The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ...The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.展开更多
在Matlab仿真平台下建立光伏电池的非线性工程模型。针对光伏电池的最大功率追踪(maximum power point tracking,MPPT)问题,分析目前典型的最大功率跟踪算法,即变步长扰动观察法和梯度变步长电导增量法;针对其所存在的缺陷,提出基于改...在Matlab仿真平台下建立光伏电池的非线性工程模型。针对光伏电池的最大功率追踪(maximum power point tracking,MPPT)问题,分析目前典型的最大功率跟踪算法,即变步长扰动观察法和梯度变步长电导增量法;针对其所存在的缺陷,提出基于改进电导增量法的MPPT控制算法。并采用Matlab仿真平台对不同算法的跟踪效果进行对比分析,仿真结果表明:所提出的改进型MPPT算法实用性强,跟踪精度高,而且动态性和稳定性更加优越。展开更多
The compensation current of the arc-suppressing coil makes the phase and amplitude of zero-sequence measurement current of the earthed fault feeder to vary. It is very hard to detect the fault feeder by using existing...The compensation current of the arc-suppressing coil makes the phase and amplitude of zero-sequence measurement current of the earthed fault feeder to vary. It is very hard to detect the fault feeder by using existing detectors based on single method. In this paper, integrative feeder selection strategy—zero sequence current increment method and the direction of transient current— is put forward. Based on the integrative feeder selection strategy, the design of fault-feeder selection device for one-phase-to ground fault on resonance grounding system is presented. For the purpose of testing and validating the operating principle of the device, the experiment of single-phase-to-ground fault has been carried out on the simulation of 1.2 kV power network. The results from many repeat experiments show that stability of the fault selection device is satisfactory.展开更多
基金funded by the Bavarian State Ministry of ScienceResearch and Art(Grant number:H.2-F1116.WE/52/2)。
文摘The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.
文摘在Matlab仿真平台下建立光伏电池的非线性工程模型。针对光伏电池的最大功率追踪(maximum power point tracking,MPPT)问题,分析目前典型的最大功率跟踪算法,即变步长扰动观察法和梯度变步长电导增量法;针对其所存在的缺陷,提出基于改进电导增量法的MPPT控制算法。并采用Matlab仿真平台对不同算法的跟踪效果进行对比分析,仿真结果表明:所提出的改进型MPPT算法实用性强,跟踪精度高,而且动态性和稳定性更加优越。
文摘The compensation current of the arc-suppressing coil makes the phase and amplitude of zero-sequence measurement current of the earthed fault feeder to vary. It is very hard to detect the fault feeder by using existing detectors based on single method. In this paper, integrative feeder selection strategy—zero sequence current increment method and the direction of transient current— is put forward. Based on the integrative feeder selection strategy, the design of fault-feeder selection device for one-phase-to ground fault on resonance grounding system is presented. For the purpose of testing and validating the operating principle of the device, the experiment of single-phase-to-ground fault has been carried out on the simulation of 1.2 kV power network. The results from many repeat experiments show that stability of the fault selection device is satisfactory.
