Optimal Location and Control of Multi Hybrid Model Based Wind-Shunt FACTS to Enhance Power Quality 47 0 20 40 60 80 100 676 678 680 682 684 686 688 690 692 694 Iteration Cost ($/h) Fig. 14. Convergence characteristic of the 6 generating units with consideration of wind source and STATCOM 0 5 10 15 20 25 30 35 40 45 0 20 40 60 80 100 120 140 Branches (i-j) Power Transit (Pij) With Wind-Statcom Pij Max Without: Wind/Statcom Fig. 15. Active power transit (Pij) with and without wind and STATCOM, Case1: Normal Condition: IEEE 30-Bus Electrical Generation and Distribution Systems and Power Quality Disturbances 48 Table 1 shows the results based on the flexible integration of the hybrid model, the goal is to have a stable voltage at the candidate buses by exchanging the reactive power with the network, the active power losses reduced to 7.554 MW compared to the base case: 10.05 MW, without integration of the hybrid controllers, the total cost also reduced to 676.4485 $/h compared to the base case (802.2964 $/h), Fig. 14 shows the convergence characteristic of fuel cost for the IEEE 30-Bus with consideration of the hybrid models, Fig. 15 shows the distribution of power transit in the different branches at normal condition, Fig. 17 shows the distribution of power transit in the different branches at contingency situation (without line 1-2). The active power transit reduced clearly compared to the case without integration of wind source which enhance the system security. Fig. 16 shows the improvement of voltage profiles based hybrid model. Results at abnormal conditions (contingency) are also encouragement. 0 5 10 15 20 25 30 0.92 0.94 0.96 0.98 1 1.02 1.04 1.06 1.08 1.1 Bus N° Voltage (pu) With Wind-STATCOM Without/Wind, STATCOM Max V Min V Fig. 16. Voltage profiles with and without hybrid model (wind and STATCOM): IEEE 30-Bus Case2: Under Contingency Situation The effeciency of the integrated hybrid model installed at different critical location is tested under contingency situation caused by fault in power system, so it is important to maintain the voltage magnitudes and power flow in branches within admissible values. In this case a contingency condition is simulated as outage at different candidate lines. Table 2 shows sample results related to the optimal power flow solution under contingency conditions (Fault at line 1-2). Optimal Location and Control of Multi Hybrid Model Based Wind-Shunt FACTS to Enhance Power Quality 49 Buses STATCOM 10 12 15 17 20 21 23 24 29 Q (MVAR) 42.76 -15.65 -11.00 -20.10 -2.90 -20.83 0.28 4.09 -4.52 Pw (MW) 5.8791 5.8803 6.0105 6.092 6.2671 6.2934 5.9560 5.8050 5.8164 V (p u) 1.02 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0  = NW i w P 1 (MW) 54 MW (19.05%), PD =283.4 MW Ploss (MW) 5.449 Pg1 (MW) 64.12 Abnormal Condition Without line 1-2 Pg2 (MW) 67.98 Pg5 (MW) 26.86 Pg8 (MW) 34.65 Pg11(MW) 21.00 Pg13(MW) 20.24 Qg1 1.76 Qg2 41.3 Qg5 20.98 Qg8 35.55 Qg11 8.08 Qg13 39.26  = NG i G P 1 (MW) 235.610MW (83.14%) Cost ($/h) 686.1220 Table 2. Power Quality Results based Hybrid Model: IEEE-30Bus: Abnormal Condition Electrical Generation and Distribution Systems and Power Quality Disturbances 50 0 5 10 15 20 25 30 35 40 45 0 20 40 60 80 100 120 140 Branche (i-j) Power Transit (MW) Pij with wind/Facts Pij Max Fig. 17. Active power transit (Pij) with hybrid model: Case 2: Abnormal Condition: without line 1-2: IEEE 30-Bus 6. Conclusion A three phase strategy based differential evolution (DE) method is proposed to enhance the power quality with consideration of multi hybrid model based shunt FACTS devices (STATCOM), and wind source. The performance of the proposed approach has been tested with the modified IEEE 30-Bus with smooth cost function, at normal condition and at critical loading conditions with consideration of contingency. The results of the proposed hybrid model integrated within the power flow algorithm compared with the base case with only conventional units (thermal generators units). It is observed that the proposed dynamic hybrid model is capable to improving the indices of power quality in term of reduction voltage deviation, and power losses. Due to these efficient properties, in the future work, author will still to apply this algorithm to solve the practical optimal power flow of large power system with consideration of multi hybrid model under severe loading conditions and with consideration of practical constraints. 7. References Acha E, Fuerte-Esquivel C, Ambiz-Perez (2004) FACTS Modelling and Simulation in Power Networks. John Wiley & Sons. Adamczyk, A.; Teodorescu, R.; Mukerjee, R.N.; Rodriguez, P., Overview of FACTS devices for wind power plants directly connected to the transmission network, IEEE International Symposium on Industrial Electronics (ISIE), Page(s): 3742– 3748, 2010. Optimal Location and Control of Multi Hybrid Model Based Wind-Shunt FACTS to Enhance Power Quality 51 Bansal, R. 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Introduction Photovoltaic systems are attractive renewable energy sources for Thailand because of high daily solar irradiation, about 18 MJ/m 2 /day. Furthermore, renewable energy is boosted by the government incentive on adders on electricity from renewable energy like solar PV, wind and biomass, introduced in the second half of 2000s. For PV systems, domestic rooftop PV units, commercial rooftop PV units and ground-based PV plants are appealing. Applications of electricity supply from PV plants that have been filed total more than 1000 MW. With the adder incentive, more households will be attracted to produce electricity with a small generating capacity of less than 10 kW, termed a very small power producer (VSPP). A possibility of expanding domestic roof-top grid-connected units draw our attention to study single phase PV-grid connected systems. Increased PV penetration can have significant [1-2] and detrimental impacts on the power quality (PQ) of the distribution networks [3-5]. Fluctuation of weather condition, variations of loads and grids, connecting PV-based inverters to the power system, requires power quality control to meet standards of electrical utilities. PV can reduce or improve power quality levels [6-9]. Different aspects should be taken into account. In particular, large current variations during PV connection or disconnection can lead to significant voltage transients [10]. Cyclic variations of PV power output can cause voltage fluctuations [11]. Changes of PV active and reactive power and the presence of large numbers of single phase domestic generators can lead to long-duration voltage variations and unbalances [12]. The increasing values of fault currents modify the voltage sag characteristics. Finally, the waveform distortion levels are influenced in different ways according to types of PV connections to the grid, i.e. direct connection or by power electronic interfaces. PV can improve power quality levels, mainly as a consequence of increase of short circuit power and of advanced controls of PWM converters and custom devices. [13] Grid-connected inverter technology is one of the key technologies for reliable and safety grid interconnection operation of PV systems [14-15]. An inverter being a power Electrical Generation and Distribution Systems and Power Quality Disturbances 54 conditioner of a PV system consists of power electronic switching components, complex control systems [16]. In addition, their operations depends on several factors such as input weather condition, switching algorithm and maximum power point tracking (MPPT) algorithm implemented in grid-connected inverters, giving rise to a variety of nonlinear behaviors and uncertainties [17]. Operating conditions of PV based inverters can be considered as steady state condition [18], transient condition [19-20], and fault condition such islanding [21-22]. In practical operations, inverters constantly change their operating conditions due to variation of irradiances, temperatures, load or grid impedance variations. In most cases, behavior of inverters is mainly considered in a steady state condition with slowly changing grid, load and weather conditions. However, in many instances conditions suddenly change, e.g. sudden changes of input weather, cloud or shading effects, loads and grid changes from faults occurring in near PV sites [23]. In these conditions, PV based inverters operate in transient conditions. Their average power increases or decreases upon the disturbances to PV systems [24]. In order to understand the behavior of PV based inverters, modeling and simulation of PV based inverter systems is the one of essential tools for analysis, operation and impacts of inverters on the power systems [25]. There are two major approaches for modeling power electronics based systems, i.e. analytical and experimental approaches. The analytical methods to study steady state, transient models and islanding conditions of PV based inverter systems, such as state space averaging method [26], graphical techniques [27-28] and computation programming [29]. In using these analytic methods, one needs to know information of system. However, PV based inverters are usually commercial products having proprietary information; system operators do not know the necessary information of products to parameterize the models. In order to build models for nonlinear devices without prior information, system identification methods are exposed [33-34]. In the method reported in this paper, specific information of inverter is not required in modeling. Instead, it uses only measured input and output waveforms. Many recent research focuses on identification modeling and control for nonlinear systems [35-37]. One of the effective identification methods is block oriented nonlinear system identification. In the block oriented models, a system consists of numbers of linear and nonlinear blocks. The blocks are connected in various cascading and parallel combinations representing the systems. Many identification methods of well known nonlinear block oriented models have been reported in the literature [38-39]. They are, for example, a NARX model [40], a Hammerstein model [41], a Wiener model [42], a Wiener-Hammerstein model and a Hammerstein-Wiener model [43]. Advantages of a Hammerstein model and a Wiener model enables combination of both models to represent a system, sensors and actuators in to one model. The Hammerstein-Wiener model is recognized as being the most effective for modeling complex nonlinear systems such PV based inverters [44]. In this paper, real operating conditions weather input variation, i.e. load variations and grid variations, of PV- based inverters are considered. Then two different experiments, steady state and transient condition, are designed and carried out. Input-output data such as currents and voltages on both dc and ac sides of a PV grid-connected systems are recorded. The measured data are used to determine the model parameters by a Hammerstein-Wiener nonlinear model system identification process. In the Section II, PV system characteristics are introduced. The I-V characteristic, an equivalent model, effects of radiation and temperature on voltage and current of PV are described. In the Section III, system identification methods, particularly a Hammerstein-Wiener Model is explained. In the Modeling of Photovoltaic Grid Connected Inverters Based on Nonlinear System Identification for Power Quality Analysis 55 following section, the experimental design and implementation to model the system is illustrated. After that, the obtained model from prior sections is analyzed in terms of control theories. In the last section, the power quality analysis is discussed. The output prediction is performed to obtain electrical outputs of the model and its electrical power. The power quality nature is analyzed for comparison with outputs of model. Subsequently, voltage and current outputs from model are analyzed by mathematical tools such the Fast Fourier Transform-FFT, the Wavelet method in order to investigate the power quality in any operating situations. 2. PV grid connected system (PVGCS) operation In this section, PV grid connected structures and components are introduced. Structures of PBGCS consist of solar array, power conditioners, control systems, filtering, synchronization, protection units, and loads, shown in Fig. 1. PCC Solar Array Power Converter Filtering Control Unit Synchronization & Protection Load Utility Fig. 1. Block diagram of a PV grid connected system 2.1 Solar array Environmental inputs affecting solar array/cell outputs are temperature (T) and irradiance (G), fluctuating with weather conditions. When the ambient temperature increases, the array short circuit current slight increases with a significant voltage decrease. Temperature and I- V characteristics are related, characterized by array/cell temperature coefficients. Effects of irradiance, radiant solar energy flux density in W/m 2 , apart from solar radiation at sea level, are determined by incident angles and array/cell envelops. Typical characteristics of relationship between environmental inputs (irradiance and temperature) and electrical parameters (current and voltage of array/cells) are shown in Fig. 2 [45]. In our experimental designs, operating conditions of PV systems under test is designed and based on typical operating conditions. 2.2 Operating conditions of a PV grid connected system A PV system, generating power and transmitting it into the utility, can be categorized in three cases, i.e. a steady state condition, a transient condition and a fault condition like islanding. Three factors affecting the operation of inverters are input weather conditions, local loads and utility grid variations. Electrical Generation and Distribution Systems and Power Quality Disturbances 56 Fig. 2. Temperature and irradiance effects on I-V characteristics of PV arrays/cells [46] 200 400 600 800 1000 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 Time High solar intensity High Temperature Medium solar intensity Medium Temperature Low Solar intensity Low Temperature Solar Irridiance (W/m 2 ) Fig. 3. Variations of solar irradiance and temperature throughout a day conditioning PVGCS operation Firstly, under a steady state condition, input, load and utility under consideration are treated as being constant with slightly change weather condition. Installed capacities of PV systems in a steady state are low, medium and high capacity. According to the weather conditions throughout a day as shown in Fig. 3 [47-48], a low radiation about 0-400 W/m 2 is common in an early morning (6:00 AM-9:00 AM) and early evening (16:00 PM-19:00 PM), medium radiation of 400-800 W/m 2 in late morning (9:00 AM-11:00 PM) and early afternoon (14:00 PM-16:00 PM) and high radiation of 800-1000 W/m 2 around noon (11.00 [...]... output offset as and aw are a scaling coefficient and a wavelet coefficient bs and bw are a scaling dilation coefficient and a 64 Electrical Generation and Distribution Systems and Power Quality Disturbances wavelet dilation coefficient cs and cw are scaling translation and wavelet translation coefficients The scaling function f (.) and the wavelet function g(.) are both radial functions, and can be written... composes of the inverter, a variable DC power supply (representing DC output from a PV array), real and complex loads, a digital power meter, a digital oscilloscope, , a AC power system and a computer, shown 66 Electrical Generation and Distribution Systems and Power Quality Disturbances schematically in Fig 14 The system is connected directly to the domestic electrical system (low voltage) As we consider... introduced and particularly a Hammerstein-Wiener model is explained Finally, a MIMO (multi input multi output model with equation and characteristic is illustrated 58 Electrical Generation and Distribution Systems and Power Quality Disturbances 3.1 Principle of system identification A dynamical system can be classified in terms of known structures and parameters of the system, shown in Fig.5, and classified... System Identification for Power Quality Analysis 59 mathematical and physical characteristics and details of systems for the purposes of controlling, maintenance and trouble shooting of systems, and planning of managing the power system 3.1.2 Physical modeling Photovoltaic inverters, particularly commercial products, compose of two parts, i.e a power circuit and a control circuit Power electronic components... b2 q −1 + + bnq − bn + 1 F(q ) = 1 + f 1q −1 + + f nq − fn (2) (3) 62 Electrical Generation and Distribution Systems and Power Quality Disturbances 3.2.2 Nonlinear subsystem The Hammerstein-Wiener Model composes of input and output nonlinear blocks which contain nonlinear functions f(•) and h(•) that corresponding to the input and output nonlinearities The both nonlinear blocks are implemented using... abnormal conditions such islanding, the gird-connected PV systems may collapse The PV systems are black out and cut out of the utility grid Such can affect power quality, stability and reliability of power systems 2.3 Power converter There are several topologies for converting a DC to DC voltage with desired values, for example, Push-Pull, Flyback, Forward, Half Bridge and Full Bridge [49 ] The choice for a... Exogenous (NARX), Nonlinear Output Error (NOE), and Nonlinear Auto-Regressive with 60 Electrical Generation and Distribution Systems and Power Quality Disturbances Moving Average Exogenous (NARMAX), Nonlinear Box-Jenkins (NBJ), Nonlinear State Space, Hammerstein model, Wiener Model, Hammerstein-Wiener model and WienerHammerstein model [56] In practice, all systems are nonlinear; their outputs are a nonlinear... DC current-AC voltage, DC voltage-AC current and DC current-AC current respectively The output voltage and output current are key components for expanding to the other electrical values of a system such power, harmonic, power factor, etc The linear parameters, zeros, poles and delays are used to represent properties and relation between the system input and output There are two important steps to identify...  F2 (q )    B3 (q )   B4 ( q )  I ac (t ) = h  f ( vdc (t − nk 3 )) + e(t )  ⊗ h  f (idc (t − nk 4 )) + e(t )    F3 (q )   F4 (q )  Bi (q ) = b1 + b2 + + bnbi q − nbi + 1 Fi ( q ) = f 1 + f 2 + + f n fi q − n fi + 1 (10) (11) (12) Where nbi , n fi and nki are pole, zero and delay of linear model Where as number of subscript i are 1,2,3 and 4 which stand for relation between DC voltage-AC... corresponding set of nonlinear estimators for input and output, and one set of linear parameters, i.e pole bn, zero fn and delay nk , as written in the equation (9) For SIMO, MISO and MIMO models, there would be more than one set of Modeling of Photovoltaic Grid Connected Inverters Based on Nonlinear System Identification for Power Quality Analysis 65 nonlinear estimators and linear parameters The relationships . Abnormal Condition Electrical Generation and Distribution Systems and Power Quality Disturbances 50 0 5 10 15 20 25 30 35 40 45 0 20 40 60 80 100 120 140 Branche (i-j) Power Transit (MW) Pij. reliable and safety grid interconnection operation of PV systems [ 14- 15]. An inverter being a power Electrical Generation and Distribution Systems and Power Quality Disturbances 54 conditioner. coefficient. bs and bw are a scaling dilation coefficient and a Electrical Generation and Distribution Systems and Power Quality Disturbances 64 wavelet dilation coefficient. cs and cw are scaling