Deterioration Of Power Quality Research Articles

Robust Assessment of Hosting Capacity of Renewable Generation in Distribution Systems Considering the Active Network Management

Abstract As the penetration rate of renewable distributed generation (RDG) increases, its random fluctuation characteristics have brought many problems to the safe operation of the active distribution network (ADN), such as voltage over-limit, reverse flow, and deterioration of power quality. Therefore, it has effectively affected the new energy hosting capacity of the ADN. Therefore, the effective assessment of the new energy hosting capacity of the ADN is a key part of the distribution network planning. In order to cope with the random fluctuation characteristics of new energy, this paper presents a two-stage robust assessment method of RDG hosting capacity of distribution networks by considering active network management (ANM) techniques such as network reconfiguration, reactive power compensation and on-load voltage regulation. Finally, the improved IEEE-33 distribution system is used to verify the results. The proposed method has been shown to be effective for evaluating the ADN’s RDG hosting capacity.

Tri-Level Cascading Control to Augment Power Quality Concerns in Distribution Networks

Power distribution systems have been affected by harmonic problems for decades. Harmonic currents can affect line voltage and direct to numerous adversative results including equipment overheating, failure of solid-state equipment, and restraint with communication systems. They deteriorate power quality by causing poor power factors and increasing total harmonic distortion. To reduce harmonics and to improve power quality, a grid-tie active power filter (APF) is introduced which eliminates both lower and upper-order harmonics. In this proposed paper, APF is used along with an LCL filter with versatile controlling techniques, Clarke’s and Park’s transformations are implemented in the paper. The proposed scheme can fulfill the problem faced in the power system due to non-linear loads. The results are simulated and validated using MATLAB/Simulink environment.

Construction and Optimization of Power Quality Risk Spillover Trend Prediction Model Based on Deep Learning

This study aims to establish and refine a deep learning-based predictive model for power quality risk spillover trends, thereby addressing the challenges posed by the escalating diversity and adaptability of power systems in the evolution of the renewable energy industry. Initially, the study introduces the concept of power quality risk spillover, referring to the influence exerted by power quality issues in one region on other regions. This influence initiates a chain reaction, ultimately leading to a more widespread deterioration in power quality. Then, the advantages and applications of the deep learning model in power quality risk spillover trend prediction are expounded. During the model development process, we employed deep learning algorithms such as the multilayer perceptron and extended short-term memory network, tailored specifically to the unique characteristics of power quality data. To enhance the model's predictive accuracy and stability, we implemented optimization strategies encompassing data preprocessing, feature engineering, and model integration. Our experimental findings demonstrate that the optimized model exhibits remarkable precision and stability in anticipating power quality risk spillover trends, thereby offering robust support for risk management and ensuring the stable operation of power systems. This study provides a valuable exploration and practice for the application and development of a power quality risk spillover trend prediction model based on deep learning.

A deep dive into enhancing frequency stability in integrated photovoltaic power grids

Voltage control strategies (VCS) and frequency stability analysis (FSA) are essential for power system reliability, particularly during high-load periods. Stable voltage and frequency levels prevent malfunction, power quality deterioration, and supply interruptions. Grid operators must skillfully manage VCS and FSA control to ensure system stability. Nonlinear loads, especially under transient conditions, significantly affect voltage stability (VS), introducing harmonics, waveform distortion, and stability complexities. Accurate modeling of these nonlinear loads is vital when traditional static load models fall short. Frequency fluctuations from power generation-demand imbalances require vigilant monitoring and regulation. Effective frequency control mechanisms are indispensable for preserving desired frequencies. Using a Western Algeria case study, this paper underscores FSA's significance in integrating photovoltaic (PV) systems into power grids. It addresses challenges from frequency fluctuations due to dynamic ZIP load profiles, emphasizing the importance of FSA for reliable grid operation. The study offers insights and practical approaches to enhance VS, FSA control, and energy management (EM), improving grid reliability and ensuring uninterrupted power supply. We must look into FSA's benefits in integrating PV systems to improve performance and lower grid interruptions. This includes looking into its control mechanisms and feedback systems.

Emergency Prevention Control as a Means of Power Quality Improvement in a Shipboard Hybrid Electric Power System

The problems associated with the greenhouse effect have increased the desire to limit carbon dioxide (CO2) emissions into the atmosphere, including emissions produced by shipboard electrical power systems. This has led to a further search for solutions in this area, such as shipboard hybrid electric power systems (SHEPSs). These systems do not yet have a wide application compared with traditional shipboard electrical power systems for several reasons, including the lack of studies establishing the fault tolerance of such systems. Therefore, in this work, problems related to generated power quality deterioration under large disturbances are studied. To achieve the fault-free operation of SHEPS, an emergency prevention control (EPC) system based on controlled parameter forecasting, along with a system structure and operation algorithm, is developed. The goals of improving electrical power quality are achieved by increasing the control efficiency of the power system’s generating sources. To validate the feasibility of the proposed control system, a computer simulation was carried out after developing a mathematical model of the SHEPS under study. The results of the study show that the use of the proposed EPC system will improve power quality when the controlled parameters are within acceptable limits. At the same time, further research is needed, as the problem of false control action as a consequence of EPC system hardware or software faults remains unstudied.

Identification of composite power quality disturbances based on relative position matrix

With the integration of large-scale nonlinear loads and distributed power sources into the grid, composite power quality disturbances (PQDs) events are becoming increasingly common, which significantly degrade the quality of power supply. Therefore, this paper focuses on studying the accurate classification of composite PQDs to mitigate the risk of power quality deterioration. However, traditional classification methods perform barely satisfactory in terms of accuracy and robustness in the classification of PQDs. To address these issues, this paper proposes a method for recognizing composite PQDs based on relative position matrix (RPM). Initially, utilizing the RPM method, the initial one-dimensional PQD time series data is transformed into two-dimensional image data while preserving its high-frequency characteristics. This process results in the creation of an informative and feature-rich image training set. Subsequently, an end-to-end framework for PQDs classification was developed. The framework utilizes convolutional neural networks to automatically extract multi-scale spatial and temporal features from image data. This design aims to automate the classification of composite PQDs, eliminating the need for labor-intensive manual signal processing and feature extraction. This integration ensures a more accurate and robust classification. Finally, the proposed method is tested on a case involving 30 types of PQDs at varying noise levels and compared with existing power quality disturbance classification methods, and results show that the proposed method has better performance than the previously established methods.

Power Distribution Systems’ Vulnerability by Regions Caused by Electrical Discharges

Energy supply interruptions or blackouts caused by faults in power distribution feeders entail several damages to power utilities and consumer units: financial losses, damage to power distribution reliability, power quality deterioration, etc. Most studies in the specialized literature concerning faults in power distribution systems present methodologies for detecting, classifying, and locating faults after their occurrence. In contrast, the main aim of this study is to prevent faults by estimating the city regions whose power grid is most vulnerable to them. In this sense, this work incorporates a geographical-space study via a spatial data analysis using the local variable electrical discharge density that can increase fault risks. A geographically weighted spatial analysis is applied to data aggregated by regions to produce thematic maps with the city regions whose feeders are more vulnerable to failures. The spatial data analysis is implemented in QGIS and R programming environments. It is applied to the real data of faults in distribution power grid transformers and electrical discharges in a medium-sized city with approximately 200,000 inhabitants. In this study, we highlight a moderate positive correlation between electrical discharge density and the percentage of faults in transformers by regions in the central and western areas of the city under study.

Investigation of harmonics analysis power system due to non-linear loads on the electrical energy quality results

With the increase in the demand for electrical energy, more reliable and better quality energy is needed. This concept has taken its place in the field of electrical engineering with the title of "power quality". Today, power electronics elements, transformers, arc furnaces, converters, etc. used in industry, harmonic emitting elements have caused deterioration of power quality and as a result, it has led us to ways to get rid of harmonics. In order to provide quality electrical energy, some criteria such as continuity of energy, stability of voltage and frequency, proximity of power factor to 1, balance of phase voltages and harmonic amounts in voltage should be taken into consideration. In this study, the energy quality of different units in Giresun University Güre Campus was investigated with CA 8333 energy analyzer. The results obtained were transferred to the computer environment with the help of graphics. In order to analyze the harmonics and inter harmonics in the power system and to see the effect on the system, series active power filter is modulated and simulated against different nonlinear loads in MATLAB/Simulink program. The results are shown with the FFT (Fast Fourier Transform) analysis program to see the elimination of harmonics in the system and their response to filtering. The results of harmonics in the power system with nonlinear loads before and after filtering are analyzed and examined. As a result of the analysis, suggestions are made regarding the measures that can be taken.

EXPLORING CHARGING-DISPATCH APPROACHES AND VEHICLE-TO-GRID TECHNOLOGIES FOR ELECTRIC VEHICLES IN DISTRIBUTION NETWORKS

Various electric vehicle charging and discharging strategies (EVs) and V2G technologies are discussed in this article as their impacts on energy distribution networks. The V2G application that can be used on vehicles offers many benefits, as demonstrated. Features such as active power regulation, reactive power support, load balancing, and current harmonic filtering are incorporated into this technology.Although V2G technology has many benefits, there are also several challenges. These challenges include reduced battery life, communication overhead between EVs and grids, and changes in distribution network infrastructure. The article briefly discusses the effects of electric vehicle penetration levels,charging profiles, and various other aspects of controlled charging and discharging from a performance perspective. This includes over loading, deteriorating power quality, and power loss. A comprehensive analysis of controlled and uncontrolled charging–discharging methods, delayed charging discharging methods, indirect controlled discharging methods, bidirectional charging–discharging methods, and intelligent scheduling is presented in this study. Several challenges and issues regarding electric vehicle applications are discussed from an aggregator’s perspective. Analysis shows that Li-ion batteries can be recharged 2000–4000 times, and a mass-produced Li-ion battery costs $200–$500 per kWh.Degradation costs of batteries at 80% discharge depth are estimated to be $130 per MWh at 300 kWh investment cost. 10% of peak capacity could come from PEVs in the 20% range. Around 87.5% of PEVs are properly charged. Key Words: Electric vehicles(EV),penetration,overloading, Communication,Deteriorating,Degradation.

Optimization of Algorithm for Solving Railroad Power Conditioner Compensation Power Reference Value and System Power Quality Analysis Based on Optimal Compensation Model

At present, electrified railroads with complex road conditions are facing the problems of the existence of a single power supply method, the deterioration in power quality, and the difficulty in recycling regenerative braking energy. In order to improve the above problems, this paper establishes a traction power supply system containing photovoltaic units, proposes an optimized compensation model for the RPC (railroad power conditioner), and improves the particle swarm algorithm for solving the reference value of RPC compensation power. First, the structure of the RPC-based traction photovoltaic power generation system and the establishment of the integrated energy management strategy of the system are constructed. Then, the back-to-back converter compensation model with power quality index parameter constraints is established, which establishes the optimization function with the objectives of minimizing the negative sequence current, maximizing the power factor, and minimizing the RPC compensation power, as well as establishes constraints on the active converter capacity and three-phase voltage imbalance. Then, the particle swarm algorithm for solving the RPC compensation power reference value is improved, specifically in the original PSO by introducing dynamically changing inertia weights and learning factors. This not only solves the problem of the single power supply and realizes the nearby consumption of photovoltaic units in the traction system, but also realizes the recycling of regenerative braking energy and the coordinated control of the traction photovoltaic power generation system, improves the power quality of the system, and meets the demand of the RPC for real-time control. In order to verify the effectiveness of the optimized compensation model established in this paper and improve the convergence of the particle swarm algorithm for solving the RPC compensation power reference value, a simulation model of the traction PV power generation system is established in MATLAB/Simulink, and a real-time simulation is carried out to verify it based on the preset working condition data. The results show that the RPC optimization compensation model developed in this paper can coordinate the control system energy flow and improve the system power quality (the power factor increases and the negative sequence current decreases). The improved particle swarm algorithm is more convergent.

Analysis Of Power Quality Parameters and Devices for Their Measurement

Purpose. The main objective of this study is to assess the phenomena that affect the parameters of power quality, to consider ways to counteract the deterioration of power quality, to assess the effect of electromagnetic interference on the human body, and to review the available devices for measuring the parameters of electric energy. Methodology. To obtain relevant data, the authors conducted a literature review on the topic of the work using full-text and abstract databases. The main causes of electromagnetic compatibility (EMC) violations and ways to counteract these violations are considered. The main parameters of electricity quality, conditions for their measurement and permissible ranges according to the State Standards of Ukraine (SSU) are highlighted. A review of devices for measuring the parameters of electricity quality and the search for a suitable one that meets Ukrainian standards was carried out. Findings. The authors have proved: 1) the problem of measuring electricity quality parameters is relevant and important for its accurate accounting; 2) control of electricity quality parameters and compliance with state standards allows avoiding negative impact on the electricity supply system; 3) the use of modern digital measuring devices provides greater measurement accuracy than analog ones, and they are able to take into account more parameters in one measurement. Originality. The authors conducted a study in the field of electromagnetic compatibility in the field of electromagnetic compatibility using a digital device that gives more accurate and reliable results of power quality parameters. Practical value. Based on the results obtained, it is possible both to correct the personal research of individual scientists or teams of scientists and to predict further prospects for the development of the subject area «Electromagnetic Compatibility» in traction power supply systems in railway transport. The research can also be useful in the study of the disciplines «Electromagnetic Compatibility of Railway Automation Systems» and «Electrical Circuits and Lines of Railway Automation», organization of scientific and practical seminars, advanced training courses, etc.

Comparative analysis of control techniques using a PV-based SAPF integrated grid system to enhance power quality

The deterioration of power quality due to the increased adoption of Power Electronic Switching Devices is a major concern for electrical engineers. In addition to industrial loads, household loads cause numerous disturbances on the utility side, such as sags, ripples, flickers, and harmonics. It is possible to create power quality problems in a system or to have them arise due to a faulty consumer. This paper implements a renewable energy system with a Shunt Active Power Filter (SAPF) that injects PV energy into a power grid in the presence of nonlinear loads and under various load conditions to maintain power quality and improve power grid capacity. The Perturb and Observe (P&O) algorithm adjusts boost converter duty cycles to track the Maximum Power Point (MPP). The boost converter output is connected to the grid via a SAPF. The effectiveness of SAPF in reducing total harmonic distortion has been investigated using various reference current generation strategies of control methods and improved maximum power factor. The voltage source inverter switches are controlled through hysteresis current control. Moreover, this paper explains how to regulate the dc link capacitor of a three-phase SAPF with PI, fuzzy, and Adaptive Fuzzy Neuro Inference System (ANFIS) controllers to enhance power quality and compensate for reactive power and harmonic current caused by nonlinear loads. This system is developed under balanced and unbalanced nonlinear loads in MATLAB-Simulink. Furthermore, SRF-based reference current generation techniques with ANFIS controller to achieve better performance tested for control performance in proto-type hardware model to validate the proposed performance of this system.

Integration of PV Sources in Prosumer Installations Eliminating Their Negative Impact on the Supplying Grid and Optimizing the Microgrid Operation

This paper concerns the mitigation of voltage disturbances deteriorating power quality and disrupting the operation of LV distribution grids due to the high penetration of PV energy sources in prosumer installations. A novel control strategy for 3-phase 4-wire PV inverters is proposed, which ensures the transmission of PV active power and simultaneous compensation of load unbalance and reactive power, making the prosumer installation balanced and purely active. It results in the balance of phase voltages and the mitigation of their variability. Unlike other methods used for voltage regulation in LV grids, the proposed solution contributes to the reduction in losses, is simple, and does not require additional costs. In the paper, a control algorithm for the PV inverter is described. Its effectiveness was tested by simulation using a model of the real LV distribution grid developed in the PSCAD/EMTDC program. The results of the simulations are presented and evaluated.

MODELING AND ANALYZING THE EFFECT OF CONNECTION TO THE NETWORK OF A HARMONIC SOURCE HAVING VARIOUS TOTAL HARMONIC DISTORTION FACTORS ON LOAD SIGNAL WAVEFORMS

This article examines the effect of a network connected source of harmonics having a total harmonic distortion factor varying from 5% to 15% on load voltage and current waveforms. When a source of higher harmonics is connected to the network, both in the network and in the load, the effective values of voltage and current increase, that can negatively affect the cable line insulation, accelerating its destruction and aging.
 To analyze the consequences of a power quality deterioration, a 20 kV network was simulated, consisting of a 20 kV symmetrical generator, an XRUHAKXS-20(1x120/50) power cable line 20 km long, a step-down transformer 20/0.4 kV with a power of 2 MVA, with windings connected in delta-star, and a three-phase symmetrical load.
 The values of the currents flowing through the cable conductor, obtained as the result of simulation were used to calculate the voltage drop between the cable conductor and its shield. Results obtained show that the connection of a harmonic distortion source to a network leads to a magnification of a current flowing through the cable conductor by more than 2%. The model proposed in the article can be used further for a more detailed study of solar photovoltaic plants connection to the grid.
 One of the biggest problems regarding solar power plants is that its electricity generation is intermittent. Thus, future efforts should be focused on modeling and studying the higher harmonics generation during switching on and off of the solar photovoltaic plants.

A Unified Open-Circuit-Fault Diagnosis Method for Three-Level Neutral-Point-Clamped Power Converters

Fast and accurate fault diagnosis is important to enhance the reliability of power converters. Since both open-circuit faults in insulated gate bipolar translator (IGBTs) and diodes deteriorate power quality, diagnosis methods only focusing on IGBT failures are easily disturbed by diode failures. In this work, we propose a unified effective residual-based open-circuit fault diagnosis method for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> semiconductor devices including IGBTs, clamping diodes, and freewheeling diodes in three-level neutral-point-clamped power converters. The voltage and current residuals are calculated from the mismatches between the actual current path and the estimated one, requiring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">solely</i> the already existing current measurement of the system. By analyzing the variation of the voltage and current residuals in all open-circuit failure cases, the proposed diagnosis method can locate the faulty switch within several sampling periods, i.e., less than 1 ms. Finally, the proposed diagnosis method is incorporated in the model predictive current control framework. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Both</i> experimental <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> hardware-in-the-loop results confirm the effectiveness and robustness of the proposed diagnosis method at various operation scenarios.

Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads

The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS–MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS–MVO framework is tested and validated by estimating the harmonics present in multiple test signals with different noise levels. Amplitudes and phases of harmonics present in the polluted signal were estimated, and the framework computational time was compared with the previously developed technique’s results which are reported in the literature. There was 80% reduction in computational time and 82% improvement in terms of accuracy in estimating harmonics using WLS–MVO as compared to previously developed techniques. The performance of the developed framework is further validated by estimating the harmonics present in the real-time voltage and current waveforms obtained from axial flux permanent magnet generator (AFPMSG), uninterruptible power supply (UPS), and light-emitting diode (LED). The purposed technique technique outperforms the already-developed techniques, in terms of accuracy and computational time.

ВИКОРИСТАННЯ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ ПРОГНОЗУВАННЯ ВИРОБЛЕННЯ СОНЯЧНОЇ ЕНЕРГІЇ НА ФОТОЕЛЕКТРИЧНИХ ЕЛЕМЕНТАХ

Purpose. Development of an artificial neural network for modeling time series of forecasting electricity generation taking into account the use of a renewable energy source using MATLAB&amp;Simulink software. Methodology. Theoretical and experimental research with the help of software implementation of fundamental knowledge in the field of electricity generation using renewable energy sources; simulation of artificial neural network prediction; methodology of mathematical statistics and forecast analysis. Results. Based on the conducted research and implemented solutions, this article developed a time series modeling system for forecasting solar energy production using Simulink. Defined control parameters and corresponding time series; the analysis of the input data showed the influence on the generated electrical energy of such meteorological parameters as solar radiation on the surface of the earth and on the surface of the panel, temperature of the panel and the environment, cloud cover and time of day. In order to ensure the appropriate accuracy of forecasting the generated power, there was a task to ensure the accuracy limit of forecasting meteorological parameters. Scientific novelty. The novelty of the given research lies in the formation of the balance of electricity in the power system with the help of an artificial neural network, influencing the choice of mode parameters and calculated loads. The balance between production and consumption of electricity is a fundamental aspect of the technological sustainability of the energy system. Violation of this balance can lead to deterioration of power quality, including changes in frequency and voltage in electrical networks, which can affect the efficiency of equipment. Practical significance. Neural network forecasting allows effectively distribute the load between different objects of the power system. This makes it possible to manage electricity consumption by adjusting the load of equipment. For example, the main electricity generation can be shifted to hours and zones of the wholesale electricity market with lower prices to reduce the cost of electricity generated from renewable energy sources.

Status and Prospect of Islanding Detection in Active Distribution Network

A large number of distributed power sources are injected to the distribution network, which can cause island operation in planned or unplanned situations. The islanded operation caused by the fault will cause the inverter to go off the grid in serious fault conditions such as short circuit between phases and disconnection. After the island is formed, if the common connection point before the island exchanges more power, a sudden change in the voltage within the island occurs. The frequency and phase angle changes of the voltage in the island are closely related to the characteristics of the inverter phase-locked loop, and the frequency mutation generally does not cause sudden changes in frequency due to a certain inertia of the phase-locked loop. Due to sudden changes in load power angle, input fault ride-through, etc., sudden changes in the phase angle of voltage in islands may occur. In order to solve the possible problems of power quality deterioration in islands, passive, active, and remote islanding detection schemes can be adopted, and the three schemes have their own advantages and disadvantages. In actual operation, it is necessary to consider the cooperation relationship between island detection, protection, safe automatic control and fault ride through.

An Automatic Identification Framework for Complex Power Quality Disturbances Based on Ensemble CNN

A large number of electric vehicles (EVs) are connected to the grid, increasing the risk of power quality deterioration. Meanwhile, power quality disturbances (PQDs) directly affect EV charging safety. Intelligent identification of complex PQDs is the basis for solving the power quality problem, which is very meaningful for improving EV charging quality. This paper proposes an automatic recognition framework for complex PQDs based on ensemble convolution neural network (ECNN). Firstly, a multifusion structure on account of the time and frequency domain feature of PQDs signals is introduced. In addition, a composite convolution is proposed to reduce network complexity, which is using the standard convolution and depthwise separable convolution. Then, we design an adaptive-context mechanism to extend the versatility of ECNN. At the same time, the need to use batch normalization to accelerate training convergence and prevent training overfitting is verified. Furthermore, some visualization methods are performed to analyze the inner mode and illustrate the working mechanism of ECNN. Finally, we apply various experiments to prove the effectiveness of ECNN. Compared to other advanced deep neural networks and traditional methods, ECNN has better noise resistance, higher accuracy, and lower training cost.

Analiza kvaliteta električne energije na satnom nivou kod priključenja elektrana na obnovljive izvore energije

Analyses necessary for the new power plant connection contain the following power quality analyses carriedout using the simulation model: Estimation of the emission level of the voltage waveform asymmetry at the point of connection, estimation of the high harmonic components at the connection point and estimation of the emission level of long-term and shortterm flicker. Limits for the previously mentioned parameters have been defined in the National Grid Code (at the national level) or in the international standards. According to the international standards, the limit is considered violated if the duration of the parameter value violation exceeds 5% of the monitored period. Conventional approach of power quality analysis through software tool assumes only analysis for the most critical regime: when the short-circuit power at the point of connection is the lowest and the new power plant produces maximum active power. Based on this approach mitigation measures for improving power quality supply are sometimes suggested to the plant investor. In should be noted that Renewable Energy Sources (RES) do not have maximum output power during the whole year, and consequently, power quality deterioration is much less than in the critical regime observed in the simulations. For power quality simulation purposes, technical sheets of the equipment contain high harmonic levels for the full operation range are used from which usually the worst (highest) harmonic injections are considered for modelling RES as a harmonic source. In this paper, the results of power quality analyses for the RES connection using hourly resolution data for the one-year period based on DPL (DIgSILENT Programming Language) in DIgSILENT PowerFactory software package will be presented. This approach gives the duration of the limit violation, and consequently, more reliable results in comparison with the results of the conventional approach.