The power quality is becoming an extensively addressing aspect of the power system because of the sensitive operation of the smart grid, awareness of power quality, and the equipment of modern power systems. In this p...The power quality is becoming an extensively addressing aspect of the power system because of the sensitive operation of the smart grid, awareness of power quality, and the equipment of modern power systems. In this paper, we have conceived a new hybrid Quantum inspired particle swarm optimization and least square(QPSO-LS) framework for real-time estimation of harmonics presented in time-varying noisy power signals. The technique has strong, robust, and reliable search capability with powerful convergence properties. The proposed approach is applied to various test systems at different signal to noise ratio(SNR) levels in the presence of uniform and Gaussian noise. The results are presented in terms of precision, computation time, and convergence characteristics. The computation time decreases by 3-5 times as compared to the existing algorithms. The technique is further authenticated by estimating harmonics of real-time current or voltage waveforms, obtained from light emitting diode(LED) lamp and axial flux permanent magnet synchronous generator(AFPMSG). The results demonstrate the superiority of QPSO-LS over other methods such as LS-based genetic algorithm(GA), particle swarm optimization(PSO), bacterial foraging optimization(BFO), artificial bee colony(ABC), and biogeography based optimization with recursive LS(BBO-RLS) algorithms, in terms of providing satisfactory solutions with a significant amount of robustness and computation efficiency.展开更多
A method to separate a harmonic signal from multiplicative and additive noises is proposed. The method is to square the signal x(t), which consists of a harmonic signal embedded in multiplicative and additive noises, ...A method to separate a harmonic signal from multiplicative and additive noises is proposed. The method is to square the signal x(t), which consists of a harmonic signal embedded in multiplicative and additive noises, to form another signal y(t) = x2(t)-E[x2(t)]. After y(t) having been gotten, the Fourier transform is imposed on it. Because the information of x(t) (especially about frequency) is included in y(t), the frequency of x(t) can be estimated from the power spectrum of y(t). According to the simulation, under the condition where frequencies divided by resolution dω are integer, the maximum relative error of estimated frequencies is less than 0.4% when the signal-to-noise ratio (SNR) is greater than -23 dB. If frequencies divided by resolution dω are not integer, the maximum relative error will be less than 2.9%. But it is still small in terms of engineering.展开更多
Harmonic state estimation in distribution networks is essential for identifying harmonic sources.However,issues such as limited measurement redundancy,asynchronous measurements,and unbalanced load distributions in thr...Harmonic state estimation in distribution networks is essential for identifying harmonic sources.However,issues such as limited measurement redundancy,asynchronous measurements,and unbalanced load distributions in three-phase networks undermine the reliability of existing methods in practical applications.To address these issues,this paper proposes an interval harmonic state estimation method in three-phase unbalanced distribution networks,integrating data from multiple sources.First,the interval multi-source harmonic measurement dataset is constructed by integrating asynchronous harmonic measurement data from multiple sources.The time asynchrony of measurement data from power quality monitoring devices is calibrated using the sliding window weighted dynamic time warping algo-rithm.Second,the interval harmonic state estimation model for the three-phase asymmetric distribution network is constructed.The model is solved using the interval-weighted least squares method,enhanced by the improved Krawczyk operator,thereby minimizing the expansion resulting from interval operations.Finally,the feasibility and accuracy of the proposed interval harmonic state estimation method are validated.展开更多
文摘The power quality is becoming an extensively addressing aspect of the power system because of the sensitive operation of the smart grid, awareness of power quality, and the equipment of modern power systems. In this paper, we have conceived a new hybrid Quantum inspired particle swarm optimization and least square(QPSO-LS) framework for real-time estimation of harmonics presented in time-varying noisy power signals. The technique has strong, robust, and reliable search capability with powerful convergence properties. The proposed approach is applied to various test systems at different signal to noise ratio(SNR) levels in the presence of uniform and Gaussian noise. The results are presented in terms of precision, computation time, and convergence characteristics. The computation time decreases by 3-5 times as compared to the existing algorithms. The technique is further authenticated by estimating harmonics of real-time current or voltage waveforms, obtained from light emitting diode(LED) lamp and axial flux permanent magnet synchronous generator(AFPMSG). The results demonstrate the superiority of QPSO-LS over other methods such as LS-based genetic algorithm(GA), particle swarm optimization(PSO), bacterial foraging optimization(BFO), artificial bee colony(ABC), and biogeography based optimization with recursive LS(BBO-RLS) algorithms, in terms of providing satisfactory solutions with a significant amount of robustness and computation efficiency.
基金the National Natural Foundation of China(No.59635140).
文摘A method to separate a harmonic signal from multiplicative and additive noises is proposed. The method is to square the signal x(t), which consists of a harmonic signal embedded in multiplicative and additive noises, to form another signal y(t) = x2(t)-E[x2(t)]. After y(t) having been gotten, the Fourier transform is imposed on it. Because the information of x(t) (especially about frequency) is included in y(t), the frequency of x(t) can be estimated from the power spectrum of y(t). According to the simulation, under the condition where frequencies divided by resolution dω are integer, the maximum relative error of estimated frequencies is less than 0.4% when the signal-to-noise ratio (SNR) is greater than -23 dB. If frequencies divided by resolution dω are not integer, the maximum relative error will be less than 2.9%. But it is still small in terms of engineering.
基金supported by the National Natural Science Foundation of China(No.52377087).
文摘Harmonic state estimation in distribution networks is essential for identifying harmonic sources.However,issues such as limited measurement redundancy,asynchronous measurements,and unbalanced load distributions in three-phase networks undermine the reliability of existing methods in practical applications.To address these issues,this paper proposes an interval harmonic state estimation method in three-phase unbalanced distribution networks,integrating data from multiple sources.First,the interval multi-source harmonic measurement dataset is constructed by integrating asynchronous harmonic measurement data from multiple sources.The time asynchrony of measurement data from power quality monitoring devices is calibrated using the sliding window weighted dynamic time warping algo-rithm.Second,the interval harmonic state estimation model for the three-phase asymmetric distribution network is constructed.The model is solved using the interval-weighted least squares method,enhanced by the improved Krawczyk operator,thereby minimizing the expansion resulting from interval operations.Finally,the feasibility and accuracy of the proposed interval harmonic state estimation method are validated.
