Application of ANFIS Controlled ShuntActive Filterfor Harmonic Reduction
Authors :Chun-Tang Chao, Chi-Jo Wang, Cheng-Ting Hsu, Nguyen Thi Hoai Nam Presented by: Nguyen Thi Hoai Nam OUTLINE Introduction Shunt Active Filter Modeling
Control System Design Simulation Results Conclusions 1. Introduction Reason choosing this research topic
Reduction harmonic method Proposed controller: ANFIS (Adaptive Neuro FuzzyInference System) In recent years, with the rapid economic development, all kinds of nonlinear loads based on power electronic devices (diode and thyristor rectifiers, electronic starters, uninterruptible power supplyand high voltage direct current systems, arc furnaces, etc.) have been used in power systems and induced the appearance of the dangerous phenomenon of harmonic currents flow in the electrical feeder networks, producing distortions in the current and voltage waveforms. As a result, harmful consequences occur: equipment overheating, malfunction of solid-state material, interferences with telecommunication systems, etc. So power quality distortion has become a serious problem in electrical power systems due to the increase of nonlinear loads drawing non-sinusoidal currents Active filter ANFIS is acronym of Adaptive Neuro Fuzzy Inference System In this research, first we design fuzzy logic controller to controller and then ANFIS-based adaptive AF is presented for different operation conditions. 2. Shunt Active Filter Modeling
Active filter is a power electronic device based on the use of inverters Shunt Active Power Filter is connected in a common point connection between the source of power system and the load system which present the source of the polluting currents circulating in the power system lines Fig. 1. Power system with non-linear load and shunt active filter. 2. Shunt Active Filter Modeling
(1) (2) Fig. 1. Power system with non-linear load and shunt active filter. - Suppose that iL, iF, iS are receiver absorbed current, desired power supply current and active filter must provide current respectively then we have the relationship between them in formula given (1) - Where ifis fundamental component magnitude of load current, also the power supply current iS, iH is the harmonic current generated in load branch. From (1), (2) we have: (3) Formula (3) indicates that purpose of shunt active power filter is intended to generate exactly the same harmonics contained in the polluting current iL but with opposite phase. 2. Shunt Active Filter Modeling
The mathematical model can be extracted from the single-phase equivalent scheme by Fig. 2. Fig. 2. Single-phase equivalent scheme Fig. 2 shows the mathematical model extracted from the single-phase equivalent circuit Applying the Kirchhoffs laws, the relationship between AFs voltage vF and current iF is described (4) where vs, vF is calculated by (5) and (6). E is power supply for the AF and is a switching state taking the values of either 1 or 1 corresponding to the two inverter levels +E or E. Finally, the whole supply- AF-rectifier for 3 phases can be modeled by the following equations (7), (8) (4) (7) (5) (8) (6) 3. Control System Design 3.1 Control structure of Active Filter
Fig. 3 is applied to control AF producing current track with the load current harmonic Fig. 3. The active filter control structure. Where: AF is active filter; BPF is band pass filter; LPF is low pass filter; PWM is pulse width modulation. 3. Control System Design 3.1 Control structure of Active Filter
Fig. 4 shows the simulation model of three-phase power system with AF in Matlab/Simulink environment for implementing the system architecture in Fig. 3 Fig. 4. The simulation model of electrical power system with active filter. 3. Control System Design 3.1 Control structure of Active Filter
The controller structure and the structure of AF are shown in Fig. 5 and Fig. 6 respectively. Fig. 5. The controller structure. Fig. 6. Active Filter structure using IGBTs 3. Control System Design 3.2 Fuzzy Logic Controller for AF
Fig. 7. Fuzzy controller synoptic diagram Figure 7 shows the synoptic scheme of a PD-like FLC for the AF. The FLC possesses one output (u) andtwo inputs, the error (e) and derivate of the error (de), where: e = iref iF Fig. 8 shows the rule viewer window of FLC. In this designed controller, the Gaussian membership function is employed for the two inputs and the output uses triangle shape. Fig. 9 shows the relationship between 2 inputs (e,de) and 1 output (u) Fig. 8 Rule viewer window. Fig. 9 Relationship between e, de, u 3. Control System Design 3.3 ANFIS Architecture for AF
Jang originally presented the Adaptive Neuro-Fuzzy Inference System technique in 1993 [16]. Jang combined both Fuzzy Logic and Neural Network to produce a powerful processing tool named Neuro-Fuzzy Systems that have both Neural Network and Fuzzy Logic advantages and the most common one is ANFIS. Actually, this tool is like a fuzzy inference system, but the difference is in the use of a back propagation algorithm for minimizing the error. 3. Control System Design 3.3 ANFIS Architecture for AF
Fig. 10. ANFIS architecture The signal propagation and the function in each layer, as described below. - Layer 1 is the fuzzy layer, in which the membership degree of different fuzzy sets for input variables are specified. - Layer 2 is the rule layer that the output of this layer is the product of the input signals. Each node performs the precondition matching of the fuzzy rules, i.e. they compute the activation level of each rule. The node number of this layer is equal to the number of fuzzy rules. - Layer 3 is the normalized layer. Its function is to normalize the weight function to produce the normalized firing strengths. - Layer 4 is the defuzzification layer and provides the output values resulting from the inference of rules. Connections between layer 3 and layer 4 are weighted by the fuzzy singletons that represent another set of parameters for the neuro-fuzzy network. - Layer 5 is the output layer which sums up all the inputs from layer 4 and transforms the fuzzy classification results into a crisp. Layer 1 consists of input variables Layer 2 is membership layer Layer 3 is rule layer Layer 4 is defuzzification layer Layer 5 is output layer 3. Control System Design 3.3 ANFIS Architecture for AF
Fig patterns are loaded into the ANFIS editor tool Anfis editor tool in matlab/simulink is used to train the data. Fig. 11 shows 500 training patterns are loaded in ANFIS editor tool The ANFIS model uses hybrid optimization method. The number of training epochs is 30 and training error tolerance sets to zero. The average testing error of training data are 6.63e-6 as shown in Fig. 12 Fig. 12. Result of the ANFIS model testing with training data 3. Control System Design 3.3 ANFIS Architecture for AF
Fig. 13. Membership functions of input e. Fig. 13 and Fig.14 shows the membership function form of input (e) is changed after training Fig. 14 Tuned membership functions of input e 3. Control System Design 3.3 ANFIS Architecture for AF
Fig. 15. Membership functions of input de. Fig. 15 and Fig.16 shows the membership function form of input (de) is changed after training Fig. 16 Tuned membership functions of input de 4. Simulation Results Table 1. Simulation parameters
Simulation results with methodologies of FLC and the ANFIS controller are implemented by Matlab/Simulink. Table 1 summarizes the simulation parameters. 4. Simulation Results Fig. 17. Supply current isa waveform before applying the AF. Fig. 17 and Fig. 18 show the supply current waveform and its harmonic spectrum before applying shunt active filter. Serious distortions is indicated in Fig. 18 with a THD of 12.54%. Fig. 18. Harmonic spectrum of isa before applying AF 4. Simulation Results Fig. 19. Supply current isa waveform after applying AF using FLC - Consider applying shunt active filter and the proposed FLC, after all of the preliminary operations have been applied, the simulation is executed for 0.12s in Matlab R2009a. The effectiveness of the fuzzy control strategy is illustrated in Fig. 19 and Fig. 20. Fig. 19 shows the supply current waveform is much closer to sinusoidal wave. The harmonic spectrum in Fig. 20 shows the significant improvement with THD reducing to 1.04%. Fig. 20. Harmonic spectrum of isa after applying AF using FLC 4. Simulation Results Fig. 21. Supply current isa waveform after applying AF using ANFIS The simulation result becomes better after we use ANFIS, as shown in Fig. 21 and Fig. 22. The THD value drops again to 0.98%. Fig. 22. Harmonic spectrum of isa after applying AF using ANFIS 4. Simulation Results Fig. 23 shows how AF current tracks its reference signal iref . Fig. 23. AF current and its reference with ANFIS. 4. Simulation Results Table 2. Total Harmonic Distortion (THD) (%) in different running conditions of load Moreover, consider that the motor runs in different conditions: heavy load, medium load, and light load. The respective fundamental currents are 710A, 470A, and 255A. Table 2 reports the THD values in each case. It indicates that with active filter and the proposed FLC or ANFIS design, the system will have better response under different running conditions of load. Also, the THD value doesnt exceed 5% by the IEEE 519 standards 5. Conclusions In this work, the FLC and ANFIS are developed to reduce the harmonic current for nonlinear loads through running simulation in Matlab/Simulink environment. Importantly, the applied ANFIS controller is better than the fuzzy controller and can also be used to improve the control performance of nonlinear systems. Experimental results and simulations show that the resulting shunt active filter presents good dynamic and steady-state response. Harmonic pollution is always kept under IEEE 519 standards. Thank you for listening