This is a self-archived – parallel published version of this article in the publication archive of the University of Vaasa. It might differ from the original. Artificial intelligence (AI)-based optimization of power electronic converters for improved power system stability and performance Author(s): Gros, Ioana-Cornelia; Lü, Xiaoshu; Oprea, Claudiu; Lu, Tao; Pintilie, Lucian Title: Artificial intelligence (AI)-based optimization of power electronic converters for improved power system stability and performance Year: 2023 Version: Accepted manuscript Copyright ©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Please cite the original version: Gros, I-C., Lü, X., Oprea, C., Lu, T. & Pintilie, L. (2023). Artificial intelligence (AI)-based optimization of power electronic converters for improved power system stability and performance. In 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). IEEE. https://doi.org/10.1109/SDEMPED54949.2023.10271490 Artificial intelligence (AI)-based optimization of power electronic converters for improved power system stability and performance Ioana-Cornelia Gros Department of Electrical Machines and Drives Faculty of Electrical Engineering, Technical University of Cluj-Napoca Cluj-Napoca, Romania ioana.gros@emd.utcluj.ro https://orcid.org/0000-0003-1270-1042 Tao Lu Department of Electrical Engineering and Energy Technology, University of Vassa Vaasa, Finland Tao.Lu@uwasa.fi Xiaoshu Lü Department of Electrical Engineering and Energy Technology, University of Vassa Vaasa, Finland xiaoshu.lu@uwasa.fi https://orcid.org/0000-0002-1928-8580 Lucian Pintilie Department of Electrical Machines and Drives Faculty of Electrical Engineering, Technical University of Cluj-Napoca Cluj-Napoca, Romania lucian.pintilie@emd.utcluj.ro Claudiu Oprea Department of Electrical Machines and Drives Faculty of Electrical Engineering, Technical University of Cluj-Napoca Cluj-Napoca, Romania claudiu.oprea@emd.utcluj.ro https://orcid.org/0000-0001-6602-1507 Abstract—The present review paper provides an overview of the recent advances in AI-based techniques for the design and optimization of power electronic converters. There is an increased demand on power converters in applications like renewable energy generation, microgrids, electric and hybrid vehicles, high-voltage DC power transmission etc. with focus on their design and optimization. In this context, various AI techniques are discussed, such as: machine learning, deep learning, reinforcement learning, and evolutionary algorithms, artificial neural networks, fuzzy logic control, expert systems and their applications in power electronics and electric drives. Some case studies from the literature are referred and potential benefits of AI-assisted design and optimization of power electronic converters with aspects of enhanced power system stability and performance are highlighted. Keywords—power electronic converters, artificial intelligence, optimization, renewable energy generation, I. INTRODUCTION There has been a significant increase recently in applications of power converters in renewable energy generation, electric and hybrid vehicles, microgrids, and high- voltage DC power transmission. In an overview of the recent advances and industrial applications of multilevel converters [1], different types of DC/DC converters were described which are commonly used in the voltage step-up or step-down of DC power generated by renewable energy resources (RES), such as solar energy, to match the voltage requirements of the load or the power grid. Wang et al. (2015) provides a review of the various power electronics technologies used for the grid connection of utility-scale battery energy storage systems [2]. It covers the advantages and disadvantages of different converter topologies, control strategies, and their impact on the overall system efficiency and reliability. The review also discusses the key challenges and future directions in this area. These review papers have demonstrated that the design and optimization of these power electronic converters are critical for ensuring optimal performance and stability of renewable energy systems. However, as the integration of RES and energy storage systems into power grids is dramatically increasing, the complexity of these systems poses significant challenges to traditional optimization techniques, leading to a growing interest in artificial intelligence (AI)-based optimization techniques, in particularly, the optimization of power electronic converters for improved power system stability and performance is of critical importance and complex. The paper provides a unique perspective by highlighting recent developments in the application of AI techniques such as machine learning, deep learning, and evolutionary algorithms for converter optimization. Furthermore, the paper also explores the potential benefits of integrating AI-based techniques with traditional control methods to enhance converter performance. This review paper can serve as a constructive resource for researchers and engineers who are working on designing and developing innovative solutions to address the challenges of power system stability and reliability. II. CURRENT DEMANDS OF POWER ELECTRONIC SYSTEMS Power electronic systems are currently used in most electric equipment, but the focus of this paper is on high power systems, like the electric drive of EVs or HEVs and their charging systems, industrial applications and energy production in conventional or renewable energy systems. Regardless of the application of the power electronic system, the general requirements are energy efficiency, reliability, high power density and communication capabilities. The biggest challenge in adding RES facilities to the existing production infrastructure is trying to overlap the production and consumption daily curves; this can be obtained in either by using energy-storage systems or by shifting the energy consumption by using variable fees or incentives. Carrasco et al. presents new trends in power electronics technology for the integration of RES and energy-storage systems [3] showing several multi-level converter topologies used in wind turbines applications and PV systems. DC-DC converter topologies for automotive applications are presented in [4], covering both the drive system for EVs and HEVs and fast charging stations, concluding that Multi-Device Interleaved DC-DC Boost Converter (MDIBC) topology is best suited for high-power applications, while other topologies, such as Sinusoidal Amplitude High Voltage Converter (SAHVC), Zero Voltage Switching Resonant Converter (ZVSC) is more suitable for low-power EVs or HEVs. The power densities of conventional DC-DC converter with Si-based semiconductors is limited, so Wide-Bandgap Semiconductors (WBSC) with SiC semiconductors seem to be more suited for applications that require higher power densities [5,6]. In the case of the power electronic systems used in power generation, more specific requirements are considered, like the electric power quality or Voltage Ride Through capabilities. Al-Shetwi et al. presents a survey of the requirements and control methods used for grid-connected RES, highlighting the differences between standards in different countries regarding the interconnectivity of these systems [7]. Power quality requirements include frequency stability (managing the active power input in the network depending on the frequency variations), reactive current injection/absorption (aiding the system to recover after a fault by injecting reactive currents [8]), voltage regulation and reactive power control (ability of the power electronic system to provide reactive power with a variable power factor), harmonics, voltage unbalance, flicker. Another important aspect regarding the power electronic systems for RES is their ability to recover from a fault; Tarafdar et al present a review of Fault Ride Through legislation of different countries [9], showing the requirements for Low-Voltage Ride Through – LVRT, Zero-Voltage Ride Through – ZVRT and High- Voltage Ride Through – HRVT.Beside these requirements, the power electronic systems must be able to consider two other aspects, very important in the current environmental and economic context: sustainability and cons-effectiveness. The application of the circular economy concept in the field of power electronics means that these systems must use recyclable materials, have a low environmental impact and consider the regulations related to the use of hazardous materials. III. AI METHODOLOGIES Different AI-based approaches have been proposed and applied to improve the performance, efficiency, and reliability of power systems, electronic converters and drives. Common algorithms include neural networks, genetic algorithms, back- propagation neural network, decision tree regression, random forest, support vector regression, fuzzy logic controllers, explainable artificial intelligence, adaptive neuro-fuzzy inference system and deep reinforcement learning controllers, Fig. 1 shows broad categories of the commonly applied AI methods. Fig. 1 Broad categories of the commonly applied AI methods reviewed in this study. Machine learning is the most general category, with supervised, unsupervised, and reinforcement learning as its subcategories. Optimization and control systems are other major categories, with genetic algorithms and fuzzy logic controllers falling under them respectively. Explainable artificial intelligence is a distinct category due to its focus on transparency and interpretability. Among reviewed AI methods shown in Fig. 1, neural networks can learn patterns in data and make predictions based on the learning. They have the advantage of being able to learn complex non-linear relationships between inputs and outputs, however, can be computationally expensive. Back- propagation neural network is a learning algorithm used in neural networks to train them on a given dataset. It works by iteratively adjusting the weights of the connections between neurons to minimize the error between the predicted output and the actual output. They can be sensitive to overfitting and require a large amount of data to train. Decision tree regression uses a tree-like model to predict the value of a target variable based on input variables. It works by recursively splitting the data into smaller subsets based on the input variables until the target variable can be accurately predicted. It has the advantage of being able to handle both categorical and numerical data, but it can be sensitive to overfitting. Random forest is an ensemble learning algorithm that uses a combination of decision trees to make predictions. It works by randomly selecting subsets of features and data to train each decision tree, and then combining the predictions of all the trees to make a final prediction. It has the advantage of being able to handle large datasets with many features, but it can be computationally expensive and require careful tuning to avoid overfitting. Support vector regression applies a linear or non-linear function to approximate a target variable based on several input variables. It works by finding the hyperplane that maximizes the margin between the data points and the hyperplane. The methods can be computationally expensive and require careful tuning of parameters. Fuzzy logic controllers use fuzzy logic to make decisions based on uncertain or imprecise data. They are often used in applications where the inputs and outputs are not well-defined. Fuzzy logic controllers have the advantage of being able to handle imprecise data and uncertain conditions, but they can be difficult to design and require careful tuning of parameters. Genetic algorithms use principles of natural selection to search for the best solution which are often used for optimization problems. Genetic algorithms have the advantage of being able to find solutions to complex problems that traditional optimization algorithms cannot, but they can be computationally expensive and require a large number of iterations to converge. Explainable artificial intelligence (XAI) is often used in applications where the decisions made by the AI need to be easily understood by humans. XAI has the advantage of being able to provide insights into the systems. Depending on the applications, each AI methods has its merits and limits. Practically applications of the AI models are extensive for improving the performance, stability, and efficiency of power electronic converters and power systems [10-19]. Xu et al. (2021) proposes an AI-based control design for reliable virtual synchronous generators in power systems [10], while Tian et al. (2022) proposes an AI-based approach for modeling and optimizing the efficiency of a DC-DC converter [11]. Ratapon Phosung et al. (2022) presents an AI- based approach for designing and optimizing instability mitigation strategies for AC-DC feeder systems [12]. Gómez- Bravo et al. (2021) discusses the application of XAI methods to improve the performance of deep reinforcement learning controllers used for photovoltaic maximum power point tracking [13]. Vinodhini et al. (2022) proposes a fuzzy logic controller-based power factor correction converter for industrial applications [14]. Gao et al. (2023) proposes a method for designing the control of power converters using an inverse approach and artificial neural networks [15]. Vinay et al. (2014) discusses the use of AI methods in maximum power point tracking techniques for photovoltaic applications [16]. Márquez-Rubio et al. (2019) proposes an online learning method to train an artificial neural network controller for a buck converter [17]. Kukreja et al. (2020) proposes the use of an AI-based controller for improving power quality in grid- connected distribution systems [18]. Akpolat et al. (2021) initiates a sensorless control strategy for a DC microgrid based on an artificial neural network (ANN) algorithm. The proposed method uses a single ANN to estimate the grid voltage, load current, and power flow direction [19]. These AI-based methods were proposed as alternatives to traditional control methods and were shown to achieve better performance in terms of efficiency, stability, and reliability. Additionally, several papers highlighted the potential of AI- based methods for improving the interpretability of control systems through the use of XAI techniques. XAI techniques can be applied to a variety of power electronics and electric converters application areas to improve the transparency and interpretability of AI models and algorithms. This can help to ensure that AI systems are making decisions that are understandable and accountable to humans. Overall, these papers demonstrate the growing importance and potential of AI-based approaches in the field of power systems and converters. An excellent book for detailed description of these AI algorithms is [20]. IV. IMPLEMENTATION OF AI IN THE CONTROL LEVEL A. AI in the control level In a very exhaustive study on the topic of AI applications in power electronics [21], three related life cycle phases are distinguished by the authors: design, maintenance, and control, each of them referred with practical implementation examples, to reveal the opportunities that the AI development is creating [22]: Design: AI is used to optimize the design of power electronic systems, such as component selection, topology, and control algorithms. Machine learning algorithms have been applied to model and simulate different design options which allow for rapid prototyping and optimization. Maintenance: AI is used to predict maintenance purposes, detect faults, and diagnose defects in power electronic systems. Machine learning models have been employed to analyze data from sensors, historical maintenance records, and other sources to predict when maintenance is needed and to identify the root cause of faults. This can reduce downtime and improve system reliability. From maintenance point of view, reliability, condition monitoring and remaining usable life (RUL) forecast are all topics covered in the section of the power electronics literature [21]. Not many AI-based fault detection techniques are reported in the literature, only AI- based parameter identification techniques are included. The widespread adoption of condition monitoring in industry is another significant feature of the applications of electric drives. In order to optimize the potential of electric machines, new methods for problem diagnosis and preventive maintenance have been developed taking advantage of the novel AI-based computational tools. Selected summarized review aspects of AI techniques and their applications to fault diagnosis of electrical machines and power converters are presented in Table 1, with the corresponding references and details. Control: AI is used to optimize the control of power electronic systems, such as closed-loop control, pulse-width modulation, and grid synchronization. Machine learning algorithms have been utilized to develop control algorithms that are adaptive to changing conditions and can optimize system performance. The main aim is improved system efficiency, reduced losses, and better system stability. The multidisciplinary nature of these applications has led to various innovations in the electrical and energy fields. renewable energy systems [23], electrical vehicle [24], DC microgrids [25] etc. Within the control level, for example, a description of the recent and impactful cases is furtherly presented. Nevertheless, given the interdisciplinary aspect of the problem, the subject is not limited to these subdomains, or it is situated at the boundary of more than one; energy management applications, fault-tolerant operation or PWM converters can be also considered as viable candidates for AI approaches in power converters [21]. i. DC microgrids and DC/DC converters for renewable applications / electric vehicles. The authors in [26] investigate the influence of power converter parameters on grid stability, proposing AI-based stability assessments concept for the converter-dominated grids. An artificial-intelligence-based design (AI-D) approach is described in [27], for the design of power converter parameters and optimization using genetic algorithms. The model is validated on a synchronous buck converter for an electric vehicle accessory system. Fig.2. Buck converter implementation in an EV application, using AI approach [27] An ANN based control of a DC-DC boost power converter is proposed in [28], with application for a wind energy converter system. To obtain the reliability of the power balance, the study proposes a type o control the DC-DC boost converter using ANN, with more accurate outputs and increased reliability, by reducing the used sensors. Table 1. Review of AI techniques and case applications to fault diagnosis of electrical machines and power electronic converters. AI Techniques Applications to Fault Diagnosis of Electrical Machines Application Cases in Power Electronic Converters Machine Learning Supervised learning algorithms such as decision trees, support vector machines (SVM), and neural networks to develop a fault diagnosis strategy for induction motors based on multi-class classification [29] Open-circuit fault detection (Supervised learning algorithms) [30] Unsupervised learning algorithms like clustering and anomaly detection to detect patterns and identify unknown faults by analyzing data without prior labeling; they can identify abnormal behaviors and anomalies in electrical machines [31] Anomaly detection (Unsupervised learning algorithms) [30] Reinforcement learning techniques to optimize the maintenance and fault diagnosis strategies of electrical machines; they learn through interactions with the environment and improve fault detection and diagnosis over time [32] Component aging detection (Reinforcement learning techniques) [33] Deep Learning Convolutional Neural Networks (CNNs) to show significant success in fault diagnosis tasks by extracting meaningful features from images or sensor data of electrical machines. They can detect and classify various faults accurately [34] Component aging detection (Deep learning) [33] Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to demonstrate effective in handling sequential data, such as time-series sensor data. They can capture temporal dependencies and detect faults based on patterns over time [35] Transient fault detection (Recurrent neural networks and long short-term memory networks) [36] Generative Adversarial Networks (GANs) to generate synthetic data samples to augment training datasets, enhancing fault diagnosis performance; they can also be used to generate realistic fault scenarios for testing and validation purposes [37] Data augmentation and testing (Generative adversarial networks) [38] Expert Systems Rule-based expert systems to employ a set of predefined rules and knowledge bases to diagnose faults in electrical machines; these systems rely on expert knowledge to interpret symptoms and make accurate diagnoses [39] Fault localization (Expert systems) [40] Fuzzy logic-based systems to leverage fuzzy sets and linguistic variables to handle imprecise and uncertain information in fault diagnosis; they can effectively deal with incomplete or noisy data, improving diagnosis accuracy [41] Fault localization (Fuzzy logic-based systems) [40] Knowledge-based systems to utilize a knowledge base and reasoning mechanisms to diagnose faults; they incorporate domain-specific knowledge and can provide explanations for the diagnosed faults [42] Fault localization (Knowledge-based systems) [40] Other AI Techniques Genetic algorithms and swarm intelligence-based techniques to optimize fault diagnosis processes by searching for optimal fault detection strategies or feature selection algorithms [43] Optimization of fault diagnosis (Genetic algorithms and swarm intelligence-based techniques) [38] Bayesian networks and probabilistic graphical models to enable probabilistic reasoning and fault diagnosis based on statistical inference; they can handle uncertainty and make probabilistic predictions about potential faults [44] Probabilistic fault diagnosis (Bayesian networks and probabilistic graphical models) [36] Natural Language Processing (NLP) techniques to aid in fault diagnosis by analyzing textual data, such as maintenance logs or user manuals, to extract relevant information and assist in diagnosing faults [45] Fault diagnosis based on textual data (Natural language processing techniques) [45] ii. MPPT techniques applications. The photovoltaic power generation systems are considered the most important source of renewable energy, but they require efficient maximum power point tracking techniques (MPPT) for efficiency improvement of power generation. MPPT control has been implemented at the DC/DC conversion stage of the inverter, using for a long time, different implementation algorithms, of which Perturb and Observe (PO) algorithm has been highly used; its drawbacks reside from small response rates and high-power loss. But there are recent AI approaches used by the controller to track the maximum operating point, using Particle Swarm Optimization (PSO) [46], neural network [47], genetic algorithms [48], fuzzy logic [48, 49], reinforcement learning [50], adaptive neural fuzzy inference system genetic algorithm [47, 51]. In a specific comparation between procedures [52], Kurukuru et.al evidence the following markers: the output response (time needed to perform the MPPT), complexity of implementation (regardless the applied system), efficiency (power loss) and disturbances in the output power. From the analysis, it is identified that the PO and incremental conductance (IC) MPPT techniques are suitable to use in less complex with no high-performance systems and they have slow response rates and high-power loss [54]. The AI based MPPT techniques require a big amount of data to process and a good balance between costs and complexity is the key point in choosing one technique over another, depending on the design of the system. AI- based methods for MPPT are suitable for on-grid (electric utility) or off-grid (loads) applications. [48] Fig.3. ANN-based PI controllers for a wind energy conversion systems [28] iii. Control of motor drives: estimators-based control, controllers’ tunning, sensorless control. Progresses in AI-based approaches are also notable in the field of electrical machines and drives, sustained by the development of more robust and performant embedded systems. It is envisioned that the AI-based approaches will become more used in improved- performance control of electric drives that are the core of many industrial, automotive, and power generation applications. The authors in [53] present a very comprehensive study about the perspectives of using AI-based models in the control of electric drives with induction motors and permanent-magnet synchronous motors. The paper offers details to facilitate the reader new perspectives on: AI-based controllers [54,55], AI sensorless control [56,57], novel flux and speed estimators [58] and new tunning approaches for the proposed controllers. Fig.4. ANN approach for a sensorless control scheme [57] These innovative AI applications have greatly advanced traditional methods. The classical proportional – integral PI and proportional-integral-derivative PID controllers are frequently used in controllers design, with drawbacks in determination of its tunning parameters. Classical PI and PID controller are replaced by AI-based controllers, with increased speed and fault tolerance, using ANN [59] or fuzzy algorithms [60, 61]. B. AI and Data The types of data commonly used in AI applications in power electronics include both structured and unstructured data [63]. For example: • Time-series data: Power electronic systems generate large amounts of time-series data, including voltage and current waveforms, temperature readings, and sensor data. Machine learning algorithms can be used to analyze this data to detect faults, predict maintenance needs, and optimize energy consumption. • Image data: In power electronics, images can be used for fault detection and diagnosis, as well as for object detection and recognition in smart grid applications. Convolutional neural networks (CNNs) are often used to analyze image data in power electronics applications. • Text data: Natural language processing (NLP) techniques can be used to analyze text data in power electronics, such as documentation and service reports. NLP can be used to extract relevant information, classify documents, and identify patterns. • Big data: Power electronic systems generate large amounts of data, and big data technologies such as Hadoop and Spark can be used to manage and process this data. Machine learning algorithms can be used to analyze big data in power electronics applications to detect faults, optimize energy consumption, and improve system performance. An important aspect related to the selection of the appropriate embedded system to compute the AI algorithms is also presented in [53], highlighting the performance differences between the use of CPUs, GPUs and FPGAs. The choice of one computational device for AI-based systems depends on the complexity and cost of the implementation, the hardware and software skills of the developers and available programs and libraries. V. CONCLUSIONS Considering the vast domain of application and the dynamic development of new AI-based methodologies, the present paper presents a condensed overview on recent advanced AI techniques used in different applications of power systems and electric drives, on the maintenance and control level. Power converters and fault diagnosis of electrical machines are envisaged, with relevant application cases from the recent scientific literature: electric vehicle, energy conversion systems, control and maintenance of electrical machines and drives. 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