Poor Power Quality Research Articles

Impact of Power Quality on the Efficiency of the Mining Process

There are currently more than 30 underground mines operating in Poland. These are mines extracting hard coal, salt, and metal ores. Each of these plants has its own specifics for operation, but all operate under the same regulations. The basic principle is to ensure the safety of the crew and equipment. The progressive mechanization and automation of the mining process results in the installation of power electronic converters in the networks of mining plants, which significantly deteriorate the quality of the power in the plant supply networks. In addition, the constant reconfiguration of these networks related to the progress of the work can affect the safety conditions of the plant. This article describes problems occurring at one underground mining plant that are related to the structure of the power grid. Failures and interruptions in the production process were the result of poor power quality. They directly translated into increased production costs and significantly affected the safety level of the workforce, which could result in further consequences, not only in the financial sphere. The article also addresses the issue of existing legal regulations, the provisions of which may be insufficient in assessing the current state of power quality in mining plants.

Improved Control Strategy for Dual-PWM Converter Based on Equivalent Input Disturbance

Aiming at the problems of jittering waveforms and poor power quality caused by external disturbances during the operation of a dual-pulse-width-modulation (PWM) converter, an improved terminal sliding mode control and an improved active disturbance rejection control (ADRC) are investigated. The method is based on mathematical models of grid-side and machine-side converters to design the controllers separately, and the balance between the two sides is maintained by the capacitor voltage. An improved terminal fuzzy sliding mode control and equivalent input disturbance (EID)-error-estimation-based active disturbance rejection control are presented on the grid side to improve the voltage response rate, and an improved support vector modulation (SVM)–direct torque control (DTC)–ADRC method is developed on the motor side to improve the robustness against disturbances. Finally, theoretical simulation experiments are built in MATLAB R2023a/Simulink to verify the effectiveness and superiority of this method.

Passive Islanding Detection of Inverter-Based Resources in a Noisy Environment

Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup.

LCL-T Module Reduction of Offshore Photovoltaic Power Generation System Based on Current Feedback-routh Approximation Method

The rapid development of offshore photovoltaic power generation has attracted more and more attention. Aiming at the problem of poor output power quality caused by the randomness and intermittentness of offshore photovoltaic power generation, LCL filters and isolation transformers are widely used on the AC side of photovoltaic power generation systems to improve the power quality of Grid-connected. Although this method has a good suppression effect on harmonics and DC components, its higher order will result in a larger amount of calculation, especially in the simulation of large-scale photovoltaic grid-connected systems, the operating speed will be too slow. In order to increase the simulation rate while ensuring the quality of the output power, the high order LCL-T (LCL filter and isolation transformer) module of the photovoltaic power generation system is reduced in order. When the transformer is not saturated, the LCL-T module reduction method based on the weighted current feedback method is adopted, and the purpose of simplification is achieved through the zero-pole cancellation of the transfer function; When the transformer is saturated, the LCL-T module reduction method based on the Routh approximation method is adopted, Use advanced mathematics formulas to reduce the order of high-order system equivalent processing. The PSCAD/EMTDC simulation software verifies the off-grid characteristics and simulation rate of the reduced-order model.

Three-Dimensional Transient Electric Field Characteristics of High Pressure Electrode Boilers

An uneven electric field during the operation of an electrode boiler will lead to the emergence of a high field strength area and low field strength area in the furnace, which will endanger the safe and reliable operation and heating efficiency of the electrode boiler. A numerical study of three-dimensional transient electric field distribution characteristics in a 10 kV high-voltage electrode boiler was carried out. The distribution characteristics of the global electric field of the electrode boiler under the nominal voltage of 10 kV were studied, and the distribution law of the electric field of the electrode boiler under poor power quality, such as different bus voltage and three-phase voltage imbalance, was explored. The results show that the electric field distribution characteristics of the three-phase transient are more obvious in the section closer to the electrode disc, and the electric field distribution is the most uniform in the section that is 1.4 m away from the furnace water. In the case of poor power quality, such as different bus voltage and three-phase voltage imbalance, the points of the maximum electric field intensity of the four surfaces change periodically with time, and the greater the bus voltage fluctuation, the more severe the impact on the transient electric field. The three-phase voltage imbalance will shift the peak value of the electric field intensity. The decrease or offset of electric field intensity in the electrode boiler caused by poor power quality will directly affect its heating efficiency. The electric field simulation results have a specific reference value for improving the internal electric field distribution and on-site operation and maintenance of the electrode boiler.

Development of an IoT smart energy meter with power quality features for a smart grid architecture

Electricity consumption has been intensifying due to population growth, climate change, urbanization, and the growing use of electronic devices, which are increasingly non-linear loads that cause poor power quality conditions. The trend of the Internet of Things has led to the creation of devices that encourage the efficient and effective utilization of electrical power. This in turn facilitates the development of modern power distribution structures such as smart grids. Consequently, this paper presents in detail the design, construction, and validation of a three-phase IoT smart meter intended to form part of the end-user demand side of a smart grid. The compact embedded system, with a manufacturing cost below $80 USD, features a unique electronic design that enables its installation in any load center and employs a straightforward IoT structure that includes WiFi technology for Internet communication. Also, a deployed web application was developed specifically to display the smart meter measurements. Unlike other smart meters, the proposed meter not only provides the amount of active energy consumption, but total and fundamental RMS current and voltage, active, reactive, and apparent power, reactive energy, power factor, and some power quality parameters such as, line frequency, amplitude of 64 current harmonics, and total harmonic distortion. Additionally, this study shows that the prototype achieves an absolute error of less than 1% in all its measurements. Finally, real-life applications of the developed device are demonstrated in residential environments.

Harmonic mitigation using optimal active power filter for the improvement of power quality for a electric vehicle changing station

The idea of electric vehicles (EVs) is relatively new in the transportation industry. EVs are becoming increasingly popular due to many advantages. Apart from various advantages, some drawbacks of these vehicles are also a concern. One of the concerns for these vehicles is the substantial power requirement to charge the batteries. During charging, EVs deteriorate the power quality by injecting harmonic current into the system. As poor power quality is undesirable, the mitigation of this harmonic current is essential. Therefore, in this study, an optimal design of a shunt active power filter (SAPF) based EV charging system is suggested. The optimal design of the SAPF ensures that the DC-link voltage is efficiently regulated. To test the performance of the suggested scheme, a simulation system is developed and tested in MATLAB Simulink. The results show that the developed optimal control can efficiently regulate the DC-link voltage. The results, pre and post-compensation, show that the total harmonic distortion in the system is reduced to 4.33 % from 26.72 % using the suggested approach. This is even better by 12.93%, 7.39%, 14.32% and 20.32% in comparison to particle swarm optimization, whale optimization algorithms, bee colony optimization and ant colony optimization used for comparison.

Improving Power Quality Using D-STATCOM with Capacitor as Energy Storage System

Power electronics are used more frequently to raise awareness of power quality which must be considered when designing modern power systems for effective operation and continuous power supply to various load centers. Otherwise, it will be hazardous for residential and commercial customers, reducing the production of their work due to faults and equipment damage. Electronic technology known as Flexible AC Transmission System (FACTS) may improve power stability to make the electric power system efficient. Oscillation damping in the Power system is still a big concern for operating a safe and dependable power system. The static synchronous compressor (STATCOM) is connected to assist power networks with poor power quality and voltage regulation. Although there are several uses, voltage stability is the most frequently used. An average model of the D-STATCOM control approach is presented in this work to improve the transmission of power quality flow and voltage stability in electric power systems. An increase in system voltage stability, reactive power flow control for different loads, and dynamic response of STATCOM are the primary goals of this research work.

Analysis of Remote Islanding Detection Methods for Distributed Resources

Penetration of distributed generation in the power grid is increasing. This situation leads to new possibilities but also new issues, such as anti-islanding protection. During a loss-of-mains or islanding situation, distributed generation (DG) units remain feeding a part of the electrical network without connection to the main power system. Currently, DG units have to be disconnected from the grid during islanding as soon as possible, due to potential risks for repair crews and components, poor power quality in the island and reclosing problems. A fast and reliable islanding detection method could avoid mentioned issues. Among the many existing methods, remote schemes could meet these requirements. Hence, this paper presents the operating principles of remote islanding detection methods, as well as the current state of development of communication-based remote islanding detection methods to be applied in distributed power generation systems.

Application of Advanced Soft Computing Techniques for Power Quality Improvement Using Active Power Filters in Radial Distribution System

This paper investigates the application of advanced soft computing techniques: northern goshawk optimization (NGO) and coati optimization algorithm (COA) for improving power quality (PQ) using active power filter (APF) in radial distribution systems (RDS). While beneficial, the growing adoption of solar photovoltaic (SPV) systems introduces power quality concerns like harmonic distortion due to their nonlinear nature. This distribution generator (DG) type is called nonlinear DG (NLDG). Harmonics is the leading cause of poor PQ. This study considers nonlinear loads (NLs) at two end nodes and nonlinear DG (NLDG) integrated into the RDS. APFs are optimally placed to reduce the harmonics and enhance PQ. The proposed approach utilizes soft computing techniques to minimize the APF current while adhering to inequality constraints. The NGO, inspired by natural processes, is employed for optimal APF sizing. This method prioritizes a balance between exploration and exploitation for efficient searching. The effectiveness of NGO is evaluated through simulations on the IEEE-69 bus RDS and compared with another recent soft computing technique, the COA. Here, four different cases are considered: a) only NL+NLDG, b) APF at bus 27, c) APF at bus 35, and d) APFs are at 27 and 35 buses. These cases are considered to analyze the effect of placement and sizing of APFs on PQ in RDS. The results validate the stability and efficacy of NGO in addressing this optimization problem for PQ improvement in RDS.

Analysis of Distribution Static Compensator Control Strategies for Mitigating Voltage Dip Impact on Distribution Network

Voltage dip, over-voltage, load unbalanced, and current and voltage harmonics distortions are the key characteristics of poor power quality (PQ) issues on the power distribution network with a significant negative impact. The performance of a custom power device, distribution static compensator (D-STATCOM), in reducing current total harmonic distortion (THD) during the mitigation process of voltage dip with fault is investigated in this study. To control the load side voltage, the D-STATCOM utilizes a three-phase voltage source converter and is coupled at the point of common coupling (PCC). To mitigate voltage dip effects, this study implements and compares the effectiveness of the conventional PI controller with an intelligent optimization-based PI controller using the dynamic gravitational search algorithm (DGSA). The performance of these controllers is validated by the MATLAB/Simulink simulation results obtained. Analysis of the results demonstrates that D-STATCOM operates flawlessly with an intelligently optimized PI control strategy reducing the current THD from 11.68% to 3.74%.

Evolutionary and Swarm Based Optimization of Fit k-Nearest Neighbor Classifier for Classification of Power Quality Disturbances

In the electric network, integration of renewable energy sources and switching in large industrial loads and non-linear loads result in Power Quality Disturbances (PQDs). PQDs arise malfunctioning the sensitive equipment integrated with power grid; therefore, it is necessary to overcome the effects of poor power quality (PQ) in electric power supply. It is possible to monitor PQ with the advancement of signal processing and artificial intelligence automatically. In this paper, techniques of automatic detection and classification of PQDs are proposed using discrete wavelet transform–multi-resolution analysis (DWT–MRA) and evolutionary and swarm intelligence algorithms in renewable integrated power grid. Fit k-nearest neighbor (KNN) classifier along with DWT–MRA technique is used to classify the PQDs and it is optimized using evolutionary and swarm intelligence algorithms like, genetic algorithms, particle swarm optimization, and grey wolf optimization. Performance of algorithm is compared using accuracy of classification and confusion matrix. Further robustness of classifier is tested with Simulink model of IEEE bus test system that indicated the renewable integrated power grid environment.

Techno-economic analysis of reactive power management in a solar PV microgrid: A case study of Sunyani to Becheam MV feeder, Ghana

Solar photovoltaic (PV) electricity offers a promising solution for the increasing demand for clean and sustainable energy, particularly in developing countries like Ghana. Power utility operators maintain power quality as their utmost priority per benchmarks established by regulators. Some researchers argue that increasing variable renewable sources in the grid compromises its stability, leading to poor power quality. This paper aims to evaluate the suitability of reactive power management (technical aspect) in a solar PV microgrid in meeting regulators' power quality benchmarks. It also presents the economics associated with reactive power management. The study uses Python optimisation algorithms to achieve the technical objective. The net present value (NPV), profitability index (PI) and discounted payback period (DPP) are the econometrics used to assess the economic viability of reactive power management in this study. The results show that optimising reactive power in microgrids reduces power losses, increasing the utility's energy sales. An annual energy savings of 1,985,600 MWh was made due to the reactive power optimisation, leading to significant economic returns for the microgrid. The NPV, PI and DPP calculated from the study were GHS 12,528,583,512, 10,404.3, and one year, respectively. Therefore, it suggests reactive power optimisation to microgrid and grid operators as a viable solution to meet regulators' benchmarks.

Economic flexible AC transmission system devices placement/sizing with (N-1) contingency using genetic algorithm

<span>The rise in demand for electricity and the high cost of constructing new power networks reckons optimal utilization of electric power overloading and excessive power transfer along transmission lines, high losses, voltage instability, poor power quality, reliability issues. Flexible AC transmission system (FACTS) boosts the static and dynamic performance of power systems. Although efficient power transmission by improving power quality and voltage profile enhancement is controlled using the FACTS devices the placement, types, and sizing are important parameters to be optimized for the power system. This paper develops the economic multiple FACT placements and sizing solution during N-1 contingency conditions. Placement and sizing being the stochastic problem meta-heuristic algorithm genetic algorithm (GA) is used and applied on the standard IEEE 9 bus system. MATLAB-based simulation is developed for economic placement of multiple FACTS and single FACTS devices in different scenarios (without FACTS devices, with single FACTS devices, and with multiple FACTS devices). Both static VAR compensators (SVC) and thyristor-controlled series capacitors (TCSC) are used (either single or multiple) to optimize the transmission loss and total cost. The results show that transmission loss and cost reduction with 70% compensation is working better with about 0.1 MW lesser loss in many cases.</span>

Multiverse Recurrent Expansion With Multiple Repeats: A Representation Learning Algorithm for Electricity Theft Detection in Smart Grids

Electricity theft, known as “Non-Technical Loss” (NTL) is certainly one of the priorities of power distribution utilities. Indeed, NTL could lead to serious damage ranging from massive financial losses to loss of reputation resulting from poor power quality. With advances in metering infrastructure technologies, the availability of user data has fueled the emergence of data-driven methods in NTL detection. Among these methods, deep learning (DL) is an indisputable alternative to conventional human-centric approaches. Typically, modeling based on NTL data is subject to three main challenges, including (i) missing information; (ii) class imbalance; and (iii) data complexity. In this context, this paper contributes to solving these three main problems while paying more attention to data complexity related to cardinality. Accordingly, a multiverse recurrent expansion with multiple repeats (MV-REMR) algorithm is proposed in this paper. MV-REMR is able to provide deeper representations than ordinary DL networks and take advantage of different trained deep network responses to build an efficient model. For MV-REMR efficiency analysis, a realistic NTL dataset is considered. As a result, MV-REMR has shown that it can achieve what is considered excellent feature mapping proven by both scatter visualization and variations in widely used classification metrics. Moreover, MV-REMR shows its ability to marginalize the distance of data classes with superior performance. In addition, thanks to the new mapping scheme, MV-REMR shows its ability to correct outliers resulting from errors in missing values filling techniques. Finally, a comparison with some recent successful works also confirms the superiority of the MV-REMR model.

An adaptive dynamic power management approach for enhancing operation of microgrid with grid ancillary services

The increasing prevalence of photovoltaic (PV)-wind-battery-based microgrids and their integration into distribution networks have brought new challenges in grid ancillary services, such as poor power quality (PQ), grid instability, voltage and current fluctuations, and low efficiency of microgrids. These adverse effects are due to the intermittent nature of PV and wind power generation. The present work proposes an adaptive dynamic power management (ADPM) approach and enhanced instantaneous symmetrical component theory (EISCT) to improve the microgrid operation and grid ancillary services. The proposed ADPM-based EISCT not only utilizes a single grid side converter (GSC) but also reduces the sensor requirement and control structure complexity and thereby eliminates communication mismatch. The control structure, proposed in this research, has two major segments. The first segment focuses on active power management control through ADPM to facilitate efficient power flow between the utility grid and microgrid sources which helps in ensuring efficient power sharing. The second component is dedicated to synchronizing the renewable energy sources of the microgrid with the utility grid and simultaneously maintaining PQ compliance as per IEEE 519–2022 standards. The efficacy and adaptability of the proposed structure are validated through MATLAB®/Simulink® environment and the practical superiority of the system has been tested by using MicroLabBox® DS1202-based real-time hardware set-up. The experimental result shows improved performance of the developed structure under various microgrid operating conditions including dynamic loading situations, and battery system state-of-charge (SOC). The simulation and experimental results suggested that the DC-link ripples have been reduced to 2%, peak overshoot to 1.8%, settling time during the transition to 1.2s, battery response time to 0.3s, and the total harmonic distortion (THD) to less than 5%. These results demonstrated the superiority of the ADPM-based EISCT over conventional strategies.

Simulasi Analisis Kualitas Daya Menggunakan Logika Fuzzy Untuk Meningkatkan Efisiensi Sistem Tenaga Listrik

This study aims to simulate power quality analysis using fuzzy logic to increase the efficiency of the power system. Poor power quality can cause disturbances in the electric power system and reduce the efficiency of the energy used. Therefore, improving power quality is one of the main focuses in the electric power industry.The fuzzy logic method is used in this study to evaluate power quality based on relevant parameters, such as voltage, current, frequency and harmonics. Fuzzy logic is able to overcome the uncertainty and complexity of data related to electric power systems, and provide more adaptive and intelligent solutions. In this study, simulations were carried out using special software that implements fuzzy logic to analyze power quality. The input data obtained from the electric power system is used to build a fuzzy logic model. Then, the simulation results are used to identify existing power quality problems and propose appropriate improvement strategies.The simulation results show that the use of fuzzy logic can significantly increase the efficiency of the power system by minimizing disturbances and optimizing power quality. In addition, fuzzy logic also provides the ability to predict the future conditions of the electric power system and take the necessary precautions. This research has important practical implications in the field of power systems. By using fuzzy logic in power quality analysis, power companies and grid operators can optimize power system operations, reduce power losses, improve energy efficiency, and ensure reliable electricity supply to consumers.

A novel framework for enhancing the power quality of electrical vehicle battery charging based on a modified Ferdowsi Converter

A DC–DC converter in an EV charging station is to regulate the DC voltage from the rectifier to the required voltage for battery charging. This ensures that a constant, stable, and safe charging voltage is provided to the battery However, if the EV charging is done only with a DC–DC​ converter, it results in poor Power Quality (PQ), with high levels of harmonic distortion and a low power factor. This can lead to longer charging times, increased energy costs, decreased battery performance and life, and introduce serious PQ issues. To address these issues, the Ferdowsi Converter is proposed is a combination of two interleaved Buck-Boost converters with a common input inductor as a solution with simplified PT control strategy presented, operated in operating in discontinuous conduction mode (DCM) is presented, which can achieve nearly unity power factor (PF) over universal input voltage range studied. The converter performs with fewer components, providing a charger input current that is in phase with the mains voltage, reducing the THD to below 5% under steady state and during variations in line and load side.

Performance Enhancement of Single-Phase Two-Stage AC-DC Converter With Reduced DC-Link Capacitance

Single-phase two-stage AC-DC converters employed in electric vehicle (EV) chargers have the tendency to produce low frequency ripple due to input/ output power mismatch, making it necessary to use bulky DC-link capacitors. High voltage ripple at DC-link due to reduced capacitance, prompts poor input power quality and high low-line frequency ripple at the output of secondary-stage. In state-of-art, input power quality in converters with low DC-link capacitance is enhanced by using a digital low-pass filter (LPF). However, employing LPF to sensed DC-link voltage results in a non-optimum dynamic response and deteriorated stability margins. With the objective to improve input power quality, enhance dynamic performance and reduce secondary-side ripple for low valued DC-link capacitance AC-DC converters, a modified unit-template based model predictive (MUT-MPC) and a DC-link feedforward (DFF) controls are presented in this paper. The current feed-forward for the unit-template is controlled by predictive voltage error through an optimization process of cost function to improve input power quality and dynamic performance for primary AC-DC converter. Furthermore, a DC-link feedforward (DFF) control is implemented for secondary DC-DC stage, which reduces the low-frequency current ripple at the load. Finally, to verify the effectiveness of presented multi-objective control method in comparison with traditional controls, MATLAB simulations and experimental results are provided.

LM2L rule based adaptive filter controlled multi-functional SPV plant to enhance DG set utilization

In the existing solar photovoltaic (SPV) and diesel generator (DG) based standalone microgrid, the power factor of the DG set reaches around 0.5-0.6. Further, it encounters poor power quality performance due to harmonics currents of the local loads, coupled at the microgrid's point of interconnection (POI). All these issues significantly affect the DG set performance, causing additional losses and reducing its fuel efficiency. In addition, the SPV plant does not provide any ancillary services in the existing system and remains ideal most of the time, causing the under-utilization of microgrid's power converters. In this context, this work presents a least mean 2Lth (LM2L) rule-based adaptive filter-controlled SPV system, where its power converters are regulated to deliver multifunctional capabilities such as enhancing the DG set's power factor and power quality performance. As a result, the DG set power factor improves to unity, and the %THD value of its voltage and current reduce to below 5%. Hence, the fuel efficiency of the DG set is improved, line losses are decreased, and the additional load can be committed to the DG set capacity, which is kept reserved to supply reactive power to the load. Moreover, the modifications, which are earlier required in the DG set design to accommodate the distortion in its voltages and currents due to the nonlinear load, can be avoided. The LM2L algorithm is used as a prefilter for the measured load currents and determines their fundamental component. It has two operating rules, which decide the fundamental component's estimation depending on the system conditions. As a result, it demonstrates an improved steady state and dynamic performances compared to the existing adaptive filter algorithms. Test results verify the control and design of the microgrid on a laboratory prototype.