摘要
故障诊断是准确故障分析、快速恢复供电的基础,而谐波源定位是谐波治理、谐波污染责任划分的前提。随着科学技术的发展以及社会的进步,配电系统的规模不断扩大,安装的保护、监控等设备越来越多,信息中心接收的数据量越来越大。如何综合利用上送信息,实现配电网的故障诊断和谐波源定位对提高电网运行管理水平有着非常重要的理论和现实意义。本论文从配电网故障诊断和谐波源定位问题的特点出发,挑选有效的电气输入量,利用合理的处理方法,对所涉及的子课题进行了针对性的研究。
首先,提出了一种基于自适应神经模糊推理系统的小电流接地系统故障分类方法。该方法采用小波变换提取故障特征频带内的暂态信号,利用统计量构造故障特征量,研究所构造特征量在不同故障类型下的规律,利用自适应神经模糊推理系统对特征量进行融合,进而得到分类结果。在PSCAD/EMTDC中建立了配电网仿真模型,通过仿真对分类方法进行了训练和测试。结果表明,该分类方法具有很高的准确性。仿真研究了分类方法在中性点接地方式变化、电弧故障、不同系统等效阻抗变化、系统拓扑结构变化、噪声环境以及不同负荷水平六种工况下的适应性。结果表明,前四种工况下分类方法几乎不受影响；当系统拓扑结构变化不大时,分类方法的适应性良好,但是在拓扑发生剧烈变化时,方法的正确率有所下降；在信噪比较低以及重负荷工况下,分类的正确率大大降低。鉴于此,可通过增加前置滤波器环节和重负荷工况下的故障训练样本提高分类方法的准确性。
其次,研究了基于S变换的小电流接地系统单相接地故障选线方法。在研究S变换提取幅频特性和相频特性的基础上,提出了一种基于S变换比相原理的故障选线方法。该方法利用S变换提取零序电流在不同频率点的模值和相角信息,通过模值比较,找出了特征频率点；通过相角比较,制定了故障选线投票机制。考虑到特征频率点的模值能刻画该频率点相角信息的可靠性,在投票机制中定义了投票信心度。通过对故障后1/4工频周期内的采样点逐一进行投票,形成投票统计图。利用仙农模糊熵定义的选线信心度对投票统计图进行了融合计算,在选出故障馈线的同时,给出选线信心度。该方法的特点在于通过综合多个采样点的投票结果,大大提高了选线方法的可靠性,并且给出的选线信心度是对当前选线结果可靠性的度量。建立了一个四馈线仿真模型,通过仿真验证表明,定义的选线信心度合理,选线正确率高。后续在研究健全馈线和故障馈线瞬时功率特征区别的基础上,构造了基于S变换的瞬时能量函数,提出了基于S变换瞬时能量函数法的故障选线方法。对以上两种选线方法在电弧故障和噪声环境下的适应性进行了研究。结果表明,两种方法几乎不受电弧故障的影响,并且具有较强的抗噪声能力。最后,通过对两种选线方法进行综合得到了基于仙农模糊熵的综合选线方法。通过仿真测试表明,综合方法的抗噪性比基于单一选线原理的方法有所提升。
再次,理论分析了S注入法中影响故障定位信号衰减的主要因素,建立仿真模型对这些因素进行了研究。结果表明,定位信号受过渡电阻的影响最大,并且当网络拓扑结构一定时,定位信号的衰减程度主要取决于过渡电阻和故障距离两个因素。以铁路配电网为应用对象,设计实现了一套基于S注入法的铁路电力贯通线路故障定位系统,该系统通过基于无线传感器网络通信思想的无线节点上送定位信号的检测信息,大大提高了信号寻迹的效率。通过在一条实际线路上的试验,结果表明该定位系统定位准确、耗时短。
最后,针对配电网谐波源定位问题进行了研究。首先改进了基于电流源注入法的谐波潮流直接算法。该谐波潮流算法通过矩阵初等变换对同时含有环网、滤波器和补偿电容器的网络进行求解,通过和IEEE-519中一个配电网模型的计算数据比较,验证了算法的正确性。将该方法和前推回代法进行了对比,结果表明本文的算法速度快、收敛性好,并且随着环路的增加,方法的优势更加明显。研究了基于最小二乘估计和稀疏最大化的两种谐波源定位方法,根据IEEE-123节点测试模型建立了计算模型,对两种方法在测量点个数不同时的定位能力进行了分析。对比分析了两种方法在非主要谐波源存在注入干扰、量测矩阵存在误差、网络存在环路以及接有补偿电容器组四种工况下的适应性。研究表明,基于最小二乘估计的谐波源定位方法对四种工况的适应性最好,但是以测量点的增加为代价；基于稀疏最大化的谐波源定位方法可以用少量测量点实现谐波源定位,并且在谐波注入干扰、网络含有环路以及量测矩阵存在误差时表现良好,但当网络含有补偿电容时,方法的准确性下降甚至失效。因此,应测量补偿电容上流过的谐波电流以减小其影响。
Fault diagnosis is the foundation of precise fault analysis and fast fault restoration. Harmonic source identification is the premise of harmonic treatment and responsibility partition of harmonic pollution. With the development of science and technology and the progress of society, the size of distribution network keeps growing, and protection and monitor devices are more installed. As a result, large amount of data was accepted by information center in nowadays. How to use these data for fault diagnosis and harmonic source identification in distribution network is both theoretically and practically significant for improving operation level in power system. According to the characteristics of fault diagnosis and harmonic source identification in distribution, the effective electrical components are selected, and the reasonable techniques are employed for studying the related issues.
At first, the fault classification technique in neutral non-effectively grounded distribution network based on Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed. The transient signals in fault characteristic frequency band are extracted by wavelet transform. From the transient signals, the fault characteristic quantities are constructed by statistics. Afterward, their performance under different fault types is studied. ANFIS is employed as the fault characteristic quantities fusion tool to obtain the final fault classification result. The proposed fault classification technique is trained and tested in the simulated distribution network in PSCAD/EMTDC environment. The results show its high correctness. The adaptability of the proposed method is studied in six distribution operating conditions, which are neutral grounded style changing, arc fault, different system equivalent impedances, network topology variation, noise interference and different load levels. From the results, the classification method is almost immune in the first four conditions. When nework topology changes slightly, the adaptability is good. But, the correctness of results would decrease if topology changes intensively. The proposed technique is seriously degraded by small signal noise ratio or heavy load in the system. So, adding noise filter and training samples of heavy load should be considered for improving the correctness.
At second, the fault line selection methods based on S-Transform (ST) in neutral non-effectively grounded distribution network is well studied. Through analyzing the amplitude-frequency and phase-frequency characteristics of ST, the fault line selection technique based on phase-comparison principle is proposed. This technique utilizes the modulus and phase information extracted by ST at different frequencies of zero sequence currents. Through modulus comparison, the characteristic frequency is found. Through phase comparison, the fault line vote mechanism is constructed. Since the modulus at characteristic frequency can reflect the reliability of phase at the corresponding frequency, the vote confidence degree is defined in vote process. Through one by one vote in1/4fundamental cycle after fault, the vote statistic diagram is obtained. The Fault Line Selecting Confidence Degree (FLSCD), which is defined by Shannon Fuzzy Entropy (SFE), is used to calculate the vote statistic diagram. In the proposed fault line selection method, the fault line result is given with FLSCD. Its advantages are that the result reliability is highly enhanced by utilizing multiple-points vote results, and FLSCD is the reliability measument of the suspected fault line. The simulation model with four feeders is established. The simulating results show that the proposed technique possesses high correctness, and the defined FLSCD is rational. Later, the difference between instant power of faulty line and that of healthy lines are studied. The instant power functions for fault line selection based on ST are constructed for proposing the other fault line selection method. The performance of two above methods in arc fault and noise environment is researched. The results show that both of them exibihit good in arc fault, and their anti-noise ability is also strong. In the end, two methods are synthetized for obtaining the fusion method based on SFE. Through simulation, the anti-noise ability of fusion method is improved in comparison with the single principle based methods.
At third, the main factors which attenuate the fault location signal in S-injection method are theoretically studied. Moreover, these factors are researched by simulation. It is found that fault resistance has the most serious influence on the fault location signal. When the network topology is fixed, the attenuation of fault location signal mainly depends on fault resistance and fault distance. To make the railway distribution network as application object, the fault location system based on S-injection method is developed. It employs wireless detectors which are based on the communication idea of wireless sensor network to upload the sensing information of fault location signal. As a result, the efficiency of tracing location signal is highly improved. From the field test on a real feeder, the location system locates fault correctly and quickly.
At last, the harmonic source identification in power distribution is researched. The harmonic power flow direct algorithm based on current injection idea is modified first. The algorithm can calculate the network with loops, capacitors and filters through matrix initial transform. In comparison with the results of distribution in IEEE-519, the correctness of algorithm is verified. Compared with backward-forward sweep method, the algorithm in this paper shows fast calculation speed and good convergence. Moreover, its advantages are more obvious as the number of loops increases. Then, two harmonic source identification methods based on Least-Square Estimation (LSE) and Sparsity Maximizatioin (SM) respectively are studied carefully. According to IEEE-123test network, the calculation model for testing the two methods is established. The identification ability of two methods is analyzed when there are different numbers of measument devices in network. The adaptability of two techniques is compared when non-main harmonic sources have injecting disturbances, measument matrix has errors, and network has loops or capacitors. From the study results, the LSE method shows better adaptability, however the expense is more measument devices. The SM technique can identify harmonic sources with the small number of measument devices. Also, it has good adaptability under the conditions of harmonic injection distrubance, loops and inaccurate measument matrix. But when capacitors are connected into network, the SM technique is degraded seriously or even invalid. So, the harmonic current flowing in capacitors should be measured in order to avoid their impact.
引文
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