a powerful demand-side management tool, proves beneficial for both the network operator and the end-user, especially when self-consumption is desirable in order to achieve a Nearly Zero Energy Building.

РАЗРАБОТКА АППАРАТНОЙ ЧАСТИ И 3D-МОДЕЛИРОВАНИЕ КОРПУСА СИСТЕМЫ ТЕХНИЧЕСКОГО ЗРЕНИЯ ДЛЯ ОПРЕДЕЛЕНИЯ ПАРАМЕТРОВ КОРНЕПЛОДОВ САХАРНОЙ СВЁКЛЫ

One of the problems of the sugar industry is the high quality loss of sugar beet roots during storage. The development of a technical vision system is aimed at ensuring greater safety of raw materials during harvesting and storage by analyzing raw materials arriving at factories. The concepts of technical vision and technical vision systems and its components are described in the work. The process of distribution of trucks at the receiving point of sugar factories by humans is considered. The purpose of the article is formulated, which is to develop the hardware of the technical vision system, as well as 3D modeling of the body. All characteristics of all components for the technical vision system have been determined. Based on the characteristics, components were selected to create the system. A block diagram of a technical vision system has been developed and described, including a power supply, a twoposition toggle switch, a DC-DC converter unit, a fuse block, a controller, an eight-channel relay module, a galvanic isolation module, a USB splitter with active power supply, a USB-UART converter, spotlight, laser distance sensor, industrial camera, USB modem, LED display, thermostat, heating plate, case fan, thermistor. The 3D modeling method is the KOMPAS-3D environment, in which models such as the protective housing of the technical vision system and the bracket for mounting it on site were created. The case is printed from PLA plastic, as it has high hardness and density, as a result of which the likelihood of the case breaking is low. The bracket is made of heat treated steel for high strength and durability. Based on the results of the work performed, the technical vision system was sent for testing and identification of deficiencies to the receiving point of a sugar factory in the Kursk region.

Integration of optimal power flow with combined heat and power dispatch of renewable wind energy based power system using chaotic driving training based optimization

Combined heat and power economic dispatch (CHPED) based optimal power flow (OPF) problem has been studied in this article using a new, practical approach based on chaotic driving training optimization (DTBO) (CDTBO). In the proposed technique (CDTBO), the chaotic based learning is integrated with DTBO to overcome the local optimal problem and inferior convergence speed of the existing algorithms. OPF is an important concern to retain the power system running effectively. In order to meet the demand for reasonably priced power generation with optimal power flow in transmission lines, the authors combined CHPED and OPF. Since fuel is changing daily in the current environment, using renewable energy sources to generate electricity economically is crucial. The renewable energy source like wind energy is integrated with thermal units for economic power generation with reducing the thermal fuel consumption of CHPED-based OPF system. The proposed technique implemented on CHPED based IEEE-30 bus system for renewable and without renewable energy sources with considering different cases. The suggested problem considering with valve point loading of thermal units, transmission losses and uncertainties of wind speed to address the non-linearity of the renewable-based CHPED-OPF system. Cost minimization, voltage deviation control, transmission losses minimization and stability index are the single objectives of the prospective system. Furthermore tested on multi-objective functions for simultaneously minimization of cost with emission and simultaneously minimization of active power loss with voltage profile. It is observed that the proposed CDTBO technique helps to reduce the cost by 2% and 12.8% for renewable based system as compared to non-renewable system for multi-objective function. The robustness of the proposed solution has been verified by implementing the statistical analysis on two systems with least variation of mean and optimal values of cost with the tolerance of less than 0.0035%. A comparison has been made with recent well known optimization techniques to address the superiority of the suggested CDTBO algorithm.

Backstepping Control of an AC/DC/AC Converter in a Grid Connected Wind Power System

The contribution of this paper is to study a nonlinear control approach known as the backstepping control method to manage reactive and active power using new parametric values for the grid connected wind power generation system. Specifically, the research focuses on a system containing a wind turbine system, a permanent magnet synchronous generator (PMSG), and a grid connected via AC/DC/AC converter. A comprehensive mathematical model for both the wind turbine system, PMSG, and the grid connected via AC/DC/AC converter was developed for this purpose. The Simulation results of the grid-connected wind power generation system are presented and analyzed to show the performance of the nonlinear backstepping control method. The characteristics of the response to variations in generator speed and the power injected into the grid show the highlight of the performance of the backstepping method, under varying wind conditions.

Finite Set-Model Predictive Control for Modified quasi Switched Z Source Level 3 Off-board Charger for Active Power Oscillations Damping in Weak Grids

Finite Set-Model Predictive Control for Modified quasi Switched Z Source Level 3 Off-board Charger for Active Power Oscillations Damping in Weak Grids

Reduction of Power Distribution System Losses by Using Novel Heuristic Algorithm

The Distribution Networks are the most widespread part of electrical power systems. Researcher has been trying to in the solutions to reduce high energy losses associated with them due to the low voltage level of the supply framework. Capacitors based techniques have been proposed to reduce these losses. These techniques minimize power losses, improve stability, and enhance the voltage profile of the distribution system under normal operating conditions. Finding optimal place and proper size of the capacitor is still a very hot area of research. In this research work, an innovative procedure based on the Archimedes optimization Algorithm (AOA) is presented to solve the optimal capacitor placement as well as sizing problem to improve the voltage profile and reduce the energy losses in the network. The suggested AOA based technique, is implemented on IEEE 33 bus network to find the optimal locations to place the capcitors in the distribution network. The performance of AOA is also compared with a standard model to verify the effectiveness of the scheme. By using AOA active power losses reduced upto 23.62% and reacrive losess by 21.86% respectively. The simulation results reveal that AOA based technique offer better results regarding the capacitor placement for voltage profile improvement of buses and significant reduction in active power losses as comapred to previous studies.

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant–grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG reactive power pricing model. The objective functions aimed to minimize the cost of reactive power purchased by generators, the price of active power network losses, the total deviation of node voltages, and the depreciation costs of discrete variable actions, thereby establishing a dynamic reactive power optimization model for power systems including doubly-fed wind farms. By introducing Logistic chaotic mapping, the CSA is improved by using the highly stochastic characteristics of chaotic systems, which is known as the Chaotic Cuckooing Algorithm. Meanwhile, the basic cuckoo search algorithm was improved in terms of adaptive adjustment strategies and global convergence guidance strategies, resulting in an enhanced cuckoo search algorithm to solve the established dynamic reactive power optimization model, improving global search capability and convergence speed. Finally, using the IEEE 30-bus system as an example and applying the improved chaotic cuckoo search algorithm for solution, simulation results show that the proposed reactive power optimization model and method can reduce reactive power costs and the number of discrete device actions, demonstrating effectiveness and adaptability. When the improved chaotic cuckoo algorithm is applied to optimize the objective function, the optimization result is better than 7.26% compared to the standard cuckoo search algorithm, and it is also improved compared to both the PSO algorithm and the GWO algorithm.

Modeling and control of a single-phase grid-tied medium-frequency isolated converter for active and reactive power management in photovoltaic applications

A single-phase photovoltaic converter formed by the full-bridge dc–dc converter with a capacitive output filter and a grid-tied full-bridge inverter is studied in this paper. The switched model, the average model based on a modified-conventional approach, and the linear state-space model of the dc stage operating in discontinuous conduction mode are presented and compared with the behavior of the state variables obtained from a simulator. A duty-ratio constraint is obtained from the modified average model, which is eliminated by expressing its dependency on the control and the state variables. In addition, the modeling of the ac stage is carried out in the dq reference frame. Linear controllers are designed to guarantee the maximum power point tracking, the regulation of the dc-link voltage, the injection of the active power generated, and the reactive power management as an ancillary function. The controllers proposed, according to the transfer functions derived from the linear models, are validated via simulation for a 1.41 kVA converter.

Model-Based Algorithm for Flexible Power Point Tracking for Photovoltaic Participation in Primary Frequency Regulation

Grid-connected photovoltaic (PV) systems are commonly designed for maximum energy production. However, as their presence grows, revised grid regulations increasingly require these systems to partially adhere to the primary frequency regulation (PFR) by reducing their power production when the grid frequency exceeds the nominal value. Nevertheless, full participation in PFR of PV systems, without battery storage, would require operating with a dedicated active power reserve to be utilized in the event of underfrequency conditions. This paper presents a model-based (MB) flexible power point tracking (FPPT) algorithm for PV systems. Results of a microgrid simulation show that the proposed algorithm enables a PV system with a nominal power of 100 kW to maintain a 20 kW active power reserve under various irradiation and temperature conditions, with a relative error lower than 3%. Furthermore, a simulation of the PV system assisting in primary frequency regulation is presented. Thanks to the proposed strategy, when employing the algorithm, the frequency deviation is reduced both in the event of underfrequency and overfrequency conditions. By enabling operation with a constant active power reserve, as well as a quick adjustment of the produced active power, in the event of frequency imbalance, the proposed MB algorithm is an interesting candidate for achieving complete PFR capabilities of PV systems.

Coordinated risk-averse distributionally robust optimization for maintenance and generation schedules of offshore wind farm cluster

The coordinated optimization of maintenance and generation schedules of multiple offshore wind farms (OWFs) bring great benefits in reducing the maintenance and operation cost of OWF cluster. Firstly, a two-stage coordinated risk-averse distributionally robust optimization (CRDRO) model for maintenance and generation schedules of wind turbines (WTs) in OWF cluster is proposed. This model takes into account uncertainties related to the prediction errors in WTs’ maximum available active power outputs, the travel time of maintenance vessels, and the high operational cost risks associated with these uncertainties. A feasible route generating algorithm is designed to obtain all feasible maintenance combinations for WTs and the corresponding routes of maintenance vessels for the CRDRO model. The risk-averse term, which is a weighted sum of expected operation costs and GlueVaR value, is used to measure the tail risk of the worst probability distribution. To solve the two-stage CRDRO model, the risk-averse term is simplified into the form of expected value calculation, which can be further transformed into the tractable form by using the strong duality theorem. Finally, the two-stage CRDRO model is solved by the column-and-constraint generation algorithm, with the nonconvex sub-problem being addressed by the outer approximation algorithm. Case studies on an real-world cluster comprising three OWFs verify the correctness and efficiency of the proposed method, and demonstrate that the CRDRO model enhances the overall economic efficiency and reduces the high operational cost risk under extreme scenarios.

Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm

To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.

Optimization of energy efficiency for offshore wind farms via wake modeling-free NMPC

This paper presents an energy efficiency improvement scheme for offshore wind farms. This scheme uses learning-based nonlinear model predictive control (NMPC) and does not require wake modeling. The main feature is that each wind turbine can independently complete all optimal control, without the need for wind farm wake model calculation and iterative optimization. Specifically, this paper studies the critical factors for increasing the active power of wind farms and reducing the structural load of wind turbines, and reconstructs the optimal control objectives of wind turbines. Furthermore, combined with the nonlinear model of wind turbines containing uncertainty, a nonlinear model predictive control algorithm based on deep neural networks was designed, which can achieve multi-objective optimal control at the wind turbine level. The simulation results show that the uncertainty estimation method can effectively improve the nonlinear control performance, achieve an increase in the active power of the wind farm without wake iterative optimization, and at the same time suppress the structural loads of all wind turbines.