企业配电网电能质量检测及控制方法研究
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摘要
电力工业是关系国计民生的基础产业,随着国民经济的发展和人民生活水平的不断提高,人们对电力的需求快速增长,同时对配电网电能质量提出了更高的要求。
本文重点针对企业配电网的谐波及无功电流的实时检测方法、动态补偿控制方法和综合补偿技术进行了深入研究。本文所做研究的特色和创新之处以及获得的有益结论主要体现在以下几个方面：
1.首先介绍了自适应噪声消除原理及传统LMS(Least Mean Square)算法,在归一化LMS算法基础上提出了改进LMS算法,该算法具有较快的收敛速度和良好的平稳性。将改进LMS算法用于调整自适应滤波器的权值,提出了两种基于定步长的自适应谐波与无功检测算法。对两种算法进行观测比较,并且在配电网单相系统仿真及三相滤波器系统的谐波补偿控制仿真中对两算法进行检验。仿真结果显示应用检测算法二的滤波器系统拥有较好的动态响应性能,但在稳态精度方面仍有待改进。以检测算法二为基础,提出一种模糊自适应谐波与无功电流检测方法,通过建立起对应的模糊规则并利用模糊推理来实时调整算法的步长,能够协调好系统稳态性能与收敛速度之间的矛盾,并能在一定程度上改善滤波器的补偿精度。最后对模糊自适应谐波与无功电流检测方法进行仿真研究,仿真结果显示了所提方法的正确性和有效性。
2.以IHAPF(Hybrid Active Power Filter With Injection Circuit)为研究对象,提出了基于π补偿smith预估器和BP神经网络(Neural Network, NN)的谐波电流信号跟踪控制方法,能够降低系统控制延时对滤波器性能的影响。使用π补偿smith预估器可以将系统延时从控制闭环内部转移到外部,提高系统的响应速度和稳定性。基于PSO-BP(Particle Swarm Optimization and BP Neural Network)算法的神经网络则用来在线寻求PI控制器的最优参数,PSO-BP算法不仅可以克服传统BP算法的收敛速度慢和易陷入局部极值的局限,而且较好的提高了BP神经网络学习能力。对于π补偿smith预估器模型的辨识,则是利用时间乘以误差绝对值积分(Integral Time Absolute Error, ITAE)准则建立起预估器参数与PI控制器参数间的数学关系式,应用该等式关系便可得到预估器模型的具体参数值,完成对预估器模型的辨识。最后,对所提控制方法进行仿真和实验研究,仿真结果显示所提方法的可行性和有效性,实验结果进一步验证了方法的正确性。
3.针对TCR-MSC型静止无功补偿器(Static Var Compensator, SVC)的无功动态补偿控制方法进行了深入研究,提出了将SVC电压稳定控制和SVC负荷不平衡补偿相结合的复合控制方法,该方法易于实现、计算量小。首先介绍了SVC系统的拓扑结构,并设计了SVC的整体控制器,然后分别对电压的稳定控制方法和SVC负荷不平衡补偿方法进行了介绍。在电压稳定控制的实现过程中采用了一种改进的Z-N(Ziegler-Nichols)方法对控制器参数进行了优化,获得了优越的SVC系统控制性能。对三相负荷不平衡的补偿控制采用了基于虚拟对称三相系统的同步参考旋转坐标变换的补偿电纳计算方法,应用该方法计算量小,使用该方法的SVC不需要利用锁相环来获取同步旋转角,能够快速准确地补偿负荷的无功功率。最后对所提控制方法进行了仿真研究,仿真结果验证了方法的可行性与有效性。
4.针对IHPQC(Injection Hybrid Power Quality Compensator)系统的控制技术进行了深入研究,提出了将SVC模式控制与IHAPF电流控制相复合的控制方法。在第四章所提电压稳定和负荷不平衡补偿控制方法的基础上,结合提出的电网功率因数校正控制方法,即构成了本文的SVC模式控制方法,通过该方法可以实现企业配电网电压的稳定、负荷电流负序分量的补偿和电网功率因数的校正。IHAPF电流控制主要包含配电网谐波与无功电流的检测及跟踪补偿控制环节,在谐波与无功的检测中使用了特定次数谐波检测并进行相位补偿的方法,可以实现电网谐波分量的准确检测,并能够减小控制延时在检测环节对系统产生的影响,该方法不仅可用于三相对称系统,同样适用于三相不对称系统。采用电流的反馈-前馈跟踪控制方法实现对电网谐波电流的动态补偿,其中电流的反馈控制环节应用了提出的迭代学习控制算法,该算法克服了传统迭代学习算法在IHPQC应用中的不足,改善了配电网谐波电流的跟踪补偿效果。采用单纯形加速算法对电流反馈控制器的参数进行在线优化,能够获得控制器的最优参数,提高系统的控制性能。PSIM仿真和实验结果验证了所提方法的可行性和有效性,采用复合控制方法后的IHPQC系统能够有效地实现电网的动态谐波治理和无功补偿。
Power industry is a basic industry relative to the people's livelihood. With the development of national economy and the improvement of people's living standards, the people's demand for electric power have rapid growth, and there are more and more requests for the power quality of distribution network.
In this paper the real-time detection method and dynamic compensation method as well as synthesis compensation technique are proposed. The characteristics, innovation and beneficial conclusion are mainly embodied in the following aspects:
1. In this chapter the adaptive noise cancellation principle and conventional LMS (Least Mean Square) algorithm are introduced firstly. An improved LMS algorithm based on normalized LMS is further proposed, and it owns a better convergence speed comparing with conventional LMS algorithm and good stability. Improved LMS algorithm is applied to adjust the weights of adaptive filters, and this dissertation proposes two adaptive detection algorithms of harmonic and reactive current with invariable step-length. Two proposed detection methods are tested and compared in single-phase system simulation and harmonic compensation control simulation of three-phase filter system. Simulation results show that the filter system with the second detection algorithm possesses a good dynamic response performance, but steady precision of using it still needs to be improved. An adaptive detection method of harmonic and reactive current based on fuzzy algorithm optimization is further proposed. The step-length of algorithm is adjusted through establishing the corresponding fuzzy rules and using fuzzy inference, which result in the improvement of compensation precision of filter. Finally the adaptive detection method of harmonic and reactive current based on fuzzy optimization is applied in simulation of three-phase filter system, and the simulation results have shown that the proposed method is correct and effective.
2. In this chapter IHAPF (Hybrid Active Power Filter with Injection Circuit) is regarded as research object. A novel current tracking control method, which is based on n compensation smith predictor and neural network with a modified weight algorithm, is proposed. The use of π compensation smith predictor, which can transfer the system delay to external of control closed-loop from inside of closed-loop, has some advantages such as eliminating system delay effectively, as well as improving system stability and response speed. The neural network, which is based on PSO-BP (Particle Swarm Optimization and BP Neural Network) algorithm, is used for optimizing PI controller parameters and then improving control system precision. PSO-BP algorithm can not only overcome the problems such as slow convergence speed and sinking into the local extremum easily, but also has higher control precision and better learning ability of BP Neural Network. Meanwhile, after obtaining the relation of π compensation smith predictor parameters and PI controller parameters by ITAE (Integrated Time Absolute Error) criterion, parameter estimation of two controllers can be got, which avoids the case of identifying two controller parameters separately, and reduces the sensitive dependence on the grid parameters of two controllers. Simulation and experimental results have verified the effectiveness of proposed method.
3. In this chapter, it makes an intensive study for the reactive current dynamic compensation method of SVC (Static Var Compensator). A compound control method composed of the voltage stability control and the compensation method of imbalance load in SVC system is proposed. The proposed method is easily carried out and the calculation with the method is small, so it is worth using for reference. In the paper, it introduces the topology structure of SVC, and designs the whole controller of SVC, then introduces the voltage control method and compensation method of the imbalance load respectively. In the voltage control method, an improved Ziegler-Nichols method is applied to optimize the parameters of controller and get a good control performance, and the method is easy to be realized and its calculation is small. In the compensation control of three-phase imbalance load, a calculation method of electrical susceptance based on synchronization reference rotating coordinate transformation of structuring symmetrical three-phase system is proposed. Proposed computing method has the advantage of small computation. The SVC system based on the proposed computing method can acquire synchronous rotation angle of not using phaselocked loop, and it can compensate reactive power of load quickly and accurately. Simulation results have verified the feasibility and effectiveness of proposed method.
4. In this chapter a control technology of IHPQC (Injection Hybrid Power Quality Compensator) is studied, and control method composed of SVC mode control and IHAPF current control is proposed. In the paper, SVC mode control method consists of proposed reactive power control method in the fourth chapter and the grid power factor correction method proposed in this chapter. Stabilizing voltage of distribution system and compensating negative sequence components of load current as well as improving power factor can be realized by SVC mode control method, and it is easy to be realized and has reference value for large-scale SVC engineering application. IHAPF current control mainly consists of detection method of harmonic and reactive current and tracking compensation control. The detection method of detecting selected harmonic and compensating phase can realize the accurate detection of the harmonic component in the power grid, and can reduce the influence of detection delay. Proposed detection method can be used not only in three-phase symmetric system, but also for three-phase asymmetric system. Current tracking control method based on feedback-feedforward control is proposed to realize the dynamic compensation of harmonic in the power grid. An improved iterative learning control algorithm is applied in current feedback control, and proposed iterative algorithm avoids some defects of conventional iterative algorithm. In order to improve the control performance for tracking harmonic, the simplex method is used to optimize parameters of iterative controller. A feedforward link based on derivative learning law of harmonic current error as the control input is presented to the iterative feedback controller, which can improve the response performance of tracking harmonic. Simulation and experimental results have confirmed that IHPQC system with the proposed control method can effectively realize dynamic supression for load harmonic current and TCR harmonic, and dynamic compensation for reactive power of power grid.
本文重点针对企业配电网的谐波及无功电流的实时检测方法、动态补偿控制方法和综合补偿技术进行了深入研究。本文所做研究的特色和创新之处以及获得的有益结论主要体现在以下几个方面：
1.首先介绍了自适应噪声消除原理及传统LMS(Least Mean Square)算法,在归一化LMS算法基础上提出了改进LMS算法,该算法具有较快的收敛速度和良好的平稳性。将改进LMS算法用于调整自适应滤波器的权值,提出了两种基于定步长的自适应谐波与无功检测算法。对两种算法进行观测比较,并且在配电网单相系统仿真及三相滤波器系统的谐波补偿控制仿真中对两算法进行检验。仿真结果显示应用检测算法二的滤波器系统拥有较好的动态响应性能,但在稳态精度方面仍有待改进。以检测算法二为基础,提出一种模糊自适应谐波与无功电流检测方法,通过建立起对应的模糊规则并利用模糊推理来实时调整算法的步长,能够协调好系统稳态性能与收敛速度之间的矛盾,并能在一定程度上改善滤波器的补偿精度。最后对模糊自适应谐波与无功电流检测方法进行仿真研究,仿真结果显示了所提方法的正确性和有效性。
2.以IHAPF(Hybrid Active Power Filter With Injection Circuit)为研究对象,提出了基于π补偿smith预估器和BP神经网络(Neural Network, NN)的谐波电流信号跟踪控制方法,能够降低系统控制延时对滤波器性能的影响。使用π补偿smith预估器可以将系统延时从控制闭环内部转移到外部,提高系统的响应速度和稳定性。基于PSO-BP(Particle Swarm Optimization and BP Neural Network)算法的神经网络则用来在线寻求PI控制器的最优参数,PSO-BP算法不仅可以克服传统BP算法的收敛速度慢和易陷入局部极值的局限,而且较好的提高了BP神经网络学习能力。对于π补偿smith预估器模型的辨识,则是利用时间乘以误差绝对值积分(Integral Time Absolute Error, ITAE)准则建立起预估器参数与PI控制器参数间的数学关系式,应用该等式关系便可得到预估器模型的具体参数值,完成对预估器模型的辨识。最后,对所提控制方法进行仿真和实验研究,仿真结果显示所提方法的可行性和有效性,实验结果进一步验证了方法的正确性。
3.针对TCR-MSC型静止无功补偿器(Static Var Compensator, SVC)的无功动态补偿控制方法进行了深入研究,提出了将SVC电压稳定控制和SVC负荷不平衡补偿相结合的复合控制方法,该方法易于实现、计算量小。首先介绍了SVC系统的拓扑结构,并设计了SVC的整体控制器,然后分别对电压的稳定控制方法和SVC负荷不平衡补偿方法进行了介绍。在电压稳定控制的实现过程中采用了一种改进的Z-N(Ziegler-Nichols)方法对控制器参数进行了优化,获得了优越的SVC系统控制性能。对三相负荷不平衡的补偿控制采用了基于虚拟对称三相系统的同步参考旋转坐标变换的补偿电纳计算方法,应用该方法计算量小,使用该方法的SVC不需要利用锁相环来获取同步旋转角,能够快速准确地补偿负荷的无功功率。最后对所提控制方法进行了仿真研究,仿真结果验证了方法的可行性与有效性。
4.针对IHPQC(Injection Hybrid Power Quality Compensator)系统的控制技术进行了深入研究,提出了将SVC模式控制与IHAPF电流控制相复合的控制方法。在第四章所提电压稳定和负荷不平衡补偿控制方法的基础上,结合提出的电网功率因数校正控制方法,即构成了本文的SVC模式控制方法,通过该方法可以实现企业配电网电压的稳定、负荷电流负序分量的补偿和电网功率因数的校正。IHAPF电流控制主要包含配电网谐波与无功电流的检测及跟踪补偿控制环节,在谐波与无功的检测中使用了特定次数谐波检测并进行相位补偿的方法,可以实现电网谐波分量的准确检测,并能够减小控制延时在检测环节对系统产生的影响,该方法不仅可用于三相对称系统,同样适用于三相不对称系统。采用电流的反馈-前馈跟踪控制方法实现对电网谐波电流的动态补偿,其中电流的反馈控制环节应用了提出的迭代学习控制算法,该算法克服了传统迭代学习算法在IHPQC应用中的不足,改善了配电网谐波电流的跟踪补偿效果。采用单纯形加速算法对电流反馈控制器的参数进行在线优化,能够获得控制器的最优参数,提高系统的控制性能。PSIM仿真和实验结果验证了所提方法的可行性和有效性,采用复合控制方法后的IHPQC系统能够有效地实现电网的动态谐波治理和无功补偿。
Power industry is a basic industry relative to the people's livelihood. With the development of national economy and the improvement of people's living standards, the people's demand for electric power have rapid growth, and there are more and more requests for the power quality of distribution network.
In this paper the real-time detection method and dynamic compensation method as well as synthesis compensation technique are proposed. The characteristics, innovation and beneficial conclusion are mainly embodied in the following aspects:
1. In this chapter the adaptive noise cancellation principle and conventional LMS (Least Mean Square) algorithm are introduced firstly. An improved LMS algorithm based on normalized LMS is further proposed, and it owns a better convergence speed comparing with conventional LMS algorithm and good stability. Improved LMS algorithm is applied to adjust the weights of adaptive filters, and this dissertation proposes two adaptive detection algorithms of harmonic and reactive current with invariable step-length. Two proposed detection methods are tested and compared in single-phase system simulation and harmonic compensation control simulation of three-phase filter system. Simulation results show that the filter system with the second detection algorithm possesses a good dynamic response performance, but steady precision of using it still needs to be improved. An adaptive detection method of harmonic and reactive current based on fuzzy algorithm optimization is further proposed. The step-length of algorithm is adjusted through establishing the corresponding fuzzy rules and using fuzzy inference, which result in the improvement of compensation precision of filter. Finally the adaptive detection method of harmonic and reactive current based on fuzzy optimization is applied in simulation of three-phase filter system, and the simulation results have shown that the proposed method is correct and effective.
2. In this chapter IHAPF (Hybrid Active Power Filter with Injection Circuit) is regarded as research object. A novel current tracking control method, which is based on n compensation smith predictor and neural network with a modified weight algorithm, is proposed. The use of π compensation smith predictor, which can transfer the system delay to external of control closed-loop from inside of closed-loop, has some advantages such as eliminating system delay effectively, as well as improving system stability and response speed. The neural network, which is based on PSO-BP (Particle Swarm Optimization and BP Neural Network) algorithm, is used for optimizing PI controller parameters and then improving control system precision. PSO-BP algorithm can not only overcome the problems such as slow convergence speed and sinking into the local extremum easily, but also has higher control precision and better learning ability of BP Neural Network. Meanwhile, after obtaining the relation of π compensation smith predictor parameters and PI controller parameters by ITAE (Integrated Time Absolute Error) criterion, parameter estimation of two controllers can be got, which avoids the case of identifying two controller parameters separately, and reduces the sensitive dependence on the grid parameters of two controllers. Simulation and experimental results have verified the effectiveness of proposed method.
3. In this chapter, it makes an intensive study for the reactive current dynamic compensation method of SVC (Static Var Compensator). A compound control method composed of the voltage stability control and the compensation method of imbalance load in SVC system is proposed. The proposed method is easily carried out and the calculation with the method is small, so it is worth using for reference. In the paper, it introduces the topology structure of SVC, and designs the whole controller of SVC, then introduces the voltage control method and compensation method of the imbalance load respectively. In the voltage control method, an improved Ziegler-Nichols method is applied to optimize the parameters of controller and get a good control performance, and the method is easy to be realized and its calculation is small. In the compensation control of three-phase imbalance load, a calculation method of electrical susceptance based on synchronization reference rotating coordinate transformation of structuring symmetrical three-phase system is proposed. Proposed computing method has the advantage of small computation. The SVC system based on the proposed computing method can acquire synchronous rotation angle of not using phaselocked loop, and it can compensate reactive power of load quickly and accurately. Simulation results have verified the feasibility and effectiveness of proposed method.
4. In this chapter a control technology of IHPQC (Injection Hybrid Power Quality Compensator) is studied, and control method composed of SVC mode control and IHAPF current control is proposed. In the paper, SVC mode control method consists of proposed reactive power control method in the fourth chapter and the grid power factor correction method proposed in this chapter. Stabilizing voltage of distribution system and compensating negative sequence components of load current as well as improving power factor can be realized by SVC mode control method, and it is easy to be realized and has reference value for large-scale SVC engineering application. IHAPF current control mainly consists of detection method of harmonic and reactive current and tracking compensation control. The detection method of detecting selected harmonic and compensating phase can realize the accurate detection of the harmonic component in the power grid, and can reduce the influence of detection delay. Proposed detection method can be used not only in three-phase symmetric system, but also for three-phase asymmetric system. Current tracking control method based on feedback-feedforward control is proposed to realize the dynamic compensation of harmonic in the power grid. An improved iterative learning control algorithm is applied in current feedback control, and proposed iterative algorithm avoids some defects of conventional iterative algorithm. In order to improve the control performance for tracking harmonic, the simplex method is used to optimize parameters of iterative controller. A feedforward link based on derivative learning law of harmonic current error as the control input is presented to the iterative feedback controller, which can improve the response performance of tracking harmonic. Simulation and experimental results have confirmed that IHPQC system with the proposed control method can effectively realize dynamic supression for load harmonic current and TCR harmonic, and dynamic compensation for reactive power of power grid.
引文
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