Solar inverters - Department of Electrical, Computer, and Energy
Transcription
Solar inverters - Department of Electrical, Computer, and Energy
R. W. Erickson Department of Electrical, Computer, and Energy Engineering University of Colorado, Boulder An Inverter System vpv(t) vbus(t) DC-DC Inverter EMI PV vac(t) H1 d vac(t) dinv PWM PWM ibus Gc3(s) vbus(t) iac(t) Phase-locked loop H3 Gc3(s) MPPT – + vpv(t) –+ Vref-pv H2 iref(t) Vref-bus – + Gc2(s) × Sinusoid unit amplitude phase-locked to vac Standards IEEE 1547: standard for connecting a renewable energy source to the utility grid • Current harmonic limit (THD < 5%) • Anti-islanding (detect loss of grid, shut down within 1 sec) • Disconnection when grid frequency or grid voltage is out of bounds National Electric Code UL 1741 Weighted Efficiency standards: California Energy Commission (CEC) Power level, % of rated Weight 100% 0.05 75% 0.53 50% 0.21 30% 0.12 20% 0.05 10% 0.04 • Provides a way to compare products of different companies • Weightings reflect typical distribution of array power experienced in California Microinverters One inverter per panel • Mounted on or near the panel—on roof • MPPT on per-panel basis • Conventional AC wiring reduces Balance-of-system cost • Straightforward expandability • Reliability? Efficiency? Rated temperature? Ascension Tech. microinverter, 1998 Enphase microinverter, 2008 Elements of a Microinverter System Pdc i + PV Cells i v v(t) i(t) Pac Energy storage DC-DC Converter + DC-AC Inverter iac + Microinverter power train: • DC-DC converter (high boost ratio) v vcap vac – – – Pdc Pac vac(t) iac(t) t Central box Microinverter Microinverter Power stage MPPT Current control Anti-islanding Power stage MPPT Current control Anti-islanding Roof AC Transient protection AC utility AC disconnect Communications Computer Smart grid • Energy storage capacitor • Inverter Rooftop system • Microinverters include most or all of grid interface control • Central box boosts the low PV input voltage to a higher voltage. The inverter stage generates the AC current that is injected to the AC line. Despite various new topologies that have been demonstrated in recent literature [5], the typical low-cost micro-inverter is still designed either as a full-bridge stage, or as a buck stage with an unfolder stage. The unfolder Microinverter Approaches stage, if present, switches at the zero-crossings of the line voltage to convert the rectified sinusoid at the buck output to a full sinusoid on the AC line. H-bridge inverter Buck converter plus unfolder Unfolder: similar to bridge rectifier, but power flows in reverse Fig. 1. Common micro-inverter power stages. (a) Full bridge. (b) Buck stage with an unfolder stage. direction. Implemented using transistors that switch at ac line frequency An illustration of the Boundary Conduction Mode (BCM) waveform is shown in Fig. 2. Although it is softswitching, and operates with low RMS current, a disadvantage of BCM is its high average switching frequency, which causes high switching losses. As demonstrated by equation (1), the BCM waveform has the highest switching Inverter sinewave synthesis approaches We can employ any of the approaches we have already discussed for PWM rectifier systems: • Average current control • Peak current control • Boundary conduction mode • Hysteretic control • Discontinuous conduction mode control • Cycle-by-cycle control (and there are a few we didn’t discuss, most notably harmonic elimination, that could be employed for either rectifiers or inverters) lowest possible peak current in DCM, and as a result the switching frequency in BCM is maximal. Equation (1) also predicts that the switching frequency of BCM increases at low output powers, creating a switching frequency profile that causes disproportional switching losses at low powers. This is demonstrated by the last expression in equation (1) Synthesizing a Sinusoidal Current: results in a higher switching frequency, so the switching frequency and switching losses in BCM substantially increase Boundary Conduction Mode (BCM) at low voltages and low powers. for which the power-factor is unity and the switching frequency is proportional to Rout. A lower output voltage vout(t) Inductor current waveform, BCM Fig. 2. Illustration of the inductor current in BCM, showing soft switching transitions (ZVS, ZCS) and the variations in switching frequency over the line cycle. The switching frequencies in DCM and BCM are given by: DCM: fs t vout t 2 L iout t BCM: fs t vout t 2 L iout t v t 1 out vdc t 1 2iout t i pk t vout t vdc t 2 Loss components at different solar irradiance levels, BCM (300 W, 240 Vac example) (1) BCM with unity power factor : fs t Fig. v t Rout 1 out 2L vdc t , where Rout vout t iout t Fig. 3. Distribution of losses in BCM. The vertical bars represent average losses over an AC line cycle. The losses are shown in percent the average AC outputat power. Switching losses dominateThe at low powers. 3 shows how the totalrelative loss into BCM distributes various output powers. losses in this figure are averaged over a line cycle, and are shown in percent relative to the cycle averaged output power. The total loss is composed of BCM waveform. The DC operating points are selected with constant ratio of voltage and current vout/iout = Rout, and thus reside on the same output sinusoid. The efficiency is computed according to the calibrated loss model presented in section IV, with conditions as follows: Rout = 215.1 Ω, average AC power of 225 W, bus voltage of vbus = 425 V, and an inductor of 300µH built on a PQ 26/20 core. Each curve is label by its output power pout = voutiout, which is Discontinuous conduction mode (DCM) given in percent relative to the maximal instantaneous output power of 450 W. • Higher conduction loss • program Lower that switching loss To find the optimal values of the inductance L and magnetic flux density ΔBmax, we used a computer • A net improvement in numerically scanned the weighted efficiency of every combination of these two parameters. The program runs over CEC efficiency each output power level, and over single DC operating points in the output sinusoid, and computes the efficiency at each operating point, using the calibrated loss model. The resulting efficiency data is averaged according to the CEC efficiency formula (see section V). Typical results of this simulation are shown in Fig. 10, for a PQ 26/20 magnetic core, and conditions as detailed in Table I. CEC efficiency [%] Fig. 4. By increasing the peak current 99.2 in DCM, the switching frequency is reduced, while the average inductor current (iout) is unchanged. 99 98.8 98.6 98.4 98.2 98 97.8 97.6 97.4 Weighted efficiency vs. inductor size, DCM vs. BCM 300 W, 240 Vac example constant peak current BCM 250 350 450 550 650 750 850 inductance [µH] Fig. 10. Weighted CEC efficiency with a PQ 26/20 magnetic core, for a BCM controller and the proposed constant peak current controller. 0 200 400 DC output power pout [W] 600 Fig. 14. Efficiency measurements at DC operating points, for various average AC powers Fig. 12. Inverter waveforms at efficiency a DC operating Ch1 (yellow) cycle-by-cycle capacitor over voltagea vline int(t) , Ch2 (blue) auxiliary At each power level, the AC is point. computed by averaging theintegration DC efficiencies cycle. The results are Measured Results: 300 W Microinverter Prototype winding voltage sensor, at comparator output, Ch4 (green) inductor current iL(t). Conditions: Vdc= 426.8 V, Idc= 0.98 A, vout= 330.1 V, iout= A. again averaged by the CEC weighted average formula 1.259 to obtain the overall CEC efficiency. The AC efficiency is computed by equation (9), and the results are summarized in Table II. / ac AC line voltage pout t dt AC efficiency 0 AC / ac 0 pout t DC pout t where pout t Pac sin 2 ac t (9) Current reference dt Filtered inverter current In this equation, Pac is the average AC power, pout(t) is the output power at a DC operating point, and ηDC(pout) is the inductor current efficiency at those operating points, as shown in Fig. 14. The weighted CEC efficiency is found toInstantaneous be 99.15 %. Fig. 13. Inverter waveforms over a line cycle. Ch1 (yellow) AC line voltage sensor, Ch2 (blue) AC current iac(t), Ch3 (magenta) reference signal iref(t), Ch4 (green) inductor current iL(t). Conditions: Vdc= 425 V, Idc= 0.462 A, Rload=253 Ω TABLE II CEC Efficiency CEC Average average AC average AC power weight loss over power P The efficiency of the inverter points.efficiency These tests are done with a power supply levelis measured at static DCac operating AC cycle 100resistor % 0.05 300 W 2.6 W 99.13 % at the input and a variable load (R load) at the output. To increase the accuracy of the measurements, the meters 75 % 0.53 225 W 1.97 W 99.12 % at the input and output are filtered The1.26 efficiency results 50 % by large 0.21 EMI inductors. 150 W W 99.16 % are shown in Fig. 14. The various 30 % 0.12 90 W 0.7 W 99.22 % curves in the figure correspond to tests with different average AC powers (P ac). At each such test the load resistor is 20 % 0.05 60 W 0.45 W 99.24 % 10 instantaneous % 0.04 output 30power W 0.24 W in the99.2 % 0 … 2Pac. set to Rload=Vac,rms2/Pac, and the is scanned range Overall weighted CEC efficiency = 99.15 % Development of Electrical Model of the Photovoltaic Cell, slide 1 Photogeneration Semiconductor material absorbs photons and converts into hole-electron pairs if Photon energy h > Egap (*) • Energy in excess of Egap is converted to heat • Photo-generated current I0 is proportional to number of absorbed photons satisfying (*) photon + – Charge separation Electric field created by diode structure separates holes and electrons Open circuit voltage Voc depends on diode characteristic, Voc < Egap/q Development of Electrical Model of the Photovoltaic Cell, slide 2 Current source I0 models photo-generated current I0 is proportional to the solar irradiance, also called the “insolation”: I0 = k (solar irradiance) Solar irradiance is measured in W/m^2 Development of Electrical Model of the Photovoltaic Cell, slide 3 Diode models p–n junction Diode i–v characteristic follows classical exponential diode equation: Id = Idss (eLVd – 1) The diode current Id causes the terminal current Ipv to be less than or equal to the photo-generated current I0. Development of Electrical Model of the Photovoltaic Cell, slide 4 Modeling nonidealities: R1 : defects and other leakage current mechanisms R2 : contact resistance and other series resistances Cell characteristic Cell output power is Ppv = IpvVpv At the maximum power point (MPP): Vpv = Vmp Ipv = Imp At the short circuit point: Ipv = Isc = I0 Ppv = 0 At the open circuit point: Vpv = Voc Ppv = 0 Maximum Power Point Tracking Automatically operate the PV panel at its maximum power point Some possible MPPT algorithms: • Perturb and observe • Periodic scan I-V curve with partial shading • Newton s method, or related hillclimbing algorithms • What is the control variable? Where is the power measured? Power vs. voltage Example MPPT: Perturb and Observe • A well-known approach" • Works well if properly tuned" • When not well tuned, maximum power point tracker (MPPT) is slow and can get confused by rapid changes in operating point" • A common choice: control is switch duty cycle" Basic algorithm! ! Measure power" Loop:" • Perturb the operating point in some direction" • Wait for system to settle" • Measure power" • Did the power increase?" Yes: retain direction for next perturbation" N: reverse direction for next perturbation" Repeat" ! Control Issues: MPPT by Perturb-and-Observe , , Measured power vs. commanded PV voltage Key elements of digital controller • • • • • Find PV voltage that maximizes power output Switching converter is high noise environment This “noise” is partly correlated to the control, and hence isn’t entirely random The highly-filtered dc control characteristic exhibits many small peaks (“traps”), where P&O algorithm gets stuck More noise makes P&O work better! Magnified view Typical experimental data () α () α → • Perturb-and-observe step time of 15 msec • Perturb-and-observe algorithm may take minutes to find max power • Weather can change in seconds • Improved algorithm achieves max power in seconds • Adaptive algorithm finds max power quickly, then reduces jitter size to improve equilibrium MPPT accuracy