Reliability Of Power Grid Research Articles

Improving the efficiency and reliability of India’s power grid through targeted EHV transmission line investments: a case of UPPCL

This study analyzes the optimization of Extra High Voltage (EHV) transmission line design parameters by Uttar Pradesh Power Corporation Limited (UPPCL) in India to improve power grid efficiency and reliability. UPPCL reduced insulation specifications for 400 kV lines from 27 to 21 disc insulators and decreased live metal clearance from 2600 to 2400 mm, resulting in 7–9% cost savings per line and reduced right-of-way requirements. While initial test lines performed satisfactorily for over 5 years, longer lines experienced operational challenges related to insulator quality, bird activity, and design margins. Tripping issues emerged during adverse weather conditions, particularly in foggy winter periods. Root cause analysis revealed the need for stringent insulator quality control, regular cleaning to mitigate bird dropping contamination, and potential corona ring design improvements. This case study demonstrates how targeted EHV transmission line optimization can yield significant cost and environmental benefits, while highlighting the importance of balancing economic gains with reliability and safety considerations. The findings provide valuable insights for future EHV transmission line designs and upgrades across India’s power grid.

Economic evaluation of kinetic energy storage systems as key technology of reliable power grids.

In recent years, energy-storage systems have become increasingly important, particularly in the context of increasing efforts to mitigate the impacts of climate change associated with the use of conventional energy sources. Renewable energy sources are an environmentally friendly source of energy, but by their very nature, they are not able to supply the required amount of energy in a uniform distribution. This study evaluated the economic efficiency of short-term electrical energy storage technology based on the principle of high-speed flywheel mechanism using vacuum with the help of an innovative approach based on life cycle cost analysis (LCC). The innovative potential of high-speed flywheel energy storage systems (FESS) can be seen in increasing the reliability of the electricity transmission system with the possibility of providing control power to compensate for residual loads caused by volatile renewable power sources and power sinks. Based on the research conducted, the LCC method was selected in this study as the most appropriate method to evaluate the economic efficiency of a high-speed FESS used to compensate for short-term fluctuations in an upgraded electric transmission system. As a result, the adjusted LCC per MWh values were compared with the average intra-hour margin realisable in the Intra-Day OTE Market, while the margin calculation also considered the efficiency of the inertial storage. Under the modelled technical and economic conditions, it was found that a high-speed FESS project that can compensate for short-term fluctuations in the electricity transmission system can be economically efficient in the Czech Republic.

A Multi-Decadal Hourly Coincident Wind and Solar Power Production Dataset for the Contiguous United States

As renewable energy continues its rapid expansion in the Unites States, multi-decadal hourly datasets of electricity production are needed to asses reliability and resource adequacy of power grids. Recent years have seen the release of grid-cell-level simulated meteorological variables, however these are not extended to the power domain, are not developed from a dynamically consistent numerical weather model, and only cover a historical baseline of less than a decade. To fill this gap, this work provides a dataset of 43 years of coincident plant-level wind and solar power production data. The dataset is designed to be aggregated to appropriate scales of interest for bulk system studies such as Balancing Authorities (BAs), states, and nodes of a production cost model. The dataset covers every plant in the contiguous U.S. that is reported in the U.S. Energy Information Administration (EIA) Form 860 as of 2020. When compared with the EIA-923 monthly generation, we find minimal bias (less than 5%). When compared with BA-reported hourly generation, we find low bias in solar (less than 7%), and slight underdispersion in wind. This coincident multi-decadal historical dataset provides a documented and evaluated multi-resource baseline for studies on reliability, resource adequacy, climate change impacts, and characterization of emergent climate threats on renewable resources.

Energy management of hybrid AC/DC microgrid considering incentive‐based demand response program

AbstractIncreasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming.

Identifying interaction boundary of inverter-based generation in assessing system strength of power grids using relative electrical distance concept

The increasing use of inverter-based generation (IBG) in power grids raises concern about system strength. This is partly due to the inherent interactions among multiple IBGs in close proximity to one another. This paper proposes an approach to identifying the existential boundary of interaction in a network using the relative electrical distance (RED) concept. The mathematical formulation of the RED concept to address the interaction problem among the IBGs involved utilising the power system network’s admittance matrix to capture its structural characteristics. An interaction matrix derived from the RED values of all IBG pairs was then developed to identify the interacting IBG groups. The proposed approach was demonstrated using the IEEE 39-bus system and a practical 72-bus Nigerian power grid. Results showed that RED values effectively group interacting IBGs, with values closer to 0 signifying higher interaction levels, values closer to 1 indicating lower interaction, and a value of 1 denoting no interaction. Time-domain simulations confirmed the accuracy of the approach, demonstrating that the effect of control interaction propagates proportionally to neighbouring IBGs based on RED values. However, fault currents can influence the impact of control interactions. This approach, which requires less computational effort, provides a quick identification tool for potential areas of concern based on the degree of interaction, enhancing the reliability of power grids with high IBG penetration.

Accurate Measurement of Tower Grounding Resistance for Single-Tower and Multi-Tower Parallel Scenarios Based on the Clamp Meter Method: For the Sustainable Operation of Towers

Transmission tower grounding safety is a critical element that significantly impacts the reliability and sustainability of power grids. Tower grounding resistance is a significant grounding characteristic parameter. In order to accurately measure the grounding resistance of towers using the clamp meter method in both single-tower and multi-tower parallel scenarios, this paper establishes theoretical calculation models for measuring tower grounding resistance using the clamp meter method. Considering the interaction between the artificial grounding device and the tower foundation, this paper models and simulates transmission towers and lightning shield wires, analyzing the influencing factors on the grounding resistance measurement results using the clamp meter method in both single- and multi-tower parallel scenarios. The results show that for single towers, the clamp meter measurement results increase with decreasing foundation root spacing and increasing length of the artificial device’s extension lines, but the changes are relatively small. In multi-tower grounding scenarios, due to the shunting effect of the parallel branches formed by the towers and lightning shield wires, the value is smaller than in single-tower grounding scenarios. Changing the number of parallel towers and the type of lightning shield wire produces little effect on the measurement results. However, changes in soil resistivity have the most significant impact. Therefore, correction formulas for the impact of soil resistivity on clamp meter measurements are proposed and then verified by applying them to field tests.

A review of Green hydrogen production and the obstacles to hydrogen green economy

The Energy sector, a significant contributor to greenhouse emissions due to its reliance on fossil energy sources, is on the brink of a significant transformation. However, there is hope on the horizon. Hydrogen is leading the charge in the energy industry's decarbonisation processes with its cleaner and more sustainable features. The adoption of Hydrogen could pave the way for a global energy transition towards a carbon-neutral society. Green Hydrogen, in particular, holds immense promise. It can facilitate the shift to clean and renewable energy while ensuring the reliability and sustainability of power grids, inspiring a sustainable and carbon-neutral future for the energy industry. Moreover, Hydrogen, with its potential to displace fossil fuels in various industries as an energy source, has sparked a growing demand for hydrogen supply chain management structures. This versatility is the recipe for economic development. However, there are significant obstacles to implementing a cleaner Hydrogen economy in Nigeria, such as the high production costs of green Hydrogen and the need for substantial development of specific technologies. This review article will delve into these obstacles and assess potential solutions.

On soft measurement modeling for predicting photovoltaic power with uncertainty based on the Takagi–Sugeno model

Abstract Accurate solar power forecast is becoming more essential for safe and reliable power grid operation with the increasing number of grid-connected photovoltaic (PV) power production units. However, PV power exhibits significant output fluctuation due to both external inputs and intrinsic stochasticity in system dynamics. Therefore, an efficient and reliable soft measurement model of PV power with uncertainty is demanded in practice applications. The technique described in this paper captures the impacts produced by the fundamental uncertainty observed in the data, instead of relying on unrealistic assumptions about uncertainty. A soft measurement model using the Takagi-Sugeno (TS) Fuzzy Logic System (FLS) based on several input-output time series of PV plants is presented in this study. Chebyshev's inequality from probability theory and statistics is adopted to create the confidence interval-based response envelopes for these time series at each moment. An envelope-based measure of output uncertainty and a center-valued response forecasting model can be obtained by the proposed identification technique. PV datasets are employed to demonstrate the concept, which indicates the proposed soft measurement may outperform existing methods in terms of prediction accuracy. The average values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Correlation Coefficient (R) are only 0.0787, 0.1113, and 0.9979. The average values of the prediction interval coverage probability (PICP) and the prediction interval normalized average width (PINAW) are 0.9806 and 0.1051.

Aperiodic small signal stability method for detection and mitigation of cascading failures in smart grids

The occurrence of cascading failures poses significant risks to the stability and reliability of modern smart grids. This article presents a novel hybrid algorithm designed to assess and mitigate these failures. The technique combines advanced clustering algorithms, specifically Affinity Propagation Graph (APG) and Self-Propagating Graph (SPG), to detect critical nodes, and Unified Power Flow Controllers (UPFCs) to provide compensation to grid networks. The algorithm first uses APG to divide the network into clusters and considers the center bus as the critical node. If the center bus is not critical, SPG is applied to identify the critical node. This hybrid approach identifies the critical node in just 0.02 s (for both APG and SPG) and with improved accuracy compared to existing methods. After identifying critical nodes, UPFCs are strategically installed to regulate power flow and reduce the probability of cascading failures, with compensation taking approximately 0.14 s. Simulation results demonstrate the effectiveness of the proposed method in enhancing grid resilience and reducing the likelihood of cascading failures. By strategically deploying UPFCs at critical nodes, this approach ensures resilient grid operation in various scenarios. This research significantly contributes to the development of smart grid technologies by providing a comprehensive framework to address cascading failures in power distribution networks. The proposed method shows potential for improving the reliability and stability of power grids amid changing system dynamics and uncertainties.

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Accurate fault diagnosis in transmission lines is crucial to ensure the reliability and stability of power grids. Conventional approaches often rely on expert knowledge or complex feature extraction methods, which are subjective and time-consuming. Additionally, many existing approaches use separate sub-algorithms for fault classification and localization. These are operating independently and sequentially. This research work proposes an innovative method for fault classification and localization in transmission lines using phasor measurement unit (PMU) data. The proposed method employs a Weighted Extreme Learning Machine (WELM) algorithm, which uses the variable data distribution across different fault classes through a weighted approach. The PMU data is generated by simulation using an IEEE 9-bus test system in the MATLAB simulation environment. The Maximal Overlap Discrete Wavelet Transform-based feature extraction technique is applied to derive input feature data to facilitate fault classification and localization. The WELM classifier is also optimized using the Grey Wolf Optimization (GWO) algorithm. The resulting GWO-optimized WELM (GWO-WELM) model, when trained on PMU data, achieves a remarkable fault classification accuracy of 99.83 % and fault localization accuracy of 95.48 %, respectively. These results demonstrate that the GWO-WELM model outperforms commonly used classifiers. Moreover, the proposed model shows robustness by accurately classifying the noisy data with a signal-to-noise ratio (SNR) of 10 dB (achieving 91.5 % accuracy in classification and 88.1 % accuracy in localization, respectively).

Consequence simulation of cyber attacks on key smart grid business cases

The increasing threat of cyber-attacks on modern power systems highlights the need for a comprehensive examination through simulations. This study conducts an in-depth simulation of cyber-attacks on critical smart grid components, including smart meters, substation automation, and battery management systems, to expose and analyze potential disruptions to power system operations. We identify vulnerabilities that can lead to severe grid instabilities, such as voltage variations, system collapses, and inverter failures. Our analysis underscores the complex interactions between cyber threats and grid components, revealing how disruptions extend beyond mere load interruptions to affect the core infrastructure. We advocate for integrating established cybersecurity frameworks like NIST, ISO/IEC 27001, and IEC 62443, essential in fortifying grid stability against these dynamic threats. Our findings highlight the urgent need for continuous adaptation and enforcement of these frameworks to enhance resilience and ensure the reliability of modern power grids against cyber-attacks.

Comparative analysis of the effectiveness of passive and active filters of higher harmonics as part of the electrical complex of metal heat treatment

The presence of a nonlinear load connected to the power supply network causes a distortion of the sinusoidal shape of the current and voltage curves, resulting in higher harmonics. Higher harmonics lead to overloading and overheating of equipment, reduced reliability and reduced service life, and can have a negative impact on the quality of electricity. As part of the task of ensuring the reliability of power grids, an important task is to control the level of harmonic components and use special devices to minimize their impact. To combat higher harmonics in power grids, various devices have been developed and used, including line chokes, passive filters, and active filters. The choice of a particular device depends on the parameters of the power grid, the type and characteristics of the connected load, the level of harmonic components, and the requirements for the quality of electricity. Linear chokes are capable of reducing the level of harmonics in the power grid, but, having a number of disadvantages, require additional justification when deciding on their use. A passive filter has a circuit consisting of a choke, resistor, and capacitor, can have different configurations, and is tuned to the frequency of a specific harmonic to suppress its effects. Passive filters are more effective in combating higher harmonics generated by nonlinear noise, more economical and easier to maintain, but have a limited frequency range, fixed parameters and significant weight and dimensions, which limits their use. Active filters include active components to generate signals that are out of phase with harmonic components, thus compensating for higher harmonics. Active filters are more flexible, capable of correcting distorted and shaped signals, are much more efficient than chokes and passive filters, but are less cost-effective and more power-consuming. In the presented work, a passive six-link LC filter and a shunt active filter based on the principle of instantaneous compensation of active and reactive currents are investigated. In the active filter, harmonic distortion compensation is provided by a proportional-integral controller. In this paper, we have carried out simulation modeling of passive and active filters as part of an electrical complex for metal heat treatment and analyzed the results of harmonic distortion compensation

Enhancing the reliability of power grids: A YOLO based approach for insulator defect detection

Insulators are crucial components of power grid systems, safeguarding against electrical conductor breaks. However, their prolonged exposure to complex outdoor environments renders them susceptible to defects. In this study, we address the importance of accurate insulator defect detection and propose an approach using the You Only Look Once (YOLO) object detection framework. In particular, we compare the performance of YOLO v8 against YOLO v7 in detecting two specific types of insulator defects—broken insulators and flashover damaged insulators. Leveraging the Insulator Defect Image Dataset, our results demonstrate that YOLO v8 achieves superior accuracy with a rate of 98.99 percent along with a mean average precision (mAP) of 99.10 percent. The findings underscore the efficacy of YOLO v8 in improving the reliability and resilience of power grid systems by allowing timely and accurate detection of insulator defects in complex outdoor environments. This research contributes to advancing the field of power grid infrastructure monitoring and maintenance, ultimately facilitating more effective strategies for mitigating the consequence of insulator defects on power grid system performance and reliability.

Virtual Power Plants: Challenges, Opportunities, and Profitability Assessment in Current Energy Markets

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs.

СУЧАСНІ ТЕНДЕНЦІЇ РОЗВИТКУ СИСТЕМ КОМПЕНСАЦІЇ РЕАКТИВНОЇ ПОТУЖНОСТІ

This paper surveys current trends and developments in reactive power compensation systems, elucidating their significance in enhancing system capacity and flexibility. By analyzing the capacity factor enhancement enabled by reactive power compensation, the study underscores its role in optimizing electricity delivery, thus conserving energy resources and facilitating the integration of renewable energy sources. It explores the key models and concepts driving advancements in reactive power compensation within power grids. As modern electrical systems grow in complexity, ongoing research into improving compensation methods becomes imperative to ensure the reliability and efficiency of industrial power grids. Future endeavors will involve developing a model utilizing real-world enterprise data to investigate the effects of technological variations in filter parameters on the performance of complex reactive power compensation schemes, encompassing both filters and capacitor banks. Keywords: reactive power, compensation systems, filter-compensating circuit, industrial power grid, electric power. Context. The paper considers the current trends used in the field of reactive power compensation, and the main directions of development of modern methods of reactive power compensation. Objective. The purpose of the work is to overview of current trends in the development of reactive power compensation systems, as well as an overview of possible ways of their effective application in various industrial networks. Methods. The paper presents a key aspect of reactive power compensation is its ability to increase system capacity and flexibility. By improving the capacity factor, utilities can deliver electricity more efficiently, reducing the need for excess capacity and thus conserving energy resources. In addition, this process plays an important role in supporting the integration of renewable energy sources such as wind and solar power, which often introduce volatility into electricity generation schemes. Results. The paper presents the main models and concepts in the field of reactive power compensation in power grids. Conclusions. Given the growing complexity of modern electrical systems, it is important to continue research into improving methods and means of reactive power compensation to ensure the reliability and efficiency of industrial power grids. In further research, it is planned to develop a model using a real enterprise as an example and investigate the impact of technological deviations of filter parameters on the steady-state and transient performance of complex reactive power compensation schemes, including not only filters but also capacitor banks.

Research and application of power equipment monitoring and fault diagnosis

Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

A Novel Approach for Dynamic Analysis of Wind-Integrated Multi-Machine Power Systems

Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.

The combination mode of forest and SVM for power network disaster response failure identification

The damage to power transmission and distribution equipment in different regions is inseparable from the erosion of natural and artificial disasters, affecting the power grid's regular power supply and users' typical power consumption. Therefore, research on failure risk prediction technology for power transmission and distribution equipment under different disasters has increasingly become critical to power grid reliability. Based on the combination mode of forest and Support Vector Machine (SVM), this study proposes a disaster response failure prediction model that considers the time cumulative effect of disaster elements. Based on this model, the failure degree indicators of different disaster elements are constructed by analyzing the relationship between disaster frequency and time. Then, the historical disaster data is used to calculate the index and form a training set with the monitoring data. The binary decision graph transformation method is combined with the adjacent node priority method to obtain the minimum cut set, and the SVM regression method is used to train to get the prediction model. The experimental results show that the effectiveness and accuracy of the proposed method are verified through the analysis of numerical examples and comparison of various techniques. The model's accuracy is 8.63 % higher than that of the traditional disaster prediction model, and the error rate of disaster failure prediction analysis is not more than 0.01.

Electricity demand forecasting at distribution and household levels using explainable causal graph neural network

Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.

Performance and harmonic detection algorithm of phase locked Loop for parallel APF

As the boost of power electronics technology, the harmonic problem in the power system is becoming increasingly prominent. Fourier decomposition is performed on the load current in the power system, and components with a frequency that is an integer multiple of the fundamental wave are referred to as harmonic components. Harmonic control is essential to establish a safe and reliable power grid environment and provide high-quality and clean electricity to power users. The study focused on parallel active power filters, proposed a specific harmonic detection method on the grounds of synchronous harmonic rotating coordinate system, and developed a phase-locked loop design on the grounds of order generalized integrator. Meanwhile, a compensation current control method on the grounds of space vector pulse width modulation was introduced. The results showed that in the full compensation simulation experiment, the compensated A-phase grid side current waveform was significantly improved and presented a sinusoidal shape. After 0.05 s, the actual output compensation current closely followed the command current. Meanwhile, after compensation, the total harmonic distortion rate decreased from 26.58 to 3.06%. In specific harmonic compensation simulation experiments, when the sum of 5th, 7th, and 11th harmonic components was used as the command current for compensation, the distortion of the current waveform was improved after the load undergoes a sudden change. After compensation, the 5th, 7th, and 11th harmonic content significantly decreased, and the total harmonic distortion rate decreased to 4.08%. This indicated that the proposed phase-locked loop design and harmonic detection method for active power filters had high stability and effectiveness. The study’s primary contribution is to enhance the utilization efficiency of DC voltage and improve the dynamic response ability of current. Additionally, it offers a new method for reducing the impact of harmonics on the power grid and improving power quality. It provided an effective method reference for technological progress in related fields such as power electronics and control engineering.