Optimal PFC corrector of single stage power converter using BC tuned PID controller

Abstract

In this paper, Bee Colony (BC) algorithm based tuning of the proportional, integral and derivative (PID) controller is proposed. The novelty of the proposed method is enhanced searching ability, reliability and suitable for complex problems. Here, the proposed method is used to optimize the proportional, integral and derivative gains of the PID controller based on the output power variation from the input power. With the optimized gains, PID controller determines the control output to reduce the error between the actual and reference value of the instantaneous input power. This instantaneous input reference is computed with the aid of Radial Basis Function Network (RBFN). In the presented work, the topology of the AC to DC power converter is modelled and controlled with BC optimized PID controller. The performance of the proposed BC-PID controller for AC to DC converter is analyzed in terms of power factor correction and efficiency. The validation of the proposed control algorithm is performed on MATLAB/Simulink working platform and the effectiveness of the proposed method is analyzed by using the comparison analysis with the existing techniques.

Keywords

Power factor correction AC to DC Converter single stage efficiency PSO algorithm BC algorithm PID control optimal control

1 Introduction

The Converters have been extensively employedwith the generation of the power electronics, for variable speed drives like the induction motor drives and DC motors, to name a few, in micro-grid scenarios of the renewable energy sources such as the PV panel, wind energy, fuel cells, battery energy storage systems, super capacitors, micro turbines, and so on [1 –4]. In addition, they are utilized for the DC loads in commercial, industrial and residential applications and for parallel functions involving FACTS devices, UPSs, CPDs, to name a few [5, 6]. In every aspect, the converters have a very vital part to play in power system procedures.Ever-zooming advancement in the technology of power electronics have enable the converters to be cost-conscious, grid connectable and capable of tackling superior power applications in the realm of power systems and electrical drives [7]. Normally, there are diverse phases involved in conversions though there are two kinds of universal conversions available such as the AC to DC conversion and DC to AC conversion. In a large majority of the cases, these conversions depend invariably on the state the of conversion applications.

Normally, an AC to DC converter is also called the rectifier which arranges the line supply or the parallel AC sources as input and provides a regulated DC output voltage. With a particular range of voltage values the corresponding controlled DC voltage may be changed with the help of an independent boost or buck or a blend of the DC/DC conversions [8]. With the intention of offering a fresh DC power for the miniature and the mega applications such as Light-Emitting Diode (LED) lights, portable AC-DC adapters, Hybrid Electric Vehicles (HEVs), and so on [9], such kind of power conversions with various phases have been devoted. Broadly, there are two distinctive converters such as the linear converters and the switched mode converters. With the help of a low frequency transformer the linear converters withstand the voltage and adapt the output voltage with a linear regulator in which the switched mode converters utilize the power switches to achieve superlative efficiency with minimal form factor [10].

The fundamental control is offered by the AC to DC converters with the sparkling advantages of low price and simplicity of use [11]. Conversely, the innate defects are such that the power factor (PF) tends to decrease when the firing angle goes up and the ripples are present in the output DC voltage [12 –15] in unrestrained rectifiers or line-commutated phase-controlled rectifiers. The most important difficulties and may create some difficult effects to the system, such as overloading of the feeders, harmonic pollution, high investment cost, low efficiency, and low dependability, which hold back their extensive use. On other hand, these rectifiers perform as nonlinear loads and draw line current harmonics which twist the mains voltage [16 –19]. Other sensitive tools linked in the similar network may get badly affected by these current harmonics [20]. However, the size and cost of a converter is based on its optimal control which should as high as feasible. Nevertheless, higher frequencies result in the raise of transistor switching losses, and hence, the converter’s efficiency gets restricted [21, 22]. In existing works, many control topologies such as artificial neural network Control lyapunov function, discrete energy function (ANN) and fuzzy logic controller (FLC) [23] are involved for PFC of the power converter. But these methodologies are struggles to find the optimal controlling performance based on the load. These factors cause an incredible decrease in the power quality of the power system and hence call for an instantaneous attention for the viable economic compensation.

In this document, a novel BC-PID controller for AC to DC converter is envisioned for the regulation of the input power factor and the related power quality challenges. With regard to the power factor rectification, output DC voltage control and superlative efficiency, the presentation of the innovative optimal control algorithm for AC to DC converter is carried out. In Section 3, the precise version of the novel power converter’s topology model and control algorithm is illustrated. In the preceding Section 2, the details of latest investigation work related to the topic are furnished. The validation of the novel technique is proficiently presented in Section 4 substantiated by the captivating simulation outcomes and the resultant debates. Section 5 formally discharges the conclusion task of the document.

2 Recent research work: A brief review

With higher competence for power factor correction, there are many works offered on modeling and controlling of AC-DC converters in literature. A few of them are assessed here. Three novel single-stage full-bridge AC-DC topologies has been brought in by Ribeiro H S et al. [24] with some optimized features and compared them with the ones of the presented full-bridge single-stage topologies. The approach employed consisted in the meaning of the operating principles recognizing the boost function for each topology, their operating limits, and the dependence among the two involved conversion processes. A novel bridgeless single-stage half-bridge AC-DC converter for PF correction has been suggested by Woo-Young Choi et al. [25]. The suggested converter incorporated the bridgeless boost rectifier with the asymmetrical pulse-width modulation half-bridge DC-DC converter. A high efficiency, high PF and low cost are presented by the suggested converter.

A novel control approach has been offered by Pahlevaninezhad Majid et al. [26] that controlled the input power of the converter instead of the output voltage by applying an optimal nonlinear control approach based on the Control-Lyapunov Function (CFL). A novel control approach has been offered by Das P et al. [27] based on a new separate energy function minimization control law that permitted the front-end AC/DC boost PF correction converter to function with faster dynamic response than the conventional controllers and at the same time sustained near unity input PF.

With high PF and high competence, a single power-conversion AC-DC converter has been suggested by Yong-Won Cho et al. [28]. The suggested converter was obtained by integrating a full-bridge diode rectifier and a series-resonant active-clamp DC-DC converter. Narimani Mehdi et al. [29] have proposed a new interleaved single stage AC to DC converter to reduce line current harmonics while achieving power factor correction. The proposed rectifier could produce input currents that did not have dead band regions with high power factor, operated with a continuous output current, and minimized the input electromagnetic interference filter size.

Weise N et al. [30] have proposed a dual active-bridge based single stage AC to DC converter. It could be used due to unique features of high-frequency isolation resulting in, high power density and safety andvoltage matching, bidirectional power flow, soft switching leading to higher efficiency.

From the associated work, it explains that, there are more than a few novel topologies such as optimized full bridge single stage converter, bridgeless single stage half bridge converter, etc. and numerous control algorithms based on Control Lyapunov function, Discrete energy function, etc. have been proposed for AC to DC converters. Their principle was to perfect the input PF and step down or up output DC voltage as per the system necessities in the stated papers. However, the full bridge single stage converter encloses more power switches count which provides the more switching losses and in bridgeless single stage half bridge converter, there might be the possibility of less usage of accessible power sources which probably lower the competence of the converter. Although these control approaches were much quicker than the conventional ones coming to control algorithms, however it was feasible at the price of complex computations and its plan was not an uncomplicated job. As a result, there was a chance of modeling a novel topology and better control algorithm with simple design structure for AC to DC converter in order to attain higher input PF and regulated DC output voltage with lower current harmonics and higher competence. Alternatively, for increasing the output DC voltage and PF correction some papers have been suggested separate stages. It was reasonably useful, if both the intentions were attained in single stage. In literature, regarding this situation, a very few research works were offered. Hence, the above stated disadvantages have inspired me to do this research work.

In the previous work, PSO was utilized for tuning the gains of PID controller. The performance of the PSO appreciably depends on its updating equation of particle positions. In this equation, two random vectors have significant impact on updating the particle position to next iteration. This random nature in search process leads to slow and local convergence. Thus, affects the performance of proposed PID controller for AC to DC power conversion. In order to avoid such immature performance of PSO, here, BC algorithm is utilized. The detail explanation of the proposed work is as follows.

3 Proposed optimal control strategy development for AC to DC converter

Here, the optimal PFC control technique for single stage AC to DC converter is designed. The concept of the Single stage AC to DC converter involves the fact there is a single stage for the power factor rectification and the DC to DC voltage regulation. Now, the objective of the novel control technique is to rectify the power factor of specified supply and control the DC-link voltage at the output terminals. In this scenario, a single stage AC to DC converter is designed at the outset and thereafter the novel control approach is envisaged. From the designed AC to DC converter and novel control technique, an optimal controller is envisioned.

3.1 AC to DC converter model and proposed control approach

The proposed AC to DC converter is a single stage and single phase circuit diagram. It is shown in Fig. 1 with the proposed control approach. Here, S₁, D_S1, L_F, C_o and D_F are main switch, its body diode, main input inductor, output capacitor and main diode. Other than these circuit elements, there is an active snubber circuit which consists of auxiliary switch (S₂), four auxiliary diodes (D₁, D₂, D₃, D₄), snubber inductances (L_R1, L_R2, L_ol) and snubber capacitor (C_R). In order to correct the power factor and DC output voltage regulation, the gating signals of switches S₁, S₂ must be pulsated as per determined by the proposed optimal controller. Now, the proposed control approach with the proposed AC to DC converter is given as below.

In Fig. 1, the proposed AC to DC converter model along with the proposed control approach is presented. The operating modes of the proposed AC to DC converter are detailed clearly in [31]. From Fig. 1, it says that, AC to DC converter has AC power supply as input. Let the input voltage be V_S (t) and the input voltage and input current drawn by converter be V_in (t) and I_in (t) respectively. Their mathematical representations are given as follows. $V_{s} (t) = V_{m} Sin ω t$ (1) $V_{in} (t) = V_{m} | sin ω t |$ (2) $I_{in} (t) = I_{m} | sin ω t |$ (3) where, V_m and I_m are the amplitudes of input voltage and current respectively. ω is the angular frequency of the AC voltage supply.

Here, whenever the AC to DC converter is fed with AC power supply, the main inductor and other converter elements combine and draw the current even if the power switches are turned ON. In the paper, input power of the converter is controlled to follow the reference input power such that the input current follows the input voltage. This corrects the power factor of input power supply. Now, the determination of reference value of input power is defined as, $P_{ref} = 2 \cdot V^{2} \cdot P_{com}; V = (\frac{V_{in}}{V_{m}})$ (4) where, P_ref is the reference value of input power, P_com is the command power determined by RBFN. The detail version of determining P_com by RBFN is explained in subsection 3.2. Here, the determination of reference value depends on the P_com value and V² value. V value is the fraction of actual input voltage to its amplitude. So, the reference value is another version of actual input voltage in terms of the product of its fraction value (V) and (P_com) value. Using this reference value of the input power, the input power to the converter is controlled by adjusting the input current to the converter which is possible by optimal switching of the converter by proposed optimal controller. Now, the determination of error signal is given as, $e (t) = P_{ref} (t) - P_{in} (t)$ (5) $P_{in} (t) = V_{in} (t) \cdot I_{in} (t)$ (6) where, P_in (t) is the actual input power to the converter. Here, input current is controlled in terms of input power such that it follows the input voltage which is constant for whole converter operation. Therefore, the power factor correction is achieved, as input current follows the input voltage. In other words, the input power follows the reference value.

Now, the error signal computed from Equation (5) is used to compute the control signal for PWM modulator to determine the gating pulses for power switches (S₁, S₂). The determination of control signal for PWM modulator by the proposed BC-PID controller is given as, $u (t) = K_{p \cdot} e (t) + K_{I \cdot} \int_{0}^{t} e (t) + K_{D^{\cdot}} \frac{d}{dt} (e (t))$ (7) where, u (t) is the control signal determined by proposed BC-PID controller. K_P, K_I, K_D are the proportional, integral and derivative gains of the PID controller respectively. These gains are tuned by the BC algorithm. The procedure of the tuning these gains is detailed in subsection 3.3. Once the control signal u (t) is determined by the BC-PID controller, it is given to the PWM modulator to generate gate pulses for both switches (S₁, S₂). Now, the detailed explanation of RBFN is as follows.

3.2 The development of RBFN

Due to the RBFN better approximation capabilities, simpler network structures and faster learning algorithms, RBFNs have certain advantages over other types of ANNs and have been widely applied in many science and engineering fields [31, 32]. So, in the paper, it is applied to provide the power command based on the given output voltage of the converter. Therefore, it has one input and one output. Basically, the development of RBFN involves two stages. They are training and testing stage. In order to train the RBFN, it needs a data of input and output patterns. In Fig. 2, the structure of proposed RBFN is illustrated.

It consists of three layers; Input layer, Hidden layer and Output layer. The proposed structure consists of one input and n hidden nodes with one output. The nodes in each layer are respectively given as. $(I), ({RBN}_{1}, {RBN}_{2}, {RBN}_{3}, \dots \dots {RBN}_{n}), (O)$

The connecting weights from input layer to the hidden layer are given as W₁₁, W₁₂, W₁₃, …, W_1n. These connecting weights are unit weights only. The connecting weights from hidden layer to output layer are given as W₀, W₂₁₁, W₂₂₁, W₂₃₁, …, W_2n1. Here, W₀ is the weight connecting the bias to the output node. The proposed RNFN input is the output voltage of the converter and the output is the power command value. The representation of the RBFN output in terms of its input is given as follows. $P_{com} = \sum_{i = 1}^{n} W_{2 i 1} . φ_{i}$ (8) $φ_{i} = exp (- \frac{d_{i}^{2}}{2 \cdot σ_{i}^{2}})$ (9) $d_{i} = ∥ V_{0} - {\tilde{V}}_{0, i} ∥$ (10) $σ_{i} = (\frac{1}{m} \sum_{j = 1}^{m} ∥ \tilde{V}_{o, i} - \tilde{V}_{o, j} ∥)$ (11) where, φ_i is the output of ith hidden node whose radial basis function Gaussian exponential function. exp(•) is the representation of exponential function in Gaussian exponentional function. d_i is the Euclidean norm of the distance between the input (V_o) and the node center ( ${\tilde{V}}_{o, i}$ ). σ_i is the width of an RBF node, which is computed from the root mean-square distance to the nearest ith RBF node as shown in Equation (11). Equation (8) represents the output of the RBFN for given input (V_o). With the characteristics of inputs and outputs from Fig. 2 and Equation (8), RBFN can be trained and utilized for assisting the reference generator for aiding the proposed BC-PID controller. The point of training involves determining the number of RBF nodes and the output layer weight values if the centre of input vector is known [33]. Now, purpose of training the RBFN is achieved by the minimization of total square errors. It is given as follows $E_{s} = \frac{1}{2} \sum_{s = 1}^{S} {P_{com}^{target} - P_{com}^{RBFN}}^{2}$ (12) where, E_S is the total square error of the RBFN to be minimized. S is the total number of input-output patterns and $P_{com}^{target}$ and $P_{com}^{RBFN}$ are target and actual power command values of RBFN structure respectively.

Now, the training of the RBFN is performed. Here, the training of the neural network and the weight adjustments of the network is achieved by using the back propagation algorithm. The procedure used for FFNN is applicable for RBFN too. Once the training process gets completed, the RBFN is ready to provide power command value for any kind of converter output voltage. This determined power command value is forwarded to compute the reference value of instantaneous input power. It is then compared with actual instantaneous input power and error signal is generated. Based on this error signal, proposed optimal controller determines the control signal for PWM modulator. Now, the development of the proposed optimal BC-PID controller is detailed in the following subsection.

3.3 Optimal controller design by BC algorithm

BC algorithm is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behavior of a honey bee swarm [34 –36]. Here, BC algorithm is used to tune the gains of PID controller for better performance in power factor correction by proposed AC to DC converter. The tunable gains of PID controller are given as, K_P, K_I, K_D. The fitness function to be minimized by the proposed BC algorithm is Equation (5). In general, the BC algorithm comprised five phases for optimization process. The phase wise description of BC algorithm is presented here. Now, the procedure to find the optimum P, I and D gains by BC algorithm’s five phases is given as follows.

A. Initialization Phase

Step 1: Initialize the food sources randomly in the search space. Let the food sources in search space is given as, ${fd}_{i} = [K_{P}^{i} K_{I}^{i} K_{D}^{i}]; 1 \leq i \leq N$ (13) where, N is the population size or number of food sources in the search space. fdⁱ is the food source of the ith position in search space with its dimensional positions as $K_{P}^{i}$ , $K_{I}^{i}$ and $K_{D}^{i}$ .

Step 2: Start the iteration count, t = 0.

B. Employed Bee Phase

Step 3: Each employee bee selects one solution and goes for it.

Step 4: Each employ bee performs neighborhood search at the selected solution. It is given as follows. $\begin{matrix} {fd}_{j}^{new} = {fd}_{j}^{i} + φ_{j}^{i} ({fd}_{j}^{i} - {fd}_{j}^{k}); \\ j = K_{P}, K_{I}, K_{D}; 1 \leq k \leq N \end{matrix}$ (14) where, fd^new is the neighbor solution/food source, $φ_{j}^{i}$ is the random number in [-1 1] and ${fd}_{j}^{k}$ is the randomly selected food source for determining the neighbor food source.

Step 5: Evaluate the fitness of each solution using Equation (5).

Step 6: Each employ bee applies greedy operation on both neighbor and present solutions and remembers the best solution.

Step 7: Employ bees provide this information to onlooker bees.

C. Onlooker Bee Phase

Step 8: Each onlooker bee selects one solution based on their probability value (P_Pr) and goes for it. The probability value of selection of solution by an onlooker bee is derived in Equation (15). $P_{i} = \frac{{ft}_{i}}{\sum_{m = 1}^{P} {ft}_{m}} with {ft}_{i} = \frac{1}{1 + e}$ (15)

Step 9: Similarly, each onlooker bee performs neighborhood search at the selected solution as employ bee does using Equation (14).

Step 10: Evaluate the fitness of each solution using Equation (5).

Step 11: Each onlooker bee applies greedy operation on both neighbor and present solutions and remembers the best solution.

D. Scout Bee Phase

Step 12: If there is any solution not improved by no. generations called as limit (L), it is abandoned. And, a new solution is randomly initialized by scout bee like in initialization phase.

Step 13: Iteration counts, t = t + 1.

E. Termination Phase

Step 14: Check the termination condition. It is given as follows: $t > t_{max} ?$ (16) where, t_max is the maximum number of iteration. If the termination condition is satisfied, stop the algorithm and provide the tuned K_P, K_I, K_D values. Otherwise, repeat the steps from 3.

These are the total five phases by BC algorithm for optimizing the given parameters. Here, P_Pr, L and t_max are parameters to be preset by the user according to the problem of interest.

This is the procedure of BC algorithm. When the procedure is completed the optimized gains of PID controller are determined. These tuned gains of PID controller are included in the control structure. Hence, the proposed controller is called as BC-PID controller or optimal controller. Now, the validation of the proposed work is performed by implementing in MATLAB platform. It is given in the following section.

4 Results & discussions

Here, the performance of the novel adapted PID controller based on BC algorithm is assessed for the purpose of the power factor rectification. The efficiency of the proposed optimal controller is estimated with regard to various parameters such as the power factor rectification, efficacy of the power conversion. The innovative optimal BC-PID controller intended for the objectives as stated above is performed in MATLAB/Simulink working platform. At first, a single stage AC to DC converter is developed namely the SIMULINK model of the novel AC to DC converter which is demonstrated below.

It is crystal clear from Fig. 3 that, the novel Simulink technique of the single stage single phase AC to DC converter comprises the uncontrolled bridge rectifier circuit, main inductor, main capacitor, main switch and load along with a snubber circuit. Now, the AC power supply is furnished as input to the bridge rectifier which offers the pulsated DC voltage as input. Depending on the gate pulses offered to the power switches, input current to the converter is controlled. In our technique, the input DC current is controlled with regard to the input power by the novel optimal controller to pursue the input voltage for power factor rectification. The design parameters of the novel converter are appropriately included in Table 1 [38].

In Table 1, the implemented values of segments of the AC to DC converter are tabulated. In accordance with these parameters, the innovative controller is developed. To develop the novel optimal controller, the BC algorithm is activated based on the data offered in Table 2 [39]. Deploying the algorithm data, it is able to fine-tune the regulation gains of the PID controller by reducing the error value between reference and real value of input current to the AC to DC converter. The improved gains by the novel BC algorithm are exhibited in Table 3 [40].

In Table 2, the parameters of the proposed BC algorithm are presented. Similarly, the PSO parameters were chosen from the reference [41, 42]. With the presented parameter details, the gains of PID controller are determined. The determined PID gains by BC algorithm are tabulated in Table 3 along with PSO tuned PID gains. The PID gains of both algorithms have different gain values. Once the PID gains are computed by BC algorithm, PID controller is ready for power factor correction. To test the proposed optimal controller, the AC voltage of 200 V is given as the input to the proposed single stage AC to DC converter. The input voltage and current waveforms of the proposed AC to DC converter is shown in Fig. 5.

In Fig. 4, the input voltage waveform and the current drawn by the innovative AC to DC converter by the proposed controller are exhibited. It is crystal clear that the input current pursues the waveform of input voltage, thereby exhibiting the near unity power factor of the input AC supply. In the X-axis time in seconds is taken parameter and in the Y-axis, the input voltage and current are considered as the parameters. It is clear from the X-axis parameter that, simulation is carried out up to 0.5 seconds only.

Now, in order to validate the performance of the proposed optimal controller, the simulation results of AC to DC converter for voltage and current waveforms of main inductor, resonance capacitor and main switch are also analyzed along with output DC voltage. The comparison is performed in terms of the proposed optimal controller based on BC and PSO and without any controller to the converter. For this purpose, the scale of the following graphs has time in seconds as X-axis parameter and corresponding parameter as its Y-axis parameter. Here after, to perform the detail analysis, the simulation has been increased from 0.5 seconds to 5 seconds. The comparison performance graphs are shown as follows.

In Figs. 5 and 6, the performance of voltage and current waveforms of main inductor is illustrated. Here, the comparison is performed between BC algorithm and PSO algorithm based PID controller and without any controller to the converter. From Figs. 5 and 6, it says that, for proposed and PSO based PID controller, the waveforms are having less ripples the converter without controller operation.

In Figs. 7 and 8, the performance of voltage and current waveforms of resonant capacitor are illustrated. From Figs. 7 and 8, it says that, for proposed and PSO based PID controller, the waveforms are having better performance than the converter operation when there is no controller i.e., lower ripples in both the waveforms.

In Figs. 9 and 10, the waveforms of voltage and current of main switch are illustrated. From Fig. 9, it says that, the voltage waveform of the main switch by proposed controller has slight lower value than the PSO-PID and one without controller. From Fig. 10, it says that, the current drawn by the converter operation without controller has more value the when PSO-PID and proposed controller are applied. The performance comparison graph of the proposed AC to DC controller with and without controller for output voltage regulation is shown in Fig. 11.

In Fig. 11, the performance appraisal of the novel optimal controller is elegantly exhibited. It is crystal clear that, for soft switching devoid of the controller, the output voltage curve has miserably failed in arriving at the set point and its rise time period is high and the settling time period also far exceeds the replication period. Now, the performance of the efficiency is assessed and beautifully pictured in Fig. 12. In Fig. 12, the performance comparison of the proposed BC-PID controller with PSO-PID controller, no controller and hard switching to the AC to DC converter is presented in terms of efficiecny vs output power.

In Fig. 13, the convergence performance is illustrated between BC and PSO algorithms. From the analysis, it states that, the BC algorithm has achieved less fitness value than the PSO algorithm. Therefore, the proposed BC algorithm is effective and better optimization tool than the PSO algorithm in determining the PID controller gains. With the reduced fitness value, it demonstrates the determination of better optimal values of PID controller than the PSO algorithm. Hence, the proposed BC-PID controller is superior to PSO-PID controller.

5 Conclusion

In the paper, a BC optimized PID controller was developed for single phase and single stage AC to DC converter. It was designed for power factor correction, output DC link voltage regulation, efficiency improvement. In the proposed work, BC algorithm was utilized to tune the gains of the PID controller such that the error between the actual and reference value of input power to the converter gets minimized. Hence, the optimal control action was performed on AC to DC controller for power factor correction. The validation of the proposed BC-PID controller was performed on MATLAB working platform. The implementation details of BC algorithm and tuned PID controller gains were also presented. Simulation results were examined for the voltage and current waveforms of main inductor, resonance capacitor and main switch. The power factor correction by proposed controller showed it to be unity power factor and the efficiency of the converter was examined for different load characteristics. These comparisons have proved that the proposed BC-PID controller is superior to PSO-PID controller for AC to DC converter. From the convergence point of view, the superiority of the proposed BC algorithm over PSO algorithm was also demonstrated. Is also possible to studying this problem by including fractional order PID (FOPID) controller with different artificial intelligent techniques. In general is possible to extend or change the objective function to include the need of the controller parameters and consumer benefits.

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