Localization And Contribution Assessment Method Of Multiple Harmonic Sources In Distribution Network Under Dynamic Conditions

When a large number of harmonics are injected into the power system, it will not only endanger the operation safety, but also incur huge economic losses. With the drastic increase of non-linear loads in the power grid both in terms of capacity and quantity, the operating conditions with coexistence of multiple harmonic sources have increased greatly. This situation can also intensify the time-varying characteristics of the power grid parameters,including fluctuations and mutations. As a consequence, it becomes extremely difficult to identify and distinguish the contribution of harmonic sources. Therefore, the main objective of this paper is to study the harmonic source localization, the harmonic pollution level assessment and the harmonic contribution assessment under the condition of varying power grid parameters, in order to provide theoretical basis for the reward/punishment and treatment of harmonic pollution.In consideration of the economics of harmonic measurement configuration and the impact of the newly-increased harmonic interference current, a method for harmonic source localization is proposed in this paper based on the complementary optimization measurement and the smooth approximation sparse reconstruction method. Firstly, with the objectives to minimize the number of measurement instruments and maximize the measurement redundancy, a 0-1 programming model of harmonic measurement optimization configuration is established under the constraint of complete observability of the electric power system. The binary genetic algorithm is then employed to get the solution.In the premise of ensuring the observability of the suspected area, the smooth approximation sparse reconstruction method and the orthogonal matching pursuit method are adopted respectively to localize the harmonic sources under the condition of underdetermined measurement equation (partial measurement), taking the node harmonic voltage as the known variable and the harmonic injection current as the estimated variable. The validity of the proposed algorithm is verified on the IEEE 13-bus and 34-bus systems, and the performance is compared with that of the least square method. The simulation results suggest that both the smooth approximation sparse reconstruction method and the orthogonal matching pursuit method can localize the harmonic sources more accurately under the condition of partial measurement. Specifically, the smooth approximation sparse reconstruction method delivers the best performance.In order to indentify the harmonic sources under the condition of unknown or varying harmonic impedance, a harmonic source localization method is proposed based on the fast kernel entropy optimization independent component analysis (FKEO-ICA) and the minimum conditional entropy. Firstly, the FKEO-ICA method is applied to estimate the injected harmonic current in the absence of prior knowledge of harmonic impedance. On such basis,the piecewise conditional entropies of the harmonic voltage and the harmonic current are calculated. Then, the harmonic sources can be localized based on the minimum conditional entropy. This method is validated on the 34-bus systems, and the performance of the FKEO-ICA method is compared with that of other three ICA methods. The results suggest that the accuracy of the FKEO-ICA method on the estimation of harmonic current is very high, and the minimum conditional entropy can precisely localize the harmonic sources.In order to analyze the harmonic pollution level on the bus, a new method is proposed for assessing the harmonic pollution level based on the technique for order preference by similarity to ideal solution (TOPSIS). Firstly, an assessment index set is formed according to the main characteristics of harmonic pollution. Then, in consideration of the group decision-making behavior of the harmonic pollution level assessment, the group eigenvalue method is used to integrate the weight information collected from several experts so as to calculate the overall weight. On such basis, the TOPSIS method is employed to obtain the harmonic pollution level assessment value of each bus. Furthermore, in view of the variations of harmonic parameters in a certain period of time, the assessment method based on the extended cloud similarity measurement is also investigated. The triangular fuzzy numbers are used to express the assessment information. Lastly, the triangle extended cloud and the TOPSIS method are applied to obtain the final assessment results. The validity of the proposed assessment method has been verified on seven 10kV buses.In consideration of the effect of the variations in utility harmonic impedance on the harmonic contribution, a method based on piecewise bound constrained optimization is proposed in this paper to assess the load harmonic contribution. Firstly, the wavelet packet transform method is adopted to determine the change times of the utility harmonic impedance and to divide the harmonic monitoring data into several segments. Then, the piecewise bound constrained optimization model of harmonic contribution assessment is established for the various data segments, and the interior point method, the sequential quadratic programming method and the active set method are employed respectively to calculate all the instantaneous harmonic contributions of harmonic loads. Lastly, the weighted summation method is applied to compute the total harmonic contribution.According to the results of simulation tests performed on an experimental circuit and the IEEE 13-bus system, the proposed method can assess the harmonic contribution accurately in the presence of variations in the utility harmonic impedance.In consideration of the significant effect of the utility harmonic voltage fluctuation on the harmonic contribution, an adaptive method for harmonic contribution assessment is proposed in this paper based on hierarchical K-means clustering and Bayesian partial least squares (BPLS). The purpose is to address the multiple harmonic loads phenomenon. After determining the utility harmonic impedance using the dominant fluctuation filtering method,the hierarchical K-means clustering method is adopted to cluster the harmonic voltage data into different segments automatically (the number of clusters of the harmonic data needs not to be given in advance). Then, the BPLS method is employed to calculate the harmonic contribution of each cluster. Lastly, the total harmonic contribution of harmonic loads is obtained by weighted summation according to the length of each harmonic data segment.According to the results of simulation tests performed on the IEEE 13-bus and 69-bus systems, the proposed method can well adapt to the fluctuation of the utility harmonic voltage and generate accurate assessment results.A technology system is formed in this dissertation including multiple harmonic sources localization, bus harmonic pollution level assessment and harmonic contribution assessment in distribution network under dynamic conditions, which can provide technical reference for the harmonic pollution management in the harmonic dynamic change conditions of utility and loads.