Research On Intelligent Algorithm For On-line Monitoring Of Metal Oxide Arrester (MOA) Aging

In addition to the threat of overvoltage,the MO A operating in the system also subjects to external environmental factors such as temperature,humidity,and contamination.With the increase of operating time,the aging of the MOA becomes more and more serious,which eventually leads to MOA failure and severely affects the security of the system.At present,the preventive test is usually used to solve the problem of MOA aging monitoring.This method is not economical,and the damage caused by the test is irreversible.The existing online monitoring technology is affected by environmental factors,grid factors,monitoring indexes,etc.The error of monitoring results is large and the monitoring accuracy is not enough.Aiming at this problem,the impact of environmental factors and power factors on online monitoring indicators is studied.In order to obtain the resistive leakage current under harmonic voltage more accurately,the basic time delay superposition method is optimized.After that,the relationship between the power loss characteristics of the ZnO varistors and the aging is studied in this paper.A new variable of aging degree and its quantization method is proposed.Finally,genetic thought,simulated annealing thought and particle swarm optimization are combined to form the optimization algorithm GA-PSO and SA-PSO respectively.The Human Group Optimization is optimized to form the optimization algorithm OHGO,and the optimization algorithms are applied to the online monitoring of MOA aging.The main conclusions are as follows:The impact of environmental factors and power grid factors on MOA online monitoring indicators shows that:under the voltage fluctuation,the maximum amplitude of resistive leakage current(Imr),the rms value of fundament harmonic of the resistive leakage current(Ir1),the rms value of third harmonic of the resistive leakage current(Ir3)is larger with the power grid voltage fluctuation.In contrast,Ir1 has better anti-interference,which can be used as an index for evaluating the aging of MOA.But when there are third harmonics and fifth harmonics in the power grid,all the indicators lose the credibility of the evaluation.The full current(Ix),resistive leakage current(Ir),the rms value of fundament harmonic of the resistive leakage current(Ir1),the rms value of third harmonic of the resistive leakage current(Ir3)increases along with the increase of relative humidity and temperature,especially the leakage current resistance component increased by nearly 20 times.It has great influence on the accurate monitoring of MOA aging.The performance of the optimized time delay superposition method is simulated by MATLAB and compared with the basic time delay superposition method,the current orthogonal method and the capacitive current compensation method.The results show that because the non-optimized time delay superposition method neglects the influence of the harmonic voltage,the high harmonic component of the capacitive leakage current is superimposed on the resistive component,which makes the extracted leakage current error large,so the non-optimized time delay superposition method can only extract the resistive current component under the pure sinusoidal voltage.Compared with the basic time delay superposition method,the optimization algorithm has no obvious difference under the fundamental harmonic of the leakage current,while under the third harmonic and fifth harmonic of the leakage current,the highest error of the basic delay superposition method is over 500%.Compared with the current orthogonal method and capacitive current compensation method,the optimized algorithm and the orthogonal method are consistent in extracting the resistive leakage current undert the harmonic voltage.The two algorithms are basically the same on extract the fundament harmonic of the resistive leakage current,and the error of extracting the third harmonic and fifth harmonics of the resistive leakage current is also small.The relationship between the power loss characteristics of MOA valves and aging shows that the power loss under the static state of the MO A valve increases with the increase of the aging degree and is positively related to the temperature.The increasing rate of power loss shows an inflection point at a certain temperature value(around 65?).When the temperature of MOA valves is lower than this temperature,the power loss becomes slower with increasing temperature,and vice versa.The inflection point temperature depends on the aging degree of MOA valves.In order to study the performance of each optimization algorithm,benchmark test functions are used for testing and comparison,and MATLAB was used to simulate the harmonics and fluctuations in the power grid,so the performance of each optimization algorithm in MOA aging online monitoring was studied.The results show that GA-PSO performs better in all aspects,followed by SA-PSO,and finally OHGO in terms of computing time,convergence success rate and solution accuracy.GA-PSO and SA-PSO show good immunity to harmonic voltage,frequency fluctuation and voltage fluctuation.In most cases,the parameter error soluted by GA-PSO and SA-PSO is closest to 0,but the performance of the SA-PSO is relatively stable,and the accuracy of the solution is the highest.Therefore,these algorithms can be applied to the online monitoring technology of MO A aging well.