Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier u...Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.展开更多
Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispat...Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines,photovoltaics,diesel engine unit,load,and battery energy storage system.The economic cost,environmental concerns,and power supply consistency are expressed via subobjectives with varying priorities.Then,the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives.The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG.Finally,the validity of the proposed model and solution methodology are con firmed by case studies.This study provides refere nee for mathematical model of multiojective optimizati on of MG and can be widely used in current research field.展开更多
The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonl...The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.展开更多
Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition domain.This field has gained much interest lately due to its diverse ap...Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition domain.This field has gained much interest lately due to its diverse application potentials.Nowadays,different methods are available for automatic script recognition.Among most of the reported script recognition techniques,deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms.However,the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error,which renders them unfeasible.This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources.To alleviate this shortcoming,this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization(QPSO),which is capable of automatically evolving the meaningful convolutional neural network(CNN)topologies.The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts,namely Bangla,Devanagari,and Dogri,consisting of handwritten characters and digits.Empirically,the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.展开更多
This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniqu...This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.展开更多
To enhance the performance of quantum-behaved PSO, some improvements are proposed. First, an encoding method based on the Bloch sphere is presented. In this method, each particle carries three groups of Bloch coordina...To enhance the performance of quantum-behaved PSO, some improvements are proposed. First, an encoding method based on the Bloch sphere is presented. In this method, each particle carries three groups of Bloch coordinates of qubits, and these coordinates are actually the approximate solutions. The particles are updated by rotating qubits about an axis on the Bloch sphere, which can simultaneously adjust two parameters of qubits, and can automatically achieve the best matching of two adjustments. The optimization process is employed in the n-dimensional space [-1, 1]n, so this approach fits to many optimization problems. The experimental results show that this algorithm is superior to the original quantum-behaved PSO.展开更多
This paper proposes a systematic method, integrating the uniform design (UD) of experiments and quantum-behaved particle swarm optimization (QPSO), to solve the problem of a robust design for a railway vehicle suspens...This paper proposes a systematic method, integrating the uniform design (UD) of experiments and quantum-behaved particle swarm optimization (QPSO), to solve the problem of a robust design for a railway vehicle suspension system. Based on the new nonlinear creep model derived from combining Hertz contact theory, Kalker's linear theory and a heuristic nonlinear creep model, the modeling and dynamic analysis of a 24 degree-of-freedom railway vehicle system were investigated. The Lyapunov indirect method was used to examine the effects of suspension parameters, wheel conicities and wheel rolling radii on critical hunting speeds. Generally, the critical hunting speeds of a vehicle system resulting from worn wheels with different wheel rolling radii are lower than those of a vehicle system having original wheels without different wheel rolling radii. Because of worn wheels, the critical hunting speed of a running railway vehicle substantially declines over the long term. For safety reasons, it is necessary to design the suspension system parameters to increase the robustness of the system and decrease the sensitive of wheel noises. By applying UD and QPSO, the nominal-the-best signal-to-noise ratio of the system was increased from -48.17 to -34.05 dB. The rate of improvement was 29.31%. This study has demonstrated that the integration of UD and QPSO can successfully reveal the optimal solution of suspension parameters for solving the robust design problem of a railway vehicle suspension system.展开更多
The use of the supplementary controllers of a High Voltage Direct Current (HVDC) based on Voltage Source Converter (VSC) to damp low Frequency oscillations in a weakly connected system is surveyed. Also, singular valu...The use of the supplementary controllers of a High Voltage Direct Current (HVDC) based on Voltage Source Converter (VSC) to damp low Frequency oscillations in a weakly connected system is surveyed. Also, singular value decomposition (SVD)-based approach is used to analyze and assess the controllability of the poorly damped electromechanical modes by VSC-HVDC different control channels. The problem of supplementary damping controller based VSC-HVDC system is formulated as an optimization problem according to the time domain-based objective function which is solved using quantum-behaved particle swarm optimization (QPSO). Individual designs of the HVDC controllers using QPSO method are evaluated. The effectiveness of the proposed controllers on damping low frequency oscillations is checked through eigenvalue analysis and non-linear time simulation under various disturbance conditions over a wide range of loading.展开更多
To enhance the optimization ability of particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of t...To enhance the optimization ability of particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of the basic states of the multi-qubits system. The rotation angles of multi-qubits are determined based on the local optimum particle and the global optimal particle, and the multi-qubits rotation gates are employed to update the particles. At each of iteration, updating any qubit can lead to updating all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization show that, although its single step iteration consumes long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.展开更多
Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of...Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.展开更多
This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state errors.In this paper, the specific control laws under the three forms are given.S...This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state errors.In this paper, the specific control laws under the three forms are given.Stability is analyzed by the La Salle invariance principle and the numerical simulation is carried out in a 2D test system.The calculation process for the Lyapunov function is based on a combination of the average of virtual mechanical quantities, the particle swarm algorithm and a simulated annealing algorithm.Finally, a unified form of the control laws under the three forms is given.展开更多
This paper investigates the security and reliability of information transmission within an underlay wiretap energy harvesting cognitive two-way relay network.In the network,energy-constrained secondary network(SN)node...This paper investigates the security and reliability of information transmission within an underlay wiretap energy harvesting cognitive two-way relay network.In the network,energy-constrained secondary network(SN)nodes harvest energy from radio frequency signals of a multi-antenna power beacon.Two SN sources exchange their messages via a SN decode-and-forward relay in the presence of a multiantenna eavesdropper by using a four-phase time division broadcast protocol,and the hardware impairments of SN nodes and eavesdropper are modeled.To alleviate eavesdropping attacks,the artificial noise is applied by SN nodes.The physical layer security performance of SN is analyzed and evaluated by the exact closed-form expressions of outage probability(OP),intercept probability(IP),and OP+IP over quasistatic Rayleigh fading channel.Additionally,due to the complexity of OP+IP expression,a self-adaptive chaotic quantum particle swarm optimization-based resource allocation algorithm is proposed to jointly optimize energy harvesting ratio and power allocation factor,which can achieve security-reliability tradeoff for SN.Extensive simulations demonstrate the correctness of theoretical analysis and the effectiveness of the proposed optimization algorithm.展开更多
高压SF6断路器弧触头的接触电阻和质量损失预测在断路器状态评估中起着重要作用。该文提出了一种基于量子粒子群优化和支持向量回归(quantum particle swarms optimization and support vector regression,QPSO-SVR)的方法,能够有效预...高压SF6断路器弧触头的接触电阻和质量损失预测在断路器状态评估中起着重要作用。该文提出了一种基于量子粒子群优化和支持向量回归(quantum particle swarms optimization and support vector regression,QPSO-SVR)的方法,能够有效预测断路器弧触头在不同电弧电流条件下的接触电阻增量和质量损失,并结合实验数据获得了SVR算法的最佳训练参数。将该文方法与其他预测方法进行比较,QPSO-SVR方法对不同电弧电流条件下的实验数据表现出良好的预测能力。其中,对于接触电阻增量的预测相对误差为3.023%,而对于质量损失的预测相对误差为4.61%,均表现出较好的鲁棒性。最后将QPSO-SVR预测得到的质量损失和接触电阻增量以及监测到的累积电弧能量进行模糊逻辑推理,构建了基于QPSO-SVR算法的触头烧蚀状态评估系统,将触头烧蚀状态分为O级烧蚀、Ⅰ级烧蚀、Ⅱ级烧蚀、Ⅲ级烧蚀4等级,可为高压SF6断路器检修提供参考。展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R263)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura Universitysupporting this work by Grant Code:(22UQU4310373DSR36).
文摘Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.
基金State Grid Corporation Science and Technology Project(520605190010).
文摘Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines,photovoltaics,diesel engine unit,load,and battery energy storage system.The economic cost,environmental concerns,and power supply consistency are expressed via subobjectives with varying priorities.Then,the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives.The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG.Finally,the validity of the proposed model and solution methodology are con firmed by case studies.This study provides refere nee for mathematical model of multiojective optimizati on of MG and can be widely used in current research field.
基金Sponsored by the Scientific and Technological Project of Heilongjiang Province(Grant No.GD07A304)
文摘The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.
文摘Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition domain.This field has gained much interest lately due to its diverse application potentials.Nowadays,different methods are available for automatic script recognition.Among most of the reported script recognition techniques,deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms.However,the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error,which renders them unfeasible.This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources.To alleviate this shortcoming,this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization(QPSO),which is capable of automatically evolving the meaningful convolutional neural network(CNN)topologies.The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts,namely Bangla,Devanagari,and Dogri,consisting of handwritten characters and digits.Empirically,the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.
基金Project supported by the National Natural Science Foundation of China (Grant No 10647141)
文摘This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.
文摘To enhance the performance of quantum-behaved PSO, some improvements are proposed. First, an encoding method based on the Bloch sphere is presented. In this method, each particle carries three groups of Bloch coordinates of qubits, and these coordinates are actually the approximate solutions. The particles are updated by rotating qubits about an axis on the Bloch sphere, which can simultaneously adjust two parameters of qubits, and can automatically achieve the best matching of two adjustments. The optimization process is employed in the n-dimensional space [-1, 1]n, so this approach fits to many optimization problems. The experimental results show that this algorithm is superior to the original quantum-behaved PSO.
基金the Ministry of Science and Technology of Taiwan (Grants MOST 104-2221-E-327019, MOST 105-2221-E-327-014) for financial support of this study
文摘This paper proposes a systematic method, integrating the uniform design (UD) of experiments and quantum-behaved particle swarm optimization (QPSO), to solve the problem of a robust design for a railway vehicle suspension system. Based on the new nonlinear creep model derived from combining Hertz contact theory, Kalker's linear theory and a heuristic nonlinear creep model, the modeling and dynamic analysis of a 24 degree-of-freedom railway vehicle system were investigated. The Lyapunov indirect method was used to examine the effects of suspension parameters, wheel conicities and wheel rolling radii on critical hunting speeds. Generally, the critical hunting speeds of a vehicle system resulting from worn wheels with different wheel rolling radii are lower than those of a vehicle system having original wheels without different wheel rolling radii. Because of worn wheels, the critical hunting speed of a running railway vehicle substantially declines over the long term. For safety reasons, it is necessary to design the suspension system parameters to increase the robustness of the system and decrease the sensitive of wheel noises. By applying UD and QPSO, the nominal-the-best signal-to-noise ratio of the system was increased from -48.17 to -34.05 dB. The rate of improvement was 29.31%. This study has demonstrated that the integration of UD and QPSO can successfully reveal the optimal solution of suspension parameters for solving the robust design problem of a railway vehicle suspension system.
文摘The use of the supplementary controllers of a High Voltage Direct Current (HVDC) based on Voltage Source Converter (VSC) to damp low Frequency oscillations in a weakly connected system is surveyed. Also, singular value decomposition (SVD)-based approach is used to analyze and assess the controllability of the poorly damped electromechanical modes by VSC-HVDC different control channels. The problem of supplementary damping controller based VSC-HVDC system is formulated as an optimization problem according to the time domain-based objective function which is solved using quantum-behaved particle swarm optimization (QPSO). Individual designs of the HVDC controllers using QPSO method are evaluated. The effectiveness of the proposed controllers on damping low frequency oscillations is checked through eigenvalue analysis and non-linear time simulation under various disturbance conditions over a wide range of loading.
文摘To enhance the optimization ability of particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of the basic states of the multi-qubits system. The rotation angles of multi-qubits are determined based on the local optimum particle and the global optimal particle, and the multi-qubits rotation gates are employed to update the particles. At each of iteration, updating any qubit can lead to updating all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization show that, although its single step iteration consumes long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.
基金National Natural Science Foundation of China(No.519667013)Institution of Higher Learning Scientific Research Project of Gansu Province of China(No.2016B-032)。
文摘Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.
基金Project supported by the National Natural Science Foundation of China (Grant No.62176140)。
文摘This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state errors.In this paper, the specific control laws under the three forms are given.Stability is analyzed by the La Salle invariance principle and the numerical simulation is carried out in a 2D test system.The calculation process for the Lyapunov function is based on a combination of the average of virtual mechanical quantities, the particle swarm algorithm and a simulated annealing algorithm.Finally, a unified form of the control laws under the three forms is given.
基金supported in part by the National Natural Science Foundation of China under Grant 61971450in part by the Hunan Provincial Science and Technology Project Foundation under Grant 2018TP1018+1 种基金in part by the Natural Science Foundation of Hunan Province under Grant 2018JJ2533in part by Hunan Province College Students Research Learning and Innovative Experiment Project under Grant S202110542056。
文摘This paper investigates the security and reliability of information transmission within an underlay wiretap energy harvesting cognitive two-way relay network.In the network,energy-constrained secondary network(SN)nodes harvest energy from radio frequency signals of a multi-antenna power beacon.Two SN sources exchange their messages via a SN decode-and-forward relay in the presence of a multiantenna eavesdropper by using a four-phase time division broadcast protocol,and the hardware impairments of SN nodes and eavesdropper are modeled.To alleviate eavesdropping attacks,the artificial noise is applied by SN nodes.The physical layer security performance of SN is analyzed and evaluated by the exact closed-form expressions of outage probability(OP),intercept probability(IP),and OP+IP over quasistatic Rayleigh fading channel.Additionally,due to the complexity of OP+IP expression,a self-adaptive chaotic quantum particle swarm optimization-based resource allocation algorithm is proposed to jointly optimize energy harvesting ratio and power allocation factor,which can achieve security-reliability tradeoff for SN.Extensive simulations demonstrate the correctness of theoretical analysis and the effectiveness of the proposed optimization algorithm.
文摘高压SF6断路器弧触头的接触电阻和质量损失预测在断路器状态评估中起着重要作用。该文提出了一种基于量子粒子群优化和支持向量回归(quantum particle swarms optimization and support vector regression,QPSO-SVR)的方法,能够有效预测断路器弧触头在不同电弧电流条件下的接触电阻增量和质量损失,并结合实验数据获得了SVR算法的最佳训练参数。将该文方法与其他预测方法进行比较,QPSO-SVR方法对不同电弧电流条件下的实验数据表现出良好的预测能力。其中,对于接触电阻增量的预测相对误差为3.023%,而对于质量损失的预测相对误差为4.61%,均表现出较好的鲁棒性。最后将QPSO-SVR预测得到的质量损失和接触电阻增量以及监测到的累积电弧能量进行模糊逻辑推理,构建了基于QPSO-SVR算法的触头烧蚀状态评估系统,将触头烧蚀状态分为O级烧蚀、Ⅰ级烧蚀、Ⅱ级烧蚀、Ⅲ级烧蚀4等级,可为高压SF6断路器检修提供参考。
