From Patents to Progress: Genetic Algorithms in Harmonic Distortion Monitoring Technology

Abstract

Sustainable energy sources, such as wind energy, are pivotal in driving our energy landscape towards a more environmentally conscious and responsible future. Wind power, as an exemplar of clean and renewable energy solutions, adeptly harnesses the kinetic energy of the wind to generate electricity. While wind energy significantly contributes to our sustainability objectives, the quality of the energy it produces is equally essential. A critical challenge in this context is harmonic distortion, which manifests as unwanted fluctuations in the frequency and amplitude of electrical waveforms. Effectively mitigating these distortions within wind energy systems is vital to maintaining the stability and reliability of power grids, guaranteeing that the electricity supplied adheres to high-quality standards. The objective of this study was to conduct a technological prospection focused on the contemporary scenario of genetic algorithm applications in addressing harmonic variations. This investigation unearthed a total of 634 relevant documents. The findings suggest that the utilization of genetic algorithms for enhancing energy quality is a relatively recent but promising field. The State Grid Corp of China emerged as the principal contributor, with ten noteworthy inventors identified. Remarkably, both China and the United States lead in patent filings. The insights gleaned from these documents underscore the potential for further exploration and the synergistic application of these techniques. These collaborative efforts have the potential to yield processes and devices that offer significant economic and environmental advantages for the energy industry, solidifying our commitment to a cleaner and more sustainable energy future.

1. Introduction

The use of renewable energy as the main energy source has been widely encouraged in recent decades. This strategy emerges as an option to meet the increase in energy demand generated by industrialization and world population growth [1]. Because it derives from non-polluting natural resources such as the sun, wind, and water, it is known as clean energy and has become promising in replacing non-renewable energies derived from fossil fuels [2,3]. In view of this, financial investments in sustainable energies is crucial to solve the imminent problems of shortage of fossil-based resources as well as the pollution generated during their burning [4].

Due to the high costs, government investment and support have been key in financing this transition [5]. The shift from the primary source of energy to photovoltaic and wind energy sources has been largely stimulated through the development of policies involving electricity pricing, fines for high CO₂ emissions, and investment in innovative clean energy tools [5,6]. In 2020, China invested more than USD 77 billion in wind power installations, and it is estimated that these amounts will reach 426 billion by 2050 to increase its transmission and storage capacity and achieve carbon neutrality in the country [6]. Globally, investment in onshore and offshore wind is expected to reach USD 211 billion and USD 100 billion, respectively, by 2050 [7].

Among the available forms of renewable energy, wind energy is the most accepted because of its advanced technology and similar cost to traditional methods of energy generation [8]. Due to their reliance on wind speed, low maintenance costs, and the various turbine models available on the market, wind power plants can be found in coastal regions and offshore and on smallholder farms [9]. Presently, the worldwide wind energy capacity stands at 837 gigawatts (GW), and it is anticipated to expand by 94 GW each year. This trajectory is set to yield a cumulative increase of 459 GW in new capacity over the next five years [10,11]. Due to the Incentive Programs for Alternative Sources of Electricity (Proinfa), wind energy represents Brazil’s second largest source of energy, behind hydraulic energy [12].

In addition to concerns about meeting the energy demand of residential, commercial, and industrial consumers, the electricity sector is concerned with providing quality energy supply [13]. Nowadays, electricity distribution companies are increasingly concerned with properly communicating the quality of their electrical products due to various compelling reasons. Compliance with regulations ensures legal adherence and avoids penalties, while customer satisfaction hinges on delivering reliable, safe, and efficient products. Quality offerings enhance brand reputation and competitiveness, leading to long-term cost savings and reduced liabilities. Conforming to industry standards demonstrates professionalism and attracts stakeholders [14].

The quality of energy is closely related to the presence of interruptions in power supply, that is, the presence of electrical failures in the system, whether in the companies that distribute energy or at the final consumer, causing oscillations or disturbances that affect the quality of energy [15]. In the view of that, quality energy must be properly transmitted continuously and efficiently to avoid financial losses and discomfort to users [16]. Under normal conditions, the electrical system should operate in the frequency range of 60 ± 0.5 Hz and with constant voltage forming a pure sine wave with a defined amplitude [17]. Any oscillation in these quantities, whether events or variations, deteriorates the quality of the energy supply and can compromise the operation, especially in the case of large industries [18,19]. While events may involve voltage interruptions that are only measurable once they surpass the designated threshold, the slight voltage and current deviations noticed during variations can be measured at any given moment [20]. Among the range of variations, harmonic distortions stand out as one of the most significant factors employed for evaluating the power supply’s quality [20].

Harmonic distortions refer to voltages or currents with frequencies that are multiples of the fundamental signal frequency in the active electrical system. These distortions result in changes in the sinusoidal waveforms, decreasing their amplitude while increasing their frequency [21]. These distortions come from equipment and non-linear electronic loads installed in the power system that consume 60 Hz currents and, therefore, generate energy considered dirty or contaminated [21]. Additionally, harmonic distortions can alter the operation of cables, transformers, motors and machines, energy meters, and even protection devices [22,23]. Due to these problems, the installation of compensating and modular filters is carried out as preventive and corrective measures to mitigate the possible damages generated in the presence of harmonic levels in the electrical lines [22,23].

Regarding the diagnosis, the Fourier mathematical method is used to identify and quantify the harmonic distortions [24]. The mathematical tool allows for the decomposition of the waveform, where each component can be checked separately, with the analysis performed in the frequency domain [25]. As it is influenced by the direct current component (CCC), modeling techniques and computational intelligence have been developed to improve the accuracy and speed of identification of harmonic distortions as well as the parts of the network where they are generated [26]. In this sense, genetic algorithm methods have an innovative potential to solve the existing limitations, since they can estimate the harmonic components that are slightly affected by the CCC [26].

Genetic algorithms (GAs), inspired by the processes of natural selection and genetics, match the creation of suitable forms through a progression of structures. This mechanism involves the stochastic exchange of information to shape an adaptive algorithm. After every interaction, a new set of synthetic entities emerges, using components from a previous set. Frequently, this interactive procedure generates better individuals. In this way, past information is effectively used to discover a new set of results, producing improved performance [27,28].

In terms of harmonic variations, genetic algorithms can be used to improve power quality by optimizing control parameters or device settings in the power system to directly mitigate unwanted harmonic variations. This process involves representing potential solutions as chromosomes that encode parameter values, evaluating fitness based on an objective function and selecting high-fitness chromosomes for reproduction. Through crossover and mutation operations, new offspring chromosomes are generated, facilitating the exploration of parameter settings. The parameter values of the fittest chromosome are extracted as the optimal solution for rectifying harmonic variations, which can subsequently be implemented in the real electrical system to achieve a high standard of power quality [29,30,31].

Nowadays, the development of new materials, new methods, and the use of computer science to promote new technological solutions in the field of renewable energy is a notable area of research and innovative efforts. The direct connection between the success of innovation research and the development of technology linked to the interests of commercial markets is widely known as patents. These patents are typically submitted by corporations, universities, and research institutions and play a crucial role in safeguarding the intellectual properties possessing substantial technological and economic significance. Patent documents also provide a comprehensive overview of the current technological situation of the technology of interest, as well as the state of the art, development prospects, potential competitors, patent applicability, and potential alliance partners. In addition, they provide policymakers and economists with a rich source of data for assessing the effectiveness of innovation and subsidy policies [32,33,34]. In this sense, the aim of this study was to carry out a technological survey centered on patents, to verify the technological panorama of the use of genetic algorithms to measure harmonic distortions that affect the quality of energy production and distribution.

2. Materials and Methods

The aim of this research was to conduct an investigative exploration to gather technological data from patent documents. The technological inquiry took place on 20 July 2023, using the Derwent Innovation Index (DWPI), Thomson Innovation©, licensed for exclusive use by the University Center SENAI CIMATEC. Following a meticulous refinement process, specific keywords and Boolean operators were incorporated to construct the search strategy implemented in the prospecting phase:

Harmonic monitoring AND wind power AND genetic algorithms AND power quality.

Furthermore, International Patent Classification (IPC) codes were also applied in this work.

Table 1. IPC codes employed during the current patent technology prospecting process.

IPC Code	Related to
G06N3/00	Computing arrangements based on biological models
G06N3/006	Based on simulated virtual individual or collective life forms, e.g., social simulations or particle swarm optimization [PSO]
H02J3/00	Circuit arrangements for ac mains or ac distribution networks

shows the codes utilized during the prospecting, along with their corresponding thematic areas. Table 1 provides a comprehensive overview of the connections between each code and its associated subject matter.

The database search encompassed the title, abstract, and claims fields of the patent documents, without any restrictions on the data collection timeframe. To visualize the temporal analysis of the patent documents (year of priority and year of expiration), the primary applicants and inventors, as well as the International Patent Classification (IPC) codes assigned to the document indicators, the GraphPad Prism 9.2 software (San Diego, CA, USA), licensed for use by the University Center SENAI CIMATEC, was utilized. The outcomes regarding the geographical distribution of the main applicant countries/regions, the principal potential markets for the technologies, and the primary technological areas associated with the inventions were directly obtained from the DWPI database, with necessary adaptations. Figure 1 represents the research process used.

3. Results and Discussion

The development of patents is fundamental to guaranteeing legal certainty for companies and stimulating investment in new technologies and innovation [35]. In this study, a patent search was carried out to assess the technology of interest, the algorithms used to identify harmonic distortions, and the methods available to assess power quality. In total, 634 DWPIs were found. Figure 2 shows an analysis of the annual distribution of patent applications relating to the technology described; the first applications were found in 2003 (Figure 1). Patent applications have gradually increased over the period analyzed, indicating a growing interest in developing new technologies for the supply of quality energy. It can be observed that there was a significant rise in the number of publications between 2017 and 2021, followed by a peak in 2021 with 98 documents.

After the Paris Climate Agreement, where countries set a target to net zero emissions of carbon dioxide by the second half of the century emission (CO₂), a growing number of national governments incorporated this reality into national strategies and set the vision of a carbon-free future. Five years later, there was a noticeable surge in interest surrounding energy and the quality of energy production and distribution, driven by several influential factors. This era marked a significant juncture in technological advancement, with innovations in power generation, transmission, and distribution fostering the exploration of new avenues for enhancing the efficiency and dependability of energy systems [36]. Concurrently, the escalating global demand for energy, spurred by economic growth and urbanization, underscored the imperative of maintaining a stable and high-quality energy supply. Environmental concerns, particularly related to air pollution and greenhouse gas emissions, were gaining widespread attention, emphasizing the pivotal role of improved energy quality in mitigating environmental impact [37]. Moreover, the pursuit of energy efficiency was emerging as a central goal, with a focus on minimizing energy losses during transmission and distribution to enhance sustainability and cost-effectiveness. Ensuring the reliability and stability of energy grids also came to the forefront, as voltage fluctuations and harmonic distortions posed disruptions to power supply, leading to economic losses and inconvenience. The integration of renewable energy sources further heightened the need for advanced technologies to ensure the smooth and reliable operation of energy grids [38]. Alongside these factors, regulatory changes, globalization of energy markets, and a flourishing landscape of research and innovation collectively propelled endeavors aimed at elevating the overall quality of energy production and distribution worldwide [39]. This scenario points to the need to promote technologies aimed at better identifying and combating harmonic distortions, promoting the supply of quality energy.

The first related patent (EP1340988A2) was filed in 2003 by the Institute for Solar Energy Supply Technology (ISET) at the University of Kassel (Hessen, Germany) and refers to a method and an apparatus for measuring the impedance of a power supply network at its nominal frequency by impressing a test current into the network and measuring the changes in the network current and the network voltage obtained thereby. The patent describes the measurement using a microprocessor device for two-way communication between the grid control point and the electrical loads [40]. The constant need to produce clean, high-quality energy is intensifying, driven by global initiatives to harness renewable sources and, thus, reduce CO₂ emissions resulting from the combustion of fossil fuels. According to IRENA (International Renewable Energy Agency), energy transition to renewable sources is the only strategy that would allow for global warming to be controlled by just 1.5° by 2050 [41,42]. In this sense, it is worth highlighting the increase in renewable energy generation capacity installed between 2001 and 2020 [41]. According to the UNEP (United Nations Environment Program) Global Renewables Status Report, 2021 will see record growth in the deployment of renewable energies [43]. In 2019, the Institute for Applied Economic Research (IPEA) released the sustainable development goals of the United Nations (UN) 2030 agenda [44]. Among the goals set, the substantial increase in renewable energies in the global energy matrix and global cooperation to finance new infrastructure and clean energy technologies deserve to be highlighted [44]. Taken together, these data point to an increase in investment in the area, which can also be reflected in an increase in patent filings.

It is important to note that with the growing increase in alternative sources of electricity generation, such as wind and solar power, power quality studies are becoming increasingly important due to the possible contributions of disturbances in the network [45]. In this way, power quality studies play a fundamental role in verifying variations in voltage, current, frequency, or harmonic distortions that result in faults or interfere with the operation of equipment on the Basic Grid [46].

Among the patents identified in this study, most of the technologies developed focused on new methods or equipment to improve energy generation and control. The patent CN107968407A refers to the development of a device to increase energy generation and power quality [47]. The patent claims a wind power generator connected to an electric network and a control module for determining photovoltaic cell number according to the number of calculated harmonic compensation [47]. WU Zhi et al., 2018 (CN109002938A) describe a method that involves initializing genetic algorithm parameters for randomly constructing direct current (DC) grid structures according to an encoding operation, where the number of the DC grid structures is equal to a population number [48]. The DC grid structure is characterized according to robust programming by performing chromosomal individual operations. The patent filed by LI Geng-chen et al., 2022 (CN115632400A) outlines a method that proficiently mitigates system harmonics, enhancing the voltage waveform quality within the non-linear load distribution station area and thereby boosting the operational efficiency of grid-connected inverters [49]. This method ensures a consistent and dependable output voltage, maintaining the total harmonic distortion (THD) of the voltage below 3% and elevating overall electrical power quality. The method can be used in a photovoltaic power generation and energy storage system, wind power, and a 100% inverter.

The expansion of power electronics-based equipment utilization in conjunction with industrialization processes has amplified the influence of power quality disruptions. The main concern arising from these disturbances relates to their possible social and industrial ramifications. This concern extends globally and involves utilities, customers, network operators, and equipment manufacturers, leading to different degrees of shared responsibility for power quality solutions. As a consequence, this divergence results in different levels of financial and technical losses for both network operators and customers. Studies have shown that a power outage of 30% for a very short period can restart the programmable controllers of an entire assembly line [50]. Consequently, efforts are underway to improve the detection and precise identification of these interruptions, with a primary focus on the preventive forecasting of problems before they manifest themselves [46]. International standards have therefore been established to define and characterize disturbances affecting power quality: IEEE-1159, IEC-61,000-4-30, and EN 50,160 [51]. Disturbances that reduce power quality can be classified into the following types: voltage drop, voltage oscillation, harmonic distortion, notching, voltage fluctuation, interruptions, oscillatory transients, and spikes [52]. Detecting, identifying, and categorizing power quality disturbances assume a pivotal role in overseeing, strategizing, operating, and upholding power distribution systems [53].

Different methodologies have already been tested and implemented for measuring and identifying power quality. One traditional, conventional method for identifying power quality disturbances involves a point-to-point comparison of consecutive cycles. However, this approach has limitations in detecting disturbances that occur periodically, such as flat-top and phase-controlled load waveform irregularities. In recent times, numerous approaches relying on digital measurements of fundamental and harmonic voltage magnitudes, along with their respective frequencies, have been introduced. To date, algorithms leveraging the fast Fourier transform (FFT) have been extensively employed for this purpose [50]. Genetic algorithms (GA) offer a powerful approach to evaluating and mitigating harmonic distortions in energy power systems [31].

GAs represents a population-based stochastic optimization method rooted in the of evolution. They are considered a derivative-free metaheuristic technique that draws inspiration from the principles of natural selection and the evolutionary process. The process involves formulating an objective function to quantify the level of harmonic distortion, such as total harmonic distortion (THD) or individual harmonic amplitudes, and representing potential solutions in a genetic algorithm-friendly encoding scheme. Solutions typically correspond to control settings or configurations of power electronic devices like inverters or filters. The genetic algorithm proceeds by initializing a population of potential solutions, evaluating their fitness based on the objective function, and iteratively evolving the population through selection, crossover, and mutation operations. This process continues until a termination criterion is met. The end result is optimized control parameters or configurations that effectively reduce harmonic distortions in the power system [54]. Gupta and colleagues (2014) proposed an efficient genetic algorithm (GAs) to improve the reliability and power quality of distribution systems using network reconfiguration. The method was used to assess a range of power quality and reliability metrics, including feeder power loss, node voltage deviation, average interruption frequency index, average interruption unavailability index, and energy not supplied. These metrics were consolidated into a unified objective function [55]. In their 2023 study, Ugwuagbo et al. employed artificial neural networks to forecast the three-phase total harmonic distortion of current (THDi) at the Point of Common Coupling in industrial settings. The model was trained utilizing input parameters collected from power quality meters at both 33 kV and 132 kV voltage levels, drawing data from five distinct steel manufacturing plants. The findings revealed that the model incorporating two hidden layers, with four major power parameters (current, apparent power, reactive power, and active power) as inputs during the training phase, exhibited the highest performance, achieving an impressive 95.5% coefficient of correlation between the measured THDi and the predicted THDi [56].

Table 2. Articles that focus on measurement of harmonic distortion.

Title	Main Findings	Reference
Total Harmonics Distortion Prediction at the Point of Common Coupling of industrial load with the grid using Artificial Neural Network	The paper introduces a prediction model for three-phase total harmonics distortion of current (THDi) at the point of common coupling, primarily for industrial consumers. It employs an artificial neural network (ANN) with a multilayer perceptron neural network (MLPN) and easily measurable input parameters. Data from 33 kV and 132 kV voltage levels at five steel manufacturing plants were used to train eight different models. The best-performing model, with two hidden layers and four key power parameters (current, apparent power, reactive power, and active power), achieved a remarkable 95.5% correlation between the measured and predicted THDi, demonstrating its effectiveness in addressing power quality issues for industrial consumers.	[56]
Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation	The study introduces a methodology for developing a parameterized model that can estimate the generated power in a photovoltaic generation system. In addition to power estimation, the same methodology is used to create a mathematical model for estimating harmonic distortion, which helps predict both the quantity and quality of the produced power. The methodology uses a genetic algorithm to derive a mathematical model that best represents the variations in generated power over the course of a day. The results indicate that the genetic algorithm methodology outperforms the artificial neural network, showcasing its superior performance in estimating and predicting power generation behavior and quality in photovoltaic systems.	[57]
Reduction of Total Harmonic Distortion (THD) on Multilevel Inverter with Modified PWM using Genetic Algorithm	In this research, modified PWM was applied to the multilevel inverter (MLI) single-phase three-level diode clamp full bridge. The genetic algorithm method was used to obtain variable amplitude and phase shift angle, and the results showed reduced THD voltage compared to Sinusoidal Pulse Width Modulation (SPWM), with a decrease of up to 0.19 or a 65.51% reduction for modified PWM with harmonic injection n = 7 and GA optimization at ma = 0.8 (A = 0.0936 and ø = 0 radians). Similarly, for modified PWM with harmonic injection n = 22 and GA optimization at ma = 0.4 (A = 0.1221 and ø = 0 radians), there is a reduction of up to 0.08 or a 12.30% decrease in THD voltage.	[58]
Enhanced Harmonic Elimination Using Genetic Algorithm Optimization in Multilevel Inverters	The paper employed an advanced approach to computing switching angles, leveraging the power of genetic algorithms (GA). The findings involved a comprehensive comparison with the traditional selective harmonic elimination technique within a seventeen-level staircase waveform. These results unequivocally demonstrate the effectiveness of the developed method as a highly efficient approach for achieving optimal harmonic elimination in multilevel inverters.	[59]
Total Harmonic Distortion Optimization of Multilevel Inverters Using Genetic Algorithm: Experimental Test on NPC Topology with Self-Balancing of Capacitors Voltage Using Multilevel DC–DC Converter	In this research, a genetic algorithm is employed to optimize the reduction of total harmonic distortion in three-phase inverters with varying levels (three, five, seven, and nine). The outcomes indicate a remarkable reduction, with the total harmonic distortion in the three-level inverter reaching less than 23.60% and approximately 5% for the nine-level inverter. The study’s findings are not confined to optimization alone; they are also applied to address another issue related to the neutral point clamped multilevel inverter, specifically, the imbalance in DC-link capacitor voltages. To resolve this challenge, a multilevel boost DC–DC converter is proposed as a viable solution. The proposed system’s effectiveness is validated through experimental testing using a three-phase three-level neutral point clamped inverter in combination with a four-level (or two-level) DC–DC boost converter.	[60]
Total Harmonic Distortion Minimization in Multilevel Inverters using the Teaching–learning based Optimization Algorithm	In this article, the focus is on minimizing the total harmonic distortion (THD) of the output voltage in a multilevel inverter. The reduction of the harmonic components in the inverter’s output voltage is a key objective in THD minimization. This is achieved through the careful selection of switching angles, with the use of a teaching–learning-based optimization algorithm to determine the optimal angles for generating the desired voltage with the lowest possible THD. The study’s results, based on both experimental and simulation data, demonstrate the advantages of this approach compared to previous works that explored similar concepts using genetic algorithms. Experimental trials conducted on a seven-level inverter further validate the feasibility and effectiveness of this method.	[61]

presents some articles in the scientific literature that approach the use of the prospected technology.

Figure 3 shows the most relevant applicants (a) and inventors (b) among the patents prospected. State Grid Corp China (Beijing, China) appears in first place with 15 documents. The company is the largest electricity distribution company in China, covering more than 88% of its territory, and in the world, with operations in Italy, Australia, Portugal, the Philippines, Hong Kong, and Brazil [62]. The group ranks second in the Fortune Global 500 and uses state-of-the-art technology to ensure the supply of clean, sustainable, and safe energy. With registered capital of RMB 536.3 billion, the company supplies energy to approximately 1.1 billion people [63]. Next up is EDSA Micro Corp (San Diego, CA, USA) with five documents. The company, founded in 1983, develops software for the simulation, implementation, and maintenance of electric power with support in the Americas, Europe, Asia, and Africa [64].

Another four Chinese institutions share third place with four papers each. They invest in operating and maintaining power grids as well as building and testing power equipment. It is important to highlight the substantial presence of research institutes and universities among the list of main applicant companies. These institutions represent half of the companies selected, and this reflects their significant participation in patent applications. Among the patents developed by universities, Sichuan University (Chengdu, Sichuan, China) and China Three Gorges University (Yichang, Hubei, China) published in 2019 and 2021, respectively. While the first invention relates to a method for controlling hybrid alternating current distribution and optimizing energy storage, the second relates to a new device for increasing power quality by compensating for calculated harmonics.

Figure 4 illustrates the key countries and regions at the forefront of technology production in this field. A closer examination of the leading nations in patent applications reveals China’s significant lead in technology development, with the highest number of patent submissions—110 documents. Following is the United States, with 12 documents. China holds a pivotal position in the advancement and global dissemination of renewable energy technologies because its dependence on non-renewable energy for electricity generation is largely responsible for its incessant CO₂ levels.

In 2015, renewable energy constituted less than 24% of China’s total power generation, underscoring the enduring dominance of fossil fuels as the principal energy source for electricity generation and the overall energy supply in the country [65]. The nation has undertaken ambitious environmental commitments, aiming to attain peak carbon emissions by 2030 and carbon neutrality by 2060 [32]. In pursuit of these goals, wind power has emerged as a compelling and practical alternative, further reinforcing China’s leadership in sustainable energy solutions. Moreover, the country’s commitment to renewable energy not only benefits its own environmental initiatives but also contributes significantly to shaping the international landscape of clean and sustainable technologies [66].

To accomplish these objectives, China’s energy regulations are central to its path towards carbon neutrality, given that the energy sector contributes to approximately 90% of the nation’s greenhouse gas emissions. While China has made significant strides towards a sustainable-energy future, it faces substantial challenges [67]. Conversely, China has been a global leader in annual solar power capacity expansion, outpacing all other nations in this regard. Consequently, the Chinese government has committed substantial investments in renewable energy development to facilitate the attainment of its carbon reduction objectives. Over the past decade and a half, public funding for renewable energy has surged by more than USD 286 billion [68]. Beyond financial investments, it is widely acknowledged in the literature that achieving both carbon emission reduction and renewable energy expansion hinges on technological innovation (TI) and robust research and development (R&D) investments. These endeavors not only hold the potential to stimulate economic growth but also ensure a sustainable ecological balance [69].

It is worth emphasizing that one of the main drivers of China’s remarkable technological progress lies in the growing collaboration between research institutions and private companies through research and innovation. This collaborative mindset is evident in several patent documents discovered during this study, exemplifying the tangible results of such partnerships. The patent (CN113572163A) filed by Zhongshan Power Supply Bureau Guangdong Power Grid Co. Ltd., the Zhejiang University, and Southern Power Grid Digital Power Network Research Institute Co. Ltd. refers to an adaptive discrete particle swarm optimization-based distribution network optimization method for photovoltaic power generation and wind power generation used in intelligent operation and control technology of digital grids. The method involves performing random initialization to form multiple particle position matrices and multiple particle velocity matrices, which are used to update particle positions and set fitness functions [70]. Xinjiang University, Dalian University of Technology, and the company State Grid Xinjiang Electric Power Co. Ltd. filed a patent (CN113887912A) related to a non-invasive load identification method of embedded devices under deep learning, combining the results obtained by a one-dimensional convolutional neural network with the load power feature and harmonic feature for second identification in a single-hidden-layer neural network [71].

In this research, the United States emerges as the second country on the list, with mere 12 registered patents, underscoring a considerable gap in patent applications. Despite this modest patent record, it is essential to note that the United States holds the dubious distinction of being the largest cumulative carbon emitter globally, responsible for emitting over 509 billion tons of carbon dioxide from 1850 to 2021. This astonishing figure accounts for a significant 20.3% of the world’s total carbon emissions and has contributed to a 0.2 °C increase in global temperatures [72]. The United States has a diverse range of energy resources and production methods, which can be classified into several broad categories, including primary and secondary sources, renewable energy, and fossil fuels. The primary energy sources encompass fossil fuels (petroleum, natural gas, and coal), nuclear energy, and renewable sources of power. On the other hand, electricity serves as a secondary energy source, derived from the generation of primary energy resources. In 2021, the consumption of renewable energy (excluding hydroelectricity) reached a remarkable 7.48 EJ (input-equivalent), marking a substantial growth of approximately 130% since 2011 when it stood at 3.25 EJ. Meanwhile, renewable energy generation in the United States (excluding hydroelectricity) experienced a remarkable surge, more than tripling from 201.9 TW-hours in 2011 to an impressive 624.5 TW-hours in 2021 [73].

According to the Lawrence Berkeley National Laboratory, public policies aimed at advancing renewable energy, such as state renewable portfolio standards (RPS) mandating a minimum percentage of electricity from renewable sources, have played a pivotal role, contributing to approximately 45% of the growth in renewable energy within the United States [74]. The renewable portfolio standard (RPS), a key state-level policy, requires electricity providers to incorporate a designated percentage or quantity of renewable energy into their retail sales. As of now, this policy has been embraced by 29 states, encompassing an impressive 54% of the total electricity generation within the U.S. electricity market.

The adoption of RPS programs yields numerous significant benefits for both the economy and the industrial sector, including diversifying energy portfolios, reducing dependence on fossil fuels, minimizing externalities associated with fossil fuels (e.g., GHG emissions, air pollution, and thermal effluent), fostering economies of scale to mitigate renewable technology costs, and promoting the social acceptance of renewable energy through information spillovers (e.g., raising awareness about the negative externalities and economic costs of fossil fuels) [75]. The expansion of renewable energy is significantly bolstered by crucial federal incentives, such as the Investment Tax Credit, which can reduce initial expenses by a notable 10–30%. Furthermore, various state-level incentives, including tax credits, grants, and rebates, further contribute to this growth. In contrast, during the fiscal years spanning 2020 to 2024, Congress allocated over USD 5.7 billion in tax relief to the oil and gas sectors [76,77]. These policies prioritized the development and promotion of energy technology and invested substantial technical R&D subsidies [78].

Furthermore, we have identified two key international organizations in this context: the World Intellectual Property Organization (WO or WIPO) and the European Patent Office (EP or EPO), each associated with 01 and 03 patents, respectively. The significance of filing patents with these organizations lies in the ability to submit multiple international applications through a single process. This approach holds immense importance for technology manufacturers, as it streamlines the process of safeguarding their inventions across borders, reducing expenses, and simplifying the often complex and time-consuming individual registration procedures in multiple countries. Ultimately, it offers a practical means to protect technology on a global scale.

According to data from the DWPI database, the abundance of technologies not only signifies recent innovations but also offers a comprehensive view of the current market status and its various segments. Figure 5 illustrates the technological areas of the prospected inventions, and for this prospection, 10 technological classifications were identified. The top three technologies presented and highlighted in the charts account for 77% of patent applications. The number of technology area assignments to patent applications can speak to a diverse portfolio or a specific technical focus. As can be seen, the two companies and one university leading development in these technological areas are State Grid Corp. China, EDSA Micro Corp, and University of Southeast.

The technological area (a), in red, refers to “blockchain, transaction, payment, inventory, item, costumer, asset”. Here, State Grid Corp. China have 62% of their patent applications classified in this area, while University of Southeast has 29%. Overall, most of the depositors highlighted in Figure 4 have filings classified in this technological area. The technological area (b), in pink, refers to “charging, wireless power, battery, pack, direct current, voltage, rechargeable”, and (c), in purple, refers to “neural network, deep learning, artificial intelligence, knowledge, computing”. The identified patent applications in these technological areas describe a new device or even a novel method for improving new energy and generating electrical energy quality.

Table 3. Patents documents involving technologies related to harmonic energy monitoring.

Priority Number	Title	Refers to	Reference
CN201610894806A	Distributed power grid electric energy quality prediction method and apparatus.	Distributed power quality prediction method; involves main grid operational data with voltage and harmonics data and contains main grid power quality prediction data with frequency factor and voltage data	[79]
CN202211270838A	Power distribution network optimization method based on adaptive discrete particle swarm optimization.	Distribution network optimization method based on adaptive discrete particle swarm optimization, involves performing global reactive power adjustments of the distribution network to stabilize the voltage of the distribution network node and reduce network losses.	[80]
CN201710783960A	Optimization planning method of power distribution network comprising new energy power generation systems and special load.	The load priority planning method for the power generating system’s power distribution network involves analyzing the subsystem’s electrical waveform distortion conditions and carrying out the power distribution network optimization process. After identification, the proposed invention solves the problem by modeling and reorganizing the network to compensate for the quality deviation.	[81]
CN202110735473A	Power distribution network planning method and system	The article reports on a new method that effectively guarantees the supply of quality energy by better adapting to heating influences on the grid. The method consists of a construction stage of grid planning based on the power output of the power supply, generator, and heater, and a resolution stage through the adoption of an algorithm to assist in optimal grid planning.	[82]
CN202210645076A	Harmonic detection method and suppression device based on ant colony optimization BP neural network.	A technique that detects the presence of harmonics with high speed and precision. Identification is carried out using a suppression device based on an ant colony optimization BP neural network that involves collecting the load current and the phase angle of phase A, and detecting the wave current obtained from the number of photovoltaic cells according to the calculated harmonic compensation number.	[83]
CN201811085672A	Bi-level planning method for hybrid AC/DC distribution network based on N-1 safety criteria.	Use of a genetic algorithm for model planning of a hybrid alternating current and direct current distribution network, guaranteeing the economy of network distribution planning. It involves achieving minimum adaptability of the alternating current/direct current network if the current iteration number reaches the upper-limit value, providing important guidance in decision-making at the time of installation.	[48]
CN202110688202A	Micro-grid optimal configuration method considering static voltage stability of power distribution network.	Use of the algorithm developed to optimize the improved multi-objective balance and increase the quality of the energy generated. It has a wind generator connected to the electricity grid and a control module to determine the number of photovoltaic cells according to the calculated harmonic compensation number.	[84]
CN202110859394A	Distributed power supply active power distribution network optimization scheduling method based on improved affine algorithm.	Improved algorithm that predicts the output power range of wind energy, improves scheduling calculations and reduces the energy flow interval. In addition, the device provides data for optimal scheduling of the power distribution network.	[70]
CN201910635250A	Incremental distribution network double-layer optimal allocation method with distributed power supplies.	Distributed grid power supply containing optimized double layer configuration method. It involves target selection using attribute decision mode to provide evaluation index and evaluation process of grid access DG the number of photovoltaic cells according to calculated harmonic compensation number.	[85]
US8180622B2	Systems and methods for a real-time synchronized electrical power system simulator for “what-if” analysis and prediction over electrical power networks.	System for real-time modeling of data center electrical system performance. It has a power system simulation engine that operates in scenario-builder mode to modify parameters in the virtual power system model (number of photovoltaic cells according to the number of harmonic compensations calculated).	[86]

shows other patents that have been prospected and are related to the use of different techniques and devices to identify and monitor harmonic distortions in the electricity network.

Among the patents selected (Table 3), it can be seen that they differ in terms of how they propose to reduce harmful harmonics. While one group of patents takes the approach of preventing/anticipating the appearance of harmonics, others focus on reducing their effects once they have been identified in the networks. In the first group, we identified applications capable of monitoring and predicting future demand for quality electricity distribution (CN201610894806A) [79], as well as planning and improving supply in distribution networks (CN202110735473A, CN202110859394A, CN201910635250A) [70,82,85]. On the other hand, in the second group, most of the patents seek to solve the problem by optimizing network distribution based on improving detection (CN201710783960A) [81], reducing voltage losses (CN202211270838A) [80] in microgrids (CN202110688202A) [84], and suppressing harmonics through specific devices and methods (CN202210645076A) [83], thus improving power quality and the reliability of power supply.

The final component of the patent search conducted is shown in Figure 5, revealing the primary IPC codes assigned to the discovered patent documents. The IPC code, established in 1971, operates on a hierarchical language of distinct symbols, independently arranged to categorize patents and utility models according to their respective technological domains [87]. This classification system serves as a vital tool for organizing and cataloging inventions based on their specific areas of technology, offering valuable insights into the global landscape of patent innovation. Figure 6 shows the main codes found in our similarity search. Of all the documents found, 63 were classified under code H02J 3/28, which is related to electricity storage, distribution, and supply systems. This classification belongs to a major code, H02J, that is related to circuit arrangements or systems for supplying or distributing electric power; systems for storing electrical energy.

The second most commonly displayed code in the patent documents was G06Q 50/60, which refers to the adapted use of information technology for management and commercial purposes, corroborating the keywords chosen in this work to search for patents. Most of the documents (7) were classified in technology area “H”, which refers to “electricity”. Three of the documents were classified as “G”, which is related to “physics”.

Table 4. IPC codes in technological prospecting.

IPC Code	Related to
H02J 3/38	Arrangements for feeding a single network in parallel by means of two or more generators, converters, or transformers
G06Q 50/06	Electricity, gas, or water supply
G06Q 10/06	Management of resources, workflows, human resources, or projects; business or organizational planning; organizational or business models
G06Q 10/04	Forecasting or optimization specially adapted for administrative or management purposes, e.g., linear programming or “stock cutting problem” (market forecasts or for commercial activities G06Q 30/0202)
H02J 3/46	Controlling the distribution of output power between generators, converters, or transformers
H02J 3/00	Circuit layouts for AC mains or distribution network
H02J 3/18	Arrangements for adjusting, eliminating, or compensating for reactive power in networks (for voltage adjustment H02J 3/16)
H02J 3/06	Control of energy transfer between connected networks; control of load distribution between connected networks
H02J 3/38	Arrangements for feeding a single network in parallel by means of two or more generators, converters, or transformers
H02J 3/48	Controlling the distribution of the phase component

shows the descriptions of the 10 main codes found.

The increase in the deposit of documents in the areas of electricity and physics may be related to the current environmental scenario faced by humanity. The effects of global warming, climate change, and pollution have had often irreparable consequences for society [88]. These effects alert us to the need to make assertive decisions regarding the reduction of pollutants and greenhouse gas emissions. Energy transition is a sustainable strategy that is increasingly being used to change the source of energy generated in the world, contributing to global decarbonization. Among the technologies used, wind energy is a promising option for mitigating these changes [89]. Due to the development and advancement of technology, the operating costs of installation and maintenance have been reduced in recent decades [90]. In addition, new devices and algorithms are constantly being developed to better identify and resolve faults in transmission systems. Several institutions have focused on identifying harmonic distortions with the aim of improving the quality of the power supplied and optimizing performance.

The main indicators used to quantify and evaluate wave distortions measure variations in amplitude, phase, and frequency over time. Through computer implementations, the spectral content of these signals can be identified [91]. Because they have limitations related to spectrum dispersion and signal stationarity, due to the data window and analysis interval chosen, genetic algorithms that help with identification and analysis are being used more frequently. They process the signals, providing precise temporal results for locating and quantifying the damage, as well as filtering out measurement noise. Patent CN202110859394A is an example of an improved algorithm for diagnosing faults in wind turbines. The algorithm predicts the power output range, improves scheduling calculations, and reduces the power flow interval [70].

Genetic algorithms offer a powerful tool in the realm of harmonic distortion diagnosis and prevention, significantly enhancing the quality of electrical energy. These algorithms enable the identification and mitigation of harmonic distortions in complex power systems [92]. By optimizing parameters like switching angles in inverters and control strategies for various electrical equipment, genetic algorithms can help reduce harmonic content in the voltage and current waveforms. This, in turn, minimizes harmonic distortions, leading to improved power quality. Genetic algorithms also facilitate the design of efficient filters and compensators, enabling precise and adaptive control mechanisms to counteract harmonics and enhance energy quality [93].

Moreover, genetic algorithms can aid in the early detection and prevention of harmonic issues. They can be employed to develop predictive models that anticipate potential disturbances in the power system, allowing for proactive measures [94]. These algorithms can fine-tune the operation of power converters and control systems to ensure the minimal introduction of harmonics into the electrical network. Additionally, genetic algorithms can be used to optimize the placement and sizing of harmonic filters, ensuring that they are strategically located to mitigate disturbances effectively. By incorporating genetic algorithms into the diagnosis and prevention of harmonic distortions, power systems can maintain cleaner and more reliable energy, reducing equipment damage and operational inefficiencies while enhancing overall energy quality [95].

Technological prospection based on patents offers a multifaceted approach to advancing the fields of harmonic distortion diagnosis, prevention, and energy quality enhancement. Through systematic patent analysis, experts can pinpoint innovative techniques and technologies relevant to these areas. The study of recent patents enables professionals to remain well informed about the latest breakthroughs, thus empowering them to integrate cutting-edge methods into their energy systems. Moreover, they can benchmark best practices by assessing patents filed by industry leaders, gaining insights into the most effective strategies for reducing harmonic distortions and optimizing power systems [96].

Furthermore, patent analysis serves as a catalyst for technology transfer. By exploring licensing or collaboration opportunities with patent holders, organizations can expedite the adoption of advanced harmonic distortion mitigation techniques and enhance energy quality. As technology trends evolve, patent data provide a window into emerging developments in the field, enabling researchers to allocate resources to areas with the highest potential for improvement. By proactively identifying these trends, institutions can position themselves as pioneers in addressing the challenges associated with power systems and electrical energy, promoting innovation and sustainable energy solutions [97].

Moreover, the estimation derived from patents reflects the novelty introduced in this work, assisting companies and organizations in making informed decisions about technological development in the field of harmonic distortion and energy quality. By leveraging patent data along with marketing surveys, consumer analysis, and evaluations of internal production capacity, companies can identify opportunities and assess the risks associated with the development of new inventions in this domain. This comprehensive approach can boost the research and development process for conceiving new products to enhance energy quality and address the intricacies of power systems, contributing to more effective solutions and innovations in the energy sector [32,34].

4. Conclusions

Sustainable energy sources like wind energy play a pivotal role in the transition towards a greener and more environmentally responsible energy landscape. Wind power offers a clean and renewable energy solution, harnessing the kinetic energy of the wind to generate electricity. While wind energy contributes significantly to sustainability goals, ensuring the quality of the energy produced is equally crucial.

The issue of harmonic distortion, characterized by unwanted variations in the frequency and amplitude of electrical waveforms, poses a challenge in the realm of energy quality. Mitigating harmonic distortions in wind energy systems is essential to maintain the stability and reliability of the power grid and to ensure that the electricity supplied meets high-quality standards. By addressing harmonic distortions in wind energy, we can not only enhance energy quality but also facilitate the seamless integration of renewable sources into the global energy mix, furthering our commitment to a sustainable and cleaner energy future.

The exploration of patent documents in this technological investigation has illuminated significant advancements in the development of innovative methods and techniques for the precise management of harmonic distortions in wind turbines, primarily through the utilization of genetic algorithms. These findings have not only identified the key technology applicants but also highlighted notable inventors, with State Grid Corp. China and EDSA Micro Corp. being prominent figures in this regard. Notably, the research underscores the leadership of China in the global wind power market, supported by its extensive portfolio of 110 patent documents and a dominant position in these applications. The outcomes of this study unequivocally underscore the pivotal role of artificial intelligence and computational analysis as the future strategies for enhancing existing methods, particularly in light of the unique challenges associated with the maintenance and sustainability of wind turbines. This emphasizes the transformative potential of these emerging technologies in ensuring the continued growth and effectiveness of the energy sector.

By pinpointing key areas and countries of technological innovation, this research provides valuable insights that companies can leverage for informed decision-making. These findings not only aid in strategic planning but also open doors to novel technologies of interest, enabling organizations to direct their research and development efforts towards creating their inventions. Moreover, this work serves as a direct resource for identifying market demands, which can serve as the catalyst for the innovation and development of new inventions. It emphasizes the role of combining traditional fault diagnosis techniques with advanced mathematical models, including artificial intelligence and deep learning, which are increasingly critical in the global pursuit of renewable and sustainable energy solutions.

It is important to acknowledge that, like any technological prospection based on patent analysis, this research may have some limitations. These limitations may stem from the specific search strategy employed, which could potentially restrict access to documents linked to the chosen keywords. Nevertheless, it is worth noting that the keywords employed in this analysis fostered a comprehensive and robust discussion on wind turbine failure diagnosis, effectively highlighting the challenges within the sector. As the field continues to evolve, further research is warranted to address these complexities and contribute to a more comprehensive understanding of the subject matter.