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The booming electronics itself carries an impact on power quality. Superconducting Magnetic Energy Storage (SMES) is proposed to enhance power quality in three-phase systems under various loads. This paper aimed to compensate the voltage sags under various faults in the grid systems. The SMES is selected as an energy storage unit to improve the capability of voltage sag compensation. Optimized Dual Fuzzy Flow (ODFF) logic controller is designed to prevent the voltage sag time during excessive phase voltage variation. Hence the proposed controller strategy reduces the total harmonic distortion during various fault conditions. To regulate the contribution of active power, the least possible value is improved using ODFF. The depth of voltage sags compensation is achieved by the over modulation and an iterative loop is designed in the control block. While protecting sensitive loads from voltage disturbances, and sags initiated by the power system, the proposed configuration is advantageous for an industrial implementation. It is found that the proposed method can result in more than 50% additional sag support time when compared with the previous methods such as PI and PSO. Utilizing MATLAB Simulink, compensation of sag and minimization of THD is established, and the simulation tests are performed to evaluate the performance of the proposed control method.

In industrial distribution systems, the grid voltage disturbances (voltage sags, swells, flicker, and harmonics) are the most common power quality problems. Sag, being the most frequent voltage disturbance, is typically caused by a fault at the remote bus and is always accompanied by a phase angle jump. The phase jump in the voltage can initiate transient current in the capacitors, transformers, and motors [

Harmonics and non-linear loads lead to major power quality issues. The Dynamic Voltage Restorer (DVR) in [

The Superconducting Magnetic Energy Storage (SMES) [

The capability of compensation is inclined by short circuit capacity of the system and SMES location. The power factor is another crucial factor to be noted for analyses.

The DVR based SMES leads to better compensation than individual performance of DVR. The series injected voltage of the DVR can be written as

Voltage sag is a momentary decrease in RMS AC voltage 0.1 - 0.9 p.u of the nominal voltage at the power frequency derived in [

In this paper, section 2 describes the brief survey on the related work and section 3 presents the proposed methodology and Optimized Dual Fuzzy Flow (ODFF) logic controller circuits are briefed in section 4 and section 5. The simulation test outputs are discussed in section 6 and finally concluded in section 7.

Numerous related research works are already existed in literature which based on power quality control, THD and Voltage sag compensation system. Some of them are reviewed here.

The emphasis is on either reducing the voltage rating of DVR by aligning the injected voltage with the source voltage (i.e., in-phase compensation) or minimizing the dc storage capacity by using the reactive power compensation/energy-optimized approach presented in [

Kumar and Mishra discussed about the first category are series active filters (SeAFs), including hybrid- type ones [

The concept of AC voltage sags and swells compensator based on three-phase hybrid transformer with buck? boost matrix-reactance chopper. Commonly known as DVR, they have a similar configuration as the SeAF [

The fuzzy system has proved the better minimization of the THD than using PI controllers. The output depicts that the THD is minimized after the BFO optimization. DVR based different controllers are designed to improve power quality regarding sag and swell characteristics under different load conditions [

The basic concept of fuzzy control strategy and problem-solving methods are discussed with clear key points [

The schematic diagram of the proposed topology with detailed phase sag supporter is shown in

To account for phase Skip compensation, this topology incorporates the merits of the inter phase ac?ac topology, by having a sag supporter with two choppers and isolation transformers in each phase. The secondary’s of the isolation transformers are connected such that they add the output voltages of the choppers and inject them. Deriving the injected voltage from two voltage sources facilitates the realization of the desired reference voltage with phase shift. In this scheme, compensation of fundamental quantities has been considered. In case of harmonics; fundamental components are extracted and compensated by the scheme.

The work presented in this paper proposes an enhanced voltage sag compensation method to extend the SMES based DVR compensation time. It optimizes the gradient of the dc link voltage (dvdc/dt) by regulating the amount of active power injected by SMES based DVR. In the proposed method, the controller restores both phase and amplitude of the load voltage to the presag value and then initiates a transition toward the Low active power (LAP) mode. The overall operation sequence and implementation of the proposed compensation method is discussed in the following subsections.

For detecting the phase Skip, two PLLs are employed (one over the load voltage and another over the source voltage), giving θ_{VL} and θ_{Vg}, respectively. As soon as the sag is detected, the first step is to determine the SMES based DVR initial injection angle that avoids the phase Skip at the load side. This is done by freezing the load voltage PLL that gives the presag angle (θ_{VLp}). On the other hand, the unrestricted grid voltage PLL gives the grid voltage phase (θ_{Vg}). The difference between these two angles gives the initial angle of injection Note that, in the steady state, both angles will be identical, and thus, the difference will be zero. For sag detection, the absolute difference between the reference load voltage (1 p.u.) and the actual grid voltage (p.u.) in synchronous reference frame is calculated as follows:

For sag detection, the absolute difference between the reference load voltage (1 p.u.) and the actual grid voltage (p.u.) in synchronous reference frame is calculated as follows

Once the presag voltage is successfully restored, after one cycle, a smooth transition toward the LAP mode is initiated and completed over the next one to two cycles. The final injection angle of SMES based DVR (θ_{fin}) is given as

The first part of Equation (5) represents the self-supporting mode of operation in which the SMES based DVR absorbs active power (relatively very small amount) from the grid to overcome the system losses and thus maintains a constant voltage across the dc link capacitor. The term γ indicates the reduction in θ_{fin} due to loss component and it is determined by the dc link (PI) controller. The second part of Equation (5) represents a case where the self-supported dc link. To ensure a smooth changeover, a transition ramp is defined between the initial and final operating points, as given in the following:

In self-supporting mode, the SMES based DVR can compensate the sag for an indefinitely long time. However, for deeper sag depths, there is certain nonzero active power injected by SMES based DVR. This causes a reduction in the energy stored in the dc link capacitor, and consequently, its voltage reduces (gradually). To maintain the required voltage at the inverter output side, the controller increases the modulation index mi until it reaches mi-max. This is the limiting case as explained by Equation (7), beyond which the controller goes into over modulation and cannot maintain the rated load voltage. To avoid this over modulation condition, an iterative control loop is used, which constantly monitors the dc link voltage and decreases θ_{fin} in Equation (6) to keep V_{dc} > V_{dc−}_{min} and is given as

Figures 2(a)-(c) depict the overall operation sequence of the proposed phase Skip compensation scheme. The transition from high active power mode (presag) to LAP mode is shown in three steps. The illustration is for the case where the sag depth is more than the limit in (5) and there is a positive phase Skip associated with the sag. As discussed previously and shown in _{r}_{1} _ V_{x}_{1}) and restores both magnitude and phase of the load voltage to presag values. After one cycle, the transition toward the LAP mode is initiated, and SMES BASED DVR gradually increases the contribution of reactive power. As seen from _{L} reaches V_{L−}_{opt}. Note that at the final operating point V_{r}_{1} _ V_{x}_{1}. The aforementioned SMES based DVR operation can be viewed as an equivalent variable impedance Z_{v} where the operation begins with dominant resistive impedance Z_{v} = R (high active power) and completes as dominant capacitive impedance Z_{ν} ≈ XC (high reactive power).

Selection of switching logic for the proposed topology to compensate voltage sags are elucidate in this section. To obtain the reference load voltage, the control system is divided into two sub modules: 1) phase Skip detection plus SMES based DVR injection angle calculation and 2) LAP injection.

To achieve a decoupled active and reactive power control, the phase of the line current is considered as the reference and is obtained by the PLL. The phase Skip detection block computes the SMES based DVR initial (presag injection) angle and final (LAP injection) angle. As shown in _{dvr} is compared with the actual voltage in the stationary reference frame. A proportional-resonant (PR) controller with a large gain at the grid fundamental frequency is used for accurate tracking of V_{dvr}. To compensate for SMES based DVR system losses, V_{dvr} is added as a feed forward signal to the output of the PR controller. The dc link voltage is constantly monitored in an iterative control loop to regulate the injected voltage angle, thus avoiding over modulation. Note that this block is only required when the sag depth is close to the system design limit.

The proposed ODFF flow is represented in

bances of the DVR. Initially, Continuous Wavelet Transform is applied to extract the features of Power system dataset block. The difference in generated and converter voltage is identified. The proposed Fuzzy uses two models. The first model is called the plant, which describes a virtual Machine model. The second type of model is known as a system model, which shows the load voltage regulatory system in a SMES based DVR model. The Proposed model is used to specify the AC load assessment whenever sags are generated, and LAP mode is injected subcutaneously. And also this model finds a suitable way of directing voltage injections.

where x and y are the input variables, A and B represents fuzzy sets in the antecedent part, and the consequent part (output) is _{0} and p_{1}, the value for these parameters are identified through a linear system of algebraic equations (

The degree the input matches i^{th} rule is typically computed using min operator:

Each specified fuzzy rule has a crisp output. The Overall output is attained through weighted average (reduce computation time of defuzzification required in a Mamdani model)

where _{i} (result of the if … part evaluation), i = 1, 2. The solution for the coefficients of the consequent in TSK Systems

There are two unknowns: p_{0} and p_{1}. So we need two simultaneous equations for two values of x, say x_{1} and x_{2}, and two values of y ? y_{1} and y_{2}

In order to determine the values of the parameters p in the consequents, one solves the LINEAR system of algebraic equations and tries to minimize the difference between the ACTUAL output of the system (Y) and the simulation [X]^{T}[P]:

・ FUZZUFICATION: The values of the membership functions for the two values x_{1} = 12 & x_{2} = 5 shown in

・ INFERENCE & CONSEQUENCE: The Sample System Rules are shown in

・ AGGREGATION:

Using a Centre of Area computation for y we get:

The simulation of system is based on the intensity of pheromone and the path length. The probability with which an ant ‘a’ selects the path from i to j is defined in Equation,

Small_{1} | Small_{2} | Big_{1} | Big_{2} | ||
---|---|---|---|---|---|

x_{1} | 12 | 0.25 | 0 | 0.2 | 1 |

x_{2} | 5 | 0.6875 | 0.375 | 0 | 0.375 |

Rule | Premise 1 | Premise 2 | Consequence | Truth Value Min (Premise 1 & Premise 2) |
---|---|---|---|---|

R_{1} | Small_{1} (x_{1}) = 0.25 | Small_{2} ( x_{2 }) = 0.375 | y^{(1)} = x_{1} + x_{2} = 12 + 5 | Min (0.25 0.375) = 0.25 |

R_{2} | Big_{1} (x_{1})= 0.2 | y^{(2)} = 2x_{1} = 24 | 0.2 | |

R_{3} | Big_{2} (x_{2}) = 0.375 | y^{(3)} = 3x_{2} = 15 | 0.375 |

where ^{th} ant. After selecting the next path of features, the trail intensity of pheromone is updated and defined in Equation (12).

Rule 1: If e(k) is PL AND De(k) is N then Du(k) = α_{1}e + β_{1}De + δ_{1}_{ }

Rule 2: If e(k) is PL AND De(k) is Z then Du(k) = α_{2}e + β_{2}De + δ_{2} ・・・ etc …_{ }

Rule 21: If e(k) is NB AND De(k) is P then Du(k) = α_{21}e + β_{21}De + δ_{21 }

α, β and δ are design parameters whose values are determined by the fuzzy system developer, based on relating the behaviour of the errors and change in errors over a fixed range of changes in control. There are 2N a total of design parameters for rules. These TSK parameters are selected by the trial and error method.

The specifications of the fault occurring system is specified with all the fundamental parameters in

De(k)Voltage Sag Deviation | ||||
---|---|---|---|---|

N | Z | P | ||

e(k) Voltage Concentration | PL | α_{1}e + β_{1}De + δ_{1} | α_{2}e + β_{2}De + δ_{2} | α_{3}e + β_{3}De + δ_{3} |

PB | α_{4}e + β_{4}De + δ_{4} | α_{5}e + β_{5}De + δ_{5} | α_{6}e + β_{6}De + δ_{6} | |

PM | α_{7}e + β_{7}De + δ_{7} | α_{8}e + β_{8}De + δ_{8} | α_{9}e +β_{9}De + δ_{9} | |

PS | α_{10}e + β_{10}De + δ_{10} | α_{11}e + β_{11}De + δ_{11} | α_{12}e + β_{12}De + δ_{12} | |

Normal | α_{13}e + β_{13}De + δ_{13} | α_{14}e + β_{14}De + δ_{14} | α_{15}e + β_{15}De + δ_{15} | |

NS | α_{16}e + β_{16}De + δ_{16} | α_{17}e + β_{17}De + δ_{17} | α_{18}e + β_{18}De + δ_{18} | |

NB | α_{19}e + β_{19}De + δ_{19} | α_{20}e + β_{20}De + δ_{20} | α_{21}e + β_{21}De + δ_{21} |

System Parameter | Specifications |
---|---|

Line voltage | 3Φ, 415 Vac |

Frequency | 50 HZ |

Impedance | 0.001 + 0.005j |

Harmonic filter | Lr = 0.5 mH, Cr = 1 Uf |

Switching frequency | 50 hz |

Injection transformer | 1:1 |

SMES coil | 7.5 h |

Fault | 350 ms |

Condition | P (kw) | Q (kw) |
---|---|---|

Without SMES based DVR | 14 | 0.9 |

With fuzzy DVR | 23 | 1.4 |

The SMES based Dynamic Voltage Restorer circuit in the distribution system is shown below in

The control block of chopper circuit is shown in

The Mamdani for fuzzy logic Error and controller is shown above in

ranges from −0.5 to +0.5.

The real and reactive power of the three phase distribution line under fault condition whose duration is from 0.1 sec to 0.45 sec without any control techniques is shown in

The Total Harmonic Distortion THD value after fault compensation primarily sag compensation is shown above in

The cosine of angle between the voltage and current is known as power factor (pf)

The power factor for the 3 phase system with the ODFF controller for the sag compensation has been obtained as 0.94.

controller are shown in

The compensation on sag in three phase system at distribution end, when eliminated, enhances the stability of

the supply power and also the sustained of the power factor without variation as it is tightly coupled to THD where the results imply the betterment on THD value reduction when compared with the SMES based DVR control without the combination of SMES coil and dual fuzzy logic control.

M. Manikandan,A. Mahabub Basha, (2016) ODFF: Optimized Dual Fuzzy Flow Controller Based Voltage Sag Compensation for SMES-Based DVR in Power Quality Applications. Circuits and Systems,07,2959-2974. doi: 10.4236/cs.2016.710254