Control Intelligence Improves Renewable Energy Efficiency

Sept. 1, 2007
Building on the success of fixed-point architectures, floating-point digital signal controllers enable implementation of more complex control algorithms in power inverters for solar- and wind-powered systems.

Throughout the world there is an increasing demand for renewable energy, yet system manufacturers continue to face the same issues that have always slowed the growth of this technology: They're required to increase the total amount of power gathered, while decreasing the cost per watt. One important way to help achieve these goals is by adding greater intelligence to the control of the inverter, which converts the variable voltage output of the energy collector into a steady voltage that is used for running applications or charging batteries. Intelligent inverters maximize power transfer from the gathering source, synchronize power output with the electrical utility and protect the local system from potentially damaging changes in the grid.

While sun- and wind-powered systems are the obvious applications, intelligent inverters also can benefit other sources of power, such as fuel cells, to maximize output. For all such applications, highly effective inverter control is available from digital signal controllers (DSCs), which have been shown to cut conversion-efficiency losses in half while significantly reducing costs. DSCs combine the high performance of digital signal processors (DSPs) with the programming ease and integration of microcontroller units (MCUs). In addition, DSCs with floating-point capabilities are now available, enhancing performance and making the job of programming complex algorithms easier.

The Inverter's Role

The main function of the inverter is to convert variable dc voltage input from the source into a clean sinusoidal 50-Hz or 60-Hz output for use by appliances and feeding back into the grid. Different applications may require single or multiple phases. In addition to dc-ac conversion, inverters perform such functions as disconnecting the circuit to protect it from power surges, charging the battery, logging data on usage and performance, and maximum power point (MPP) tracking (MPPT) to keep power generation as efficient as possible. Nominal power ranges between one and several hundred kilowatts peak (kWPK), allowing inverters to be designed around sophisticated source topologies, either with or without transformers, and with the integration of multiple control processors.

Fig. 1 shows where the inverter fits into an all-inclusive photovoltaic (PV) system that not only charges a battery and drives local ac loads, but also ties to the grid and has an alternate power source in the form of an ac generator. Similar configurations apply to wind turbines and other sources.

The blocks of the essential inverter circuit are shown in the top portion of Fig. 2. (The bottom portion will be discussed later.) First, dc-dc conversion raises or lowers the incoming voltage, adjusting its output for greatest efficiency. After some additional voltage buffering, MOSFETs in a bridge use a switching frequency, usually between 18 kHz to 20 kHz, to convert the dc to an ac voltage. Finally, a low-pass filter smooths the switched ac to a sinusoidal signal for use in generating a grid-frequency ac output. (Fig. 2 does not show the dc-dc conversion and regulation that are required for battery charging.)

Transformers and Protection

Because the source input is usually not high enough, the system can either step up the voltage with a transformer on the ac side or boost it in the dc-dc conversion stage. Just as an ac transformer inherently provides galvanic isolation, so does a phase-shifted full-bridge dc-dc converter with zero-voltage switching, thus making the latter equivalently a transformer. Fig. 3 shows a commonly used dc-ac circuit with a transformer for single-phase inversion, based on an H-bridge configuration controlled by four pulse-width modulated signals.

On one hand, transformers add weight, bulk and cost, and they also cause a reduction in efficiency of about 2%. On the other hand, they increase circuit protection and human safety by isolating the two sides of the circuit electrically, preventing a dc fault from flowing to the ac side and an ac leakage current from developing a potential issue between the PV panels and ground. The design may include a residual current protection device (RCD) that monitors the currents of all phases and then trips the relay if the current exceeds a certain value. Because of the risk of current leakage, RCDs are especially important for safety in transformerless systems.

Protection of the system mandates inclusion of a relay to protect the conversion and charging circuitry against voltage surges and spikes on the grid. In addition, if a power line is damaged or the utility has to shut it down, the inverter needs to stop feeding out electricity to the utility. A “nonislanding” inverter senses that the line has been de-energized, is undervoltage or overvoltage, or has a significant disturbance for whatever reason. When this happens, the inverter automatically disconnects from the utility grid, thereby not becoming an electricity generating “island.”

Maximizing Charging Power

The efficiency of battery charging depends on the input voltage, which can be highly variable, depending on wind conditions for a turbine or on the season, cloud cover and time of day for PV panels. Battery conditions vary, too, depending on the charge state, so sometimes it may be necessary to adjust the voltage and current ratio to increase the total power delivered and speed charging.

Maximum power output to the battery occurs when the product of voltage and current is at its peak, the MPP. MPPT is designed to determine this point and adjust the dc-dc voltage conversion to maximize the charging output. MPPT can increase the overall efficiency of a solar-power system by one-third or more during winter months, and its effect on other types of systems can be significant, too. Fig. 4 shows how the determination of MPP can vary with different conditions.

The most common algorithm for determining MPP is for the controller to perturb the panel's operating voltage with every MPPT cycle and observe the output. The algorithm continues oscillating around the MPP over a wide enough range to avoid local but misleading peaks in the power curve caused by movement in cloud cover or a brief wind lull. To the extent that the perturb-and-observe algorithm oscillates away from the MPP in each cycle, it is inefficient. An alternative, the incremental inductance algorithm solves the derivative of the power curve for zero, which is by definition a peak, then settles at the resolved voltage level.

While this approach does not have the inefficiency caused by oscillation, it risks other inefficiencies because it may settle at a local peak instead of the MPP. A combined approach maintains the level determined by the incremental inductance algorithm, but scans at intervals over a wider range to avoid selecting local peaks. This approach, while the most efficient, also requires the greatest amount of performance on the part of the controller.

Control Design Requirements

A control processor for an inverter has to meet several real-time processing challenges to execute effectively the precise algorithms that are required for efficient dc-ac conversion and circuit protection. For nonislanding requirements, the accurate measurement of voltages and currents is necessary to determine the flow of power, enabling fast disengagement.

Where the inverter output has to be synchronized with the power line, control may include a digital phase-locked loop (PLL) implemented in software along with other algorithms. MPPT and battery-charge control, while only needing near-real-time response, also involve algorithms with a high level of processing. Control is required to establish a stable dc voltage in the dc-dc conversion stage, and it may sometimes be needed to compensate for dc variation in the dc-ac stage as well. A single device that can control all the stages, and has sufficient performance for multiple algorithms, allows the designer to deal with these issues.

DSCs offer a good solution for real-time control of inverters, batteries and protective mechanisms in renewable energy systems. These DSP-based devices inherently support high-speed mathematical calculations for use in real-time control algorithms. A single DSP-based controller can control multiple conversion stages in the same inverter and have overhead remaining for performing additional functions such as MPPT, battery-charge monitoring, surge protection, data logging and communications.

New floating-point controllers extend these advantages, making programming and debugging easier and less error prone. The greater range inherent in floating-point operations makes saturation less likely and allows dynamic algorithm adjustment across all load conditions. In addition, floating-point code is more compact in math operations and requires fewer cycles than fixed point.

Integrated peripherals such as analog-to-digital converters (ADCs) and pulse-width modulators (PWMs) make it possible to directly sense inputs and control power MOSFETs, saving system space and expense. On-chip flash memories aid in programming and data collection, and communication ports simplify design for networking with units such as meters and other inverters. Fixed-point DSCs in solar-power inverters have been demonstrated to achieve significant cost reductions while cutting power-efficiency losses in half; newly available floating-point controllers will push these results even further.

An example of a DSC with floating-point capabilities is Texas Instruments' (TI's) TMS320F2833x, a 32-bit device that operates at frequencies up to 150 MHz and provides up to 300 mega floating-point operations per second (MFLOPS). Integrated features include on-chip direct memory access (DMA), fast interrupt handling, a 32-bit enhanced memory interface (EMIF), fast 12-bit ADCs supporting up to 16 input channels, multiple timers, standard communication ports, and 12 individually controlled enhanced PWM (ePWM) channels, each with its own timer and phase register (Fig. 5).

The F2833x differs from its predecessors in the F28x generation because it is based on a floating-point architecture. With a single-sign bit, 8-bit exponent and 23-bit mantissa, the device handles a normalized value range of ±~1.7 × 10-38 to ±~3.4 × 1038. For control tasks in particular, the extensive range is valuable in that it deals with scaling and saturation more efficiently than fixed-point does.

TI's testing of F2833x floating-point assembly versus F28x fixed-point assembly indicates an average improvement of nearly two and a half times in cycle efficiency for basic operations such as division, square roots and trigonometric functions, all of which scale and saturate routinely. Some signal-processing algorithms that are commonly used in control systems, including infinite impulse response (IIR) and fast Fourier transform (FFT), show nearly as much improvement.

The controller's C compiler has been designed to take advantage of the floating-point architecture with pipelining that largely eliminates wait states. The DMA controller keeps the pipeline fed while handling multiple routine transfers without interrupting the DSP core. Some examples of algorithms that are frequently used in inverter control, and in control systems in general, are listed in the table, showing the improvement in cycle counts of floating-point C code over fixed-point C.

Table. Fixed- versus floating-point digital signal controller performance for inverter control algorithms.Controlal gorithm C28x C/C++ fixed point C2833x C/C++ floating point Cycle countimprovement State estimator 1563 ~ 1137 1.37 Park transform 107 ~ 60 1.78 High-precision PID 110 ~ 70 1.57 Average performance ratio: 1.57

The F2833x compiler allows the same code to be recompiled as fixed-point code with a simple switch. Programmers can enjoy the faster development that comes with floating point, and then let the compiler make the adjustments necessary to change the code to fixed point.

Inverter Control Design

Fig. 6 shows the F2833x DSC used to control the power-stage inverter in a solar-powered system. (A wind-driven system would appear very similar, though with a wind-turbine collector.) Inputs from sensors in the panel array are fed to the controller's ADCs to provide data on the instantaneous voltage and current available from the array for conversion. Inputs may also provide information such as cell and ambient temperatures, used to protect the panels, and feedback that meters power output from the cells, used to track MPP.

All sensing inputs must be scaled so that peaks and spikes do not exceed the 3-V level of the ADCs. The data is first fed into a power control loop. And there may be more than one loop, depending on the design. Other real-time tasks that are being performed also provide inputs to the power control loop. Among these tasks are metering power returned to the grid, monitoring grid power levels for protection, regulating battery charging, tracking MPP and communicating with parallel controllers handling other systems.

Fig. 2 shows that PWMs are used to control both the dc-dc and dc-ac stages in the inverter. Depending on the power level of the system, a single or interleaved multiphase dc-dc configuration can be implemented. The dc-dc input and output voltage can be monitored and controlled using the controller's ADCs. The dc-ac stage, using an H-bridge as shown in Fig. 3, can be controlled using four PWM outputs. More than 12-bit PWM resolution is maintained at a PWM switching of 20 kHz — high enough to offer transient response and control over the ac output voltage.

This voltage is synchronized with the ac line by measuring both the line voltage and zero crossing, the latter detectable by using any of the controller's I/O lines. The low-interrupt latency of the F2833x ensures fast response and synchronization between the inverter output and the ac line voltage.

As an alternative, the system could use a three-phase inverter at the output instead of an H-bridge converter. In this case, dc-ac stage control would require six PWMs.

An important aspect of the design is in real-time fault management. A fault that occurs relatively slowly, such as overheating in the inverter, can be detected and managed using a dedicated ADC input, which monitors the temperature and initiates the appropriate system response. By contrast, a critical fault such as overvoltage, undervoltage or overcurrent requires immediate response to avoid severe system damage. The F2833x provides dedicated fault lines, called trip zones, to manage these critical faults. Trip-zone pins deactivate mapped PWM outputs within a couple of DSP cycles after receiving a fault signal, ensuring proper shutdown of the system to protect it from severe damage.

Key to Renewable Energy

Renewable energy systems are continually being improved to achieve greater efficiency and lower cost per kilowatt. While much attention is deservedly paid to improving PV panels and wind turbines, intelligent inverters can also contribute to making the technology more feasible. Floating-point DSCs represent an enabling technology for designing intelligent inverters because of their performance and flexibility. These characteristics will be especially valuable in helping designers address the varying regulatory and operational requirements of different renewable energy applications.

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